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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.
Topics: Supernova, Light curve, Galaxy, Redshift, White dwarf


Papers
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TL;DR: In this paper, the authors used forward modeling with the Tractor to search for an offset AGN in a sample of 5493 optically variable AGN detected with the Zwicky Transient Facility (ZTF).
Abstract: A supermassive black hole (SMBH) ejected from the potential well of its host galaxy via gravitational wave recoil carries important information about the mass ratio and spin alignment of the pre-merger SMBH binary. Such a recoiling SMBH may be detectable as an active galactic nucleus (AGN) broad line region offset by up to 10\,kpc from a disturbed host galaxy. We describe a novel methodology using forward modeling with \texttt{The Tractor} to search for such offset AGN in a sample of 5493 optically variable AGN detected with the Zwicky Transient Facility (ZTF). We present the discovery of 9 AGN which may be spatially offset from their host galaxies and are candidates for recoiling SMBHs. Five of these offset AGN exhibit double-peaked broad Balmer emission from an unobscured accretion disk and four show radio emission indicative of a relativistic jet. The fraction of double-peaked emitters in our spatially offset AGN sample is significantly larger than the 16\% double-peaked emitter fraction observed for ZTF AGN overall. In our sample of variable AGN we also identified 52 merging galaxies, including a new spectroscopically confirmed dual AGN. Finally, we detected the dramatic rebrightening of SDSS1133, a previously discovered variable object and recoiling SMBH candidate, in ZTF. The flare was accompanied by the re-emergence of strong P-Cygni line features indicating it likely is an outbursting luminous blue variable star.

3 citations

Journal ArticleDOI
TL;DR: The La Silla-QUEST (LSQ) Variability Survey as discussed by the authors is a wide-field synoptic survey in the Southern Hemisphere, focussing on the discovery and study of transients ranging from low redshift (z < 0.1) SN Ia, Tidal Disruption events, RR Lyr{\ae} variables, CVs, Quasars, TNOs and others.
Abstract: We describe the La Silla-QUEST (LSQ) Variability Survey. LSQ is a dedicated wide-field synoptic survey in the Southern Hemisphere, focussing on the discovery and study of transients ranging from low redshift (z < 0.1) SN Ia, Tidal Disruption events, RR Lyr{\ae} variables, CVs, Quasars, TNOs and others. The survey utilizes the 1.0-m Schmidt Telescope of the European Southern Observatory at La Silla, Chile, with the large-area QUEST camera, a mosaic of 112 CCDs with field of view of 9.6 square degrees. The LSQ Survey was commissioned in 2009, and is now regularly covering ~1000 square deg per night with a repeat cadence of hours to days. The data are currently processed on a daily basis. We present here a first look at the photometric capabilities of LSQ and we discuss some of the most interesting recent transient detections.

3 citations

Journal ArticleDOI
TL;DR: In this article, the authors presented convincing evidence of unburned carbon at photospheric velocities in new observations of 5 Type Ia supernovae (SNe Ia) obtained by the Nearby Supernova Factory.
Abstract: We present convincing evidence of unburned carbon at photospheric velocities in new observations of 5 Type Ia supernovae (SNe Ia) obtained by the Nearby Supernova Factory. These SNe are identified by examining 346 spectra from 124 SNe obtained before +2.5 d relative to maximum. Detections are based on the presence of relatively strong C II 6580 absorption "notches" in multiple spectra of each SN, aided by automated fitting with the SYNAPPS code. Four of the 5 SNe in question are otherwise spectroscopically unremarkable, with ions and ejection velocities typical of SNe Ia, but spectra of the fifth exhibits high-velocity (v > 20,000 km/s) Si II and Ca II features. On the other hand, the light curve properties are preferentially grouped, strongly suggesting a connection between carbon-positivity and broad band light curve/color behavior: Three of the 5 have relatively narrow light curves but also blue colors, and a fourth may be a dust-reddened member of this family. Accounting for signal-to-noise and phase, we estimate that 22 +10/-6% of SNe Ia exhibit spectroscopic C II signatures as late as -5 d with respect to maximum. We place these new objects in the context of previously recognized carbon-positive SNe Ia, and consider reasonable scenarios seeking to explain a physical connection between light curve properties and the presence of photospheric carbon. We also examine the detailed evolution of the detected carbon signatures and the surrounding wavelength regions to shed light on the distribution of carbon in the ejecta. Our ability to reconstruct the C II 6580 feature in detail under the assumption of purely spherical symmetry casts doubt on a "carbon blobs" hypothesis, but does not rule out all asymmetric models. A low volume filling factor for carbon, combined with line-of-sight effects, seems unlikely to explain the scarcity of detected carbon in SNe Ia by itself.

3 citations

Posted Content
TL;DR: The Type Ia supernova LSQ14fmg exhibits exaggerated properties which may help to reveal the origin of the "super-Chandrasekhar" (or 03fg-like) group.
Abstract: The Type Ia supernova (SN Ia) LSQ14fmg exhibits exaggerated properties which may help to reveal the origin of the "super-Chandrasekhar" (or 03fg-like) group. The optical spectrum is typical of a 03fg-like SN Ia, but the light curves are unlike those of any SNe Ia observed. The light curves of LSQ14fmg rise extremely slowly. At -23 rest-frame days relative to B-band maximum, LSQ14fmg is already brighter than $M_V$=-19 mag before host extinction correction. The observed color curves show a flat evolution from the earliest observation to approximately one week after maximum. The near-infrared light curves peak brighter than -20.5 mag in the J and H bands, far more luminous than any 03fg-like SNe Ia with near-infrared observations. At one month past maximum, the optical light curves decline rapidly. The early, slow rise and flat color evolution are interpreted to result from an additional excess flux from a power source other than the radioactive decay of the synthesized $^{56}Ni$. The excess flux matches the interaction with a typical superwind of an asymptotic giant branch (AGB) star in density structure, mass-loss rate, and duration. The rapid decline starting at around one month past B-band maximum may be an indication of rapid cooling by active carbon monoxide (CO) formation, which requires a low temperature and high density environment. These peculiarities point to an AGB progenitor near the end of its evolution and the core degenerate scenario as the likely explosion mechanism for LSQ14fmg.

3 citations


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TL;DR: In this paper, the mass density, Omega_M, and cosmological-constant energy density of the universe were measured using the analysis of 42 Type Ia supernovae discovered by the Supernova Cosmology project.
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.

16,838 citations

Journal ArticleDOI
TL;DR: In this article, the authors used spectral and photometric observations of 10 Type Ia supernovae (SNe Ia) in the redshift range 0.16 " z " 0.62.
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.

16,674 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).

13,246 citations

Journal ArticleDOI
TL;DR: In this article, a 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.
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.

11,309 citations

01 Jan 1998
TL;DR: The spectral and photometric observations of 10 type Ia supernovae (SNe Ia) in the redshift range 0.16 � z � 0.62 were presented in this paper.
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�

11,197 citations