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.

iPTF16geu: A multiply imaged, gravitationally lensed type Ia supernova

Abstract: We report the discovery of a multiply imaged, gravitationally lensed type Ia supernova, iPTF16geu (SN 2016geu), at redshift z = 0.409. This phenomenon was identified because the light from the stellar explosion was magnified more than 50 times by the curvature of space around matter in an intervening galaxy. We used high-spatial-resolution observations to resolve four images of the lensed supernova, approximately 0.3 arc seconds from the center of the foreground galaxy. The observations probe a physical scale of ~1 kiloparsec, smaller than is typical in other studies of extragalactic gravitational lensing. The large magnification and symmetric image configuration imply close alignment between the lines of sight to the supernova and to the lens. The relative magnifications of the four images provide evidence for substructures in the lensing galaxy.

Revisiting Optical Tidal Disruption Events with iPTF16axa

Abstract: We report the discovery by the intermediate Palomar Transient Factory (iPTF) of a candidate tidal disruption event (TDE) iPTF16axa at z = 0.108 and present its broadband photometric and spectroscopic evolution from three months of follow-up observations with ground-based telescopes and Swift. The light curve is well fitted with a t^(−5/3) decay, and we constrain the rise time to peak to be <49 rest-frame days after disruption, which is roughly consistent with the fallback timescale expected for the ~5 × 10^6 M_⊙ black hole inferred from the stellar velocity dispersion of the host galaxy. The UV and optical spectral energy distribution is well described by a constant blackbody temperature of T ~ 3 × 10^4 K over the monitoring period, with an observed peak luminosity of 1.1 × 10^(44) erg s^(−1). The optical spectra are characterized by a strong blue continuum and broad He ii and Hα lines, which are characteristic of TDEs. We compare the photometric and spectroscopic signatures of iPTF16axa with 11 TDE candidates in the literature with well-sampled optical light curves. Based on a single-temperature fit to the optical and near-UV photometry, most of these TDE candidates have peak luminosities confined between log(L [erg s^(−1)]) = 43.4–44.4, with constant temperatures of a few ×10^4 K during their power-law declines, implying blackbody radii on the order of 10 times the tidal disruption radius, that decrease monotonically with time. For TDE candidates with hydrogen and helium emission, the high helium-to-hydrogen ratios suggest that the emission arises from high-density gas, where nebular arguments break down. We find no correlation between the peak luminosity and the black hole mass, contrary to the expectations for TDEs to have M ∝ M_(BH)^(-1/2).

First cosmology results using type Ia supernovae from the Dark Energy Survey: constraints on cosmological parameters

Abstract: We present the first cosmological parameter constraints using measurements of type Ia supernovae (SNe Ia) from the Dark Energy Survey Supernova Program (DES-SN). The analysis uses a subsample of 207 spectroscopically confirmed SNe Ia from the first three years of DES-SN, combined with a low-redshift sample of 122 SNe from the literature. Our "DES-SN3YR" result from these 329 SNe Ia is based on a series of companion analyses and improvements covering SN Ia discovery, spectroscopic selection, photometry, calibration, distance bias corrections, and evaluation of systematic uncertainties. For a flat ΛCDM model we find a matter density ${{\rm{\Omega }}}_{{\rm{m}}}=0.331\pm 0.038$. For a flat wCDM model, and combining our SN Ia constraints with those from the cosmic microwave background (CMB), we find a dark energy equation of state $w=-0.978\pm 0.059$, and ${{\rm{\Omega }}}_{{\rm{m}}}=0.321\pm 0.018$. For a flat w 0 w a CDM model, and combining probes from SN Ia, CMB and baryon acoustic oscillations, we find ${w}_{0}=-0.885\pm 0.114$ and ${w}_{a}=-0.387\,\pm \,0.430$. These results are in agreement with a cosmological constant and with previous constraints using SNe Ia (Pantheon, JLA).

RAPIDLY DECAYING SUPERNOVA 2010X: A CANDIDATE '.Ia' EXPLOSION

Abstract: We present the discovery, photometric, and spectroscopic follow-up observations of SN 2010X (PTF 10bhp). This supernova decays exponentially with τ_d = 5 days and rivals the current recordholder in speed, SN 2002bj. SN 2010X peaks at M_r = −17 mag and has mean velocities of 10,000 km s^(−1). Our light curve modeling suggests a radioactivity-powered event and an ejecta mass of 0.16M_⊙. If powered by Nickel, we show that the Nickel mass must be very small (≈0.02 M_⊙) and that the supernova quickly becomes optically thin to γ -rays. Our spectral modeling suggests that SN 2010X and SN 2002bj have similar chemical compositions and that one of aluminum or helium is present. If aluminum is present, we speculate that this may be an accretion-induced collapse of an O-Ne-Mg white dwarf. If helium is present, all observables of SN 2010X are consistent with being a thermonuclear helium shell detonation on a white dwarf, a “.Ia” explosion. With the 1 day dynamic-cadence experiment on the Palomar Transient Factory, we expect to annually discover a few such events.

Verifying the Cosmological Utility of Type Ia Supernovae: Implications of a Dispersion in the Ultraviolet Spectra

Abstract: We analyze the mean rest-frame ultraviolet (UV) spectrum of Type Ia Supernovae (SNe) and its dispersion using high signal-to-noise ratio Keck-I/LRIS-B spectroscopy for a sample of 36 events at intermediate redshift (z=0.5) discovered by the Canada-France-Hawaii Telescope Supernova Legacy Survey (SNLS). We introduce a new method for removing host galaxy contamination in our spectra, exploiting the comprehensive photometric coverage of the SNLS SNe and their host galaxies, thereby providing the first quantitative view of the UV spectral properties of a large sample of distant SNe Ia. Although the mean SN Ia spectrum has not evolved significantly over the past 40percent of cosmic history, precise evolutionary constraints are limited by the absence of a comparable sample of high-quality local spectra. The mean UV spectrum of our z~;;=0.5 SNe Ia and its dispersion is tabulated for use in future applications. Within the high-redshift sample, we discover significant UV spectral variations and exclude dust extinction as the primary cause by examining trends with the optical SN color. Although progenitor metallicity may drive some of these trends, the variations we see are much larger than predicted in recent models and do not follow expected patterns. An interesting new result is a variation seen in the wavelength of selected UV features with phase. We also demonstrate systematic differences in the SN Ia spectral features with SN light curve width in both the UV and the optical. We show that these intrinsic variations could represent a statistical limitation in the future use of high-redshift SNe Ia for precision cosmology. We conclude that further detailed studies are needed, both locally and at moderate redshift where the rest-frame UV can be studied precisely, in order that future missions can confidently be planned to fully exploit SNe Ia as cosmological probes.

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�