arXiv subjects

Sam M. Ward

Publications and source records attributed to Sam M. Ward.

7 recordsLinked to original sources

arXiv2025

Characterising the Standardisation Properties of Type Ia Supernovae in the z band with Hierarchical Bayesian Modelling

Type Ia supernovae (SNe Ia) are standardisable candles: their peak magnitudes can be corrected for correlations between light curve properties and their luminosities to precisely estimate distances. Understanding SN Ia standardisation across wavelength improves methods for correcting SN Ia magnitudes. Using 150 SNe Ia from the Foundation Supernova Survey and Young Supernova Experiment, we present the first study focusing on SN Ia standardisation properties in the z band. Straddling the optical and near-infrared, SN Ia light in the z band is less sensitive to dust extinction and can be collected alongside the optical on CCDs. Pre-standardisation, SNe Ia exhibit less residual scatter in z-band peak magnitudes than in the g and r bands. SNe Ia peak z-band magnitudes still exhibit a significant dependence on light-curve shape. Post-standardisation, the z-band Hubble diagram has a total scatter of RMS $ = 0.195$ mag. We infer a z-band mass step of $\gamma_{z} = -0.105 \pm 0.031$ mag, which is consistent within $1\sigma$ of that estimated from gri data, assuming $R_{V} = 2.61$. When assuming different $R_{V}$ values for high and low mass host galaxies, the z-band and optical mass steps remain consistent within $1\sigma$. Based on current statistical precision, these results suggest dust reddening cannot fully explain the mass step. SNe Ia in the z band exhibit complementary standardisability properties to the optical that can improve distance estimates. Understanding these properties is important for the upcoming Vera Rubin Observatory and Nancy G. Roman Space Telescope, which will probe the rest-frame z band to redshifts 0.1 and 1.8.

astro-ph.CO↗

arXiv2024

Scalable hierarchical BayeSN inference: Investigating dependence of SN Ia host galaxy dust properties on stellar mass and redshift

We apply the hierarchical probabilistic SED model BayeSN to analyse a sample of 475 SNe Ia (0.015 < z < 0.4) from Foundation, DES3YR and PS1MD to investigate the properties of dust in their host galaxies. We jointly infer the dust law $R_V$ population distributions at the SED level in high- and low-mass galaxies simultaneously with dust-independent, intrinsic differences. We find an intrinsic mass step of $-0.049\pm0.016$ mag, at a significance of 3.1$\sigma$, when allowing for a constant intrinsic, achromatic magnitude offset. We additionally apply a model allowing for time- and wavelength-dependent intrinsic differences between SNe Ia in different mass bins, finding $\sim$2$\sigma$ differences in magnitude and colour around peak and 4.5$\sigma$ differences at later times. These intrinsic differences are inferred simultaneously with a difference in population mean $R_V$ of $\sim$2$\sigma$ significance, demonstrating that both intrinsic and extrinsic differences may play a role in causing the host galaxy mass step. We also consider a model which allows the mean of the $R_V$ distribution to linearly evolve with redshift but find no evidence for any evolution - we infer the gradient of this relation $\eta_R = -0.38\pm0.70$. In addition, we discuss in brief a new, GPU-accelerated Python implementation of BayeSN suitable for application to large surveys which is publicly available and can be used for future cosmological analyses; this code can be found here: https://github.com/bayesn/bayesn.

astro-ph.CO↗

arXiv2023

Bird-Snack: Bayesian Inference of dust law $R_V$ Distributions using SN Ia Apparent Colours at peaK

To reduce systematic uncertainties in Type Ia supernova (SN Ia) cosmology, the host galaxy dust law shape parameter, $R_V$, must be accurately constrained. We thus develop a computationally-inexpensive pipeline, Bird-Snack, to rapidly infer dust population distributions from optical-near infrared SN colours at peak brightness, and determine which analysis choices significantly impact the population mean $R_V$ inference, $\mu_{R_V}$. Our pipeline uses a 2D Gaussian process to measure peak $BVriJH$ apparent magnitudes from SN light curves, and a hierarchical Bayesian model to simultaneously constrain population distributions of intrinsic and dust components. Fitting a low-to-moderate-reddening sample of 65 low-redshift SNe yields $\mu_{R_V}=2.61^{+0.38}_{-0.35}$, with $68\%(95\%)$ posterior upper bounds on the population dispersion, $\sigma_{R_V}<0.92(1.96)$. This result is robust to various analysis choices, including: the model for intrinsic colour variations, fitting the shape hyperparameter of a gamma dust extinction distribution, and cutting the sample based on the availability of data near peak. However, these choices may be important if statistical uncertainties are reduced. With larger near-future optical and near-infrared SN samples, Bird-Snack can be used to better constrain dust distributions, and investigate potential correlations with host galaxy properties. Bird-Snack is publicly available; the modular infrastructure facilitates rapid exploration of custom analysis choices, and quick fits to simulated datasets, for better interpretation of real-data inferences.

astro-ph.GA↗

arXiv2023

The DEHVILS Survey Overview and Initial Data Release: High-Quality Near-Infrared Type Ia Supernova Light Curves at Low Redshift

While the sample of optical Type Ia Supernova (SN Ia) light curves (LCs) usable for cosmological parameter measurements surpasses 2000, the sample of published, cosmologically viable near-infrared (NIR) SN Ia LCs, which have been shown to be good "standard candles," is still $\lesssim$ 200. Here, we present high-quality NIR LCs for 83 SNe Ia ranging from $0.002 < z < 0.09$ as a part of the Dark Energy, H$_0$, and peculiar Velocities using Infrared Light from Supernovae (DEHVILS) survey. Observations are taken using UKIRT's WFCAM, where the median depth of the images is 20.7, 20.1, and 19.3 mag (Vega) for $Y$, $J$, and $H$-bands, respectively. The median number of epochs per SN Ia is 18 for all three bands ($YJH$) combined and 6 for each band individually. We fit 47 SN Ia LCs that pass strict quality cuts using three LC models, SALT3, SNooPy, and BayeSN and find scatter on the Hubble diagram to be comparable to or better than scatter from optical-only fits in the literature. Fitting NIR-only LCs, we obtain standard deviations ranging from 0.128-0.135 mag. Additionally, we present a refined calibration method for transforming 2MASS magnitudes to WFCAM magnitudes using HST CALSPEC stars that results in a 0.03 mag shift in the WFCAM $Y$-band magnitudes.

astro-ph.CO↗

arXiv2022

A BayeSN Distance Ladder: $H_0$ from a consistent modelling of Type Ia supernovae from the optical to the near infrared

The local distance ladder estimate of the Hubble constant ($H_0$) is important in cosmology, given the recent tension with the early universe inference. We estimate $H_0$ from the Type Ia supernova (SN~Ia) distance ladder, inferring SN~Ia distances with the hierarchical Bayesian SED model, BayeSN. This method has a notable advantage of being able to continuously model the optical and near-infrared (NIR) SN~Ia light curves simultaneously. We use two independent distance indicators, Cepheids or the tip of the red giant branch (TRGB), to calibrate a Hubble-flow sample of 67 SNe~Ia with optical and NIR data. We estimate $H_0 = 74.82 \pm 0.97$ (stat) $\pm\, 0.84$ (sys) km\,s$^{-1}$\,Mpc$^{-1}$ when using the calibration with Cepheid distances to 37 host galaxies of 41 SNe~Ia, and $70.92 \pm 1.14$ (stat) $\pm\,1.49$ (sys) km\,s$^{-1}$\,Mpc$^{-1}$ when using the calibration with TRGB distances to 15 host galaxies of 18 SNe~Ia. For both methods, we find a low intrinsic scatter $\sigma_{\rm int} \lesssim 0.1$ mag. We test various selection criteria and do not find significant shifts in the estimate of $H_0$. Simultaneous modelling of the optical and NIR yields up to $\sim$15\% reduction in $H_0$ uncertainty compared to the equivalent optical-only cases. With improvements expected in other rungs of the distance ladder, leveraging joint optical-NIR SN~Ia data can be critical to reducing the $H_0$ error budget.

astro-ph.CO↗

arXiv2022

Relative intrinsic scatter in hierarchical Type Ia supernova siblings analyses: Application to SNe 2021hpr, 1997bq & 2008fv in NGC 3147

We present Young Supernova Experiment $grizy$ photometry of SN 2021hpr, the third Type Ia supernova sibling to explode in the Cepheid calibrator galaxy, NGC 3147. Siblings are useful for improving SN-host distance estimates, and investigating the contributions towards the SN Ia intrinsic scatter (post-standardisation residual scatter in distance estimates). We thus develop a principled Bayesian framework for analyzing SN Ia siblings. At its core is the cosmology-independent relative intrinsic scatter parameter, $\sigma_{Rel}$: the dispersion of siblings distance estimates relative to one another within a galaxy. It quantifies the contribution towards the total intrinsic scatter, $\sigma_0$, from within-galaxy variations about the siblings' common properties. It also affects the combined-distance uncertainty. We present analytic formulae for computing a $\sigma_{Rel}$-posterior from individual siblings distances (estimated using any SN-model). Applying a newly trained BayeSN model, we fit the light curves of each sibling in NGC 3147 individually, to yield consistent distance estimates. However, the wide $\sigma_{Rel}$-posterior means $\sigma_{Rel}\approx\sigma_0$ is not ruled out. We thus combine the distances by marginalizing over $\sigma_{Rel}$ with an informative prior: $\sigma_{Rel}\sim U(0,\sigma_0)$. Simultaneously fitting the trio's light curves improves constraints on distance, and each sibling's individual dust parameters, compared to individual fits. Higher correlation also tightens dust parameter constraints. Therefore, $\sigma_{Rel}$-marginalization yields robust estimates of siblings distances for cosmology, and dust parameters for siblings-host correlation studies. Incorporating NGC 3147's Cepheid-distance yields $H_0=78.4\pm 6.5\,$km/s/Mpc. Our work motivates analyses of homogeneous siblings samples, to constrain $\sigma_{Rel}$, and its SN-model dependence.

astro-ph.CO↗

arXiv2021

Testing the Consistency of Dust Laws in SN Ia Host Galaxies: A BayeSN Examination of Foundation DR1

We apply BayeSN, our new hierarchical Bayesian model for the SEDs of Type Ia supernovae (SNe Ia), to analyse the $griz$ light curves of 157 nearby SNe Ia ($0.015<z<0.08$) from the public Foundation DR1 dataset. We train a new version of BayeSN, continuous from 0.35--0.95 $μ$m, which we use to model the properties of SNe Ia in the rest-frame $z$-band, study the properties of dust in their host galaxies, and construct a Hubble diagram of SN Ia distances determined from full $griz$ light curves. Our $griz$ Hubble diagram has a low total RMS of 0.13 mag using BayeSN, compared to 0.16 mag using SALT2. Additionally, we test the consistency of the dust law $R_V$ between low- and high-mass host galaxies by using our model to fit the full time- and wavelength-dependent SEDs of SNe Ia up to moderate reddening (peak apparent $B-V \lesssim 0.3$). Splitting the population at the median host mass, we find $R_V=2.84\pm0.31$ in low-mass hosts, and $R_V=2.58\pm0.23$ in high-mass hosts, both consistent with the global value of $R_V=2.61\pm0.21$ that we estimate for the full sample. For all choices of mass split we consider, $R_V$ is consistent across the step within $\lesssim1.2σ$. Modelling population distributions of dust laws in low- and high-mass hosts, we find that both subsamples are highly consistent with the full sample's population mean $μ(R_V) = 2.70\pm0.25$ with a 95% upper bound on the population $σ(R_V) < 0.61$. The $R_V$ population means are consistent within $\lesssim1.2σ$. We find that simultaneous fitting of host-mass-dependent dust properties within our hierarchical model does not account for the conventional mass step.

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