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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University of Portsmouth1, University of Southampton2, University of California, Berkeley3, Argonne National Laboratory4, Texas A&M University5, University of Illinois at Urbana–Champaign6, Fermilab7, University of Pennsylvania8, Lawrence Berkeley National Laboratory9, University of Chicago10, Australian Astronomical Observatory11, University College London12, Space Telescope Science Institute13, Carnegie Institution for Science14, Ludwig Maximilian University of Munich15, California Institute of Technology16, University of Michigan17, Max Planck Society18, Ohio State University19, Autonomous University of Barcelona20, Catalan Institution for Research and Advanced Studies21, Brookhaven National Laboratory22, University of Sussex23, SLAC National Accelerator Laboratory24, Universidade Federal do Rio Grande do Sul25, Stanford University26, University of Manchester27
TL;DR: The first spectroscopically confirmed superluminous supernova (SLSN) from the Dark Energy Survey (DES) is DES13S2cmm as mentioned in this paper, which is located in a low metallicity (sub-solar), low stellar-mass host galaxy (log(M/M_sun) = 9.3 +/- 0.3).
Abstract: We present DES13S2cmm, the first spectroscopically-confirmed superluminous supernova (SLSN) from the Dark Energy Survey (DES). We briefly discuss the data and search algorithm used to find this event in the first year of DES operations, and outline the spectroscopic data obtained from the European Southern Observatory (ESO) Very Large Telescope to confirm its redshift (z = 0.663 +/- 0.001 based on the host-galaxy emission lines) and likely spectral type (type I). Using this redshift, we find M_U_peak = -21.05 +0.10 -0.09 for the peak, rest-frame U-band absolute magnitude, and find DES13S2cmm to be located in a faint, low metallicity (sub-solar), low stellar-mass host galaxy (log(M/M_sun) = 9.3 +/- 0.3); consistent with what is seen for other SLSNe-I. We compare the bolometric light curve of DES13S2cmm to fourteen similarly well-observed SLSNe-I in the literature and find it possesses one of the slowest declining tails (beyond +30 days rest frame past peak), and is the faintest at peak. Moreover, we find the bolometric light curves of all SLSNe-I studied herein possess a dispersion of only 0.2-0.3 magnitudes between +25 and +30 days after peak (rest frame) depending on redshift range studied; this could be important for 'standardising' such supernovae, as is done with the more common type Ia. We fit the bolometric light curve of DES13S2cmm with two competing models for SLSNe-I - the radioactive decay of 56Ni, and a magnetar - and find that while the magnetar is formally a better fit, neither model provides a compelling match to the data. Although we are unable to conclusively differentiate between these two physical models for this particular SLSN-I, further DES observations of more SLSNe-I should break this degeneracy, especially if the light curves of SLSNe-I can be observed beyond 100 days in the rest frame of the supernova.
83 citations
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Princeton University1, Carnegie Institution for Science2, University of California, Berkeley3, Lawrence Berkeley National Laboratory4, Weizmann Institute of Science5, University of California, Santa Barbara6, Las Cumbres Observatory Global Telescope Network7, University of Southampton8, Stockholm University9, California Institute of Technology10, University of Maryland, College Park11, Goddard Space Flight Center12, University of Oxford13, INAF14, Max Planck Society15, San Diego State University16, University of Tokyo17
TL;DR: In this paper, the authors presented the results of a systematic study of 1077 Type I supernovae discovered by the Palomar Transient Factory, leading to nine new members of this peculiar class.
Abstract: Since the discovery of the unusual prototype SN 2002cx, the eponymous class of Type I (hydrogen-poor) supernovae with low ejecta speeds has grown to include approximately two dozen members identified from several heterogeneous surveys, in some cases ambiguously. Here we present the results of a systematic study of 1077 Type I supernovae discovered by the Palomar Transient Factory, leading to nine new members of this peculiar class. Moreover, we find there are two distinct subclasses based on their spectroscopic, photometric, and host galaxy properties: "SN 2002cx-like" supernovae tend to be in later-type or more irregular hosts, have more varied and generally dimmer luminosities, have longer rise times, and lack a Ti ii trough when compared to "SN 2002es-like" supernovae. None of our objects show helium, and we counter a previous claim of two such events. We also find that the occurrence rate of these transients relative to Type Ia supernovae is 5.6^(+22)_(-3.8)% (90% confidence), lower compared to earlier estimates. Combining our objects with the literature sample, we propose that these subclasses have two distinct physical origins.
82 citations
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TL;DR: In this article, the authors presented ten medium-resolution, high signal-to-noise ratio near-infrared (NIR) spectra of SN 2011fe from SpeX on the NASA Infrared Telescope Facility (IRTF) and Gemini Near-Infrared Spectrograph (GNIRS) on Gemini North, obtained as part of the Carnegie Supernova Project.
Abstract: We present ten medium-resolution, high signal-to-noise ratio near-infrared (NIR) spectra of SN 2011fe from SpeX on the NASA Infrared Telescope Facility (IRTF) and Gemini Near-Infrared Spectrograph (GNIRS) on Gemini North, obtained as part of the Carnegie Supernova Project. This data set constitutes the earliest time-series NIR spectroscopy of a Type Ia supernova (SN Ia), with the first spectrum obtained at 2.58 days past the explosion and covering -14.6 to +17.3 days relative to B-band maximum. C I {\lambda}1.0693 {\mu}m is detected in SN 2011fe with increasing strength up to maximum light. The delay in the onset of the NIR C I line demonstrates its potential to be an effective tracer of unprocessed material. For the first time in a SN Ia, the early rapid decline of the Mg II {\lambda}1.0927 {\mu}m velocity was observed, and the subsequent velocity is remarkably constant. The Mg II velocity during this constant phase locates the inner edge of carbon burning and probes the conditions under which the transition from deflagration to detonation occurs. We show that the Mg II velocity does not correlate with the optical light-curve decline rate {\Delta}m15. The prominent break at ~1.5 {\mu}m is the main source of concern for NIR k-correction calculations. We demonstrate here that the feature has a uniform time evolution among SNe Ia, with the flux ratio across the break strongly correlated with {\Delta}m15. The predictability of the strength and the onset of this feature suggests that the associated k-correction uncertainties can be minimized with improved spectral templates.
81 citations
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TL;DR: In this article, a tomographic reconstruction of the 3D Ly$\alpha$ forest absorption field over the redshift range of 2.3-2.8 was presented.
Abstract: We present the first observations of foreground Lyman-$\alpha$ forest absorption from high-redshift galaxies, targeting 24 star-forming galaxies (SFGs) with $z\sim 2.3-2.8$ within a $5' \times 15'$ region of the COSMOS field. The transverse sightline separation is $\sim 2\,h^{-1}\mathrm{Mpc}$ comoving, allowing us to create a tomographic reconstruction of the 3D Ly$\alpha$ forest absorption field over the redshift range $2.20\leq z\leq 2.45$. The resulting map covers $6\,h^{-1}\mathrm{Mpc} \times 14\,h^{-1}\mathrm{Mpc}$ in the transverse plane and $230\,h^{-1}\mathrm{Mpc}$ along the line-of-sight with a spatial resolution of $\approx 3.5\,h^{-1}\mathrm{Mpc}$, and is the first high-fidelity map of large-scale structure on $\sim\mathrm{Mpc}$ scales at $z>2$. Our map reveals significant structures with $\gtrsim 10\,h^{-1}\mathrm{Mpc}$ extent, including several spanning the entire transverse breadth, providing qualitative evidence for the filamentary structures predicted to exist in the high-redshift cosmic web. Simulated reconstructions with the same sightline sampling, spectral resolution, and signal-to-noise ratio recover the salient structures present in the underlying 3D absorption fields. Using data from other surveys, we identified 18 galaxies with known redshifts coeval with our map volume enabling a direct comparison to our tomographic map. This shows that galaxies preferentially occupy high-density regions, in qualitative agreement with the same comparison applied to simulations. Our results establishes the feasibility of the CLAMATO survey, which aims to obtain Ly$\alpha$ forest spectra for $\sim 1000$ SFGs over $\sim 1 \,\mathrm{deg}^2$ of the COSMOS field, in order to map out IGM large-scale structure at $\langle z \rangle \sim 2.3$ over a large volume $(100\,h^{-1}\mathrm{Mpc})^3$.
81 citations
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University of Southampton1, University of Pittsburgh2, Korea Astronomy and Space Science Institute3, Texas A&M University4, Spanish National Research Council5, Swinburne University of Technology6, Stanford University7, SLAC National Accelerator Laboratory8, University of Chicago9, Lawrence Berkeley National Laboratory10, Australian National University11, University of Pennsylvania12, Brandeis University13, Institut d'Astrophysique de Paris14, University College London15, Fermilab16, IFAE17, Indian Institute of Technology, Hyderabad18, Steward Health Care System19, California Institute of Technology20, Autonomous University of Madrid21, National Center for Supercomputing Applications22, University of Illinois at Urbana–Champaign23, ETH Zurich24, Santa Cruz Institute for Particle Physics25, Ohio State University26, Ludwig Maximilian University of Munich27, Max Planck Society28, Harvard University29, Macquarie University30, University of São Paulo31, University of Michigan32, Catalan Institution for Research and Advanced Studies33, University of Sussex34, State University of Campinas35, Oak Ridge National Laboratory36, Institute of Cosmology and Gravitation, University of Portsmouth37
TL;DR: In this paper, the authors presented a sample of 21 hydrogen-free superluminous supernovae (SLSNe-I) and one hydrogen-rich SLSN-II detected during the five-year Dark Energy Survey (DES).
Abstract: We present a sample of 21 hydrogen-free superluminous supernovae (SLSNe-I) and one
hydrogen-rich SLSN (SLSN-II) detected during the five-year Dark Energy Survey (DES).
These SNe, located in the redshift range 0.220 < z < 1.998, represent the largest homogeneously
selected sample of SLSN events at high redshift.We present the observed g, r, i, z light
curves for these SNe,which we interpolate using Gaussian processes. The resulting light curves
are analysed to determine the luminosity function of SLSNe-I, and their evolutionary timescales.
The DES SLSN-I sample significantly broadens the distribution of SLSN-I light-curve
properties when combined with existing samples from the literature. We fit a magnetar model
to our SLSNe, and find that this model alone is unable to replicate the behaviour of many of the
bolometric light curves.We search theDES SLSN-I light curves for the presence of initial peaks
prior to the main light-curve peak. Using a shock breakout model, our Monte Carlo search finds
that 3 of our 14 eventswith pre-max data display such initial peaks.However, 10 events showno
evidence for such peaks, in some cases downto an absolutemagnitude of<−16, suggesting that
such features are not ubiquitous to all SLSN-I events. We also identify a red pre-peak feature
within the light curve of one SLSN, which is comparable to that observed within SN2018bsz.
80 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