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.

Optical and ultraviolet spectroscopic analysis of SN 2011fe at late times

Abstract: Author(s): Friesen, B; Baron, E; Parrent, JT; Thomas, RC; Branch, D; Nugent, PE; Hauschildt, PH; Foley, RJ; Wright, DE; Pan, YC; Filippenko, AV; Clubb, KI; Silverman, JM; Maeda, K; Shivvers, I; Kelly, PL; Cohen, DP; Rest, A; Kasen, D | Abstract: We present optical spectra of the nearby Type Ia supernova SN 2011fe at 100, 205, 311, 349 and 578 d post-maximum light, as well as an ultraviolet (UV) spectrum obtained with the Hubble Space Telescope at 360 d post-maximum light. We compare these observations with synthetic spectra produced with the radiative transfer code PHOENIX. The day +100 spectrum can be well fitted with models that neglect collisional and radiative data for forbidden lines. Curiously, including these data and recomputing the fit yields a quite similar spectrum, but with different combinations of lines forming some of the stronger features. At day +205 and later epochs, forbidden lines dominate much of the optical spectrum formation; however, our results indicate that recombination, not collisional excitation, is the most influential physical process driving spectrum formation at these late times. Consequently, our synthetic optical and UV spectra at all epochs presented here are formed almost exclusively through recombinationdriven fluorescence. Furthermore, our models suggest that the UV spectrum even as late as day +360 is optically thick and consists of permitted lines from several iron-peak species. These results indicate that the transition to the 'nebular' phase in Type Ia supernovae is complex and highly wavelength dependent.

AGNs on the Move: A Search for Off-nuclear AGNs from Recoiling Supermassive Black Holes and Ongoing Galaxy Mergers with the Zwicky Transient Facility

Abstract: Author(s): Ward, C; Gezari, S; Frederick, S; Hammerstein, E; Nugent, P; Van Velzen, S; Drake, A; Garcia-Perez, A; Oyoo, I; Bellm, EC; Duev, DA; Graham, MJ; Kasliwal, MM; Kaye, S; Mahabal, AA; Masci, FJ; Rusholme, B; Soumagnac, MT; Yan, L | Abstract: A supermassive black hole (SMBH) ejected from the potential well of its host galaxy via gravitational wave recoil carries important information about the mass ratio and spin alignment of the pre-merger SMBH binary. Such a recoiling SMBH may be detectable as an active galactic nucleus (AGN) broad-line region offset by up to 10 kpc from a disturbed host galaxy. We describe a novel methodology using forward modeling with The Tractor to search for such offset AGNs in a sample of 5493 optically variable AGNs detected with the Zwicky Transient Facility (ZTF). We present the discovery of nine AGNs that may be spatially offset from their host galaxies and are candidates for recoiling SMBHs. Five of these offset AGNs exhibit double-peaked broad Balmer lines, which may have arisen from unobscured accretion disk emission, and four show radio emission indicative of a relativistic jet. The fraction of double-peaked emitters in our spatially offset AGN sample is significantly larger than the 16% double-peaked emitter fraction observed for ZTF AGNs overall. In our sample of variable AGNs we also identified 52 merging galaxies, including a new spectroscopically confirmed dual AGN. Finally, we detected the dramatic rebrightening of SDSS 1133, a previously discovered variable object and recoiling SMBH candidate, in ZTF. The flare was accompanied by the reemergence of strong P Cygni line features, indicating that SDSS 1133 may be an outbursting luminous blue variable star.

Science-driven system architecture: A new process for leadership class computing

Abstract: Over the past several years, computational scientists have observed a frustrating trend of stagnating application performance despite dramatic increases in peak performance of high performance computers. In 2002, researchers at Lawrence Berkeley National Laboratory, Argonne National Laboratory, and IBM pro- posed a new process to reverse this situation (1). This strategy is based on new types of development partner- ships with computer vendors based on the concept of science-driven computer system design. This strategy will engage applications scientists well before an architecture is available for commercialization. The process is already producing results, and has further potential for dramatically improving system efficiency. This paper documents the progress to date and the potential for future benefits. An example of this process is dis- cussed, using IBM Power architecture with a computer architecture design that can lead to a sustained per- formance of 50 to 100 Tflop/s on a broad spectrum of applications in 2006 for a reasonable cost. This partner- ship will establish a collaborative approach to modifying computer architecture to enable heretofore unreal- ized achievements in computer capability-limited fields such as nanoscience, combustion modeling, fusion, climate modeling, and astrophysics.

ZTF Early Observations of Type Ia Supernovae III: Early-Time Colors as a Test for Explosion Models and Multiple Populations

Abstract: Author(s): Bulla, M; Miller, AA; Yao, Y; Dessart, L; Dhawan, S; Papadogiannakis, S; Biswas, R; Goobar, A; Kulkarni, SR; Nordin, J; Nugent, P; Polin, A; Sollerman, J; Bellm, EC; Coughlin, MW; Dekany, R; Golkhou, VZ; Graham, MJ; Kasliwal, MM; Kupfer, T; Laher, RR; Masci, FJ; Porter, M; Rusholme, B; Shupe, DL | Abstract: Colors of Type Ia supernovae (SNe Ia) in the first few days after explosion provide a potential discriminant between different models. In this paper, we present g-r colors of 65 SNe Ia discovered within 5 days from first light by the Zwicky Transient Facility in 2018, a sample that is about three times larger than that in the literature. We find that g-r colors are intrinsically rather homogeneous at early phases, with about half of the dispersion attributable to photometric uncertainties (σnoise ∼ σ int ∼ 0.18 mag). Colors are nearly constant starting from 6 days after first light (g-r ∼-0.15 mag), while the time evolution at earlier epochs is characterized by a continuous range of slopes, from events rapidly transitioning from redder to bluer colors (slope of ∼-0.25 mag day-1) to events with a flatter evolution. The continuum in the slope distribution is in good agreement both with models requiring some amount of 56Ni mixed in the outermost regions of the ejecta and with "double-detonation"models having thin helium layers MHe=0.01 M⊙) and varying carbon-oxygen core masses. At the same time, six events show evidence for a distinctive "red bump"signature predicted by double-detonation models with larger helium masses. We finally identify a significant correlation between the early-time g-r slopes and supernova brightness, with brighter events associated to flatter color evolution (p-value = 0.006). The distribution of slopes, however, is consistent with being drawn from a single population, with no evidence for two components as claimed in the literature based on B-V colors.

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�