LSST: from Science Drivers to Reference Design and Anticipated Data Products

TL;DR: The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the solar system, exploring the transient optical sky, and mapping the Milky Way.

Abstract: (Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pachon in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.

Summary

1. Introduction

• Major advances in their understanding of the universe have historically arisen from dramatic improvements in their ability to "see.".
• With all of their existing telescope facilities, the authors have still surveyed only a small fraction of the observable universe (except when considering the most luminous quasars).
• The 2010 report "New Worlds, New Horizons in Astronomy and Astrophysics" by the NRC Committee for a Decadal Survey of Astronomy and Astrophysics (National Research Council 2010) ranked LSST as its top priority for large ground-based projects, and in 2014 May the National Science Board approved the project for construction.
• The community involvement is discussed in Section 5, and broad educational and societal impacts of the project are discussed in Section 6.

2. From Science Drivers to Reference Design

• The most important characteristic that determines the speed at which a system can survey a given sky area to a given flux limit (i.e., its depth) is its étendue (or grasp), the product of its primary mirror area and the angular area of its field of view (for a given set of observing conditions, such as seeing and sky brightness).
• Guided by the community-wide input assembled in the report of the Science Working Group of the LSST in 2004 (Science Working Group of the LSST & Strauss 2004 ), the LSST is designed to achieve goals set by four main science themes:.
• Each of these four themes itself encompasses a variety of analyses, with varying sensitivity to instrumental and system parameters.
• These themes fully exercise the technical capabilities of the system, such as photometric and astrometric accuracy and image quality.
• About 90% of the observing time will be devoted to a deep-wide-fast (main) survey mode.

2.1. The Main Science Drivers

• The main science drivers are used to optimize various system parameters.
• Ultimately, in this high-dimensional parameter space, there is a manifold defined by the total project cost.
• Here the authors summarize the dozen or so most important interlocking constraints on data and system properties placed by the four main science themes: 11.
• Parameters characterizing data processing and data access (such as the maximum time allowed after each exposure to report transient sources, and the maximum allowed software contribution to measurement errors).

2.1.1. Probing Dark Energy and Dark Matter

• Current models of cosmology require the existence of both dark matter and dark energy to match observational constraints (Riess et al.
• Distinguishing competing models for the physical nature of dark energy, or alternative explanations involving modifications of the general theory of relativity, will require percent-level measurements of both the cosmic expansion and the growth of dark matter structure as a function of redshift.
• These investigations require deep wide-area multicolor imaging with stringent requirements on shear systematics in at least two bands, and excellent photometry in at least five bands to measure photometric redshifts (a requirement shared with LSS, and indeed all extragalactic science drivers).
• Rather than simply co-adding all images in a given region of sky, the individual exposures, each with their own PSF and noise characteristics, should be analyzed simultaneously to optimally measure the shapes of galaxies (Tyson et al. 2008; Jee & Tyson 2011) .

2.1.2. Taking an Inventory of the Solar System

• The small-body populations in the solar system, such as asteroids, trans-Neptunian objects (TNOs), and comets, are remnants of its early assembly.
• The history of accretion, collisional grinding, and perturbation by existing and vanished giant planets is preserved in the orbital elements and size distributions of those objects.
• Individual exposures should be shorter than about 30 s to minimize the effects of trailing for the majority of moving objects.
• The images must be well sampled to enable accurate astrometry, with absolute accuracy of at least 0 1 in order to measure orbital parameters of TNOs with enough precision to constrain theoretical models and enable prediction of occultations.

2.1.3. Exploring the Transient Optical Sky

• Recent surveys have shown the power of measuring variability of celestial sources for studying gravitational lensing, searching for SNe, determining the physical properties of gamma-ray burst sources, discovering gravitational wave counterparts, probing the structure of active galactic nuclei (AGNs), studying variable star populations, discovering exoplanets, and many other subjects at the forefront of astrophysics (SciBook, Chap.
• Such a survey would likely detect microlensing by stars and compact objects in the Milky Way, but also in the Local Group and perhaps beyond (de Jong et al. 2008) .
• Time series ranging between 1-minute and 10 yr cadence should be probed over a significant fraction of the sky.
• Observations over a decade will enable the study of long-period variables, intermediate-mass black holes, and quasars (Kaspi et al.
• The next frontier in this field will require measuring the colors of fast transients and probing variability at faint magnitudes.

2.1.4. Mapping the Milky Way

• A major challenge in extragalactic cosmology today concerns the formation of structure on subgalactic scales, where baryon physics becomes important, and the nature of dark matter may manifest itself in observable ways (e.g., Weinberg et al. 2015) .
• The Milky Way and its environment provide a unique data set for understanding the detailed processes that shape galaxy formation and for testing the smallscale predictions of their standard cosmological model.
• In order to probe the halo out to its presumed edge at ∼100 kpc (Ivezić et al. 2004 ) with main-sequence stars, the total co-added depth must reach r>27, with a similar depth in the g band.
• This value was also chosen to approximately match the accuracy anticipated for the Gaia mission 89 (Perryman et al. 2001 ; de Bruijne 2012) at its faint limit (r∼20).
• To achieve the required proper-motion and parallax accuracy with an assumed astrometric accuracy of 10 mas per observation per coordinate, approximately 1000 separate observations are required.

2.1.5. A Summary and Synthesis of Science-driven Constraints on Data Properties

• The goals of all the science programs discussed above (and many more, of course) can be accomplished by satisfying the minimal constraints listed below.
• Astrometric precision should maintain the systematic limit set by the atmosphere, of about 10 mas per visit at the bright end (on scales below 20 arcmin).
• As described above, the authors are planning to split each visit into two exposures.
• The distribution of visits per filter should enable accurate photometric redshifts, separation of stellar populations, and sufficient depth to enable detection of faint extremely red sources (e.g., brown dwarfs and high-redshift quasars).
• As a result, the LSST system can adopt a highly efficient survey strategy in which a single data set serves most science programs (instead of science-specific surveys executed in series).

2.2.1. The Aperture Size

• The product of the system's étendue and the survey lifetime, for given observing conditions, determines the sky area that can be surveyed to a given depth.
• A larger field of view would lead to unacceptable deterioration of the image quality.
• 90 For this reason, the sky area for the main survey is maximized to its practical limit, 18,000 deg 2 , determined by the requirement to avoid air masses less than 1.5, which would substantially deteriorate the image quality and the survey depth (see Equation (6)).
• The two requirements are compatible if the number of visits is several hundred per band, which is in good agreement with independent science-driven requirements on the latter.
• The required co-added survey depth provides a direct constraint, independent of the details of survey execution such as the exposure time per visit, on the minimum effective primary mirror diameter of 6.4 m, as illustrated in Figure 2 .

2.2.2. The Optimal Exposure Time

• The single-visit depth depends on both the primary mirror diameter and the chosen exposure time, t vis .
• Science drivers such as SN light curves and moving objects in the solar system require that n<4 days, or equivalently t vis <40 s for the nominal values of A sky and A FOV .
• With the adopted optical design, described below, this effective diameter corresponds to a geometrical diameter of ∼8 m.
• Apart from z<2 quasars, practically all populations have k at most 0.6 (the Euclidean value), and faint stars and galaxies have k<0.5.

2.3. System Design Trade-offs

• The authors note that the Pan-STARRS project (Kaiser et al. 2002 (Kaiser et al. , 2010)) , with similar science goals to LSST, envisions a distributed aperture design, where the total system étendue is a sum of étendue values for an array of small 1.8 m telescopes.
• Each of these clones would have to have its own 3-gigapixel camera (see below), and given the added risk and complexity (e.g., maintenance, data processing), the monolithic design seems advantageous for a system with such a large étendue as LSST.
• With an étendue about 6 times smaller than that of LSST (effective diameters of 6.4 and 3.0 m, and a field-of-view area of 9.6 deg 2 vs. 7.2 deg 2 ), and all observing conditions being equal, the PS4 system could in principle use a cadence identical to that of LSST.
• The distance limits for nearby sources, such as Milky Way stars, would drop to 60% of their corresponding LSST values, and the NEO completeness level mandated by the US Congress would not be reached.

2.4. The Filter Complement

• The authors have investigated the possibility of replacing the ugrizy system with a filter complement that includes only five filters.
• Each filter width could be increased by 20% over the same wavelength range (.

2.5. The Calibration Methods

• Precise determination of the PSF across each image, accurate photometric and astrometric calibration, and continuous monitoring of system performance and observing conditions will be needed to reach the full potential of the LSST mission.
• Extensive precursor data including the SDSS data set and their own data obtained using telescopes close to the LSST site of Cerro Pachón (e.g., the SOAR and Gemini South telescopes), as well as telescopes of similar aperture (e.g., Subaru), indicate that the photometric and astrometric accuracy will be limited not by their instrumentation or software, but rather by atmospheric effects.
• The dose of delivered photons is measured using a calibration photodiode whose quantum efficiency is known to high accuracy.
• Celestial spectrophotometric standard stars can be used as a separate means of photometric calibration, albeit only through the comparison of band-integrated fluxes with synthetic photometry calculations.
• Astrometric calibration will be based on the results from the Gaia mission (Lindegren et al. 2018 ), which will provide numerous high-accuracy astrometric standards in every LSST field.

2.6. The LSST Reference Design

• The authors briefly describe the reference design for the main LSST system components.
• Detailed discussion of the flow-down from science requirements to system design parameters and extensive system engineering analysis can be found in the LSST Science Book (Chaps. 2-3).

2.6.1. Telescope and Site

• The large LSST étendue is achieved in a novel three-mirror design (modified Paul-Baker Mersenne-Schmidt system; Angel et al. 2000) with a very fast f/1.234 beam.
• The optical design has been optimized to yield a large field of view (9.6 deg 2 ), with seeing-limited image quality, across a wide wavelength band (320-1050 nm).
• The primary-tertiary mirror cell was fabricated by CAID in Tucson and is undergoing acceptance tests.
• Hz, which is crucial for achieving the required fast slew-and-settle times.
• Furthermore, the summit support building has been oriented with respect to the prevailing winds to shed its turbulence away from the telescope enclosure.

2.6.2. Camera

• The LSST camera provides a 3.2-gigapixel flat focal plane array, tiled by 189 4K×4K CCD science sensors with 10 μm pixels .
• The sensors are deep depleted high-resistivity silicon back-illuminated devices with a highly segmented architecture that enables the entire array to be read in 2 s.
• The sixth optical filter can replace any of the five via a procedure accomplished during daylight hours.
• Each of the 21 rafts will host three front-end electronic boards (REB) operating in the cryostat (at −10°C) that read in parallel a total of 9×16 segments per CCD (144 video channels reading 1 million pixels each).

2.6.3. Data Management

• The rapid cadence and scale of the LSST observing program will produce approximately 15 TB per night of raw imaging data 95 (about 20 TB with calibration exposures).
• The detailed outputs of the LSST Data Management system are described in Section 3.3.
• Periodically process the accumulated survey data to provide a uniform photometric and astrometric calibration, measure the properties of all detected objects, and characterize objects based on their time-dependent behavior.
• Provide enough processing, storage, and network bandwidth to enable user analyses of the data without the need for petabyte-scale data transfers.
• The other half of the DR processing will be done at CC-IN2P3, which will also have the role of "long-term storage" facility.

2.6.4. The LSST Software Stack

• The LSST Software Stack is the data processing and analysis system developed by the LSST Project to enable LSST survey data reduction and delivery.
• The pipelines written for these surveys have demonstrated that it is possible to carry out largely autonomous data reduction of large data sets, automated detection of sources and objects, and the extraction of scientifically useful characteristics of those objects.
• The primary implementation language is Python and, where necessary for performance reasons, C++. 96 The LSST data management software has been prototyped for over 8 yr. Besides processing simulated LSST data (Section 2.7.3), it has been used to process images from CFHTLS (Cuillandre et al. 2012 ) and SDSS (Abazajian et al. 2009) .
• Achieving these goals requires that the source code is not only available but also appropriately documented at all levels.

2.7. Simulating the LSST System

• Typical users should not have to work directly with the C++ layer.
• A simulation framework provides such a capability, delivering a virtual prototype LSST against which design decisions, optimizations (including descoping), and trade studies can be evaluated (Connolly et al. 2014) .
• It comprises four primary components: a simulation of the survey scheduler (Section 2.7.1); databases of simulated astrophysical catalogs of stars, galaxies, quasars, and solar system objects (Section 2.7.2); a system for generating observations based on the pointing of the telescope; and a system for generating realistic LSST images of a given area of sky (Section 2.7.3).
• Computationally intensive routines are written in C/C++, with the overall framework and database interactions using Python.

2.7.1. The LSST Operations Simulator

• The LSST Operations Simulator (Delgado et al. 2014 ) was developed to enable a detailed quantitative analysis of the various science trade-offs described in this paper.
• Thus, the simulator correctly represents the variation of limiting magnitude between pairs of observations used to detect NEOs and the correlation between, for example, seasonal weather patterns and observing conditions at any given point on the sky.
• The time taken to move from one observation to the next is given by a detailed model of the camera, telescope, and dome.
• After a given exposure, all possible next observations are assigned a score that depends on their locations, times, and filters according to a set of scientific requirements that can vary with time and location.
• Results of the simulated surveys can be visualized and analyzed using a Python-based package called the Metrics Analysis Framework (MAF; Jones et al. 2014) .

2.7.2. Catalog Generation

• The simulated astronomical catalogs (CatSim; Connolly et al. 2014 ) are stored in an SQL database.
• Stellar sources are based on the Galactic structure models of Jurić et al. (2008) and include thin-disk, thick-disk, and halo star components.
• Half-light radii for bulges are estimated using the empirical absolute magnitude versus half-light radius relation given by González et al. (2009) .
• Positions for the 11 million asteroids in the simulation are stored within the base catalog (sampled once per night for the 10 yr duration of the LSST survey).

2.7.3. Image Simulations

• The framework described above provides a parameterized view of the sky above the atmosphere.
• Each photon is ray-traced through the atmosphere, telescope, and camera to generate a CCD image.
• All screens move during an exposure, with velocities derived from NOAA measurements of the wind velocities above the LSST site in Chile.
• The mirrors and lenses are simulated using geometric optics techniques in a fast ray-tracing algorithm, and all optical surfaces include a spectrum of perturbations based on design tolerances.

3. Anticipated Data Products and Their Characteristics

• The LSST observing strategy is designed to maximize the scientific throughput by minimizing slew and other downtime and by making appropriate choices of the filter bands given the realtime weather conditions.
• Using simulated surveys produced with the Operations Simulator described in Section 2.7.1, the authors illustrate predictions of LSST performance with two examples.

3.1. The Baseline LSST Surveys

• The fundamental basis of the LSST concept is to scan the sky deep, wide, and fast and to obtain a data set that simultaneously satisfies the majority of the science goals.
• The authors present here a specific realization, the so-called "universal cadence," which yields the main deep-wide-fast survey and meets their core science goals.
• At this writing, there is a vigorous discussion of cadence plans in the LSST community, exploring variants and alternatives that enhance various specific science programs, while maintaining the science requirements described in the SRD.
• The main deep-wide-fast survey will use about 90% of the observing time.
• The remaining 10% of the observing time will be used to obtain improved coverage of parameter space such as very deep (r∼26) observations, observations with very short revisit times (∼1 minute), and observations of "special" regions such as the ecliptic plane, Galactic plane, and the Large and Small Magellanic Clouds.

3.1.1. The Main Deep-wide-fast Survey and Its Extensions

• The observing strategy for the main survey will be optimized for the homogeneity of depth and number of visits.
• In times of good seeing and at low air mass, preference is given to r-and i-band observations.
• As often as possible, each field will be observed twice, with visits separated by 15-60 minutes.
• The universal cadence provides most of LSST's power for detecting NEOs and Kuiper Belt objects (KBOs) and naturally incorporates the southern half of the ecliptic within its 18,000 square degrees, with a decl.
• The anticipated total number of visits for a 10 yr LSST survey is about 2.45 million (∼4.9 million 15 s long exposures, summing over the six filters).

3.1.2. Mini-surveys and Deep Drilling Fields

• Roughly 10% of the time will be allocated to other strategies that significantly enhance the scientific return.
• Deep Drilling Fields, with a much higher number of visits (≈2500-4500 in the r band) than the main survey (a median over all fields of 200 visits in the r band), are also visible as small circles.
• The individual sequences would be sensitive to 1% variability on subminute timescales, allowing discovery of planetary eclipses and of interstellar scintillation effects, expected when the light of a background star propagates through a turbulent gas medium (Moniez 2003; Habibi et al. 2011) .
• The LSST has already selected four distant extragalactic survey fields 97 that the project guarantees to observe as Deep Drilling Fields with deeper coverage and more frequent temporal sampling than provided by the standard LSST observing pattern.
• The timing of the visits within the season is illustrated in the figure by calculating the month within the season (shown in the y-axis location in the plot), the night within the month (x-axis location in the plot), and number of visits within each night (a small additional offset in the y-axis).

3.2. Detailed Analysis of Simulated Surveys

• As examples of analysis enabled by the Operations Simulator (Section 2.7.1), the authors describe determination of the completeness of the LSST NEO sample, and estimation of errors expected for trigonometric parallax and proper-motion measurements.
• In both examples, the conclusions crucially depend on the assumed accuracy of the photometry and astrometry, as the authors now describe.

3.2.1. Expected Photometric S/N

• The output of operations simulations is a data stream consisting of a position on the sky and the time of observation, together with observing conditions such as seeing and sky brightness.
• The expected photometric error in magnitudes (roughly the inverse of the S/N) for a single visit can be written as where σ rand is the random photometric error and σ sys is the systematic photometric error (due to, e.g., imperfect modeling of the PSF, but not including uncertainties in the absolute photometric zero-point). ) .
• The constants C m depend on the overall throughput of the instrument and are computed using their current best throughput estimates for optical elements and sensors.
• In all six bands they imply single-visit depths m 5 (also listed in Table 2 ) that lie between the minimum and design specification values from the SRD listed in Table 1 .
• The differences in performance between LSST and, for example, SDSS follow directly from these relations.

3.2.2. The NEO Completeness Analysis

• Using mean asteroid reflectance spectra (DeMeo et al. 2009), combined with the LSST bandpasses, the authors calculate expected magnitudes and colors, assuming that all PHAs are C-type asteroids, of V−m=(.
• The top panels illustrate cumulative completeness for the LSST baseline cadence and MOPS configuration.

3.2.3. The Expected Accuracy of Trigonometric Parallax and Propermotion Measurements

• To model the astrometric errors, the authors need to consider both random and systematic effects.
• Random astrometric errors per visit for a given star are modeled as θ/S/N, with θ=700 mas and S/N determined using Equation (6).
• Systematic and random errors become similar at about r=22, and there are about 100 stars per LSST sensor (0.05 deg 2 ) to this depth (and fainter than the LSST saturation limit at r∼16) even at the Galactic poles.
• The astrometric transformations from pixel to sky coordinates are modeled using low-order polynomials and standard techniques developed at the US Naval Observatory (Monet et al. 2003) .
• Hence, LSST will smoothly extend Gaia's error versus magnitude curve about 4 mag fainter .

3.3. Data Products and Archive Services

• Data collected by the LSST telescope and camera will be automatically processed to data products-catalogs, alerts, and reduced images-by the LSST Data Management system (Section 2.6.3).
• Objects with motions sufficient to cause trailing in a single exposure will be identified and flagged as such when the alerts are broadcast.
• An extended source model-a constrained linear combination of two Sérsic profiles-and a pointsource model with proper motion-will generally be fitted to each detected object.
• As described in Section 3.1.2, approximately 10% of the observing time will be devoted to mini-surveys that do not follow the LSST baseline cadence.
• The authors will make it possible for the end users to create (or use) such user-generated 105 products at the LSST Data Facility, using the services offered within the LSST Science Platform (Section 3.3.1).

3.3.1. The LSST Science Platform

• The LSST Science Platform (Jurić et al. 2017 ) represents LSST's vision for a large-scale astronomical data archive that can enable effective research with data sets of LSST size and complexity.
• The LSST Science Platform will be a set of web applications and services through which the users will access the LSST data products and, if desired, conduct remote analyses or create user-generated products.
• The platform makes this possible through three user-facing aspects: 1. The web Portal, designed to provide the essential data access and visualization services through a simple-to-use website.
• The JupyterLab aspect, which will provide a Jupyter 106 Notebook-like interface and is geared toward enabling next-to-the-data remote analysis.
• This interface will open the possibility for remote access and analysis of the LSST data set using applications that the users are already comfortable with such as TOPCAT (Taylor 2005) , or libraries such as Astropy (Astropy Collaboration et al. 2013; Jenness et al. 2016) .

4. Examples of LSST Science Projects

• The design and optimization of the LSST system leverage its unique capability to scan a large sky area to a faint flux limit in a short amount of time.
• The main product of the LSST system will be a multicolor ugrizy image of about half the sky to unprecedented depth (r∼27.5).
• Each sky position within the main survey area will be observed close to 1000 times, with timescales spanning seven orders of magnitude (from 30 s to 10 yr), and produce roughly 30 trillion photometric measures of celestial sources.
• It is not possible to predict all the science that LSST data will enable.
• The authors now briefly discuss a few projects to give a flavor of anticipated studies, organized by the four science themes that drive the LSST design (although some projects span more than one theme).

4.1. Probing Dark Energy and Dark Matter

• Any given probe constrains degenerate combinations of cosmological parameters, and each probe is affected by different systematics; thus, the combination of probes allows systematics to be calibrated and for degeneracies to be broken.
• The joint analysis of LSST WL and galaxy clustering is particularly powerful in constraining the dynamical behavior of dark energy, i.e., how it evolves with cosmic time or redshift (Hu & Jain 2004; Zhan 2006) .
• The sound horizon at decoupling, which is imprinted on the mass distribution )) from LSST cosmological probes after 1 yr of data (Y1; top) and the full 10 yr survey (Y10; bottom), from each probe individually, and the joint forecast including "Stage III priors" (i.e., Planck, JLA SNe, and BOSS BAOs).
• Figures reproduced with permission at all redshifts and calibrated with the CMB, provides a standard to measure the angular diameter distance as a function of redshift (Eisenstein et al.
• Such a sample will not only provide larger statistics for the study of the Type Ia population in the universe but also be spread across the full 18,000 deg 2 LSST main survey footprint, allowing different probes of the large-scale structure of the low-redshift universe.

4.2. Taking an Inventory of the Solar System

• The small bodies of the solar system, such as main belt asteroids, the Trojan populations of the giant planets, and the KBOs, offer a unique insight into its early stages because they provide samples of the original solid materials of the solar nebula.
• The baseline LSST cadence will result in orbital parameters for several million objects; these will be dominated by main belt asteroids, with light curves and multicolor photometry for a substantial fraction of detected objects.
• The relationship between the gas-to-dust ratio in comets and their dynamical class (and places of formation) is a fundamental, and still unresolved, question in cometary science (see, e.g., A'Hearn et 1995; Bockelée-Morvan & Biver 2017).
• LSST will be some 3 mag more sensitive than current NEO surveys (like Pan-STARRS1) and will cover more sky more often.

4.3. Exploring the Transient Optical Sky

• Time domain science will greatly benefit from LSST's unique capability to simultaneously provide large-area coverage, dense temporal coverage, accurate color information, good image quality, and rapid data reduction and classification.
• LSST will extend the extrasolar planet census to larger distances within the Galaxy, thus enabling detailed studies of planet frequency as a function of stellar metallicity and parent population (e.g., Hartman et al.
• A census of light echoes of historical explosive and eruptive transients in the Milky Way and Local Group through high-resolution time series.
• Time delays between the multiple images of strongly lensed core-collapse SNe can be used to observe the elusive shock breakout phase of the light curve, providing an unprecedented look at the earliest emission from these transients (Suwa 2018).
• Relations between quasar variability properties and luminosity, redshift, rest-frame wavelength, timescale, color, radio-jet emission, black hole mass, and Eddington-normalized luminosity will be defined with massive statistics, including the potential to detect rare but important events such as jet flares and obscuration events.

4.4. Mapping the Milky Way

• The LSST will map the Galaxy in unprecedented detail, and by doing so revolutionize the fields of Galactic astronomy and near-field cosmology.
• Over 97% of all stars eventually exhaust their fuel and cool to become white dwarfs.
• Variations in the initial mass function will be studied as a function of environment (e.g., age and metallicity).
• In summary, the LSST data will revolutionize studies of the Milky Way and the entire Local Group.
• The outermost reaches of the stellar halo are predicted to bear the most unique signatures of their Galaxy's formation (Johnston et al.

4.5. Additional Science Projects

• The experience with any large survey (e.g., SDSS, 2MASS, VISTA, WISE, GALEX, to name but a few) is that much of their most interesting science is often unrelated to the main science drivers and is often unanticipated at the time the survey is designed.
• LSST will enable far more diverse science than encompassed by the four themes that drive the system design.
• The currently available samples (e.g., Greco et al. 2018 ) are highly incomplete, especially in the southern hemisphere .
• Search for strong gravitational lenses to a faint surface brightness limit (e.g., Bartelmann et al.

4.5.1. Synergy with Other Projects

• LSST will not operate in isolation and will greatly benefit from other precursor and coeval data at a variety of wavelengths, depths, and timescales.
• The Pan-STARRS surveys represent a valuable complement to LSST in providing northern sky coverage to a limit fainter than that of SDSS and SkyMapper.
• The LSST data stream will invigorate subsequent investigations by numerous other telescopes that will provide additional temporal, spectral, and spatial resolution coverage.
• The WL analyses from space and from the ground will also be highly complementary and will provide crucial cross-checks of one another.
• LSST will also enable multiwavelength studies of faint optical sources using gamma-ray, X-ray, IR, and radio data.

5. Community Involvement

• LSST has been conceived as a public facility: the database that it will produce, and the associated object catalogs that are generated from that database, will be made available with no proprietary period to the US and Chilean scientific communities, as well as to those international partners who contribute to operations funding.
• The LSST data management system (Section 3.3) will provide user-friendly tools to access this database, support user-initiated queries and data exploration, and carry out scientific analyses on the data, using LSST computers either at the archive facility or at the data access centers.
• The SDSS provides a good example for how the scientific community can be effective in working with large, publicly available astronomical data sets.
• The science collaborations are listed on the LSST web page, together with a description of the application process for each one.
• The LSST science collaborations in particular have helped develop the LSST science case and continue to provide advice on how to optimize their science with choices in cadence, software, and data systems.

6. Educational and Societal Impacts

• The impact and enduring societal significance of LSST will exceed its direct contributions to advances in physics and astronomy.
• LSST is uniquely positioned to have high impact with the interested public, planetariums and science centers, and citizen science projects, as well as middle school through university educational programs.
• The mission of LSST's Education and Public Outreach (EPO) program is to provide worldwide access to a subset of LSST data through accessible and engaging online experiences so anyone can explore the universe and be part of the discovery process.
• A dynamic, immersive web portal will enable members of the public to explore color images of the full LSST sky, examine objects in more detail, view events from the nightly alert stream, learn more about LSST science topics and discoveries, and investigate scientific questions that excite them using real LSST data in online science notebooks.
• The authors will also follow the International Planetarium Society's Data2Dome standard, to maximize the number of platforms that can use their assets.

7. Summary and Conclusions

• Until recently, most astronomical investigations have focused on small samples of cosmic sources or individual objects.
• The LSST will be unique: the combination of large aperture and large field of view, coupled with the needed computation power and database technology, will enable simultaneously fast and wide and deep imaging of the sky, addressing in one sky survey the broad scientific community's needs in both the time domain and deep universe.
• The design, development, and construction effort has been underway since 2006 and will continue through the onset of full survey operations.
• In 2014 LSST transitioned from the design and development phase to construction, and the Associated Universities for Research in Astronomy (AURA) has had formal responsibility for the LSST project since 2011.
• About 20 billion galaxies and a similar number of stars will be detected-for the first time in history, the number of cataloged celestial objects will exceed the number of living people!.

Figures

Figure 4. LSST bandpasses. The vertical axis shows the total throughput. The computation includes the atmospheric transmission (assuming an air mass of 1.2; dotted line), optics, and the detector sensitivity.

Figure 5. Example of determination of the atmospheric opacity by simultaneously fitting a three-parameter stellar model SED (Kurucz 1979) and six physical parameters of a sophisticated atmospheric model (MODTRAN; Anderson et al. 1999) to an observed F-type stellar spectrum (Fλ). The black line is the observed spectrum, and the red line is the best fit. Note that the atmospheric water feature around 0.9–1.0 μm is exquisitely well fit. The components of the best-fit atmospheric opacity are shown in Figure 6. Adapted from Burke et al. (2010).

Figure 6. Components of the best-fit atmospheric opacity used to model the observed stellar spectrum shown in Figure 5. The atmosphere model (MODTRAN; Anderson et al. 1999) includes six components: water vapor (blue), oxygen and other trace molecules (green), ozone (red), Rayleigh scattering (cyan), a gray term with a transmission of 0.989 (not shown), and an aerosol contribution proportional to λ−1 and extinction of 1.3% at λ=0.675 μm (not shown). The black line shows all six components combined. Adapted from Burke et al. (2010).

Figure 33. The LSST will deliver AGN sky densities of 1000–4000 deg−2 (top panel). The total LSST AGN yield, selected using colors and variability, should be well over 10 million objects, especially once multiwavelength data are also utilized. The bottom panel shows the expected distribution of these objects in the absolute magnitude vs. redshift plane, color-coded by the probability for an object to be detected as variable after 1 yr of observations. Note that quasars will be detected to their formal luminosity cutoff (M<−23) even at redshifts of ∼5. Adapted from Brandt & LSST Active Galaxies Science Collaboration (2007).

Figure 14. LSST data flow from the mountain facilities in Chile to the data access center and processing center in the US and the satellite processing center in France.

Figure 19. Distribution of visits over a single season at two distinct pointings, for a simulated realization of the baseline cadence. Two pointings within the “Wide Fast Deep” universal cadence footprint were chosen, one nearly overhead at the LSST site and one near the southern limit of the WFD footprint. The timing of the visits within the season is illustrated in the figure by calculating the month within the season (shown in the y-axis location in the plot), the night within the month (x-axis location in the plot), and number of visits within each night (a small additional offset in the y-axis). Each visit is color-coded according to its observed filter; the shaded gray regions show the nights around new moon.

Figure 24. Marginalized 1σ errors on the comoving distance (open triangles) and growth factor (open circles) parameters from the joint analysis of LSST LSS and WL (galaxy–galaxy, galaxy–shear, and shear–shear power spectra) with a conservative level of systematic uncertainties in the photometric redshift error distribution and additive and multiplicative errors in the shear and galaxy power spectra. The maximum multipole used for WL is 2000, and that for LSS is 3000 (with the additional requirement that ℓ D z; 0.4A2D <d ( ) ). The growth parameters are evenly spaced in log(1+z) between z=0 and 5, and the distance parameters start at z1=0.14. The error of each distance (growth) parameter is marginalized over all the other parameters, including growth (distance) parameters. The joint constraints on distance are relatively insensitive to the assumed systematics (Zhan et al. 2009).

Table 2 Parameters from Equations (5) and (6)

Figure 30. Predicted spatial distribution of stars out to 150 kpc from the center of the Milky Way, from Bullock & Johnston (2005). LSST will resolve mainsequence turnoff stars out to 300 kpc, 10 times more volume than shown here, enabling a high-fidelity spatial map over the entire observed virial volume. (Note that this is significantly larger than the 100 kpc probed by metallicity measurements in Figure 29, which is limited by the depth of the u-band observations.) Overlaid on this prediction is the observed SDSS stellar number density map based on main-sequence stars with 0.10<r−i<0.15 (Jurić et al. 2008). This map extends up to ∼20 kpc from the Sun, with the white box showing a scale of 20 kpc across and the left side aligned with the Galactic center. The revolutionary Galaxy map provided by SDSS is only complete to ∼40 kpc, or only ∼1% of the virial volume. However, the outermost reaches of the stellar halo are predicted to bear the most unique signatures of our Galaxy’s formation (Johnston et al. 2008; Cooper et al. 2010). LSST will be the only survey capable of fully testing such predictions.

Figure 29. Median metallicity map for 2.5 million main-sequence F-type stars within 10 kpc from the Sun (adapted from Ivezić et al. 2008). The metallicity is estimated using u−g and g−r colors measured by SDSS. The position and size of the mapped region, relative to the rest of the Milky Way, are illustrated in the upper right corner, where the same map is scaled and overlaid on an image of the Andromeda galaxy. The gradient of the median metallicity is essentially parallel to the Z-axis, except in the Monoceros stream region, as marked. LSST will extend this map out to 100 kpc, using a sample of over 100 million main-sequence F stars.

Figure 3. Single-visit depth in the r band (5σ detection for point sources, AB magnitudes) vs. revisit time, n (days), as a function of the effective aperture size. With a coverage of 10,000 deg2 in two bands, the revisit time directly constrains the visit exposure time, tvis=10n s. In addition to direct constraints on optimal exposure time, tvis is also driven by requirements on the revisit time, n, the total number of visits per sky position over the survey lifetime, Nvisit, and the survey efficiency, ò (see Equations (1)–(3)). Note that these constraints result in a fairly narrow range of allowed tvis for the main deep-wide-fast survey.

Figure 12. LSST Camera focal plane array. Each cyan square represents one 4K×4K pixel sensor. Nine sensors are assembled into a raft; the 21 rafts are outlined in red. There are 189 science sensors, for a total of 3.2 gigapixels. Also shown are the four corner rafts, where the guide sensors and wavefront sensors are located.

Figure 13. LSST Camera raft module, corresponding to the red squares in Figure 12, with nine sensors, integrated electronics, and thermal connections. Raft modules are designed to be replaceable.

Figure 28. The g−r vs. u−g color–color diagram for about 1 million point sources from the SDSS Stripe 82 area. Accurate multicolor photometry contains information that can be used for source classification and determination of detailed stellar properties such as effective temperature and metallicity. LSST will enable such measurements for billions of stars.

Figure 20. Cumulative completeness of the LSST survey for NEOs (left panels) and PHAs (right panels) brighter than a given absolute magnitude, H 22 (related to the size of the object and albedo; H=22 mag is equivalent to a typical 140 m asteroid). The top panels illustrate cumulative completeness for the LSST baseline cadence and MOPS configuration. In the baseline, LSST alone would discover 66% of the PHAs with H 22 (61% of NEOs); LSST combined with current and ongoing surveys can discover 81% of PHAs (73% of NEOs). The bottom panels illustrate cumulative completeness when LSST is operated for 12 yr, with extra visits around the ecliptic, and when the MOPS linking window is increased to 30 days from the baseline 15. In this case, LSST alone could discover 74% of the PHAs with H 22 (69% of NEOs); LSST combined with existing resources could discover 86% of PHAs (77% of NEOs).

Figure 23. Constraints on the dark energy equation of state (w = w w a1a0 + -( )) from LSST cosmological probes after 1 yr of data (Y1; top) and the full 10 yr survey (Y10; bottom), from each probe individually, and the joint forecast including “Stage III priors” (i.e., Planck, JLA SNe, and BOSS BAOs). The 68% confidence intervals are shown in all cases; the plotted quantitiesΔw0 andΔwa are the difference between w0 and wa and their fiducial values of −1 and 0. In the bottom panel we show, for reference, the expected constraints from the Y6 Dark Energy Survey 3x2pt analysis (brown): the corresponding LSST analysis is already expected to be higher precision than this after just 1 yr. Figures reproduced with permission from the LSST DESC Science Requirements Document v1 (LSST Dark Energy Science Collaboration et al. 2018) and related analysis.

Figure 11. Cutaway view of LSST camera. Not shown are the shutter, which is positioned between the filter and lens L3, and the filter exchange system.

Table 1 The LSST Baseline Design and Survey Parameters

Figure 1. Image quality distribution measured at the Cerro Pachón site using a differential image motion monitor (DIMM) at λ=500 nm, and corrected using an outer scale parameter of 30 m over an 8.4 m aperture. For details about the outer scale correction see Tokovinin (2002). The observed distribution is well described by a lognormal distribution, with the parameters shown in the figure.

Figure 31. Comparison of an SDSS image (2×4 arcmin2 gri composite) showing a typical galaxy at a redshift of ∼0.1 (top) with a similar BVR composite image of the same field obtained by the MUSYC survey (bottom; Gawiser et al. 2006). The MUSYC image is about 4 mag deeper than the SDSS image (and about 1 mag less deep than the anticipated LSST 10 yr co-added data). Note the rich surface brightness structure seen in the MUSYC image that is undetectable in the SDSS image.

Figure 17. Simulated image of a single LSST CCD using PhoSim (covering a 13.3×13.3 arcmin2 region of the sky). The image is a color composite (Lupton et al. 2004) from a set of 30 s gri visits.

Full text

LSST: From Science Drivers to Reference Design and Anticipated Data Products
Željko Ivezić
1,84
, Steven M. Kahn
2,3
, J. Anthony Tyson
4
, Bob Abel
5
, Emily Acosta
2
, Robyn Allsman
2
, David Alonso
6
,
Yusra AlSayyad
7
, Scott F. Anderson
1
,JohnAndrew
2
, James Roger P. Angel
8
,GeorgeZ.Angeli
9
,RezaAnsari
10
, Pierre Antilogus
11
,
Constanza Araujo
2
, Robert Armstrong
7
,KirkT.Arndt
6
, Pierre Astier
11
, Éric Aubourg
12
,NicoleAuza
2
, Tim S. Axelrod
8
,
Deborah J. Bard
13
, Jeff D. Barr
2
,AurelianBarrau
14
, James G. Bartlett
12
, Amanda E. Bauer
2
, Brian J. Bauman
15
,
Sylvain Baumont
11,16
, Ellen Bechtol
2
, Keith Bechtol
2,17
, Andrew C. Becker
1
, Jacek Becla
13
, Cristina Beldica
18
, Steve Bellavia
19
,
Federica B. Bianco
20,21
, Rahul Biswas
22
, Guillaume Blanc
10,23
,JonathanBlazek
24,25
, Roger D. Blandford
3
,JoshS.Bloom
26
,
Joanne Bogart
3
, Tim W. Bond
13
,MichaelT.Booth
2
, Anders W. Borgland
13
,KirkBorne
27
, James F. Bosch
7
,
Dominique Boutigny
28
, Craig A. Brackett
13
,AndrewBradshaw
4
, William Nielsen Brandt
29
,MichaelE.Brown
30
,
James S. Bullock
31
, Patricia Burchat
3
,DavidL.Burke
3
, Gianpietro Cagnoli
32
, Daniel Calabrese
2
,ShawnCallahan
2
,
Alice L. Callen
13
,JeffreyL.Carlin
2
, Erin L. Carlson
2
, Srinivasan Chandrasekharan
33
, Glenaver Charles-Emerson
2
,
Steve Chesley
34
, Elliott C. Cheu
35
, Hsin-Fang Chiang
18
,JamesChiang
3
, Carol Chirino
2
, Derek Chow
13
, David R. Ciardi
36
,
Charles F. Claver
2
, Johann Cohen-Tanugi
37
, Joseph J. Cockrum
2
,RebeccaColes
24
, Andrew J. Connolly
1
, Kem H. Cook
38
,
Asantha Cooray
31
, Kevin R. Covey
39
, Chris Cribbs
18
,WeiCui
40
, Roc Cutri
36
, Philip N. Daly
41
, Scott F. Daniel
1
,
Felipe Daruich
2
, Guillaume Daubard
11
, Greg Daues
18
,WilliamDawson
15
, Francisco Delgado
2
, Alfred Dellapenna
19
,
Robert de Peyster
13
, Miguel de Val-Borro
7
, Seth W. Digel
13
, Peter Doherty
42
,RichardDubois
13
, Gregory P. Dubois-Felsmann
36
,
Josef Durech
43
, Frossie Economou
2
, Tim Eifler
8
, Michael Eracleous
29
,BenjaminL.Emmons
2
, Angelo Fausti Neto
2
,
Henry Ferguson
44
, Enrique Figueroa
2
, Merlin Fisher-Levine
7
,WarrenFocke
13
, Michael D. Foss
13
,JamesFrank
19
,
Michael D. Freemon
18
, Emmanuel Gangler
45
, Eric Gawiser
46
,JohnC.Geary
47
, Perry Gee
4
,MarlaGeha
48
,
Charles J. B. Gessner
2
, Robert R. Gibson
1
, D. Kirk Gilmore
3
, Thomas Glanzman
13
, William Glick
18
, Tatiana Goldina
36
,
Daniel A. Goldstein
26,49
, Iain Goodenow
2
, Melissa L. Graham
1
, William J. Gressler
2
, Philippe Gris
45
, Leanne P. Guy
2
,
Augustin Guyonnet
42
, Gunther Haller
13
, Ron Harris
50
, Patrick A. Hascall
13
, Justine Haupt
19
, Fabio Hernandez
51
, Sven Herrmann
13
,
Edward Hileman
2
, Joshua Hoblitt
2
, John A. Hodgson
13
, Craig Hogan
52
, James D. Howard
2
, Dajun Huang
19
, Michael E. Huffer
3
,
Patrick Ingraham
2
,WalterR.Innes
3
, Suzanne H. Jacoby
2
,BhuvneshJain
53
,FabriceJammes
45
, James Jee
4
, Tim Jenness
2
,
Garrett Jernigan
54
, Darko Jevremović
55
, Kenneth Johns
35
, Anthony S. Johnson
13
, Margaret W. G. Johnson
18
, R. Lynne Jones
1
,
Claire Juramy-Gilles
11
, Mario Jurić
1
, Jason S. Kalirai
44
, Nitya J. Kallivayalil
56
, Bryce Kalmbach
1
, Jeffrey P. Kantor
2
,
Pierre Karst
57
, Mansi M. Kasliwal
58
, Heather Kelly
13
, Richard Kessler
52
,VeronicaKinnison
2
,DavidKirkby
59
, Lloyd Knox
4
,
Ivan V. Kotov
19
, Victor L. Krabbendam
2
, K. Simon Krughoff
2
, Petr Kubánek
60
,JohnKuczewski
19
,ShriKulkarni
58
,JohnKu
13
,
Nadine R. Kurita
13
, Craig S. Lage
4
, Ron Lambert
2,61
, Travis Lange
13
, J. Brian Langton
13
, Laurent Le Guillou
11,16
,DeborahLevine
36
,
Ming Liang
2
,Kian-TatLim
13
, Chris J. Lintott
6
, Kevin E. Long
62
,MargauxLopez
13
, Paul J. Lotz
2
, Robert H. Lupton
7
,
Nate B. Lust
7
,LaurenA.MacArthur
7
, Ashish Mahabal
58
, Rachel Mandelbaum
63
, Thomas W. Markiewicz
13
,
Darren S. Marsh
13
, Philip J. Marshall
3
,StuartMarshall
3
,MorganMay
19
, Robert McKercher
2
, Michelle McQueen
19
, Joshua Meyers
7
,
Myriam Migliore
14
, Michelle Miller
50
, David J. Mills
2
, Connor Miraval
19
, Joachim Moeyens
1
,FredE.Moolekamp
7,64
,
David G. Monet
65
, Marc Moniez
10
, Serge Monkewitz
36
, Christopher Montgomery
2
, Christopher B. Morrison
1
, Fritz Mueller
13
,
Gary P. Muller
2
, Freddy Muñoz Arancibia
2
, Douglas R. Neill
2
, Scott P. Newbry
13
, Jean-Yves Nief
51
, Andrei Nomerotski
19
,
Martin Nordby
13
,PaulO’Connor
19
, John Oliver
42,66
,ScotS.Olivier
15
, Knut Olsen
50
,WilliamO’Mullane
2
, Sandra Ortiz
2
,
Shawn Osier
13
, Russell E. Owen
1
, Reynald Pain
11
, Paul E. Palecek
19
, John K. Parejko
1
, James B. Parsons
18
,NathanM.Pease
13
,
J. Matt Peterson
2
, John R. Peterson
40
, Donald L. Petravick
18
, M. E. Libby Petrick
2
, Cathy E. Petry
2
, Francesco Pierfederici
67
,
Stephen Pietrowicz
18
,RobPike
68
, Philip A. Pinto
8
, Raymond Plante
18
, Stephen Plate
19
, Joel P. Plutchak
18
, Paul A. Price
7
,
Michael Prouza
60
,VeljkoRadeka
19
, Jayadev Rajagopal
50
, Andrew P. Rasmussen
40
, Nicolas Regnault
11
,KevinA.Reil
13
,
David J. Reiss
1
, Michael A. Reuter
2
, Stephen T. Ridgway
50
, Vincent J. Riot
15
, Steve Ritz
69
, Sean Robinson
19
,WilliamRoby
36
,
Aaron Roodman
13
,WayneRosing
70
, Cecille Roucelle
12
, Matthew R. Rumore
19
, Stefano Russo
13
,AbhijitSaha
50
,
Benoit Sassolas
32
, Terry L. Schalk
69
, Pim Schellart
7,71
,RafeH.Schindler
3
, Samuel Schmidt
4
, Donald P. Schneider
29
,
Michael D. Schneider
15
, William Schoening
2
, German Schumacher
2,61
, Megan E. Schwamb
72
, Jacques Sebag
2
, Brian Selvy
2
,
Glenn H. Sembroski
40
,LynnG.Seppala
15
, Andrew Serio
2
, Eduardo Serrano
2
,RichardA.Shaw
44
,IanShipsey
6
, Jonathan Sick
2
,
Nicole Silvestri
1
, Colin T. Slater
1
, J. Allyn Smith
73
,R.ChrisSmith
61
, Shahram Sobhani
74
, Christine Soldahl
13
,
Lisa Storrie-Lombardi
36
,EdwardStover
2
, Michael A. Strauss
7
, Rachel A. Street
70
, Christopher W. Stubbs
42,66
,
Ian S. Sullivan
1
, Donald Sweeney
2
, John D. Swinbank
1,7
, Alexander Szalay
75
, Peter Takacs
19
, Stephen A. Tether
13
,
Jon J. Thaler
76
, John Gregg Thayer
13
, Sandrine Thomas
2
, Adam J. Thornton
2
, Vaikunth Thukral
13
, Jeffrey Tice
13
,
David E. Trilling
77
, Max Turri
13
, Richard Van Berg
13,53
, Daniel Vanden Berk
78
,KurtVetter
19
, Francoise Virieux
12
,
Tomislav Vucina
2
, William Wahl
19
,LucianneWalkowicz
79,80
, Brian Walsh
19
, Christopher W. Walter
81
,DanielL.Wang
13
,
Shin-Yawn Wang
36
, Michael Warner
61
, Oliver Wiecha
2
, Beth Willman
2,8
, Scott E. Winters
15
, David Wittman
4
,SidneyC.Wolff
2
,
W. Michael Wood-Vasey
82
, Xiuqin Wu
36
,BoXin
2
, Peter Yoachim
1
,andHuZhan
83
1
University of Washington, Dept. of Astronomy, Box 351580, Seattle, WA 98195, USA
2
LSST Project Office, 950 N. Cherry Avenue, Tucson, AZ 85719, USA
The Astrophysical Journal, 873:111 (44pp), 2019 March 10 https://doi.org/10.3847/1538-4357/ab042c
© 2019. The American Astronomical Society. All rights reserved.
1

3
Kavli Institute for Particle Astrophysics and Cosmology, SLAC National Accelerator Laboratory, Stanford University, Stanford, CA 94025, USA
4
Physics Department, University of California, One Shields Avenue, Davis, CA 95616, USA
5
Olympic College, 1600 Chester Ave., Bremerton, WA 98337-1699, USA
6
Department of Physics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK
7
Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA
8
Steward Observatory, The University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721, USA
9
Giant Magellan Telescope Organization (GMTO), 465 N. Halstead Street, Suite 250, Pasadena, CA 91107, USA
10
Laboratoire de l’Accélérateur Linéaire, CNRS/IN2P3, Université de Paris-Sud, B.P. 34, F-91898 Orsay Cedex, France
11
Laboratoire de Physique Nucléaire et des Hautes Energies, Université Pierre et Marie Curie, Université Paris Diderot, CNRS/IN2P3,
4 place Jussieu, F-75005 Paris, France
12
AstroParticule et Cosmologie, Université Paris Diderot, CNRS/IN2P3, CEA/lrfu, Observatoire de Paris, Sorbonne Paris Cité, 10, rue Alice Domon et Léonie
Duquet, Paris Cedex 13, France
13
SLAC National Accelerator Laboratory, 2575 Sand Hill Rd., Menlo Park, CA 94025, USA
14
Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-Alpes, CNRS/IN2P3, 53 av. des Martyrs, F-38026 Grenoble cedex, France
15
Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550, USA
16
Sorbonne Universités, UPMC Univ Paris 06, UMR 7585, LPNHE, F-75005, Paris, France
17
Department of Physics, University of Wisconsin-Madison, Madison, WI 53706, USA
18
NCSA, Univers ity of Illinois at Urbana-Champaign, 1205 W. Clark St., Urbana, IL 61801, USA
19
Brookhaven National Laboratory, Upton, NY 11973, USA
20
Center for Urban Science & Progress, New York University, Brooklyn, NY 11021, USA
21
Center for Cosmology & Particle Physics, New York University, NY 10012, USA
22
Oskar Klein Centre, Department of Physics, Stockholm University, SE 106 91 Stockholm, Sweden
23
Université Paris Diderot, Sorbonne Paris Cité, F-75013 Paris, France
24
Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA
25
Institute of Physics, Laboratory of Astrophysics, École Polytechnique Fedèrale de Lausanne (EPFL), Observatoire de Sauverny, 1290 Versoix, Switzerland
26
Astronomy Department, University of California, 601 Campbell Hall, Berkeley, CA 94720, USA
27
School of Physics, Astronomy and Computational Sciences, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA
28
Université Grenoble-Alpes, Université Savoie Mont Blanc, CNRS/IN2P3 Laboratoire d’Annecy-le-Vieux de Physique des Particules, 9 Chemin de Bellevue—BP
110, F-74940 Annecy-le-Vieux Cedex, France
29
Department of Astronomy and Astrophysics, The Pennsylvania State University, 525 Davey Lab, University Park, PA 16802, USA
30
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA
31
Center for Cosmology, University of California, Irvine, CA 92697, USA
32
Laboratoire des Materiaux Avances (LMA), CNRS/IN2P3, Université de Lyon, F-69622 Villeurbanne, Lyon, France
33
Department of Computer Science, University of Arizona, 1040 E. 4th St., Tucson, AZ 85719, USA
34
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
35
Department of Physics, University of Arizona, 1118 E. Fourth Street, Tucson, AZ 85721, USA
36
IPAC, California Institute of Technology, MS 100-22, Pasadena, CA 91125, USA
37
Laboratoire Univers et Particules de Montpellier (LUPM), Université de Montpellier, CNRS/IN2P3, Place Eugène Bataillon, F-34095 Montpellier, France
38
Cook Astronomical Consulting, 220 Duxbury Ct., San Ramon, CA 94583, USA
39
Western Washington University, 516 High Street, Bellingham, WA 98225, USA
40
Department of Physics and Astronomy, Purdue University, 525 Northwestern Ave., West Lafayette, IN 47907, USA
41
University of Arizona, Tucson, AZ 85721, USA
42
Department of Physics, Harvard University, 17 Oxford St., Cambridge MA 02138, USA
43
Astronomical Institute, Charles University, Praha, Czech Republic
44
Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
45
Université Clermont Auvergne, CNRS, Laboratoire de Physique de Clermont, F-63000 Clermont-Ferrand, France
46
Department of Physics and Astronomy, Rutgers University, 136 Frelinghuysen Rd., Piscataway, NJ 08854, USA
47
Smithsonian Astrophysical Observatory, 60 Garden St., Cambridge MA 02138, USA
48
Astronomy Department, Yale University, New Haven, CT 06520, USA
49
Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
50
National Optical Astronomy Observatory, 950 N. Cherry Ave., Tucson, AZ 85719, USA
51
CNRS, CC-IN2P3, 21 avenue Pierre de Coubertin, CS70202, F-69627 Villeurbanne cedex, France
52
Department of Astronomy and Astrophysics, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637, USA
53
Department of Physics & Astronomy, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA 19104-6396, USA
54
Space Sciences Lab, University of California, 7 Gauss Way, Berkeley, CA 94720-7450, USA
55
Astronomical Observatory, Volgina 7, P.O. Box 74, 11060 Belgrade, Serbia
56
Department of Astronomy, University of Virginia, Charlottesville, VA 22904, USA
57
Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France
58
Astronomy Department, California Institute of Technology, 1200 East California Blvd., Pasadena CA 91125, USA
59
Department of Physics and Astronomy, University of California, 4129 Frederick Reines Hall, Irvine, CA 92697, USA
60
Institute of Physics, Academy of Sciences of the Czech Republic, Na Slovance 2, 182 21 Praha 8, Czech Republic
61
Cerro Tololo Inter-American Observatory, La Serena, Chile
62
Longhorn Industries, Ellensburg, WA 98926, USA
63
McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
64
Department of Chemistry, Biochemistry, and Physics, Rider University, Lawrenceville, NJ 08648, USA
65
US Naval Observatory Flagstaff Station, 10391 Naval Observatory Road, Flagstaff, AZ 86001, USA
66
Department of Astronomy, Center for Astrophysics, Harvard University, 60 Garden St., Cambridge, MA 02138, USA
67
Instituto de Radioastronomía Milimétrica, Av. Divina Pastora 7, Núcleo Central, E-18012 Granada, Spain
68
Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA
69
Santa Cruz Institute for Particle Physics and Physics Department, University of California–Santa Cruz, 1156 High St., Santa Cruz, CA 95064, USA
70
Las Cumbres Observatory, 6740 Cortona Dr., Suite 102, Santa Barbara, CA 93117, USA
71
Department of Astrophysics/IMAPP, Radboud University Nijmegen, P.O. Box 9010, 6500 GL Nijmegen, The Netherlands
72
Gemini Observatory, Northern Operations Center, 670 North A’ohoku Place, Hilo, HI 96720, USA
73
Austin Peay State University, Clarksville, TN 37044, USA
74
Belldex IT Consulting, Tucson, AZ 85742, USA
75
Department of Physics and Astronomy, John Hopkins University, 3701 San Martin Drive, Baltimore, MD 21218, USA
2
The Astrophysical Journal, 873:111 (44pp), 2019 March 10 Ivezić et al.

76
University of Illinois, Physics and Astronomy Departments, 1110 W. Green St., Urbana, IL 61801, USA
77
Department of Physics and Astronomy, Northern Arizona University, P.O. Box 6010, Flagstaff, AZ 86011, USA
78
Saint Vincent College, Department of Physics, 300 Fraser Purchase Road, Latrobe, PA 15650, USA
79
Library of Congress, 101 Independence Ave. SE, Washington, DC 20540, USA
80
The Adler Planetarium, 1300 South Lakeshore Ave., Chicago, IL 60605, USA
81
Department of Physics, Duke University, Durham, NC 27708, USA
82
Department of Physics and Astronomy, University of Pittsburgh, 3941 O’Hara Street, Pittsburgh, PA 15260, USA
83
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing 100012,
Peopleʼs Republic of China
Received 2018 May 25; revised 2019 January 8; accepted 2019 January 17; published 2019 March 11
Abstract
We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope
(LSST). The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an
inventory of the solar system, exploring the transient optical sky, and mapping the Milky Way. LSST will be a
large, wide-field ground-based system designed to obtain repeated images covering the sky visible from Cerro
Pachón in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg
2
field of
view, a 3.2-gigapixel camera, and six filters (ugrizy) covering the wavelength range 320–1050 nm. The project is in
the construction phase and will begin regular survey operations by 2022. About 90% of the observing time will be
devoted to a deep-wide-fast survey mode that will uniformly observe a 18,000 deg
2
region about 800 times
(summed over all six bands) during the anticipated 10 yr of operations and will yield a co-added map to r∼27.5.
These data will result in databases including about 32 trillion observations of 20 billion galaxies and a similar
number of stars, and they will serve the majority of the primary science programs. The remaining 10% of the
observing time will be allocated to special projects such as Very Deep and Very Fast time domain surveys, whose
details are currently under discussion. We illustrate how the LSST science drivers led to these choices of system
parameters, and we describe the expected data products and their characteristics.
Key words: astrometry – cosmology: observations – Galaxy: general – methods: observational – stars: general –
surveys
1. Introduction
Major advances in our understanding of the universe have
historically arisen from dramatic improvements in our ability to
“see.” We have developed progressively larger telescopes over
the past century, allowing us to peer further into space, and
further back in time. With the development of advanced
instrumentation—imagers, spectrographs, and polarimeters—
we have been able to parse radiation detected from distant
sources over the full electromagnetic spectrum in increasingly
subtle ways. These data have provided the detailed information
needed to construct physical models of planets, stars, galaxies,
quasars, and larger structures and to probe the new physics of
dark matter and dark energy.
Until recently, most astronomical investigations have
focused on small samples of cosmic sources or individual
objects. This is because our largest telescope facilities typically
had rather small fields of view, and those with large fields of
view could not detect very faint sources. With all of our
existing telescope facilities, we have still surveyed only a small
fraction of the observable universe (except when considering
the most luminous quasars).
Over the past two decades, however, advances in technology
have made it possible to move beyond the traditional observa-
tional paradigm and to undertake large-scale sky surveys. As
vividly demonstrated by surveys such as the Sloan Digital Sky
Survey (SDSS; York et al. 2000),theTwoMicronAllSky
Survey (2MASS; Skrutskie et al. 2006),theGalaxy Evolution
Explorer (GALEX; Martin et al. 2005),andGaia (Gaia
Collaboration et al. 2016) to name but a few, sensitive and
accurate multicolor surveys over a large fraction of the sky enable
an extremely broad range of new scientific investigations. These
projects, based on a synergy of advances in telescope construc-
tion, detectors, and, above all, information technology, have
dramatically impacted nearly all fields of astronomy—and several
areas of fundamental physics. In addition, the worldwide attention
received by Sky in Google Earth
85
(Scranton et al. 2007), the
World Wide Telescope,
86
and the hundreds of thousands of
volunteers classifying galaxies in the Galaxy Zoo project
(Lintott et al. 2011) and its extensions demonstrate that the
impact of sky surveys extends far beyond fundamental science
progress and reaches all of society.
Motivated by the evident scientific progress enabled by
large sky surveys, three nationally endorsed reports by the
US National Academy of Sciences (National Research Council
2001, 2003a, 2003b) concluded that a dedicated ground-based
wide-field imaging telescope with an effective aperture of
6–8m“is a high priority for planetary science, astronomy, and
physics over the next decade.” The Large Synoptic Survey
Telescope (LSST) described here is such a system. Located on
Cerro Pachón in northern Chile, the LSST will be a large, wide-
field, ground-based telescope designed to obtain multiband
images over a substantial fraction of the sky every few nights.
The survey will yield contiguous overlapping imaging of over
half the sky in six optical bands, with each sky location visited
close to 1000 times over 10 yr. The 2010 report “New Worlds,
New Horizons in Astronomy and Astrophysics” by the NRC
Committee for a Decadal Survey of Astronomy and Astro-
physics (National Research Council 2010) ranked LSST as
its top priority for large ground-based projects, and in 2014
May the National Science Board approved the project
for construction. As of this writing, the LSST construction
phase is close to the peak of activity. After initial tests with a
84
Corresponding author.
85
https://www.google.com/sky/
86
http://worldwidetelescope.org/home
3
The Astrophysical Journal, 873:111 (44pp), 2019 March 10 Ivezić et al.

commissioning camera and full commissioning with the main
camera, the 10 yr sky survey is projected to begin in 2022.
The purpose of this paper is to provide an overall summary
of the main LSST science drivers and how they led to the
current system design parameters (Section 2), to describe the
anticipated data products (Section 3), and to provide a few
examples of the science programs that LSST will enable
(Section 4). The community involvement is discussed in
Section 5, and broad educational and societal impacts of the
project are discussed in Section 6. Concluding remarks are
presented in Section 7.
2. From Science Drivers to Reference Design
The most important characteristic that determines the speed
at which a system can survey a given sky area to a given flux
limit (i.e., its depth) is its étendue (or grasp), the product of its
primary mirror area and the angular area of its field of view (for
a given set of observing conditions, such as seeing and sky
brightness). The effective étendue for LSST will be greater than
300 m
2
deg
2
, which is more than an order of magnitude larger
than that of any existing facility. For example, the SDSS, with
its 2.5 m telescope (Gunn et al. 2006) and a camera with 30
imaging CCDs (Gunn et al. 1998), has an effective étendue of
only 5.9 m
2
deg
2
.
The range of scientific investigations that will be enabled by
such a dramatic improvement in survey capability is extremely
broad. Guided by the community-wide input assembled in the
report of the Science Working Group of the LSST in 2004
(Science Working Group of the LSST & Strauss 2004), the
LSST is designed to achieve goals set by four main science
themes:
1. Probing dark energy and dark matter.
2. Taking an inventory of the solar system.
3. Exploring the transient optical sky.
4. Mapping the Milky Way.
Each of these four themes itself encompasses a variety of
analyses, with varying sensitivity to instrumental and system
parameters. These themes fully exercise the technical capabil-
ities of the system, such as photometric and astrometric
accuracy and image quality. About 90% of the observing time
will be devoted to a deep-wide-fast (main) survey mode. The
working paradigm is that all scientific investigations will utilize
a common database constructed from an optimized observing
program (the main survey mode), such as that discussed in
Section 3.1. Here we briefly describe these science goals and
the most challenging requirements for the telescope and
instrument that are derived from those goals, which will
inform the overall system design decisions discussed below.
For a more detailed discussion, we refer the reader to the
LSST Science Requirements Document (SRD; Ivezić &The
LSST Science Collaboration 2011), the LSST Science Book
(LSST Science Collaboration et al. 2009,hereafterSciBook),
and links to technical papers and presentations athttps://www.
lsst.org/scientists.
2.1. The Main Science Drivers
The main science drivers are used to optimize various system
parameters. Ultimately, in this high-dimensional parameter
space, there is a manifold defined by the total project cost. The
science drivers must both justify this cost and provide guidance
on how to optimize various parameters while staying within the
cost envelope.
Here we summarize the dozen or so most important
interlocking constraints on data and system properties placed
by the four main science themes:
1. The depth of a single visit to a given field.
2. Image quality.
3. Photometric accuracy.
4. Astrometric accuracy.
5. Optimal exposure time.
6. The filter complement.
7. The distribution of revisit times (i.e., the cadence of
observations), including the survey lifetime.
8. The total number of visits to a given area of sky.
9. The co-added survey depth.
10. The distribution of visits on the sky, and the total sky
coverage.
11. The distribution of visits per filter.
12. Parameters characterizing data processing and data access
(such as the maximum time allowed after each exposure
to report transient sources, and the maximum allowed
software contribution to measurement errors ).
We present a detailed discussi on of how these science-
driven data properties are transformed to system parameters
below.
2.1.1. Probing Dark Energy and Dark Matter
Current models of cosmology require the existence of both
dark matter and dark energy to match observational constraints
(Riess et al. 2007; Komatsu et al. 2009; Percival et al. 2010;
LSST Dark Energy Science Collaboration 2012; Weinberg
et al. 2015, and references therein). Dark energy affects the
cosmic history of both the Hubble expansion and mass
clustering. Distinguishing competing models for the physical
nature of dark energy, or alternative explanations involving
modifications of the general theory of relativity, will require
percent-level measurements of both the cosmic expansion and
the growth of dark matter structure as a function of redshift.
Any given cosmological probe is sensitive to, and thus
constrains degenerate combinations of, several cosmological
and astrophysical/systematic parameters. Therefore, the most
robust cosmological constraints are the result of using
interlocking combinations of probes. The most powerful
probes include weak gravitational lens cosmic shear (weak
lensing (WL)), galaxy clustering and baryon acoustic oscilla-
tions (large-scale structure, LSS), the mass function and
clustering of clusters of galaxies, time delays in lensed quasar
and supernova (SN) systems, and photometry of Type Ia
SNe—all as functions of redshift. Using the cosmic microwave
background (CMB) fluctuations as the normalization, the
combination of these probes can yield the needed precision
to distinguish among models of dark energy (see, e.g.,
Zhan 2006, and references therein) . The challenge is to turn
this available precision into accuracy, by careful modeling and
marginalization over a variety of systematic effects (see, e.g.,
Krause & Eifler 2017).
Meanwhile, there are a number of astrophysical probes
of the fundamental properties of dark matter worth exploring,
including, for example, weak- and strong-lensing observations
of the mass distribution in galaxies and isolated and merging
clusters, in conjunction with dynamical and X-ray observations
4
The Astrophysical Journal, 873:111 (44pp), 2019 March 10 Ivezić et al.

(see, e.g., Dawson et al. 2012; Newman et al. 2013;
Rocha et al. 2013), the numbers and gamma-ray emission
from dwarf satellite galaxies (see, e.g., Hargis et al. 2014;
Drlica-Wagner et al. 2015), the subtle perturbations of stellar
streams in the Milky Way halo by dark matter substructure
(Belokurov & Koposov 2016), and massive compact halo
object microlensing (Alcock et al. 2001).
Three of the primary dark energy probes, WL, LSS, and SN,
provide unique and independent constraints on the LSST
system design (SciBook, Chaps. 11–15).
WL techniques can be used to map the distribution of mass
as a function of redshift and thereby trace the history of both
the expansion of the universe and the growth of structure (e.g.,
Hu & Tegmark 1999; for recent reviews see Kilbinger 2015;
Mandelbaum 2018). Measurements of cosmic shear as a
function of redshift allow determination of angular distances
versus cosmic time, providing multiple independent constraints
on the nature of dark energy. These investigations require deep
wide-area multicolor imaging with stringent requirements on
shear systematics in at least two bands, and excellent
photometry in at least five bands to measure photometric
redshifts (a requirement shared with LSS, and indeed all
extragalactic science drivers). The strongest constraints on the
LSST image quality arise from this science program. In order to
control systematic errors in shear measurement, the desired
depth must be achieved with many short exposures (allowing
for systematics in the measurement of galaxy shapes related to
the point-spread functions [PSFs] and telescope pointing to be
diagnosed and removed). Detailed simulations of WL techni-
ques show that imaging over ∼20,000 deg
2
to a 5σ point-
source depth of r
AB
∼27.5 gives adequate signal to measure
shapes for of order 2 billion galaxies for WL. These numbers
are adequate to reach Stage IV goals for dark energy, as defined
by the Dark Energy Task Force (Albrecht et al. 2006). This
depth, as well as the corresponding deep surface brightness
limit, optimizes the number of galaxies with measured shapes
in ground-based seeing and allows their detection in significant
numbers to beyond a redshift of two. Analyzing these data will
require sophisticated data processing techniques. For example,
rather than simply co-adding all images in a given region of
sky, the individual exposures, each with their own PSF and
noise characteristics, should be analyzed simultaneously to
optimally measure the shapes of galaxies (Tyson et al. 2008;
Jee & Tyson 2011).
SNe Ia provided the first robust evidence that the expansion
of the universe is accelerating (Riess et al. 1998; Perlmutter
et al. 1999). To fully exploit the SN science potential, light
curves sampled in multiple bands every few days over the
course of a few months are required. This is essential to search
for systematic differences in SN populations (e.g., due to
differing progenitor channels), which may masquerade as
cosmological effects, as well as to determine photometric
redshifts from the SNe themselves. Unlike other cosmological
probes, even a single object gives information on the relation-
ship between redshift and distance. Thus, a large number of
SNe across the sky allows one to search for any dependence of
dark energy properties on direction, which would be an
indicator of new physics. The results from this method can be
compared with similar measures of anisotropy from the
combination of WL and LSS (Zhan et al. 2009). Given the
expected SN flux distribution at the redshifts where dark energy
is important, the single-visit depth should be at least r∼24.
Good image quality is required to separate SN photometrically
from their host galaxies. Observations in at least five
photometric bands will allow proper K-corrected light curves
to be measured over a range of redshift. Carrying out these
K-corrections requires that the calibration of the relative offsets
in photometric zero-points between filters and the system
response functions, especially near the edges of bandpasses, be
accurate to about 1% (Wood-Vasey et al. 2007), similar to the
requirements from photometric redshifts of galaxies. Deeper
data (r>26) for small areas of the sky can extend the
discovery of SNe to a mean redshift of 0.7 (from ∼0.5 for the
main survey), with some objects beyond z∼1 (Garnavich
et al. 2004; Pinto et al. 2004; SciBook, Chap. 11). The added
statistical leverage on the “pre-acceleration” era (z1) would
improve constraints on the properties of dark energy as a
function of redshift.
Finally, there will be powerful cross-checks and comple-
mentarities with other planned or proposed surveys, such as
Euclid (Laureijs et al. 2011) and WFIRST (Spergel et al. 2015),
which will provide wide-field optical–IR imaging from space;
DESI (Levi et al. 2013) and PFS (Takada et al. 2014), which
will measure spectroscopic baryon acoustic oscillations
(BAOs) with millions of galaxies; and SKA
87
(radio). Large
survey volumes are key to probing dynamical dark energy
models (with subhorizon dark energy clustering or anisotropic
stresses). The cross-correlation of the three-dimensional mass
distribution—as probed by neutral hydrogen in CHIME
(Newburgh et al. 2014), HIRAX (Newburgh et al. 2016),or
SKA, or galaxies in DESI and PFS—with the gravitational
growth probed by tomographic shear in LSST will be a
complementary way to constrain dark energy properties beyond
simply characterizing its equation of state and to test the
underlying theory of gravity. Current and future ground-based
CMB experiments, such as Advanced ACT (De Bernardis
et al. 2016), SPT-3G (Benson et al. 2014), Simons Observa-
tory, and CMB Stage-4 (Abazajian et al. 2016), will also offer
invaluable opportunities for cross-correlations with secondary
CMB anisotropies.
2.1.2. Taking an Inventory of the Solar System
The small-body populations in the solar system, such as
asteroids, trans-Neptunian objects (TNOs), and comets, are
remnants of its early assembly. The history of accretion,
collisional grinding, and perturbation by existing and vanished
giant planets is preserved in the orbital elements and size
distributions of those objects. Cataloging the orbital para-
meters, size distributions, colors, and light curves of these
small-body populations requires a large number of observations
in multiple filters and will lead to insights into planetary
formation and evolution by providing the basis and constraints
for new theoretical models. In addition, collisions in the main
asteroid belt between Mars and Jupiter still occur and
occasionally eject objects on orbits that may place them on a
collision course with Earth. Studying the properties of main
belt asteroids at subkilometer sizes is important for linking the
near-Earth object (NEO) population with its source in the main
belt. About 20% of NEOs, the potentially hazardous asteroids
(PHAs), are in orbits that pass sufficiently close to Earth’s
orbit, to within 0.05 au, that perturbations on timescales of a
century can lead to the possibility of collision. In 2005
87
https://www.skatelescope.org
5
The Astrophysical Journal, 873:111 (44pp), 2019 March 10 Ivezić et al.

Frequently asked questions

Q1. What are the contributions mentioned in the paper "Lsst: from science drivers to reference design and anticipated data products" ?

The authors describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope ( LSST ). The project is in the construction phase and will begin regular survey operations by 2022. The remaining 10 % of the observing time will be allocated to special projects such as Very Deep and Very Fast time domain surveys, whose details are currently under discussion. The authors illustrate how the LSST science drivers led to these choices of system parameters, and they describe the expected data products and their characteristics. 

Q2. What are the main constraints on the nature of dark energy?

Measurements of cosmic shear as a function of redshift allow determination of angular distances versus cosmic time, providing multiple independent constraints on the nature of dark energy. 

Q3. What are the key features of the large survey volumes?

Large survey volumes are key to probing dynamical dark energy models (with subhorizon dark energy clustering or anisotropic stresses). 

Q4. How many r bands should be used to reach the full depth of the telescope?

The images should reach a depth of at least 24.5 (5σ for point sources) in the r band to reach high completeness down to the 140 m mandate for NEOs. 

Q5. What is the next frontier in cosmology?

The next frontier in this field will require measuring the colors of fast transients and probing variability at faint magnitudes. 

Q6. How many observations are required to measure the distances to solar neighborhood stars?

In order to measure distances to solar neighborhood stars out to a distance of 300 pc (the thin-disk scale height), with geometric distance accuracy of at least 30%, trigonometric parallax measurements accurate to 1 mas (1σ) are required over 10 yr. 

Q7. How many observations are required to measure the metallicity distribution of stars?

To achieve the required proper-motion and parallax accuracy with an assumed astrometric accuracy of 10 mas per observation per coordinate, approximately 1000 separate observations are required. 

Q8. What is the significance of the u band?

An SDSS-like u band (Fukugita et al. 1996) is extremely important for separating low-redshift quasars from hot stars and for estimating the metallicities of F/G main-sequence stars. 

Q9. What are the remnants of the solar system?

The small-body populations in the solar system, such as asteroids, trans-Neptunian objects (TNOs), and comets, are remnants of its early assembly. 

Q10. How many times will the survey yield?

The survey will yield contiguous overlapping imaging of over half the sky in six optical bands, with each sky location visited close to 1000 times over 10 yr. 

Q11. How long should the exposure time be?

5. The single-visit exposure time should be less than about a minute to prevent trailing of fast-moving objects and to aid control of various systematic effects induced by the atmosphere. 

Q12. How many visits should be made to the ecliptic and Galactic planes?

11. The distribution of visits on the sky should extend over at least ∼18,000 deg2 to obtain the required number of galaxies for WL studies, with attention paid to include “special” regions such as the ecliptic and Galactic planes, and the Large and Small Magellanic Clouds (if in the Southern Hemisphere). 

Q13. What is the way to measure the orbital parameters of TNOs?

The images must be well sampled to enable accurate astrometry, with absolute accuracy of at least 0 1 in order to measure orbital parameters of TNOs with enough precision to constrain theoretical models and enable prediction of occultations. 

Q14. How many times should the SN Ia light curves be revisited?

7. The revisit time distribution should enable determination of orbits of solar system objects and sample SN light curves every few days, while accommodating constraints set by proper-motion and trigonometric parallax measurements. 

Q15. What has made it possible to move beyond the traditional observational paradigm?

Over the past two decades, however, advances in technology have made it possible to move beyond the traditional observational paradigm and to undertake large-scale sky surveys. 

Q16. What is the significance of a large number of SNe across the sky?

a large number of SNe across the sky allows one to search for any dependence of dark energy properties on direction, which would be an indicator of new physics. 

Q17. How should the image quality be maintained?

2. Image quality should maintain the limit set by the atmosphere (the median free-air seeing is 0 65 in the r band at the chosen site; see Figure 1) and not be degraded appreciably by the hardware. 

Q18. What has the evolution of telescopes allowed us to see?

The authors have developed progressively larger telescopes over the past century, allowing us to peer further into space, and further back in time.