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.

Discovery of the nearby long, soft GRB 100316D with an associated supernova

Abstract: We report the Swift discovery of the nearby long, soft gamma-ray burst GRB 100316D, and the subsequent unveiling of its low-redshift host galaxy and associated supernova. We derive the redshift of the event to be z = 0.0591 +/- 0.0001 and provide accurate astrometry for the gamma-ray burst (GRB) supernova (SN). We study the extremely unusual prompt emission with time-resolved gamma-ray to X-ray spectroscopy and find that the spectrum is best modelled with a thermal component in addition to a synchrotron emission component with a low peak energy. The X-ray light curve has a remarkably shallow decay out to at least 800 s. The host is a bright, blue galaxy with a highly disturbed morphology and we use Gemini-South, Very Large Telescope and Hubble Space Telescope observations to measure some of the basic host galaxy properties. We compare and contrast the X-ray emission and host galaxy of GRB 100316D to a subsample of GRB-SNe. GRB 100316D is unlike the majority of GRB-SNe in its X-ray evolution, but resembles rather GRB 060218, and we find that these two events have remarkably similar high energy prompt emission properties. Comparison of the host galaxies of GRB-SNe demonstrates, however, that there is a great diversity in the environments in which GRB-SNe can be found. GRB 100316D is an important addition to the currently sparse sample of spectroscopically confirmed GRB-SNe, from which a better understanding of long GRB progenitors and the GRB-SN connection can be gleaned.

A strong ultraviolet pulse from a newborn type Ia supernova.

Abstract: Type Ia supernovae are destructive explosions of carbon-oxygen white dwarfs. Although they are used empirically to measure cosmological distances, the nature of their progenitors remains mysterious. One of the leading progenitor models, called the single degenerate channel, hypothesizes that a white dwarf accretes matter from a companion star and the resulting increase in its central pressure and temperature ignites thermonuclear explosion. Here we report observations with the Swift Space Telescope of strong but declining ultraviolet emission from a type Ia supernova within four days of its explosion. This emission is consistent with theoretical expectations of collision between material ejected by the supernova and a companion star, and therefore provides evidence that some type Ia supernovae arise from the single degenerate channel.

Supernova simulations and strategies for the dark energy survey

Abstract: We present an analysis of supernova light curves simulated for the upcoming Dark Energy Survey (DES) supernova search. The simulations employ a code suite that generates and fits realistic light curves in order to obtain distance modulus/redshift pairs that are passed to a cosmology fitter. We investigated several different survey strategies including field selection, supernova selection biases, and photometric redshift measurements. Using the results of this study, we chose a 30 deg2 search area in the griz filter set. We forecast (1) that this survey will provide a homogeneous sample of up to 4000 Type Ia supernovae in the redshift range 0.05

Discovery of Precursor LBV Outbursts in Two Recent Optical Transients: The Fitfully Variable Missing Links UGC 2773-OT and SN 2009ip

Abstract: We present progenitor-star detections, light curves, and optical spectra of SN2009ip and the 2009 optical transient in UGC2773 (U2773-OT), which were not genuine SNe. Precursor variability in the decade before outburst indicates that both of the progenitor stars were LBVs. Their pre-outburst light curves resemble the S Doradus phases that preceded giant eruptions of eta Carinae and SN1954J (V12 in NGC2403), with intermediate progenitor luminosities. HST detections a decade before discovery indicate that the SN2009ip and U2773-OT progenitors were supergiants with likely initial masses of 50-80 Msun and $\ga$20 Msun, respectively. Both outbursts had spectra befitting known LBVs, although in different physical states. SN 2009ip exhibited a hot LBV spectrum with characteristic speeds of 550 km/s, plus faster material up to 5000 km/s, resembling the slow Homunculus and fast blast wave of eta Carinae. U2773-OT shows a forest of narrow absorption and emission lines comparable to that of S Dor in its cool state, plus [CaII] emission and an IR excess indicative of dust, similar to SN2008S and N300-OT. [CaII] emission is probably tied to a dusty pre-outburst environment, and not the outburst mechanism. SN2009ip and U2773-OT may provide a critical link between historical LBV eruptions, while U2773-OT may provide a link between LBVs and SN2008S and N300-OT. Future searches will uncover more examples of precursor LBV variability of this kind, providing key clues that may help unravel the instability driving LBVs.

Spectropolarimetry of SN 2001el in NGC 1448: Asphericity of a Normal Type Ia Supernova

Abstract: High-quality spectropolarimetry (range 417-860 nm; spectral resolution 1.27 nm and 0.265 nm/pixel) of the SN Ia 2001el were obtained with the ESO Very Large Telescope Melipal (+ FORS1) at 5 epochs. The spectra a week before maximum and around maximum indicate photospheric expansion velocities of about 10,000 km s$^{-1}$. Prior to optical maximum, the linear polarization of the continuum was $\approx 0.2 - 0.3 %$ with a constant position angle, showing that SN 2001el has a well-defined axis of symmetry. The polarization was nearly undetectable a week after optical maximum. The spectra are similar to those of the normally-bright SN 1994D with the exception of a strong double-troughed absorption feature seen around 800 nm (FWHM about 22 nm). This feature could be an important clue to the binary nature of SN Ia, perhaps associated with an accretion disk, or to the nature of the thermonuclear burning. If modeled in terms of an oblate spheroid, the continuum polarization implies a minor to major axis ratio of around 0.9 if seen equator-on; this level of asymmetry would produce an absolute luminosity dispersion of about 0.1 mag when viewed at different viewing angles. If typical for SNe Ia, this would create an RMS scatter of several hundredths of a magnitude around the mean brightness-decline relation.

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�