arXiv subjects

Erin E. Hayes

Publications and source records attributed to Erin E. Hayes.

10 recordsLinked to original sources

arXiv2026

Follow-up of SN 2025wny VI: The Rate and Detectable Population of Strongly Lensed SLSNe-I in ZTF

The Type I superluminous supernova (SLSN-I) SN 2025wny, multiply imaged by two foreground galaxies at $z_l = 0.375$, is the first confirmed strongly lensed SLSN and the highest-redshift ($z_s = 2.015$) multiply imaged supernova in a galaxy-scale configuration. We ask whether one discovery in seven years of Zwicky Transient Facility (ZTF) operations is consistent with expectations, and whether SN 2025wny is typical of the detectable population. We develop a forward simulation of strongly lensed SLSNe-I in ZTF, combining an empirically calibrated volumetric rate and luminosity function with a galaxy-scale deflector population, unresolved lensed light curves, and the actual ZTF observing history. We predict $0.037^{+0.022}_{-0.020}\,{\rm yr}^{-1}$, consistent with the rate of $0.14^{+0.31}_{-0.11}\,{\rm yr}^{-1}$ inferred from a single discovery in seven years of ZTF operations. Conditioning the simulation on discovery by ZTF corrects the magnifications and intrinsic luminosity of SN 2025wny for Malmquist- and magnification biases: the debiased peak bolometric luminosity, $\log_{10}(L_{\rm bol,int}/{\rm erg\,s^{-1}}) = 44.60^{+0.05}_{-0.10}$, places it at the $\sim$97th percentile of the assumed SLSNe-I luminosity function. SN 2025wny is typical of the detectable population in redshift, luminosity, and magnification, but its $4.9''$ image separation exceeds single-galaxy deflector expectations, implying our rates are conservative for wide-separation systems.

astro-ph.CO↗

arXiv2026

Follow-up of SN 2025wny IV: Photometric Time-delay Measurements of a Strongly Lensed Superluminous Supernova

We present photometric time-delay measurements of SN 2025wny, the first strongly lensed Type I superluminous supernova (SLSN-I), discovered at $z = 2.015$. Time-delay measurements from strongly lensed supernovae provide an independent probe of cosmology and the Hubble constant, $H_0$, without reliance on the local distance ladder. Using multi-facility imaging data, we performed scene-modelling photometry to deblend four of the lensed images (A-D) and construct $grizJ$-band light curves. We modelled the resolved light curves with Gaussian process regression using GausSN (Hayes et al. 2024) to infer relative time delays and magnifications between the lensed images. We found that a constant magnification model provides a suboptimal description of the data, motivating a time-dependent sigmoid magnification model to account for evolving relative magnification of image A. We measured time delays of $Δt_{AB} = -10.6^{+2.2}_{-2.5}$ days and $Δt_{AC} = 1.2^{+2.7}_{-2.6}$ days (68% credible intervals), consistent with independent spectroscopic measurements from Johansson et al. (2026). Combining the photometric time delays with the lens model of Mörtsell et al. (2026) gives $H_{0,\:\rm photo} = 80.5^{+26.4}_{-16.7}\;\rm km\,s^{-1}\,Mpc^{-1}$, while including the spectroscopic time delays as well yields $H_{0,\:\rm comb} = 70.8^{+8.2}_{-6.1}\;{\rm km\,s^{-1}\,Mpc^{-1}}$. Our results further demonstrate the potential of strongly lensed supernovae as independent probes of $H_0$.

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arXiv2026

SLSim: a strong lensing population simulation package

Gravitational lensing offers unique insights into cosmology by bending light around massive objects. Strong gravitational lensing, in particular, produces magnified and often multiple images of distant sources, crucial for precise cosmological measurements and understanding the distribution of dark matter in the universe. Current studies are limited by the number of strong gravitational lenses. From upcoming cosmological surveys, we anticipate observing a several orders of magnitude increase in the number of lenses, for both static and transient phenomena. However, detecting and analyzing these events from vast surveys like Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) presents significant challenges. To prepare for these challenges, we introduce SLSim, a versatile simulation tool tailored for the Vera C. Rubin Observatory. SLSim integrates advanced astrophysical models with computational efficiency to generate synthetic strong lens populations under realistic observational conditions. SLSim simulates static and variable lensing scenarios, essential for cosmological studies, training and testing lens search and data analysis pipelines. This paper details SLSim,'s design and implementation, emphasizing its modularity and capabilities across various astrophysical regimes. Validation against observational data and existing simulations confirms SLSim's accuracy in reproducing observed lensing phenomena. SLSim is publicly available at https://github.com/LSST-strong-lensing/slsim, and we anticipate continued development and expansion of its capabilities. Users are encouraged to check the repository for updates and to contribute to ongoing community efforts in strong lensing simulations.

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arXiv2026

The Case for Space: Estimating Precise Time Delays from Ground- and Space-Based Observations of Lensed Supernovae with Glimpse

The delay in arrival time of the multiple images of gravitationally lensed supernovae (glSNe) can be related to the present-day expansion rate of the universe, $H_{0}$. Despite their rarity, Rubin Observatory's Legacy Survey of Space and Time (Rubin-LSST) is expected to discover tens of galaxy-scale glSNe per year, many of which will not be resolved due to their compact nature. Follow-up from ground- and space-based telescopes will be necessary to estimate time delays to sufficient precision for meaningful $H_{0}$ constraints. We present the Glimpse model (GausSN Light curve Inference of Magnifications and Phase Shifts, Extended) that estimates time delays with resolved and unresolved observations together for the first time, while simultaneously accounting for dust and microlensing effects. With this method, we explore best follow-up strategies for glSNe observed by Rubin-LSST. For unresolved systems on the dimmest end of detectability by Rubin-LSST, having peak i-band magnitudes of 22-24 mag, the time delays are measured to as low as 0.7 day uncertainty with 6-8 epochs of resolved space-based observations in each of 4-6 optical and NIR filters. For systems of similar brightness that are resolved by ground-based facilities, time delays are consistently constrained to 0.5-0.8 day precision with 6 epochs in 4 optical and NIR filters of space-based observations or 8 epochs in 4 optical filters of deep ground-based observations. This work improves on previous time-delay estimation methods and demonstrates that glSNe time delays of $\sim10-20$ days can be measured to sufficient precision for competitive $H_{0}$ estimates in the Rubin-LSST era.

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arXiv2026

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 $γ_{z} = -0.105 \pm 0.031$ mag, which is consistent within $1σ$ 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 $1σ$. 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.

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arXiv2025

Multi-messenger Gravitational Lensing

We introduce the rapidly emerging field of multi-messenger gravitational lensing - the discovery and science of gravitationally lensed phenomena in the distant universe through the combination of multiple messengers. This is framed by gravitational lensing phenomenology that has grown since the first discoveries in the 20th century, messengers that span 30 orders of magnitude in energy from high energy neutrinos to gravitational waves, and powerful "survey facilities" that are capable of continually scanning the sky for transient and variable sources. Within this context, the main focus is on discoveries and science that are feasible in the next 5-10 years with current and imminent technology including the LIGO-Virgo-KAGRA network of gravitational wave detectors, the Vera C. Rubin Observatory, and contemporaneous gamma/X-ray satellites and radio surveys. The scientific impact of even one multi-messenger gravitational lensing discovery will be transformational and reach across fundamental physics, cosmology and astrophysics. We describe these scientific opportunities and the key challenges along the path to achieving them. This article is the introduction to the Theme Issue of the Philosophical Transactions of The Royal Society A on the topic of Multi-messenger Gravitational Lensing, and describes the consensus that emerged at the associated Theo Murphy Discussion Meeting in March 2024.

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arXiv2025

LensWatch. II. Improved Photometry and Time-delay Constraints on the Strongly Lensed Type Ia Supernova 2022qmx ("SN Zwicky") with HST Template Observations

Strongly lensed supernovae (SNe) are a rare class of transient that can offer tight cosmological constraints that are complementary to methods from other astronomical events. We present a follow-up study of one recently-discovered strongly lensed SN, the quadruply-imaged Type Ia SN 2022qmx (aka, "SN Zwicky") at z = 0.3544. We measure updated, template-subtracted photometry for SN Zwicky and derive improved time delays and magnifications. This is possible because SNe are transient, fading away after reaching their peak brightness. Specifically, we measure point spread function (PSF) photometry for all four images of SN Zwicky in three Hubble Space Telescope WFC3/UVIS passbands (F475W, F625W, F814W) and one WFC3/IR passband (F160W), with template images taken $\sim 11$ months after the epoch in which the SN images appear. We find consistency to within $2σ$ between lens model predicted time delays ($\lesssim1$ day), and measured time delays with HST colors ($\lesssim2$ days), including the uncertainty from chromatic microlensing that may arise from stars in the lensing galaxy. The standardizable nature of SNe Ia allows us to estimate absolute magnifications for the four images, with images A and C being elevated in magnification compared to lens model predictions by about $6σ$ and $3σ$ respectively, confirming previous work. We show that millilensing or differential dust extinction is unable to explain these discrepancies and find evidence for the existence of microlensing in images A, C, and potentially D, that may contribute to the anomalous magnification.

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arXiv2024

The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data

We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MULTIMODAL UNIVERSE contains hundreds of millions of astronomical observations, constituting 100\,TB of multi-channel and hyper-spectral images, spectra, multivariate time series, as well as a wide variety of associated scientific measurements and "metadata". In addition, we include a range of benchmark tasks representative of standard practices for machine learning methods in astrophysics. This massive dataset will enable the development of large multi-modal models specifically targeted towards scientific applications. All codes used to compile the MULTIMODAL UNIVERSE and a description of how to access the data is available at https://github.com/MultimodalUniverse/MultimodalUniverse

astro-ph.IM↗

arXiv2024

GausSN: Bayesian Time-Delay Estimation for Strongly Lensed Supernovae

We present GausSN, a Bayesian semi-parametric Gaussian Process (GP) model for time-delay estimation with resolved systems of gravitationally lensed supernovae (glSNe). GausSN models the underlying light curve non-parametrically using a GP. Without assuming a template light curve for each SN type, GausSN fits for the time delays of all images using data in any number of wavelength filters simultaneously. We also introduce a novel time-varying magnification model to capture the effects of microlensing alongside time-delay estimation. In this analysis, we model the time-varying relative magnification as a sigmoid function, as well as a constant for comparison to existing time-delay estimation approaches. We demonstrate that GausSN provides robust time-delay estimates for simulations of glSNe from the Nancy Grace Roman Space Telescope and the Vera C. Rubin Observatory's Legacy Survey of Space and Time (Rubin-LSST). We find that up to 43.6% of time-delay estimates from Roman and 52.9% from Rubin-LSST have fractional errors of less than 5%. We then apply GausSN to SN Refsdal and find the time delay for the fifth image is consistent with the original analysis, regardless of microlensing treatment. Therefore, GausSN maintains the level of precision and accuracy achieved by existing time-delay extraction methods with fewer assumptions about the underlying shape of the light curve than template-based approaches, while incorporating microlensing into the statistical error budget. GausSN is scalable for time-delay cosmography analyses given current projections of glSNe discovery rates from Rubin-LSST and Roman.

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$σ$, 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$2$σ$ differences in magnitude and colour around peak and 4.5$σ$ differences at later times. These intrinsic differences are inferred simultaneously with a difference in population mean $R_V$ of $\sim$2$σ$ 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 $η_R = -0.38\pm0.70$. 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.

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