Peter Nugent

Bio: Peter Nugent is an academic researcher from Lawrence Berkeley National Laboratory. The author has contributed to research in topics: Supernova & Light curve. The author has an hindex of 127, co-authored 754 publications receiving 92988 citations. Previous affiliations of Peter Nugent include Liverpool John Moores University & National Autonomous University of Mexico.

Astro2020 APC White Paper: The MegaMapper: a z > 2 Spectroscopic Instrument for the Study of Inflation and Dark Energy

Abstract: Author(s): Schlegel, David J; Kollmeier, Juna A; Aldering, Greg; Bailey, Stephen; Baltay, Charles; Bebek, Christopher; BenZvi, Segev; Besuner, Robert; Blanc, Guillermo; Bolton, Adam S; Bouri, Mohamed; Brooks, David; Buckley-Geer, Elizabeth; Cai, Zheng; Crane, Jeffrey; Dey, Arjun; Doel, Peter; Fan, Xiaohui; Ferraro, Simone; Font-Ribera, Andreu; Gutierrez, Gaston; Guy, Julien; Heetderks, Henry; Huterer, Dragan; Infante, Leopoldo; Jelinsky, Patrick; Johns, Matthew; Karagiannis, Dionysios; Kent, Stephen M; Kim, Alex G; Kneib, Jean-Paul; Kronig, Luzius; Konidaris, Nick; Lahav, Ofer; Lampton, Michael L; Lang, Dustin; Leauthaud, Alexie; Liguori, Michele; Linder, Eric V; Magneville, Christophe; Martini, Paul; Mateo, Mario; McDonald, Patrick; Miller, Christopher J; Moustakas, John; Myers, Adam D; Mulchaey, John; Newman, Jeffrey A; Nugent, Peter E; Palanque-Delabrouille, Nathalie; Padmanabhan, Nikhil; Piro, Anthony L; Poppett, Claire; Prochaska, Jason X; Pullen, Anthony R; Rabinowitz, David; Ramirez, Solange; Rix, Hans-Walter; Ross, Ashley J; Samushia, Lado; Schaan, Emmanuel; Schubnell, Michael; Seljak, Uros; Seo, Hee-Jong; Shectman, Stephen A; Silber, Joseph; Simon, Joshua D; Slepian, Zachary; Soares-Santos, Marcelle; Tarle, Greg; Thompson, Ian; Valluri, Monica; Wechsler, Risa H; White, Martin; Wilson, Michael J; Yeche, Christophe; Zaritsky, Dennis | Abstract: MegaMapper is a proposed ground-based experiment to measure Inflation parameters and Dark Energy from galaxy redshifts at 2

Progenitor, precursor, and evolution of the dusty remnant of the stellar merger M31-LRN-2015

Abstract: Author(s): Blagorodnova, N; Karambelkar, V; Adams, SM; Kasliwal, MM; Kochanek, CS; Dong, S; Campbell, H; Hodgkin, S; Jencson, JE; Johansson, J; Kozlowski, S; Laher, RR; Masci, F; Nugent, P; Rebbapragada, U | Abstract: ABSTRACT M31-LRN-2015 is a likely stellar merger discovered in the Andromeda Galaxy in 2015. We present new optical to mid-infrared photometry and optical spectroscopy for this event. Archival data show that the source started to brighten ∼2nyr before the nova event. During this precursor phase, the source brightened by ∼3 mag. The light curve at 6 and 1.5 months before the main outburst may show periodicity, with periods of 16n±n0.3 and 28.1n±n1.4 d, respectively. This complex emission may be explained by runaway mass-loss from the system after the binary undergoes Roche lobe overflow, leading the system to coalesce in tens of orbital periods. While the progenitor spectral energy distribution shows no evidence of pre-existing warm dust in the system, the remnant forms an optically thick dust shell at approximately four months after the outburst peak. The optical depth of the shell increases dramatically after 1.5 yr, suggesting the existence of shocks that enhance the dust formation process. We propose that the merger remnant is likely an inflated giant obscured by a cooling shell of gas with mass ∼0.2 M⊙ ejected at the onset of the common envelope phase.

Radio follow-up of gravitational wave triggers during Advanced LIGO O1

Abstract: We present radio follow-up observations carried out with the Karl G. Jansky Very Large Array during the first observing run (O1) of the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO). A total of three gravitational-wave triggers were followed-up during the ≈ 4 months of O1, from 2015 September to 2016 January. Two of these triggers, GW150914 and GW151226, are binary black hole (BH) merger events of high significance. A third trigger, G194575, was subsequently declared as an event of no interest (i.e., a false alarm). Our observations targeted selected optical transients identified by the intermediate Palomar Transient Factory in the Advanced LIGO error regions of the three triggers, and a limited region of the gravitational-wave localization area of G194575 not accessible to optical telescopes due to Sun constraints, where a possible high-energy transient was identified. No plausible radio counterparts to GW150914 and GW151226 were found, in agreement with expectations for binary BH mergers. We show that combining optical and radio observations is key to identifying contaminating radio sources that may be found in the follow-up of gravitational-wave triggers, such as emission associated with star formation and active galactic nuclei. We discuss our results in the context of the theoretical predictions for radio counterparts to gravitational-wave transients, and describe our future plans for the radio follow-up of Advanced LIGO (and Virgo) triggers.

The Acceleration of the Universe:. Measurements of Cosmological Parameters from Type Ia Supernovae

Abstract: The fate of the Universe, infinite expansion or a ldquo;big crunchrdquo;, can be determined by measuring the redshifts and brightness of very distant supernovae. These provide a record of changes in the expansion rate of the Universe over the past several billion years. The mass density, ?M, and cosmological-constant energy density ??, are measured from a data-set consisting of 42 high-redshift Type Ia supernovae discovered by the Supernova Cosmology Project. The magnitude-redshift data for these supernovae, at redshifts between 0.18 and 0.83, are fit jointly with a set of supernovae from the Cal?n/Tololo Supernova Survey, at redshifts below 0.1, to yield values for the cosmological parameters. We find ?Mflat = 0.28+0.09-0.08 (1? statistical)+0.05-0.04 (identified systematics). The data are strongly inconsistent with a ? = 0 flat cosmology, the simplest inflationary universe model. An open, ? = 0 cosmology also does not fit the data well: the data indicate that the cosmological constant is non-zero and positive, with a confidence of P(?>0) = 99%, including the identified systematic uncertainties. Thus, the Universe is found to be accelerating, i.e., q0 = ?M/2 - ?? < 0. The best-fit age of the universe relative to the Hubble time is t0flat = 14.9+1.4-1.1 (0.63/h) Gyr for a flat cosmology.

Real-time recovery efficiencies and performance of the Palomar Transient Factory’s transient discovery pipeline

Abstract: We present the transient source detection efficiencies of the Palomar Transient Factory (PTF), parameterizing the number of transients that PTF found versus the number of similar transients that occurred over the same period in the survey search area but were missed. PTF was an optical sky survey carried out with the Palomar 48 inch telescope over 2009–2012, observing more than 8000 square degrees of sky with cadences of between one and five days, locating around 50,000 non-moving transient sources, and spectroscopically confirming around 1900 supernovae. We assess the effectiveness with which PTF detected transient sources, by inserting million artificial point sources into real PTF data. We then study the efficiency with which the PTF real-time pipeline recovered these sources as a function of the source magnitude, host galaxy surface brightness, and various observing conditions (using proxies for seeing, sky brightness, and transparency). The product of this study is a multi-dimensional recovery efficiency grid appropriate for the range of observing conditions that PTF experienced and that can then be used for studies of the rates, environments, and luminosity functions of different transient types using detailed Monte Carlo simulations. We illustrate the technique using the observationally well-understood class of type Ia supernovae.

Measurements of Omega and Lambda from 42 High-Redshift Supernovae

Abstract: We report measurements of the mass density, Omega_M, and cosmological-constant energy density, Omega_Lambda, of the universe based on the analysis of 42 Type Ia supernovae discovered by the Supernova Cosmology Project. The magnitude-redshift data for these SNe, at redshifts between 0.18 and 0.83, are fit jointly with a set of SNe from the Calan/Tololo Supernova Survey, at redshifts below 0.1, to yield values for the cosmological parameters. All SN peak magnitudes are standardized using a SN Ia lightcurve width-luminosity relation. The measurement yields a joint probability distribution of the cosmological parameters that is approximated by the relation 0.8 Omega_M - 0.6 Omega_Lambda ~= -0.2 +/- 0.1 in the region of interest (Omega_M <~ 1.5). For a flat (Omega_M + Omega_Lambda = 1) cosmology we find Omega_M = 0.28{+0.09,-0.08} (1 sigma statistical) {+0.05,-0.04} (identified systematics). The data are strongly inconsistent with a Lambda = 0 flat cosmology, the simplest inflationary universe model. An open, Lambda = 0 cosmology also does not fit the data well: the data indicate that the cosmological constant is non-zero and positive, with a confidence of P(Lambda > 0) = 99%, including the identified systematic uncertainties. The best-fit age of the universe relative to the Hubble time is t_0 = 14.9{+1.4,-1.1} (0.63/h) Gyr for a flat cosmology. The size of our sample allows us to perform a variety of statistical tests to check for possible systematic errors and biases. We find no significant differences in either the host reddening distribution or Malmquist bias between the low-redshift Calan/Tololo sample and our high-redshift sample. The conclusions are robust whether or not a width-luminosity relation is used to standardize the SN peak magnitudes.

Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant

Abstract: We present spectral and photometric observations of 10 Type Ia supernovae (SNe Ia) in the redshift range 0.16 " z " 0.62. The luminosity distances of these objects are determined by methods that employ relations between SN Ia luminosity and light curve shape. Combined with previous data from our High-z Supernova Search Team and recent results by Riess et al., this expanded set of 16 high-redshift supernovae and a set of 34 nearby supernovae are used to place constraints on the following cosmo- logical parameters: the Hubble constant the mass density the cosmological constant (i.e., the (H 0 ), () M ), vacuum energy density, the deceleration parameter and the dynamical age of the universe ) " ), (q 0 ), ) M \ 1) methods. We estimate the dynamical age of the universe to be 14.2 ^ 1.7 Gyr including systematic uncer- tainties in the current Cepheid distance scale. We estimate the likely e†ect of several sources of system- atic error, including progenitor and metallicity evolution, extinction, sample selection bias, local perturbations in the expansion rate, gravitational lensing, and sample contamination. Presently, none of these e†ects appear to reconcile the data with and ) " \ 0 q 0 " 0.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Seven-year wilkinson microwave anisotropy probe (wmap *) observations: cosmological interpretation

Abstract: The combination of seven-year data from WMAP and improved astrophysical data rigorously tests the standard cosmological model and places new constraints on its basic parameters and extensions. By combining the WMAP data with the latest distance measurements from the baryon acoustic oscillations (BAO) in the distribution of galaxies and the Hubble constant (H0) measurement, we determine the parameters of the simplest six-parameter ΛCDM model. The power-law index of the primordial power spectrum is ns = 0.968 ± 0.012 (68% CL) for this data combination, a measurement that excludes the Harrison–Zel’dovich–Peebles spectrum by 99.5% CL. The other parameters, including those beyond the minimal set, are also consistent with, and improved from, the five-year results. We find no convincing deviations from the minimal model. The seven-year temperature power spectrum gives a better determination of the third acoustic peak, which results in a better determination of the redshift of the matter-radiation equality epoch. Notable examples of improved parameters are the total mass of neutrinos, � mν < 0.58 eV (95% CL), and the effective number of neutrino species, Neff = 4.34 +0.86 −0.88 (68% CL), which benefit from better determinations of the third peak and H0. The limit on a constant dark energy equation of state parameter from WMAP+BAO+H0, without high-redshift Type Ia supernovae, is w =− 1.10 ± 0.14 (68% CL). We detect the effect of primordial helium on the temperature power spectrum and provide a new test of big bang nucleosynthesis by measuring Yp = 0.326 ± 0.075 (68% CL). We detect, and show on the map for the first time, the tangential and radial polarization patterns around hot and cold spots of temperature fluctuations, an important test of physical processes at z = 1090 and the dominance of adiabatic scalar fluctuations. The seven-year polarization data have significantly improved: we now detect the temperature–E-mode polarization cross power spectrum at 21σ , compared with 13σ from the five-year data. With the seven-year temperature–B-mode cross power spectrum, the limit on a rotation of the polarization plane due to potential parity-violating effects has improved by 38% to Δα =− 1. 1 ± 1. 4(statistical) ± 1. 5(systematic) (68% CL). We report significant detections of the Sunyaev–Zel’dovich (SZ) effect at the locations of known clusters of galaxies. The measured SZ signal agrees well with the expected signal from the X-ray data on a cluster-by-cluster basis. However, it is a factor of 0.5–0.7 times the predictions from “universal profile” of Arnaud et al., analytical models, and hydrodynamical simulations. We find, for the first time in the SZ effect, a significant difference between the cooling-flow and non-cooling-flow clusters (or relaxed and non-relaxed clusters), which can explain some of the discrepancy. This lower amplitude is consistent with the lower-than-theoretically expected SZ power spectrum recently measured by the South Pole Telescope Collaboration.

Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant To Appear in the Astronomical Journal

Abstract: We present spectral and photometric observations of 10 type Ia supernovae (SNe Ia) in the redshift range 0.16 � z � 0.62. The luminosity distances of these objects are determined by methods that employ relations between SN Ia luminosity and light curve shape. Combined with previous data from our High-Z Supernova Search Team (Garnavich et al. 1998; Schmidt et al. 1998) and Riess et al. (1998a), this expanded set of 16 high-redshift supernovae and a set of 34 nearby supernovae are used to place constraints on the following cosmological parameters: the Hubble constant (H0), the mass density (M), the cosmological constant (i.e., the vacuum energy density, �), the deceleration parameter (q0), and the dynamical age of the Universe (t0). The distances of the high-redshift SNe Ia are, on average, 10% to 15% farther than expected in a low mass density (M = 0.2) Universe without a cosmological constant. Different light curve fitting methods, SN Ia subsamples, and prior constraints unanimously favor eternally expanding models with positive cosmological constant (i.e., � > 0) and a current acceleration of the expansion (i.e., q0 < 0). With no prior constraint on mass density other than M � 0, the spectroscopically confirmed SNe Ia are statistically consistent with q0 < 0 at the 2.8�