Author
Peter Nugent
Other affiliations: Liverpool John Moores University, National Autonomous University of Mexico, California Institute of Technology ...read more
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.
Topics: Supernova, Light curve, Galaxy, Redshift, White dwarf
Papers published on a yearly basis
Papers
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Instituto Superior Técnico1, Durham University2, California Institute of Technology3, University of Cambridge4, Lawrence Berkeley National Laboratory5, Space Telescope Science Institute6, University of California, Berkeley7, Stockholm University8, University of Oxford9, Massachusetts Institute of Technology10, European Southern Observatory11, University of Barcelona12, Yale University13, University of Texas at Austin14
TL;DR: In this article, a measurement of the rate of distant Type Ia supernovae derived using 4 large subsets of data from the Supernova Cosmology Project is presented. But the authors do not consider the impact of the assumed cosmological parameters on the observed supernova rate.
Abstract: We present a measurement of the rate of distant Type Ia supernovae derived using 4 large subsets of data from the Supernova Cosmology Project. Within this fiducial sample,which surveyed about 12 square degrees, thirty-eight supernovae were detected at redshifts 0.25--0.85. In a spatially flat cosmological model consistent with the results obtained by the Supernova Cosmology Project, we derive a rest-frame Type Ia supernova rate at a mean red shift z {approx_equal} 0.55 of 1.53 {sub -0.25}{sub -0.31}{sup 0.28}{sup 0.32} x 10{sup -4} h{sup 3} Mpc{sup -3} yr{sup -1} or 0.58{sub -0.09}{sub -0.09}{sup +0.10}{sup +0.10} h{sup 2} SNu(1 SNu = 1 supernova per century per 10{sup 10} L{sub B}sun), where the first uncertainty is statistical and the second includes systematic effects. The dependence of the rate on the assumed cosmological parameters is studied and the redshift dependence of the rate per unit comoving volume is contrasted with local estimates in the context of possible cosmic star formation histories and progenitor models.
106 citations
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TL;DR: In this paper, a new method was proposed to standardize type Ia supernova luminosities using flux ratios from a single flux-calibrated spectrum per supernova.
Abstract: We present a new method to standardize type Ia supernova (SN Ia) luminosities to $\la$0.13 mag using flux ratios from a single flux-calibrated spectrum per SN. Using Nearby Supernova Factory spectrophotomery of 58 SNe Ia, we performed an unbiased search for flux ratios that correlate with SN Ia luminosity. After developing the method and selecting the best ratios from a training sample, we verified the results on a separate validation sample and with data from the literature. We identified multiple flux ratios whose correlations with luminosity are stronger than those of light curve shape and color, previously identified spectral feature ratios, or equivalent width measurements. In particular, the flux ratio $\mathcal{R}_{642/443} = F(642~{\rm nm}) / F(443~{\rm nm})$ has a correlation of 0.95 with SN Ia absolute magnitudes. Using this single ratio as a correction factor produces a Hubble diagram with a residual scatter standard deviation of $0.125 \pm 0.011$ mag, compared with $0.161 \pm 0.015$ mag when fit with the SALT2 light curve shape and color parameters x 1 and c . The ratio $\mathcal{R}_{642/443}$ is an effective correction factor for both extrinsic dust reddening and instrinsic variations such as those of SN 1991T-like and SN 1999aa-like SNe. When combined with broad-band color measurements, spectral flux ratios can standardize SN Ia magnitudes to ~0.12 mag. These are the first spectral metrics that give robust improvements over the standard normalization methods based upon light curve shape and color, and they provide among the lowest scatter Hubble diagrams ever published.
106 citations
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TL;DR: In this article, the authors used a parameterized spectrum synthesis code to make a direct analysis of a high quality spectrum of the Type Ia SN 1990N that was obtained by Leibundgut et al. 14 days before the time of optical maximum.
Abstract: We use a parameterized spectrum-synthesis code to make a direct analysis of a high quality spectrum of the Type Ia SN 1990N that was obtained by Leibundgut et al. 14 days before the time of optical maximum. We suggest that the absorption feature observed near 6040 A, which has been attributed to blueshifted λ6355 of Si II, actually was produced by blueshifted λ6580 of C II, in a high-velocity (v > 26, 000 km s-1) carbon-rich region. Implications for SN Ia explosion models are briefly discussed.
105 citations
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European Southern Observatory1, University of Southampton2, University of Oxford3, Weizmann Institute of Science4, INAF5, University of California, Santa Barbara6, Las Cumbres Observatory Global Telescope Network7, Lawrence Berkeley National Laboratory8, University of California, Berkeley9, Max Planck Society10, University of Lyon11, Carnegie Institution for Science12, Tel Aviv University13, University of Texas at Austin14, Yale University15, University of Copenhagen16
TL;DR: In this paper, the optical spectra of 264 low-redshift (SNe Ia) phases were investigated and it was found that C II features are more likely to be found in SNE Ia having narrower light curves.
Abstract: We present an investigation of the optical spectra of 264 low-redshift (z 40 per cent of SNe Ia observed at these phases show signs of unburnt material in their spectra, and that C II features are more likely to be found in SNe Ia having narrower light curves.
103 citations
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TL;DR: In this paper, the authors present the results of optical, near-infrared, and midinfrared observations of M101 OT2015-1 (PSN J14021678+5426205), a luminous red transient in the Pinwheel galaxy spanning a total of 16 years.
Abstract: We present the results of optical, near-infrared, and mid-infrared observations of M101 OT2015-1 (PSN J14021678+5426205), a luminous red transient in the Pinwheel galaxy (M101), spanning a total of 16 years. The light curve showed two distinct peaks with absolute magnitudes M_r ≤ -12.4 and M_r ≃ -12, on 2014 November 11 and 2015 February 17, respectively. The spectral energy distributions during the second maximum show a cool outburst temperature of ≈ 3700 K and low expansion velocities (≈-300 km s^(−1)) for the H i, Ca ii, Ba ii, and K i lines. From archival data spanning 15–8 years before the outburst, we find a single source consistent with the optically discovered transient, which we attribute to being the progenitor; it has properties consistent with being an F-type yellow supergiant with L ~ 8.7 x 10^4 L_⊙, T_(eff) ≈ 7000 K, and an estimated mass of M_1 = 18 ± 1 M_⊙. This star has likely just finished the H-burning phase in the core, started expanding, and is now crossing the Hertzsprung gap. Based on the combination of observed properties, we argue that the progenitor is a binary system, with the more evolved system overfilling the Roche lobe. Comparison with binary evolution models suggests that the outburst was an extremely rare phenomenon, likely associated with the ejection of the common envelope of a massive star. The initial mass of the primary fills the gap between the merger candidates V838 Mon (5−10 M_⊙) and NGC 4490-OT (30 M_⊙).
103 citations
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University of California, Berkeley1, Lawrence Berkeley National Laboratory2, Instituto Superior Técnico3, Pierre-and-Marie-Curie University4, Stockholm University5, European Southern Observatory6, Collège de France7, University of Cambridge8, University of Barcelona9, Yale University10, Space Telescope Science Institute11, European Space Agency12, University of New South Wales13
TL;DR: In this paper, the mass density, Omega_M, and cosmological-constant energy density of the universe were measured using the analysis of 42 Type Ia supernovae discovered by the Supernova Cosmology project.
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.
16,838 citations
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TL;DR: In this article, the authors used spectral and photometric observations of 10 Type Ia supernovae (SNe Ia) in the redshift range 0.16 " z " 0.62.
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.
16,674 citations
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TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).
13,246 citations
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TL;DR: In this article, a 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.
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.
11,309 citations
01 Jan 1998
TL;DR: The spectral and photometric observations of 10 type Ia supernovae (SNe Ia) in the redshift range 0.16 � z � 0.62 were presented in this paper.
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�
11,197 citations