arXiv subjects

R. C. Thomas

Publications and source records attributed to R. C. Thomas.

At least 19 recordsLinked to original sources

arXiv2025

An Agnostic Approach to Building Empirical Type Ia Supernova Light Curves: Evidence for Intrinsic Chromatic Flux Variation Using Nearby Supernova Factory Data

We present a new empirical Type Ia supernova (SN Ia) model with three chromatic flux variation templates: one phase dependent and two phase independent. No underlying dust extinction model or patterns of intrinsic variability are assumed. Implemented with Stan and trained using spectrally binned Nearby Supernova Factory spectrophotometry, we examine this model's 2D, phase-independent flux variation space using two motivated basis representations. In both, the first phase-independent template captures variation that appears dust-like, while the second captures a combination of effectively intrinsic variability and second-order dust-like effects. We find that approximately 13% of the modeled phase-independent flux variance is not dust-like. Previous empirical SN Ia models either assume an effective dust extinction recipe in their architecture, or only allow for a single mode of phase-independent variation. The presented results demonstrate such an approach may be insufficient, because it could "leak" noticeable intrinsic variation into phase-independent templates.

astro-ph.HE↗

arXiv2023

Bump Morphology of the CMAGIC Diagram

We apply the color-magnitude intercept calibration method (CMAGIC) to the Nearby Supernova Factory SNe Ia spectrophotometric dataset. The currently existing CMAGIC parameters are the slope and intercept of a straight line fit to the first linear region in the color-magnitude diagram, which occurs over a span of approximately 30 days after maximum brightness. We define a new parameter, $ω_{XY}$, the size of the ``bump'' feature near maximum brightness for arbitrary filters $X$ and $Y$. We find a significant correlation between the slope of the first linear region, $β_{XY, 1}$, in the CMAGIC diagram and $ω_{XY}$. These results may be used to our advantage, as they are less affected by extinction than parameters defined as a function of time. Additionally, $ω_{XY}$ is computed independently of templates. We find that current empirical templates are successful at reproducing the features described in this work, particularly SALT3, which correctly exhibits the negative correlation between slope and bump size seen in our data. In 1-D simulations, we show that the correlation between the size of the bump feature and $β_{XY, 1}$ can be understood as a result of chemical mixing due to large-scale Rayleigh-Taylor instabilities.

astro-ph.SR↗

arXiv2022

A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time Series

We construct a physically-parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of type Ia supernovae (SNe Ia) from a sparse set of spectral time series. The PAE is a two-stage generative model, composed of an Auto-Encoder (AE) which is interpreted probabilistically after training using a Normalizing Flow (NF). We demonstrate that the PAE learns a low-dimensional latent space that captures the nonlinear range of features that exists within the population, and can accurately model the spectral evolution of SNe Ia across the full range of wavelength and observation times directly from the data. By introducing a correlation penalty term and multi-stage training setup alongside our physically-parameterized network we show that intrinsic and extrinsic modes of variability can be separated during training, removing the need for the additional models to perform magnitude standardization. We then use our PAE in a number of downstream tasks on SNe Ia for increasingly precise cosmological analyses, including automatic detection of SN outliers, the generation of samples consistent with the data distribution, and solving the inverse problem in the presence of noisy and incomplete data to constrain cosmological distance measurements. We find that the optimal number of intrinsic model parameters appears to be three, in line with previous studies, and show that we can standardize our test sample of SNe Ia with an RMS of $0.091 \pm 0.010$ mag, which corresponds to $0.074 \pm 0.010$ mag if peculiar velocity contributions are removed. Trained models and codes are released at \href{https://github.com/georgestein/suPAErnova}{github.com/georgestein/suPAErnova}

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arXiv2022

Uniform Recalibration of Common Spectrophotometry Standard Stars onto the CALSPEC System using the SuperNova Integral Field Spectrograph

We calibrate spectrophotometric optical spectra of 32 stars commonly used as standard stars, referenced to 14 stars already on the HST-based CALSPEC flux system. Observations of CALSPEC and non-CALSPEC stars were obtained with the SuperNova Integral Field Spectrograph over the wavelength range 3300 A to 9400 A as calibration for the Nearby Supernova Factory cosmology experiment. In total, this analysis used 4289 standard-star spectra taken on photometric nights. As a modern cosmology analysis, all pre-submission methodological decisions were made with the flux scale and external comparison results blinded. The large number of spectra per star allows us to treat the wavelength-by-wavelength calibration for all nights simultaneously with a Bayesian hierarchical model, thereby enabling a consistent treatment of the Type Ia supernova cosmology analysis and the calibration on which it critically relies. We determine the typical per-observation repeatability (median 14 mmag for exposures >~ 5 s), the Maunakea atmospheric transmission distribution (median dispersion of 7 mmag with uncertainty 1 mmag), and the scatter internal to our CALSPEC reference stars (median of 8 mmag). We also check our standards against literature filter photometry, finding generally good agreement over the full 12-magnitude range. Overall, the mean of our system is calibrated to the mean of CALSPEC at the level of ~ 3 mmag. With our large number of observations, careful crosschecks, and 14 reference stars, our results are the best calibration yet achieved with an integral-field spectrograph, and among the best calibrated surveys.

astro-ph.IM↗

arXiv2021

The Twins Embedding of Type Ia Supernovae II: Improving Cosmological Distance Estimates

We show how spectra of Type Ia supernovae (SNe Ia) at maximum light can be used to improve cosmological distance estimates. In a companion article, we used manifold learning to build a three-dimensional parameterization of the intrinsic diversity of SNe Ia at maximum light that we call the "Twins Embedding". In this article, we discuss how the Twins Embedding can be used to improve the standardization of SNe Ia. With a single spectrophotometrically-calibrated spectrum near maximum light, we can standardize our sample of SNe Ia with an RMS of $0.101 \pm 0.007$ mag, which corresponds to $0.084 \pm 0.009$ mag if peculiar velocity contributions are removed and $0.073 \pm 0.008$ mag if a larger reference sample were obtained. Our techniques can standardize the full range of SNe Ia, including those typically labeled as peculiar and often rejected from other analyses. We find that traditional light curve width + color standardization such as SALT2 is not sufficient. The Twins Embedding identifies a subset of SNe Ia including but not limited to 91T-like SNe Ia whose SALT2 distance estimates are biased by $0.229 \pm 0.045$ mag. Standardization using the Twins Embedding also significantly decreases host-galaxy correlations. We recover a host mass step of $0.040 \pm 0.020$ mag compared to $0.092 \pm 0.024$ mag for SALT2 standardization on the same sample of SNe Ia. These biases in traditional standardization methods could significantly impact future cosmology analyses if not properly taken into account.

astro-ph.CO↗

arXiv2021

The Twins Embedding of Type Ia Supernovae I: The Diversity of Spectra at Maximum Light

We study the spectral diversity of Type Ia supernovae (SNe Ia) at maximum light using high signal-to-noise spectrophotometry of 173 SNe Ia from the Nearby Supernova Factory. We decompose the diversity of these spectra into different extrinsic and intrinsic components, and we construct a nonlinear parameterization of the intrinsic diversity of SNe Ia that preserves pairings of "twin" SNe Ia. We call this parameterization the "Twins Embedding". Our methodology naturally handles highly nonlinear variability in spectra, such as changes in the photosphere expansion velocity, and uses the full spectrum rather than being limited to specific spectral line strengths, ratios or velocities. We find that the time evolution of SNe Ia near maximum light is remarkably similar, with 84.6% of the variance in common to all SNe Ia. After correcting for brightness and color, the intrinsic variability of SNe Ia is mostly restricted to specific spectral lines, and we find intrinsic dispersions as low as ~0.02 mag between 6600 and 7200 A. With a nonlinear three-dimensional model plus one dimension for color, we can explain 89.2% of the intrinsic diversity in our sample of SNe Ia, which includes several different kinds of "peculiar" SNe Ia. A linear model requires seven dimensions to explain a comparable fraction of the intrinsic diversity. We show how a wide range of previously-established indicators of diversity in SNe Ia can be recovered from the Twins Embedding. In a companion article, we discuss how these results an be applied to standardization of SNe Ia for cosmology.

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arXiv2020

Strong Dependence of Type Ia Supernova Standardization on the Local Specific Star Formation Rate

As part of an on-going effort to identify, understand and correct for astrophysics biases in the standardization of Type Ia supernovae (SNIa) for cosmology, we have statistically classified a large sample of nearby SNeIa into those located in predominantly younger or older environments. This classification is based on the specific star formation rate measured within a projected distance of 1kpc from each SN location (LsSFR). This is an important refinement compared to using the local star formation rate directly as it provides a normalization for relative numbers of available SN progenitors and is more robust against extinction by dust. We find that the SNeIa in predominantly younger environments are DY=0.163\pm0.029 mag (5.7 sigma) fainter than those in predominantly older environments after conventional light-curve standardization. This is the strongest standardized SN Ia brightness systematic connected to host-galaxy environment measured to date. The well-established step in standardized brightnesses between SNeIa in hosts with lower or higher total stellar masses is smaller at DM=0.119\pm0.032 mag (4.5 sigma), for the same set of SNeIa. When fit simultaneously, the environment age offset remains very significant, with DY=0.129\pm0.032 mag (4.0 sigma), while the global stellar mass step is reduced to DM=0.064\pm0.029 mag (2.2 sigma). Thus, approximately 70% of the variance from the stellar mass step is due to an underlying dependence on environment-based progenitor age. Standardization using only the SNeIa in younger environments reduces the total dispersion from 0.142\pm0.008 mag to 0.120\pm0.010 mag. We show that as environment ages evolve with redshift a strong bias on measurement of the dark energy equation of state parameters can develop. Fortunately, data to measure and correct for this effect is likely to be available for many next-generation experiments. [abstract shorten]

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arXiv2020

The SNEMO and SUGAR Companion Datasets

The Nearby Supernova Factory has made spectrophotometric observations of Type Ia supernovae since $2004$. This work presents an interim version of the data produced, including $210$ supernovae observed between $2004$ and $2013$.

astro-ph.CO↗

arXiv2020

First Cosmology Results using Type Ia Supernovae from the Dark Energy Survey: The Effect of Host Galaxy Properties on Supernova Luminosity

We present improved photometric measurements for the host galaxies of 206 spectroscopically confirmed type Ia supernovae discovered by the Dark Energy Survey Supernova Program (DES-SN) and used in the first DES-SN cosmological analysis. Fitting spectral energy distributions to the $griz$ photometric measurements of the DES-SN host galaxies, we derive stellar masses and star-formation rates. For the DES-SN sample, when considering a 5D ($z$, $x_1$, $c$, $α$, $β$) bias correction, we find evidence of a Hubble residual `mass step', where SNe Ia in high mass galaxies ($>10^{10} \textrm{M}_{\odot}$) are intrinsically more luminous (after correction) than their low mass counterparts by $γ=0.040\pm0.019$mag. This value is larger by $0.031$mag than the value found in the first DES-SN cosmological analysis. This difference is due to a combination of updated photometric measurements and improved star formation histories and is not from host-galaxy misidentification. When using a 1D (redshift-only) bias correction the inferred mass step is larger, with $γ=0.066\pm0.020$mag. The 1D-5D $γ$ difference for DES-SN is $0.026\pm0.009$mag. We show that this difference is due to a strong correlation between host galaxy stellar mass and the $x_1$ component of the 5D distance-bias correction. To better understand this effect, we include an intrinsic correlation between light-curve width and stellar mass in simulated SN Ia samples. We show that a 5D fit recovers $γ$ with $-9$mmag bias compared to a $+2$mmag bias for a 1D fit. This difference can explain part of the discrepancy seen in the data. Improvements in modeling correlations between galaxy properties and SN is necessary to determine the implications for $γ$ and ensure unbiased precision estimates of the dark energy equation-of-state as we enter the era of LSST.

astro-ph.CO↗

arXiv2019

SUGAR: An improved empirical model of Type Ia Supernovae based on spectral features

Type Ia Supernovae (SNe Ia) are widely used to measure the expansion of the Universe. Improving distance measurements of SNe Ia is one technique to better constrain the acceleration of expansion and determine its physical nature. This document develops a new SNe Ia spectral energy distribution (SED) model, called the SUpernova Generator And Reconstructor (SUGAR), which improves the spectral description of SNe Ia, and consequently could improve the distance measurements. This model is constructed from SNe Ia spectral properties and spectrophotometric data from The Nearby Supernova Factory collaboration. In a first step, a PCA-like method is used on spectral features measured at maximum light, which allows us to extract the intrinsic properties of SNe Ia. Next, the intrinsic properties are used to extract the average extinction curve. Third, an interpolation using Gaussian Processes facilitates using data taken at different epochs during the lifetime of a SN Ia and then projecting the data on a fixed time grid. Finally, the three steps are combined to build the SED model as a function of time and wavelength. This is the SUGAR model. The main advancement in SUGAR is the addition of two additional parameters to characterize SNe Ia variability. The first is tied to the properties of SNe Ia ejecta velocity, the second is correlated with their calcium lines. The addition of these parameters, as well as the high quality the Nearby Supernova Factory data, makes SUGAR an accurate and efficient model for describing the spectra of normal SNe Ia as they brighten and fade. The performance of this model makes it an excellent SED model for experiments like ZTF, LSST or WFIRST.

astro-ph.CO↗

arXiv2019

SN 2012dn from early to late times: 09dc-like supernovae reassessed

As a candidate 'super-Chandrasekhar' or 09dc-like Type Ia supernova (SN Ia), SN 2012dn shares many characteristics with other members of this remarkable class of objects but lacks their extraordinary luminosity. Here, we present and discuss the most comprehensive optical data set of this SN to date, comprised of a densely sampled series of early-time spectra obtained within the Nearby Supernova Factory project, plus photometry and spectroscopy obtained at the VLT about 1 yr after the explosion. The light curves, colour curves, spectral time series and ejecta velocities of SN 2012dn are compared with those of other 09dc-like and normal SNe Ia, the overall variety within the class of 09dc-like SNe Ia is discussed, and new criteria for 09dc-likeness are proposed. Particular attention is directed to additional insight that the late-phase data provide. The nebular spectra show forbidden lines of oxygen and calcium, elements that are usually not seen in late-time spectra of SNe Ia, while the ionisation state of the emitting iron plasma is low, pointing to low ejecta temperatures and high densities. The optical light curves are characterised by an enhanced fading starting ~60 d after maximum and very low luminosities in the nebular phase, which is most readily explained by unusually early formation of clumpy dust in the ejecta. Taken together, these effects suggest a strongly perturbed ejecta density profile, which might lend support to the idea that 09dc-like characteristics arise from a brief episode of interaction with a hydrogen-deficient envelope during the first hours or days after the explosion.

astro-ph.HE↗

arXiv2019

Superluminous Supernovae from the Dark Energy Survey

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 SLSN-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 the DES 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 events with pre-max data display such initial peaks. However, 10 events show no evidence for such peaks, in some cases down to an absolute magnitude 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.

astro-ph.HE↗

arXiv2019

First Cosmology Results Using Type Ia Supernovae From the Dark Energy Survey: Photometric Pipeline and Light Curve Data Release

We present griz light curves of 251 Type Ia Supernovae (SNe Ia) from the first 3 years of the Dark Energy Survey Supernova Program's (DES-SN) spectroscopically classified sample. The photometric pipeline described in this paper produces the calibrated fluxes and associated uncertainties used in the cosmological parameter analysis (Brout et al. 2018-SYS, DES Collaboration et al. 2018) by employing a scene modeling approach that simultaneously forward models a variable transient flux and temporally constant host galaxy. We inject artificial point sources onto DECam images to test the accuracy of our photometric method. Upon comparison of input and measured artificial supernova fluxes, we find flux biases peak at 3 mmag. We require corrections to our photometric uncertainties as a function of host galaxy surface brightness at the transient location, similar to that seen by the DES Difference Imaging Pipeline used to discover transients. The public release of the light curves can be found at https://des.ncsa.illinois.edu/releases/sn.

astro-ph.IM↗

arXiv2019

First Cosmology Results Using Type Ia Supernovae From the Dark Energy Survey: Analysis, Systematic Uncertainties, and Validation

We present the analysis underpinning the measurement of cosmological parameters from 207 spectroscopically classified type Ia supernovae (SNe Ia) from the first three years of the Dark Energy Survey Supernova Program (DES-SN), spanning a redshift range of 0.017<$z$<0.849. We combine the DES-SN sample with an external sample of 122 low-redshift ($z$<0.1) SNe Ia, resulting in a "DES-SN3YR" sample of 329 SNe Ia. Our cosmological analyses are blinded: after combining our DES-SN3YR distances with constraints from the Cosmic Microwave Background (CMB; Planck Collaboration 2016), our uncertainties in the measurement of the dark energy equation-of-state parameter, $w$, are .042 (stat) and .059 (stat+syst) at 68% confidence. We provide a detailed systematic uncertainty budget, which has nearly equal contributions from photometric calibration, astrophysical bias corrections, and instrumental bias corrections. We also include several new sources of systematic uncertainty. While our sample is <1/3 the size of the Pantheon sample, our constraints on $w$ are only larger by 1.4$\times$, showing the impact of the DES SN Ia light curve quality. We find that the traditional stretch and color standardization parameters of the DES SNe Ia are in agreement with earlier SN Ia samples such as Pan-STARRS1 and the Supernova Legacy Survey. However, we find smaller intrinsic scatter about the Hubble diagram (0.077 mag). Interestingly, we find no evidence for a Hubble residual step ( 0.007 $\pm$ 0.018 mag) as a function of host galaxy mass for the DES subset, in 2.4$σ$ tension with previous measurements. We also present novel validation methods of our sample using simulated SNe Ia inserted in DECam images and using large catalog-level simulations to test for biases in our analysis pipelines.

astro-ph.CO↗

arXiv2019

First Cosmology Results using Type Ia Supernova from the Dark Energy Survey: Simulations to Correct Supernova Distance Biases

We describe catalog-level simulations of Type Ia supernova (SN~Ia) light curves in the Dark Energy Survey Supernova Program (DES-SN), and in low-redshift samples from the Center for Astrophysics (CfA) and the Carnegie Supernova Project (CSP). These simulations are used to model biases from selection effects and light curve analysis, and to determine bias corrections for SN~Ia distance moduli that are used to measure cosmological parameters. To generate realistic light curves, the simulation uses a detailed SN~Ia model, incorporates information from observations (PSF, sky noise, zero point), and uses summary information (e.g., detection efficiency vs. signal to noise ratio) based on 10,000 fake SN light curves whose fluxes were overlaid on images and processed with our analysis pipelines. The quality of the simulation is illustrated by predicting distributions observed in the data. Averaging within redshift bins, we find distance modulus biases up to 0.05~mag over the redshift ranges of the low-z and DES-SN samples. For individual events, particularly those with extreme red or blue color, distance biases can reach 0.4~mag. Therefore, accurately determining bias corrections is critical for precision measurements of cosmological parameters. Files used to make these corrections are available at https://des.ncsa.illinois.edu/releases/sn.

astro-ph.CO↗

arXiv2019

First measurement of the Hubble constant from a dark standard siren using the Dark Energy Survey galaxies and the LIGO/Virgo binary-black-hole merger GW170814

We present a multi-messenger measurement of the Hubble constant H_0 using the binary-black-hole merger GW170814 as a standard siren, combined with a photometric redshift catalog from the Dark Energy Survey (DES). The luminosity distance is obtained from the gravitational wave signal detected by the LIGO/Virgo Collaboration (LVC) on 2017 August 14, and the redshift information is provided by the DES Year 3 data. Black-hole mergers such as GW170814 are expected to lack bright electromagnetic emission to uniquely identify their host galaxies and build an object-by-object Hubble diagram. However, they are suitable for a statistical measurement, provided that a galaxy catalog of adequate depth and redshift completion is available. Here we present the first Hubble parameter measurement using a black-hole merger. Our analysis results in $H_0 = 75.2^{+39.5}_{-32.4}~{\rm km~s^{-1}~Mpc^{-1}}$, which is consistent with both SN Ia and CMB measurements of the Hubble constant. The quoted 68% credible region comprises 60% of the uniform prior range [20,140] ${\rm km~s^{-1}~Mpc^{-1}}$, and it depends on the assumed prior range. If we take a broader prior of [10,220] ${\rm km~s^{-1}~Mpc^{-1}}$, we find $H_0 = 78^{+ 96}_{-24}~{\rm km~s^{-1}~Mpc^{-1}}$ ($57\%$ of the prior range). Although a weak constraint on the Hubble constant from a single event is expected using the dark siren method, a multifold increase in the LVC event rate is anticipated in the coming years and combinations of many sirens will lead to improved constraints on $H_0$.

astro-ph.CO↗

arXiv2018

First Cosmology Results Using Type Ia Supernovae From the Dark Energy Survey: Survey Overview and Supernova Spectroscopy

We present spectroscopy from the first three seasons of the Dark Energy Survey Supernova Program (DES-SN). We describe the supernova spectroscopic program in full: strategy, observations, data reduction, and classification. We have spectroscopically confirmed 307 supernovae, including 251 type Ia supernovae (SNe Ia) over a redshift range of $0.017 < z < 0.85$. We determine the effective spectroscopic selection function for our sample, and use it to investigate the redshift-dependent bias on the distance moduli of SNe Ia we have classified. We also provide a full overview of the strategy, observations, and data products of DES-SN, which has discovered 12,015 likely supernovae during these first three seasons. The data presented here are used for the first cosmology analysis by DES-SN ('DES-SN3YR'), the results of which are given in DES Collaboration (2018a).

astro-ph.CO↗

arXiv2018

SNEMO: Improved Empirical Models for Type Ia Supernovae

Type Ia supernova cosmology depends on the ability to fit and standardize observations of supernova magnitudes with an empirical model. We present here a series of new models of Type Ia Supernova spectral time series that capture a greater amount of supernova diversity than possible with the models that are currently customary. These are entitled SuperNova Empirical MOdels (\textsc{SNEMO}\footnote{https://snfactory.lbl.gov/snemo}). The models are constructed using spectrophotometric time series from $172$ individual supernovae from the Nearby Supernova Factory, comprising more than $2000$ spectra. Using the available observations, Gaussian Processes are used to predict a full spectral time series for each supernova. A matrix is constructed from the spectral time series of all the supernovae, and Expectation Maximization Factor Analysis is used to calculate the principal components of the data. K-fold cross-validation then determines the selection of model parameters and accounts for color variation in the data. Based on this process, the final models are trained on supernovae that have been dereddened using the Fitzpatrick and Massa extinction relation. Three final models are presented here: \textsc{SNEMO2}, a two-component model for comparison with current Type~Ia models; \textsc{SNEMO7}, a seven component model chosen for standardizing supernova magnitudes which results in a total dispersion of $0.100$~mag for a validation set of supernovae, of which $0.087$~mag is unexplained (a total dispersion of $0.113$~mag with unexplained dispersion of $0.097$~mag is found for the total set of training and validation supernovae); and \textsc{SNEMO15}, a comprehensive $15$ component model that maximizes the amount of spectral time series behavior captured.

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