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.

Type IIP Supernovae as Cosmological Probes: A SEAM Distance to SN 1999em

Abstract: Due to their intrinsic brightness, supernovae make excellent cosmological probes. We describe the SEAM method for obtaining distances to Type IIP supernovae (SNe IIP) and present a distance to SN 1999em for which a Cepheid distance exists. Our models give results consistent with the Cepheid distance, even though we have not attempted to tune the underlying hydrodynamical model, we have simply chosen the best fits. This is in contradistinction to the expanding photosphere method (EPM) which yields a distance to SN 1999em that is 50% smaller than the Cepheid distance. We emphasize the differences between SEAM and EPM. We show that the dilution factors used in the EPM analysis were systematically too small at later epochs. We also show that the EPM blackbody assumption is suspect. Since SNe IIP are visible to redshifts as high as z less than about 6, with the JWST, SEAM may be a valuable probe of the early universe.

Host-Galaxy Properties of 32 Low-Redshift Superluminous Supernovae from the Palomar Transient Factory

Abstract: We present ultraviolet through near-infrared photometry and spectroscopy of the host galaxies of all superluminous supernovae (SLSNe) discovered by the Palomar Transient Factory prior to 2013, and derive measurements of their luminosities, star-formation rates, stellar masses, and gas-phase metallicities. We find that Type I (hydrogen-poor) SLSNe are found almost exclusively in low-mass (M 0.5 Z_sun. Extremely low metallicities are not required, and indeed provide no further increase in the relative SLSN rate. Several SLSN-I hosts are undergoing vigorous starbursts, but this may simply be a side effect of metallicity dependence: dwarf galaxies tend to have bursty star-formation histories. Type-II (hydrogen-rich) SLSNe are found over the entire range of galaxy masses and metallicities, and their integrated properties do not suggest a strong preference for (or against) low-mass/low-metallicity galaxies. Two hosts exhibit unusual properties: PTF 10uhf is a Type I SLSN in a massive, luminous infrared galaxy at redshift z=0.29, while PTF 10tpz is a Type II SLSN located in the nucleus of an early-type host at z=0.04.

Type Ia Supernova Carbon Footprints

Abstract: We present convincing evidence of unburned carbon at photospheric velocities in new observations of five 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 days relative to maximum. Detections are based on the presence of relatively strong C II {lambda}6580 absorption 'notches' in multiple spectra of each SN, aided by automated fitting with the SYNAPPS code. Four of the five SNe in question are otherwise spectroscopically unremarkable, with ions and ejection velocities typical of SNe Ia, but spectra of the fifth exhibit high-velocity (v > 20, 000 km s{sup -1}) 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 broadband light curve/color behavior: three of the five 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{sup +10}{sub -6%} of SNe Ia exhibit spectroscopic C II signatures as late as -5 days with respect to maximum. We place these new objects in the context of previously recognized carbon-positivemore » 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 {lambda}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.« less

Real-time detection and rapid multiwavelength follow-up observations of a highly subluminous type ii-p supernova from the palomar transient factory survey

Abstract: The Palomar Transient Factory (PTF) is an optical wide-field variability survey carried out using a camera with a 7.8 deg^2 field of view mounted on the 48 inch Oschin Schmidt telescope at Palomar Observatory. One of the key goals of this survey is to conduct high-cadence monitoring of the sky in order to detect optical transient sources shortly after they occur. Here, we describe the real-time capabilities of the PTF and our related rapid multiwavelength follow-up programs, extending from the radio to the γ-ray bands. We present as a case study observations of the optical transient PTF10vdl (SN 2010id), revealed to be a very young core-collapse (Type II-P) supernova having a remarkably low luminosity. Our results demonstrate that the PTF now provides for optical transients the real-time discovery and rapid-response follow-up capabilities previously reserved only for high-energy transients like gamma-ray bursts.

Flash Spectroscopy: Emission Lines from the Ionized Circumstellar Material around $<10$-Day-Old Type II Supernovae

Abstract: Supernovae (SNe) embedded in dense circumstellar material (CSM) may show prominent emission lines in their early-time spectra ($\leq 10$ days after the explosion), owing to recombination of the CSM ionized by the shock-breakout flash. From such spectra ("flash spectroscopy"), we can measure various physical properties of the CSM, as well as the mass-loss rate of the progenitor during the year prior to its explosion. Searching through the Palomar Transient Factory (PTF and iPTF) SN spectroscopy databases from 2009 through 2014, we found 12 Type II SNe showing flash-ionized (FI) signatures in their first spectra. All are younger than 10 days. These events constitute 14\% of all 84 SNe in our sample having a spectrum within 10 days from explosion, and 18\% of SNe~II observed at ages $<5$ days, thereby setting lower limits on the fraction of FI events. We classified as "blue/featureless" (BF) those events having a first spectrum which is similar to that of a black body, without any emission or absorption signatures. It is possible that some BF events had FI signatures at an earlier phase than observed, or that they lack dense CSM around the progenitor. Within 2 days after explosion, 8 out of 11 SNe in our sample are either BF events or show FI signatures. Interestingly, we found that 19 out of 21 SNe brighter than an absolute magnitude $M_R=-18.2$ belong to the FI or BF groups, and that all FI events peaked above $M_R=-17.6$ mag, significantly brighter than average SNe~II.

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�