arXiv subjects

B. A. Bassett

Publications and source records attributed to B. A. Bassett.

At least 19 recordsLinked to original sources

arXiv2026

Supernovae Unite: Host-Galaxy Mass Measurements of Type Ia Supernovae and Their Impact on Cosmology

Current consensus suggests that Type Ia supernova (SN Ia) brightnesses post light-curve standardization correlate with their host-galaxy stellar masses, which must be accounted for to obtain accurate cosmological constraints. For example, Vincenzi et al. (2025) showed that different host-galaxy stellar mass measurements for the same dataset produce redshift-dependent differences of order $\sim 0.01$ mag, large enough to appreciably shift cosmological constraints. We present internally consistent host-galaxy stellar masses remeasured using aperture photometry and spectral energy distribution (SED) fitting for SN-Unite, which combines the spectroscopic Pantheon+ and the photometric Dark Energy Survey five-year (DES-SN5YR) samples into the largest SN Ia cosmology sample to date, with 2884 likely SNe Ia. We find that photometry and SED fitting choices shift SN-Unite Flat$w$CDM parameters well below statistical uncertainties. Our stellar masses differ from the Pantheon+ data release partly due to a redshift-dependent internal inconsistency within Pantheon+, while remaining largely consistent with the DES-SN5YR (DES-Dovekie) data release. When the Pantheon+ subsample of SN-Unite is combined with Baryon Acoustic Oscillations (BAO) and Cosmic Microwave Background (CMB) measurements, the significance for time-evolving dark energy increases from 3.4$σ$ to 4.0$σ$ based on the maximum a posteriori when our newly derived host-galaxy stellar masses replace the Pantheon+ data-release host-galaxy stellar masses, while DES-Dovekie remains virtually unchanged, consistent with the findings of Hoyt et al. (2026). By remeasuring the host-galaxy stellar masses using a consistent framework throughout the whole sample, we improve the robustness of the SN-Unite cosmological constraints against systematic differences in host-galaxy stellar mass measurements.

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arXiv2025

The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset

We present cosmological constraints from the sample of Type Ia supernovae (SN Ia) discovered during the full five years of the Dark Energy Survey (DES) Supernova Program. In contrast to most previous cosmological samples, in which SN are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being a SN Ia, we find 1635 DES SNe in the redshift range $0.10 0.5$ SNe compared to the previous leading compilation of Pantheon+, and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints we combine the DES supernova data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning $0.025<z<0.10$. Using SN data alone and including systematic uncertainties we find $Ω_{\rm M}=0.352\pm 0.017$ in flat $Λ$CDM. Supernova data alone now require acceleration ($q_0<0$ in $Λ$CDM) with over $5σ$ confidence. We find $(Ω_{\rm M},w)=(0.264^{+0.074}_{-0.096},-0.80^{+0.14}_{-0.16})$ in flat $w$CDM. For flat $w_0w_a$CDM, we find $(Ω_{\rm M},w_0,w_a)=(0.495^{+0.033}_{-0.043},-0.36^{+0.36}_{-0.30},-8.8^{+3.7}_{-4.5})$. Including Planck CMB data, SDSS BAO data, and DES $3\times2$-point data gives $(Ω_{\rm M},w)=(0.321\pm0.007,-0.941\pm0.026)$. In all cases dark energy is consistent with a cosmological constant to within $\sim2σ$. In our analysis, systematic errors on cosmological parameters are subdominant compared to statistical errors; paving the way for future photometrically classified supernova analyses.

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arXiv2024

The Dark Energy Survey Supernova Program: Cosmological Analysis and Systematic Uncertainties

We present the full Hubble diagram of photometrically-classified Type Ia supernovae (SNe Ia) from the Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts of 7,000 host galaxies. Based on the light-curve quality, we select 1635 photometrically-identified SNe Ia with spectroscopic redshift 0.10$< z <$1.13, which is the largest sample of supernovae from any single survey and increases the number of known $z>0.5$ supernovae by a factor of five. In a companion paper, we present cosmological results of the DES-SN sample combined with 194 spectroscopically-classified SNe Ia at low redshift as an anchor for cosmological fits. Here we present extensive modeling of this combined sample and validate the entire analysis pipeline used to derive distances. We show that the statistical and systematic uncertainties on cosmological parameters are $σ_{Ω_M,{\rm stat+sys}}^{Λ{\rm CDM}}=$0.017 in a flat $Λ$CDM model, and $(σ_{Ω_M},σ_w)_{\rm stat+sys}^{w{\rm CDM}}=$(0.082, 0.152) in a flat $w$CDM model. Combining the DES SN data with the highly complementary CMB measurements by Planck Collaboration (2020) reduces uncertainties on cosmological parameters by a factor of 4. In all cases, statistical uncertainties dominate over systematics. We show that uncertainties due to photometric classification make up less than 10% of the total systematic uncertainty budget. This result sets the stage for the next generation of SN cosmology surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time.

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arXiv2021

The Dark Energy Survey Supernova Program: Cosmological biases from supernova photometric classification

Cosmological analyses of samples of photometrically-identified Type Ia supernovae (SNe Ia) depend on understanding the effects of 'contamination' from core-collapse and peculiar SN Ia events. We employ a rigorous analysis on state-of-the-art simulations of photometrically identified SN Ia samples and determine cosmological biases due to such 'non-Ia' contamination in the Dark Energy Survey (DES) 5-year SN sample. As part of the analysis, we test on our DES simulations the performance of SuperNNova, a photometric SN classifier based on recurrent neural networks. Depending on the choice of non-Ia SN models in both the simulated data sample and training sample, contamination ranges from 0.8-3.5 %, with the efficiency of the classification from 97.7-99.5 %. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension 'BEAMS with Bias Correction' (BBC), we produce a redshift-binned Hubble diagram marginalised over contamination and corrected for selection effects and we use it to constrain the dark energy equation-of-state, $w$. Assuming a flat universe with Gaussian $Ω_M$ prior of $0.311\pm0.010$, we show that biases on $w$ are $<0.008$ when using SuperNNova and accounting for a wide range of non-Ia SN models in the simulations. Systematic uncertainties associated with contamination are estimated to be at most $σ_{w, \mathrm{syst}}=0.004$. This compares to an expected statistical uncertainty of $σ_{w,\mathrm{stat}}=0.039$ for the DES-SN sample, thus showing that contamination is not a limiting uncertainty in our analysis. We also measure biases due to contamination on $w_0$ and $w_a$ (assuming a flat universe), and find these to be $<$0.009 in $w_0$ and $<$0.108 in $w_a$, hence 5 to 10 times smaller than the statistical uncertainties expected from the DES-SN sample.

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arXiv2020

The Mystery of Photometric Twins DES17X1boj and DES16E2bjy

We present an analysis of DES17X1boj and DES16E2bjy, two peculiar transients discovered by the Dark Energy Survey (DES). They exhibit nearly identical double-peaked light curves which reach very different maximum luminosities (M$_\mathrm{r}$ = -15.4 and M$_\mathrm{r}$ = -17.9, respectively). The light curve evolution of these events is highly atypical and has not been reported before. The transients are found in different host environments: DES17X1boj was found near the nucleus of a spiral galaxy, while DES16E2bjy is located in the outskirts of a passive red galaxy. Early photometric data is well fitted with a blackbody and the resulting moderate photospheric expansion velocities (1800 km/s for DES17X1boj and 4800 km/s for DES16E2bjy) suggest an explosive or eruptive origin. Additionally, a feature identified as high-velocity CaII absorption (v $\approx$ 9400km/s) in the near-peak spectrum of DES17X1boj may imply that it is a supernova. While similar light curve evolution suggests a similar physical origin for these two transients, we are not able to identify or characterise the progenitors.

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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.

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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.

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arXiv2019

First Cosmological Results using Type Ia Supernovae from the Dark Energy Survey: Measurement of the Hubble Constant

We present an improved measurement of the Hubble constant (H_0) using the 'inverse distance ladder' method, which adds the information from 207 Type Ia supernovae (SNe Ia) from the Dark Energy Survey (DES) at redshift 0.018 < z < 0.85 to existing distance measurements of 122 low redshift (z < 0.07) SNe Ia (Low-z) and measurements of Baryon Acoustic Oscillations (BAOs). Whereas traditional measurements of H_0 with SNe Ia use a distance ladder of parallax and Cepheid variable stars, the inverse distance ladder relies on absolute distance measurements from the BAOs to calibrate the intrinsic magnitude of the SNe Ia. We find H_0 = 67.8 +/- 1.3 km s-1 Mpc-1 (statistical and systematic uncertainties, 68% confidence). Our measurement makes minimal assumptions about the underlying cosmological model, and our analysis was blinded to reduce confirmation bias. We examine possible systematic uncertainties and all are below the statistical uncertainties. Our H_0 value is consistent with estimates derived from the Cosmic Microwave Background assuming a LCDM universe (Planck Collaboration et al. 2018).

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arXiv2019

First Cosmology Results using Type Ia Supernovae from the Dark Energy Survey: Constraints on Cosmological Parameters

We present the first cosmological parameter constraints using measurements of type Ia supernovae (SNe Ia) from the Dark Energy Survey Supernova Program (DES-SN). The analysis uses a subsample of 207 spectroscopically confirmed SNe Ia from the first three years of DES-SN, combined with a low-redshift sample of 122 SNe from the literature. Our "DES-SN3YR" result from these 329 SNe Ia is based on a series of companion analyses and improvements covering SN Ia discovery, spectroscopic selection, photometry, calibration, distance bias corrections, and evaluation of systematic uncertainties. For a flat LCDM model we find a matter density Omega_m = 0.331 +_ 0.038. For a flat wCDM model, and combining our SN Ia constraints with those from the cosmic microwave background (CMB), we find a dark energy equation of state w = -0.978 +_ 0.059, and Omega_m = 0.321 +_ 0.018. For a flat w0waCDM model, and combining probes from SN Ia, CMB and baryon acoustic oscillations, we find w0 = -0.885 +_ 0.114 and wa = -0.387 +_ 0.430. These results are in agreement with a cosmological constant and with previous constraints using SNe Ia (Pantheon, JLA).

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arXiv2019

Cosmological Constraints from Multiple Probes in the Dark Energy Survey

The combination of multiple observational probes has long been advocated as a powerful technique to constrain cosmological parameters, in particular dark energy. The Dark Energy Survey has measured 207 spectroscopically--confirmed Type Ia supernova lightcurves; the baryon acoustic oscillation feature; weak gravitational lensing; and galaxy clustering. Here we present combined results from these probes, deriving constraints on the equation of state, $w$, of dark energy and its energy density in the Universe. Independently of other experiments, such as those that measure the cosmic microwave background, the probes from this single photometric survey rule out a Universe with no dark energy, finding $w=-0.80^{+0.09}_{-0.11}$. The geometry is shown to be consistent with a spatially flat Universe, and we obtain a constraint on the baryon density of $Ω_b=0.069^{+0.009}_{-0.012}$ that is independent of early Universe measurements. These results demonstrate the potential power of large multi-probe photometric surveys and pave the way for order of magnitude advances in our constraints on properties of dark energy and cosmology over the next decade.

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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).

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arXiv2018

First Cosmology Results Using Type Ia Supernovae from the Dark Energy Survey: Effects of Chromatic Corrections to Supernova Photometry on Measurements of Cosmological Parameters

Calibration uncertainties have been the leading systematic uncertainty in recent analyses using type Ia Supernovae (SNe Ia) to measure cosmological parameters. To improve the calibration, we present the application of Spectral Energy Distribution (SED)-dependent "chromatic corrections" to the supernova light-curve photometry from the Dark Energy Survey (DES). These corrections depend on the combined atmospheric and instrumental transmission function for each exposure, and they affect photometry at the 0.01 mag (1%) level, comparable to systematic uncertainties in calibration and photometry. Fitting our combined DES and low-z SN Ia sample with Baryon Acoustic Oscillation (BAO) and Cosmic Microwave Background (CMB) priors for the cosmological parameters $Ω_{\rm m}$ (the fraction of the critical density of the universe comprised of matter) and w (the dark energy equation of state parameter), we compare those parameters before and after applying the corrections. We find the change in w and $Ω_{\rm m}$ due to not including chromatic corrections are -0.002 and 0.000, respectively, for the DES-SN3YR sample with BAO and CMB priors, consistent with a larger DES-SN3YR-like simulation, which has a w-change of 0.0005 with an uncertainty of 0.008 and an $Ω_{\rm m}$ change of 0.000 with an uncertainty of 0.002 . However, when considering samples on individual CCDs we find large redshift-dependent biases (approximately 0.02 in distance modulus) for supernova distances.

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arXiv2017

Age-dating Luminous Red Galaxies observed with the Southern African Large Telescope

We measure a value for the cosmic expansion of $H(z) = 89 \pm 23$(stat) $\pm$ 44(syst) km s$^{-1}$ Mpc$^{-1}$ at a redshift of $z \simeq 0.47$ based on the differential age technique. This technique, also known as cosmic chronometers, uses the age difference between two redshifts for a passively evolving population of galaxies to calculate the expansion rate of the Universe. Our measurement is based on analysis of high quality spectra of Luminous Red Galaxies (LRGs) obtained with the Southern African Large Telescope (SALT) in two narrow redshift ranges of $z \simeq 0.40$ and $z \simeq 0.55$ as part of an initial pilot study. Ages were estimated by fitting single stellar population models to the observed spectra. This measurement presents one of the best estimates of $H(z)$ via this method at $z\sim0.5$ to date.

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arXiv2015

Machine Learning Classification of SDSS Transient Survey Images

We show that multiple machine learning algorithms can match human performance in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS) supernova survey into real objects and artefacts. This is a first step in any transient science pipeline and is currently still done by humans, but future surveys such as the Large Synoptic Survey Telescope (LSST) will necessitate fully machine-enabled solutions. Using features trained from eigenimage analysis (principal component analysis, PCA) of single-epoch g, r and i-difference images, we can reach a completeness (recall) of 96 per cent, while only incorrectly classifying at most 18 per cent of artefacts as real objects, corresponding to a precision (purity) of 84 per cent. In general, random forests performed best, followed by the k-nearest neighbour and the SkyNet artificial neural net algorithms, compared to other methods such as naïve Bayes and kernel support vector machine. Our results show that PCA-based machine learning can match human success levels and can naturally be extended by including multiple epochs of data, transient colours and host galaxy information which should allow for significant further improvements, especially at low signal-to-noise.

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arXiv2014

Generalised Fisher Matrices

The Fisher Information Matrix formalism is extended to cases where the data is divided into two parts (X,Y), where the expectation value of Y depends on X according to some theoretical model, and X and Y both have errors with arbitrary covariance. In the simplest case, (X,Y) represent data pairs of abscissa and ordinate, in which case the analysis deals with the case of data pairs with errors in both coordinates, but X can be any measured quantities on which Y depends. The analysis applies for arbitrary covariance, provided all errors are gaussian, and provided the errors in X are small, both in comparison with the scale over which the expected signal Y changes, and with the width of the prior distribution. This generalises the Fisher Matrix approach, which normally only considers errors in the `ordinate' Y. In this work, we include errors in X by marginalising over latent variables, effectively employing a Bayesian hierarchical model, and deriving the Fisher Matrix for this more general case. The methods here also extend to likelihood surfaces which are not gaussian in the parameter space, and so techniques such as DALI (Derivative Approximation for Likelihoods) can be generalised straightforwardly to include arbitrary gaussian data error covariances. For simple mock data and theoretical models, we compare to Markov Chain Monte Carlo experiments, illustrating the method with cosmological supernova data. We also include the new method in the Fisher4Cast software.

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arXiv2014

Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples

We present cosmological constraints from a joint analysis of type Ia supernova (SN Ia) observations obtained by the SDSS-II and SNLS collaborations. The data set includes several low-redshift samples (z<0.1), all 3 seasons from the SDSS-II (0.05 < z < 0.4), and 3 years from SNLS (0.2 <z < 1) and totals \ntotc spectroscopically confirmed type Ia supernovae with high quality light curves. We have followed the methods and assumptions of the SNLS 3-year data analysis except for the following important improvements: 1) the addition of the full SDSS-II spectroscopically-confirmed SN Ia sample in both the training of the SALT2 light curve model and in the Hubble diagram analysis (\nsdssc SNe), 2) inter-calibration of the SNLS and SDSS surveys and reduced systematic uncertainties in the photometric calibration, performed blindly with respect to the cosmology analysis, and 3) a thorough investigation of systematic errors associated with the SALT2 modeling of SN Ia light-curves. We produce recalibrated SN Ia light-curves and associated distances for the SDSS-II and SNLS samples. The large SDSS-II sample provides an effective, independent, low-z anchor for the Hubble diagram and reduces the systematic error from calibration systematics in the low-z SN sample. For a flat LCDM cosmology we find Omega_m=0.295+-0.034 (stat+sys), a value consistent with the most recent CMB measurement from the Planck and WMAP experiments. Our result is 1.8sigma (stat+sys) different than the previously published result of SNLS 3-year data. The change is due primarily to improvements in the SNLS photometric calibration. When combined with CMB constraints, we measure a constant dark-energy equation of state parameter w=-1.018+-0.057 (stat+sys) for a flat universe. Adding BAO distance measurements gives similar constraints: w=-1.027+-0.055.

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arXiv2013

How Flat is Our Universe Really?

Distance measurement provide no constraints on curvature independent of assumptions about the dark energy, raising the question, how flat is our Universe if we make no such assumptions? Allowing for general evolution of the dark energy equation of state with 20 free parameters that are allowed to cross the phantom divide, w(z) = -1, we show that while it is indeed possible to match the first peak in the Cosmic Microwave Background with non-flat models and arbitrary Hubble constant, H_0, the full WMAP7 and supernova data alone imply -0.12 < Ω_k < 0.01 (2σ). If we add an H_0 prior, this tightens significantly to Ω_k = 0.002 \pm 0.009 . These constitute the most conservative and model-independent constraints on curvature available today, and illustrate that the curvature-dynamics degeneracy is broken by current data, with a key role played by the Integrated Sachs Wolfe effect rather than the distance to the surface of last scattering. If one imposes a quintessence prior on the dark energy (-1 \leq w(z) \leq 1) then just the WMAP7 and supernova data alone force the Universe to near flatness: Ω_k = 0.013 \pm 0.012. Finally, allowing for curvature, we find that all datasets are consistent with a Harrison-Zel'dovich spectral index, n_s = 1, at 2σ, illustrating the interplay between early and late-universe constraints.

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arXiv2012

The sensitivity of BAO Dark Energy Constraints to General Isocurvature Perturbations

Baryon Acoustic Oscillation (BAO) surveys will be a leading method for addressing the dark energy challenge in the next decade. We explore in detail the effect of allowing for small amplitude admixtures of general isocurvature perturbations in addition to the dominant adiabatic mode. We find that non-adiabatic initial conditions leave the sound speed unchanged but instead excite different harmonics. These harmonics couple differently to Silk damping, altering the form and evolution of acoustic waves in the baryon-photon fluid prior to decoupling. This modifies not only the scale on which the sound waves imprint onto the baryon distribution, which is used as the standard ruler in BAO surveys, but also the shape, width and height of the BAO peak. We discuss these effects in detail and show how more general initial conditions impact our interpretation of cosmological data in dark energy studies. We find that the inclusion of these additional isocurvature modes leads to an increase in the Dark Energy Task Force Figure of merit by 140% and 60% for the BOSS and ADEPT experiments respectively when considered in conjunction with Planck data. We also show that the incorrect assumption of adiabaticity has the potential to bias our estimates of the dark energy parameters by $3σ$ ($1σ$) for a single correlated isocurvature mode, and up to $8σ$ ($3σ$) for three correlated isocurvature modes in the case of the BOSS (ADEPT) experiment. We find that the use of the large scale structure data in conjunction with CMB data improves our ability to measure the contributions of different modes to the initial conditions by as much as 100% for certain modes in the fully correlated case.

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