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.

Supernovae and Dark Energy

Abstract: Astronomers have begun to measure the fundamental parameters of cosmology through the observation of very distant Type Ia supernovae. Over the past decade more than 300 spectroscopically confirmed high‐redshift supernovae have been discovered. These supernovae are used as standardized candles to measure the history of the expansion of the universe. Under the current standard model for cosmology these measurements indicate the presence of a heretofore unknown dark energy causing a recent acceleration in the expansion of the universe.At this time supernova measurements of the cosmological parameters are no longer limited by statistical uncertainties, rather systematic uncertainties are the dominant source of error. These include the effects of evolution (further back in time do the supernovae behave the same way?), the effect of intergalactic dust on the brightness of the supernovae and the relationship between supernovae and their environments. Here I present exciting new developments in the field of cosmo...

Calcium-rich gap transients in the remote outskirts of galaxies

Abstract: From the first two seasons of the Palomar Transient Factory, we identify three peculiar transients (PTF09dav, PTF10iuv, PTF11bij) with five distinguishing characteristics: peak luminosity in the gap between novae and supernovae (M_R = 15.5 to -16.5), rapid photometric evolution (rise-time ~12--15 days), large photospheric velocities (~6000 to 11000 km/s), early spectroscopic evolution into nebular phase (~1 to 3 months) and peculiar nebular spectra dominated by Calcium. We also culled the extensive decade-long Lick Observatory Supernova Search database and identified an additional member of this group, SN 2007ke. Our choice of photometric and spectroscopic properties was motivated by SN 2005E (Perets et al. 2010). To our surprise, as in the case of SN 2005E, all four members of this group are also clearly offset from the bulk of their host galaxy. Given the well-sampled early and late-time light curves, we derive ejecta masses in the range of 0.4--0.7 Msun. Spectroscopically, we find that there may be a diversity in the photospheric phase, but the commonality is in the unusual nebular spectra. Our extensive follow-up observations rule out standard thermonuclear and standard core-collapse explosions for this class of "Calcium-rich gap" transients. If the progenitor is a white dwarf, we are likely seeing a detonation of the white dwarf core and perhaps, even shockfront interaction with a previously ejected nova shell. In the less likely scenario of a massive star progenitor, a very non-standard channel specific to a low-metallicity environment needs to be invoked (e.g., ejecta fallback leading to black hole formation). Detection (or lack thereof) of a faint underlying host (dwarf galaxy, cluster) will provide a crucial and decisive diagnostic to choose between these alternatives.

SN 2010jp (PTF10aaxi): A Jet-driven Type II Supernova

Abstract: Abstract We present photometry and spectroscopy of the peculiar Type II supernova SN 2010jp, also named PTF10aaxi. The light curve exhibits a linear decline with a relatively low peak absolute magnitude of only −15.9 (unfiltered), and a low radioactive decay luminosity at late times that suggests a low synthesized nickel mass of about 0.003 M⊙ or less. Spectra of SN 2010jp display an unprecedented triple-peaked Hα line profile, showing: (1) a narrow central component that suggests shock interaction with a dense circumstellar medium (CSM); (2) high-velocity blue and red emission features centered at −12,600 and +15,400 km s−1; and (3) very broad wings extending from −22,000 to +25,000 km s−1. We propose that this line profile indicates a bipolar jet-driven explosion, with the central component produced by normal SN ejecta and CSM interaction at mid and low latitudes, while the high-velocity bumps and broad line wings arise in a nonrelativistic bipolar jet. Jet-driven SNe II are predicted for collapsars resulting from a wide range of initial masses above 25 M⊙, especially at the sub-solar metallicity consistent with the SN host environment. It also seems consistent with the apparently low 56Ni mass that may accompany black hole formation. We speculate that the jet survives to produce observable signatures because the star's H envelope was very low mass, having been mostly stripped away by the previous eruptive mass loss.

A hot and fast ultra-stripped supernova that likely formed a compact neutron star binary

Abstract: Compact neutron star binary systems are produced from binary massive stars through stellar evolution involving up to two supernova explosions. The final stages in the formation of these systems have not been directly observed. We report the discovery of iPTF 14gqr (SN 2014ft), a Type Ic supernova with a fast evolving light curve indicating an extremely low ejecta mass ($\approx 0.2$ solar masses) and low kinetic energy ($\approx 2 \times 10^{50}$ ergs). Early photometry and spectroscopy reveal evidence of shock cooling of an extended He-rich envelope, likely ejected in an intense pre-explosion mass loss episode of the progenitor. Taken together, we interpret iPTF 14gqr as evidence for ultra-stripped supernovae that form neutron stars in compact binary systems.

Peculiar Type II Supernovae from Blue Supergiants

Abstract: The vast majority of Type II supernovae (SNe) are produced by red supergiants (RSGs), but SN 1987A revealed that blue supergiants (BSGs) can produce members of this class as well, albeit with some peculiar properties. This best studied event revolutionized our understanding of SNe, and linking it to the bulk of Type II events is essential. We present here optical photometry and spectroscopy gathered for SN 2000cb, which is clearly not a standard Type II SN and yet is not a SN 1987A analog. The light curve of SN 2000cb is reminiscent of that of SN 1987A in shape, with a slow rise to a late optical peak, but on substantially different time scales. Spectroscopically, SN 2000cb resembles a normal SN II but with ejecta velocities that far exceed those measured for SN 1987A or normal SNe II, above 18000 km/s for H-alpha at early times. The red colours, high velocities, late photometric peak, and our modeling of this object all point toward a scenario involving the high-energy explosion of a small-radius star, most likely a BSG, producing 0.1 solar masses of Ni-56. Adding a similar object to the sample, SN 2005ci, we derive a rate of about 2% of the core-collapse rate for this loosely defined class of BSG explosions.

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�