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Author

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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Journal ArticleDOI
TL;DR: In this paper, the authors present photometric and spectroscopic observations of galaxies hosting Type Ia supernovae (SNe Ia) observed by the Nearby Supernova Factory.
Abstract: We present photometric and spectroscopic observations of galaxies hosting Type Ia supernovae (SNe Ia) observed by the Nearby Supernova Factory. Combining Galaxy Evolution Explorer (GALEX) UV data with optical and near-infrared photometry, we employ stellar population synthesis techniques to measure SN Ia host galaxy stellar masses, star formation rates (SFRs), and reddening due to dust. We reinforce the key role of GALEX UV data in deriving accurate estimates of galaxy SFRs and dust extinction. Optical spectra of SN Ia host galaxies are fitted simultaneously for their stellar continua and emission lines fluxes, from which we derive high-precision redshifts, gas-phase metallicities, and Hα-based SFRs. With these data we show that SN Ia host galaxies present tight agreement with the fiducial galaxy mass-metallicity relation from Sloan Digital Sky Survey (SDSS) for stellar masses log(M */M ☉) > 8.5 where the relation is well defined. The star formation activity of SN Ia host galaxies is consistent with a sample of comparable SDSS field galaxies, though this comparison is limited by systematic uncertainties in SFR measurements. Our analysis indicates that SN Ia host galaxies are, on average, typical representatives of normal field galaxies.

63 citations

Journal ArticleDOI
TL;DR: In this article, the authors presented a systematic comparison of superluminous Type Ia supernovae (SNe Ia) at late epochs, including previously unpublished photometric and spectroscopic observations of SN 2007if, SN 2009dc and SNF20080723-012.
Abstract: We present a first systematic comparison of superluminous Type Ia supernovae (SNe Ia) at late epochs, including previously unpublished photometric and spectroscopic observations of SN 2007if, SN 2009dc and SNF20080723-012. Photometrically, the objects of our sample show a diverse late-time behaviour, some of them fading quite rapidly after a light-curve break at �150–200d. The latter is likely the result of flux redistribution into the infrared, possibly caused by dust formation, rather than a true bolometric effect. Nebular spectra of superluminous SNe Ia are characterised by weak or absent [Feiii] emission, pointing at a low ejecta ionisation state as a result of high densities. To constrain the ejecta and 56 Ni masses of superluminous SNe Ia, we compare the observed bolometric light curve of SN 2009dc with synthetic model light curves, focusing on the radioactive tail after �60d. Models with enough 56 Ni to explain the light-curve peak by radioactive decay, and at the same time sufficient mass to keep the ejecta velocities low, fail to reproduce the observed light-curve tail of SN 2009dc because of too much -ray trapping. We instead propose a model with �1 M⊙ of 56 Ni and �2 M⊙ of ejecta, which may be interpreted as the explosion of a Chandrasekhar-mass white dwarf (WD) enshrouded by 0.6–0.7 M⊙ of C/O-rich material, as it could result from a merger of two massive C/O WDs. This model reproduces the late light curve of SN 2009dc well. A flux deficit at peak may be compensated by light from the interaction of the ejecta with the surrounding material.

62 citations

Journal ArticleDOI
TL;DR: In this article, a sample of $57$ $R$-band Type II supernovae (SNe) light curves was used to estimate the energy per unit mass (E/M$) of the first few days after the explosion.
Abstract: During the first few days after explosion, Type II supernovae (SNe) are dominated by relatively simple physics. Theoretical predictions regarding early-time SN light curves in the ultraviolet (UV) and optical bands are thus quite robust. We present, for the first time, a sample of $57$ $R$-band Type II SN light curves that are well monitored during their rise, having $>5$ detections during the first 10 days after discovery, and a well-constrained time of explosion to within $1-3$ days. We show that the energy per unit mass ($E/M$) can be deduced to roughly a factor of five by comparing early-time optical data to the model of Rabinak & Waxman (2011), while the progenitor radius cannot be determined based on $R$-band data alone. We find that Type II SN explosion energies span a range of $E/M=(0.2-20)\times 10^{51} \; \rm{erg/(10 M}_\odot$), and have a mean energy per unit mass of $\left\langle E/M \right\rangle = 0.85\times 10^{51} \; \rm{erg/(10 M}_\odot$), corrected for Malmquist bias. Assuming a small spread in progenitor masses, this indicates a large intrinsic diversity in explosion energy. Moreover, $E/M$ is positively correlated with the amount of $^{56}\rm{Ni}$ produced in the explosion, as predicted by some recent models of core-collapse SNe. We further present several empirical correlations. The peak magnitude is correlated with the decline rate ($\Delta m_{15}$), the decline rate is weakly correlated with the rise time, and the rise time is not significantly correlated with the peak magnitude. Faster declining SNe are more luminous and have longer rise times. This limits the possible power sources for such events.

62 citations

Journal ArticleDOI
TL;DR: This paper presents the realtime image subtraction pipeline in the intermediate Palomar Transient Factory, using high-performance computing, efficient database, and machine learning algorithms to reliably deliver transient candidates within ten minutes of images being taken.
Abstract: A fast-turnaround pipeline for realtime data reduction plays an essential role in discovering and permitting follow-up observations to young supernovae and fast-evolving transients in modern time-domain surveys. In this paper, we present the realtime image subtraction pipeline in the intermediate Palomar Transient Factory. By using high-performance computing, efficient databases, and machine-learning algorithms, this pipeline manages to reliably deliver transient candidates within 10 minutes of images being taken. Our experience in using high-performance computing resources to process big data in astronomy serves as a trailblazer to dealing with data from large-scale time-domain facilities in the near future.

62 citations

Journal ArticleDOI
TL;DR: In this article, the optical afterglow of the gamma-ray burst (GRB) 130702A, identified upon searching 71 square degrees surrounding the Fermi Gamma-ray Burst Monitor (GBM) localization, was discovered and characterized by the intermediate Palomar Transient Factory (iPTF).
Abstract: We report the discovery of the optical afterglow of the gamma-ray burst (GRB) 130702A, identified upon searching 71 square degrees surrounding the Fermi Gamma-ray Burst Monitor (GBM) localization. Discovered and characterized by the intermediate Palomar Transient Factory (iPTF), iPTF13bxl is the first afterglow discovered solely based on a GBM localization. Real-time image subtraction, machine learning, human vetting, and rapid response multi-wavelength follow-up enabled us to quickly narrow a list of 27,004 optical transient candidates to a single afterglow-like source. Detection of a new, fading X-ray source by Swift and a radio counterpart by CARMA and the VLA confirmed the association between iPTF13bxl and GRB 130702A. Spectroscopy with the Magellan and Palomar 200-inch telescopes showed the afterglow to be at a redshift of z=0.145, placing GRB 130702A among the lowest redshift GRBs detected to date. The prompt gamma-ray energy release and afterglow luminosity are intermediate between typical cosmological GRBs and nearby sub-luminous events such as GRB 980425 and GRB 060218. The bright afterglow and emerging supernova offer an opportunity for extensive panchromatic follow-up. Our discovery of iPTF13bxl demonstrates the first observational proof-of-principle for ~10 Fermi-iPTF localizations annually. Furthermore, it represents an important step towards overcoming the challenges inherent in uncovering faint optical counterparts to comparably localized gravitational wave events in the Advanced LIGO and Virgo era.

62 citations


Cited by
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Journal ArticleDOI
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