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.

iPTF16fnl: a faint and fast tidal disruption event in an E+A galaxy

Abstract: We present ground-based and \textit{Swift} observations of iPTF16fnl, a likely tidal disruption event (TDE) discovered by the intermediate Palomar Transient Factory (iPTF) survey at 66.6 Mpc. The lightcurve of the object peaked at absolute $M_g=-17.2$ mag. The maximum bolometric luminosity (from optical and UV) was $L_p~\simeq~(1.0\,\pm\,0.15) \times 10^{43}$ erg/s, an order of magnitude fainter than any other optical TDE discovered so far. The luminosity in the first 60 days is consistent with an exponential decay, with $L \propto e^{-(t-t_0)/\tau}$, where $t_0$=~57631.0 (MJD) and $\tau\simeq 15$ days. The X-ray shows a marginal detection at $L_X=2.4^{1.9}_{-1.1}\times 10^{39}$ erg/s (\textit{Swift} X-ray Telescope). No radio counterpart was detected down to 3$\sigma$, providing upper limits for monochromatic radio luminosity of $ u L_{ u} < 2.3\times10^{36}$ erg/s and $ u L_{ u}<1.7\times 10^{37}$ erg/s (VLA, 6.1 and 22 GHz). The blackbody temperature, obtained from combined \textit{Swift} UV and optical photometry, shows a constant value of 19,000 K. The transient spectrum at peak is characterized by broad He II and H$\alpha$ emission lines, with an FWHM of about 14,000 km/s and 10,000 km/s respectively. He I lines are also detected at $\lambda\lambda$ 5875 and 6678. The spectrum of the host is dominated by strong Balmer absorption lines, which are consistent with a post-starburst (E+A) galaxy with an age of $\sim$650 Myr and solar metallicity. The characteristics of iPTF16fnl make it an outlier on both luminosity and decay timescales, as compared to other optically selected TDEs. The discovery of such a faint optical event suggests a higher rate of tidal disruptions, as low luminosity events may have gone unnoticed in previous searches.

The Palomar Transient Factory photometric catalog 1.0

Abstract: We constructed a photometrically calibrated catalog of non-variable sources from the Palomar Transient Factory (PTF) observations. The first version of this catalog presented here, the PTF photometric catalog 1.0, contains calibrated R_PTF-filter magnitudes for ≈2.1 × 10^7 sources brighter than magnitude 19, over an area of ≈11,233 deg^2. The magnitudes are provided in the PTF photometric system, and the color of a source is required in order to convert these magnitudes into other magnitude systems. We estimate that the magnitudes in this catalog have a typical accuracy of about 0.02 mag with respect to magnitudes from the Sloan Digital Sky Survey. The median repeatability of our catalog’s magnitudes for stars between 15 and 16 mag, is about 0.01 mag and it is over 0.03 mag for 95% of the sources in this magnitude range. The main goal of this catalog is to provide reference magnitudes for photometric calibration of visible light observations. Subsequent versions of this catalog, which will be published incrementally online, will be extended to cover a larger sky area and will also include g_PTF-filter magnitudes, as well as variability and proper-motion information.

Type II Supernova Energetics and Comparison of Light Curves to Shock-Cooling Models

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 SN II light curves that are well-monitored during their rise, with greater than 5 detections during the first 10 days after discovery, and a well-constrained time of explosion to within 13 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 2011 model of Rabinak Waxman, while the progenitor radius cannot be determined based on R-band data alone. We find that SN II explosion energies span a range of EM = (0.2-20) x 10(exp 51) erg/(10 M stellar mass), and have a mean energy per unit mass of E/ M = 0.85 x 10(exp 51) erg(10 stellar mass), 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 Ni-56 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(sub15), 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.

A SEARCH FOR NEW CANDIDATE SUPER-CHANDRASEKHAR-MASS TYPE Ia SUPERNOVAE IN THE NEARBY SUPERNOVA FACTORY DATA SET

Abstract: We present optical photometry and spectroscopy of five Type Ia supernovae discovered by the Nearby Supernova Factory selected to be spectroscopic analogs of the candidate super-Chandrasekhar-mass events SN 2003fg and SN 2007if. Their spectra are characterized by hot, highly ionized photospheres near maximum light, for which SN 1991T supplies the best phase coverage among available close spectral templates. Like SN 2007if, these supernovae are overluminous (–19.5 < MV < –20) and the velocity of the Si II λ6355 absorption minimum is consistent with being constant in time from phases as early as a week before, and up to two weeks after, B-band maximum light. We interpret the velocity plateaus as evidence for a reverse-shock shell in the ejecta formed by interaction at early times with a compact envelope of surrounding material, as might be expected for SNe resulting from the mergers of two white dwarfs. We use the bolometric light curves and line velocity evolution of these SNe to estimate important parameters of the progenitor systems, including 56Ni mass, total progenitor mass, and masses of shells and surrounding carbon/oxygen envelopes. We find that the reconstructed total progenitor mass distribution of the events (including SN 2007if) is bounded from below by the Chandrasekhar mass, with SN 2007if being the most massive. We discuss the relationship of these events to the emerging class of super-Chandrasekhar-mass SNe Ia, estimate the relative rates, compare the mass distribution to that expected for double-degenerate SN Ia progenitors from population synthesis, and consider implications for future cosmological Hubble diagrams.

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�