arXiv subjects

Kaisey S. Mandel

Publications and source records attributed to Kaisey S. Mandel.

At least 19 recordsLinked to original sources

arXiv2026

Extreme Stellar Death and Galaxy Feedback at z = 2: A Rest-frame Ultraviolet Characterization of the Strongly Lensed Superluminous Supernova 2025wny

We present photometric and spectroscopic follow-up observations of the strongly-lensed H-poor superluminous supernova (SLSN) 2025wny at $z = 2.0155$. We use integral-field Keck-II/KCWI spectroscopy to obtain rest-frame ultraviolet spectra spanning $11$ to $61$ days after maximum light. We use Pan-STARRS photometry to constrain the brightest image's magnification, finding $μ_A=19.4\pm1.6$, a factor of $\sim$2-4 higher than mass-modeling estimates. We fit the Pan-STARRS photometry with a magnetar energy-injection model including $^{56}$Ni decay using the Modular Open Source Fitter for Transients (MOSFiT); the model can reproduce the near-maximum light curve with parameters typical of local universe SLSNe-I, including a magnetar spin period $P_{\rm spin}=4.1^{+0.7}_{-1.0}\,\mathrm{ms}$ and magnetic field $B_{\perp}=2.4^{+1.1}_{-0.8}\times10^{14}\,\mathrm{G}$; however, the late-time plateau suggests an additional power source. Spectroscopically, iron-group element line blanketing is virtually absent in the rest-frame UV spectra, and the absorption features match previously identified SLSN-I features from abundance tomography. Based on narrow ISM line metallicity estimates, SN 2025wny exploded in a sub-solar-metallicity galaxy. From host-galaxy lines in our rest-frame UV-to-optical spectrum we estimate high-velocity outflows of $\sim$575 km s$^{-1}$ and an exceptionally high Ly$α$ escape fraction of $f_{esc}^{Lyα}=0.23\pm0.03$. SN 2025wny matches lower-redshift SLSNe reasonably well. Upcoming surveys like LSST and Roman will discover additional high-redshift SLSNe, further testing whether SLSNe-I explosions remain consistent at the population level in the early universe.

astro-ph.HE↗

arXiv2026

On the origin of the environmental dependence of SN Ia magnitudes: A BayeSN view of the ZTF SN Ia DR2

Astrophysical variabilities of Type Ia supernovae (SNe Ia), such as their link with their birth environment, are now one of the leading sources of systematic uncertainties on the measurement of the dark energy equation-of-state parameter $w$. Population studies of SNe Ia, using large samples, give precious insights into these variabilities. We analyse a volume-limited subsample of 932 SNe from the ZTF SN Ia DR2 with BayeSN, a hierarchical Bayesian model for SN Ia SEDs. We investigate the distributions of SN Ia light curve parameters and their link with SN environment. Using a new training of BayeSN released in a companion paper, we find a smaller scatter of Hubble residuals compared to SALT. We then investigate the magnitude step, which accounts for the correlation between SN Ia standardised absolute magnitude and host environments. We find a posteriori steps of $0.103\pm0.010$ mag (a $10.1σ$ difference from 0) when using global stellar mass as an environmental proxy, and $0.086\pm0.010$ mag ($8.3σ$) when using local colour, in accordance with steps computed using SALT light curve fits. This confirms that the large step seen in the ZTF SN Ia DR2 data was not due to the SALT fit or the associated standardisation process. We then investigate the origin of the step, using a BayeSN model which accounts for both an intrinsic magnitude step and differing dust properties with the SN environment. We find a $0.103\pm0.018$ mag ($5.6σ$) step in global mass and a $0.085\pm0.019$ mag ($4.5σ$) step in local colour. The means of the $R_V$ distribution are similar between different host environments, with $Δ\mathbb{E}(R_V)\leq0.2$ across all environment proxies, with significances ranging from $0.6σ$ to $1.2σ$. This is a strong signal of the existence of an intrinsic dependence of SN Ia absolute magnitude on environment.

astro-ph.CO↗

arXiv2026

Hawai`i Supernova Flows: Bulk Flow Measurements using SNe Ia in the Optical and NIR

The present day peculiar velocity-field was sourced by primordial density fluctuations and sculpted over the lifespan of the Universe. Cosmological models such as $Λ$CDM make predictions for various statistical properties of peculiar velocities. Bulk flow, the average velocity within a given volume, has an expectation value of $\vec{0}$ due to isotropy, and a variance directly tied to the Hubble constant, the growth-rate of structure, and the matter power spectrum. In this paper, we use the redshifts and optical and near-infrared distance estimates to Type Ia Supernovae (SNe Ia) within subsets of the Hawai`i Supernova Flows dataset to infer the bulk flow within $z \lesssim 0.1$. The inferred speeds vary between ~100 to 400 km/s but are all consistent with the predictions of $Λ$CDM. As a secondary focus, we discuss the systematic uncertainty introduced by the discrete choice of methodology using two bulk flow estimators, two types of SN Ia distance estimators, and data covering two distinct regimes in wavelength space.

astro-ph.CO↗

arXiv2026

FlowSN: Neural Simulation-Based Inference under Realistic Selection Effects applied to Supernova Cosmology

We present FlowSN, a statistical framework using simulation-based inference (SBI) with normalising flows to account for selection effects in observational astronomy. Failure to account for selection effects can lead to biased inference on global parameters. An example is Malmquist bias, where detection limits result in a sample skewed towards brighter objects. In Type Ia supernova (SN Ia) cosmology, these selection effects can systematically shift the inferred posterior distributions of cosmological parameters, necessitating the development of robust statistical frameworks to account for the biases. SBI enables us to implicitly learn probability distributions that are analytically intractable to calculate. In this work, we introduce a novel approach that employs a normalising flow to learn the non-analytic selected SN likelihood for a given survey from forward simulations, independent of the assumed cosmological model. The resulting likelihood approximation is incorporated into a hierarchical Bayesian framework and posterior sampling is performed using Hamiltonian Monte Carlo to obtain constraints on cosmological parameters conditioned on the observed data. The modular learnt likelihood approximation can be reused without retraining to evaluate different cosmological models, providing a key advantage over other SBI approaches. We demonstrate the performance of this methodology by training and testing the SBI technique using realistic LSST-like SNANA simulations for the first time. Our FlowSN approach yields accurate posterior estimates on cosmological parameters, including the dark energy equation of state $w_0$, that are an order of magnitude less biased than those obtained with conventional techniques and also exhibit improved frequentist calibration.

astro-ph.CO↗

arXiv2026

Testing Lens Models of PLCK G165.7+67.0 Using Lensed SN H0pe

Supernova H0pe is a multiply-imaged Type Ia supernova (SN~Ia) and the second lensed SN to yield a measurement of the Hubble constant by the time-delay cosmography method, finding $H_0 = 75.4^{+8.1}_{-5.5} \text{km s}^{-1} \text{Mpc}^{-1}$ (Pascale et. al 2025). We investigate the seven lens modeling approaches used to derive $H_0$, assessing their agreement with $Λ\text{CDM}$ constraints from SN~Ia surveys through a purely observational comparison. \textbf{We test each lens model by combining its predicted magnifications with the observed time delays to reconstruct the intrinsic SN~Ia luminosity and corresponding distance modulus.} While photometrically derived magnifications yield distance moduli in line with $Λ\text{CDM}$ expectations, our comparison reveals that lens model predictions, even the most precise ones, \textbf{consistently overestimate the magnification, with an offset $> 1$~mag}. This known bias, already appreciated by modeling teams, is independently confirmed through our analysis and highlights the value of lensed SNe as a tool to test model accuracy. If unaccounted for, such magnification biases can propagate into uncertainties in derived cosmological parameters, including $H_0$, a critical challenge for precision cosmology using strongly lensed transients.

astro-ph.CO↗

arXiv2026

Supernova 2025wny: High-angular resolution Keck/NIRC2 observations and preliminary lens modeling

Multiply imaged, gravitationally lensed supernovae are rare but powerful tools for providing independent measurements on cosmological parameters. Supernova (SN) 2025wny ("SN Winny") is the first gravitationally-lensed Type I superluminous supernova and the first lensed supernova in a galaxy-scale system that is suitable for time-delay cosmography studies. In this work, we present high-resolution $K_p$-band adaptive optics imaging of SN Winny obtained with the near-infrared camera (NIRC2) on the W. M. Keck II telescope. With exquisite image quality (FWHM$\approx0.^{\prime\prime}065$) we determine and make use of the precise astrometric positions of the five multiple images as constraints for our lens mass models. With lenstronomy and Glee, we parameterize the total mass of the system with a singular isothermal ellipsoid, a singular isothermal sphere, and external shear. The two independent models are in excellent agreement and reproduce the observed image positions with sub-milli-arcsecond residuals. The inferred projected total masses enclosed within the Einstein radii of the primary and secondary lens galaxies are M$_1$ = 4.44$^{+0.06}_{-0.05}\times10^{11} M_\odot$ and M$_2$ = 0.96$^{+0.02}_{-0.02}\times10^{11} M_\odot$, respectively. Likewise, the inferred effective velocity dispersion of the primary lens is $σ_{1} = $ 277.4$^{+0.9}_{-0.7}$ km/s, consistent with the independent spectroscopic measurement made by DESI of $σ_{\star,1} = $ 298$\,\pm\,37$ km/s. Our modeling results are also consistent with previous results for the same system with data from the Large Binocular Telescope (LBT), using the same lens modeling codes. We also corroborate their finding that the SN multiple image A has an anomalous excess of flux by a factor of ~2-3 beyond what our smooth mass models predict.

astro-ph.GA↗

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.

astro-ph.CO↗

arXiv2025

Misspecification-robust amortised simulation-based inference using variational methods

Recent advances in neural density estimation have enabled powerful simulation-based inference (SBI) methods that can flexibly approximate Bayesian inference for intractable stochastic models. Although these methods have demonstrated reliable posterior estimation when the simulator accurately represents the underlying data generative process (DGP), recent work has shown that they perform poorly in the presence of model misspecification. This poses a significant issue for their use in real-world problems, due to simulators always misrepresenting the true DGP to a certain degree. In this paper, we introduce robust variational neural posterior estimation (RVNP), a method which addresses the problem of misspecification in amortised SBI by bridging the simulation-to-reality gap using variational inference and error modelling. We test RVNP on multiple benchmark tasks, including using real data from astronomy, and show that it can recover robust posterior inference in a data-driven manner without adopting hyperparameters or priors governing the misspecification influence.

stat.ML↗

arXiv2025

Data-driven dust inference at mid-to-high Galactic latitudes using probabilistic machine learning

We present a method for accurately and precisely inferring photometric dust extinction towards stars at mid-to-high Galactic latitudes using probabilistic machine learning to model the colour-magnitude distribution of zero-extinction stars in these regions. Photometric dust maps rely on a robust method for inferring stellar reddening. At high Galactic latitudes, where extinction is low, such inferences are particularly susceptible to contamination from modelling errors and prior assumptions, potentially introducing artificial structure into dust maps. In this work, we demonstrate the use of normalising flows to learn the conditional probability distribution of the photometric colour-magnitude relations of zero-extinction stars, conditioned on Galactic cylindrical coordinates for stars at mid-to-high Galactic latitudes. By using the normalising flow to model the colour-magnitude diagram, we infer the posterior distribution of dust extinction towards stars along different lines of sight by marginalising over the colour-magnitude flow. We validate our method using data from Gaia, Pan-STARRS, and 2MASS, showing that we can recover unbiased posteriors and successfully detect dust along the line-of-sight in two calibration regions at mid-Galactic latitude that have been extensively studied in the context of polarisation surveys.

astro-ph.GA↗

arXiv2025

DAmodel: Hierarchical Bayesian Modelling of DA White Dwarfs for Spectrophotometric Calibration

We use hierarchical Bayesian modelling to calibrate a network of 32 all-sky faint DA white dwarf (DA WD) spectrophotometric standards ($16.5 < V < 19.5$) alongside three CALSPEC standards, from 912 Å to 32 $μ$m. The framework is the first of its kind to jointly infer photometric zeropoints and WD parameters (surface gravity $\log g$, effective temperature $T_{\text{eff}}$, extinction $A_V$, dust relation parameter $R_V$) by simultaneously modelling both photometric and spectroscopic data. We model panchromatic Hubble Space Telescope Wide Field Camera 3 (HST/WFC3) UVIS and IR photometry, HST/STIS UV spectroscopy and ground-based optical spectroscopy to sub-percent precision. Photometric residuals for the sample are the lowest yet yielding $<0.004$ mag RMS on average from the UV to the NIR, achieved by jointly inferring time-dependent changes in system sensitivity and WFC3/IR count-rate nonlinearity. Our GPU-accelerated implementation enables efficient sampling via Hamiltonian Monte Carlo, critical for exploring the high-dimensional posterior space. The hierarchical nature of the model enables population analysis of intrinsic WD and dust parameters. Inferred spectral energy distributions from this model will be essential for calibrating the James Webb Space Telescope as well as next-generation surveys, including Vera Rubin Observatory's Legacy Survey of Space and Time and the Nancy Grace Roman Space Telescope.

astro-ph.IM↗

arXiv2024

Hawai'i Supernova Flows: A Peculiar Velocity Survey Using Over a Thousand Supernovae in the Near-Infrared

We introduce the Hawai'i Supernova Flows project and present summary statistics of the first 1,217 astronomical transients observed, 668 of which are spectroscopically classified Type Ia Supernovae (SNe Ia). Our project is designed to obtain systematics-limited distances to SNe Ia while consuming minimal dedicated observational resources. To date, we have performed almost 5,000 near-infrared (NIR) observations of astronomical transients and have obtained spectra for over 200 host galaxies lacking published spectroscopic redshifts. In this survey paper we describe the methodology used to select targets, collect/reduce data, calculate distances, and perform quality cuts. We compare our methods to those used in similar studies, finding general agreement or mild improvement. Our summary statistics include various parametrizations of dispersion in the Hubble diagrams produced using fits to several commonly used SN Ia models. We find the lowest dispersions using the \texttt{SNooPy} package's EBV\_model2, with a root mean square (RMS) deviation of 0.165 mag and a normalized median absolute deviation (NMAD) of 0.123 mag. The full utility of the Hawai'i Supernova Flows data set far exceeds the analyses presented in this paper. Our photometry will provide a valuable test bed for models of SN Ia incorporating NIR data. Differential cosmological studies comparing optical samples and combined optical and NIR samples will have increased leverage for constraining chromatic effects like dust extinction. We invite the community to explore our data by making the light curves, fits, and host galaxy redshifts publicly accessible.

astro-ph.CO↗

arXiv2024

Variational Inference for Acceleration of SN Ia Photometric Distance Estimation with BayeSN

Type Ia supernovae (SNe Ia) are standarizable candles whose observed light curves can be used to infer their distances, which can in turn be used in cosmological analyses. As the quantity of observed SNe Ia grows with current and upcoming surveys, increasingly scalable analyses are necessary to take full advantage of these new datasets for precise estimation of cosmological parameters. Bayesian inference methods enable fitting SN Ia light curves with robust uncertainty quantification, but traditional posterior sampling using Markov Chain Monte Carlo (MCMC) is computationally expensive. We present an implementation of variational inference (VI) to accelerate the fitting of SN Ia light curves using the BayeSN hierarchical Bayesian model for time-varying SN Ia spectral energy distributions (SEDs). We demonstrate and evaluate its performance on both simulated light curves and data from the Foundation Supernova Survey with two different forms of surrogate posterior -- a multivariate normal and a custom multivariate zero-lower-truncated normal distribution -- and compare them with the Laplace Approximation and full MCMC analysis. To validate of our variational approximation, we calculate the pareto-smoothed importance sampling (PSIS) diagnostic, and perform variational simulation-based calibration (VSBC). The VI approximation achieves similar results to MCMC but with an order-of-magnitude speedup for the inference of the photometric distance moduli. Overall, we show that VI is a promising method for scalable parameter inference that enables analysis of larger datasets for precision cosmology.

astro-ph.IM↗

arXiv2024

Quantifying Interstellar Extinction at High Galactic Latitudes

A detailed map of the distribution of dust at high Galactic latitudes is essential for future cosmic microwave background (CMB) polarization experiments because the dust, while diffuse, remains a significant foreground in these regions. We develop a Bayesian model to identify a region of the Hertzsprung-Russell (HR) diagram suited to constrain the single-star extinction accurately at high Galactic latitudes. Using photometry from Gaia, 2MASS and ALLWISE, and parallax from Gaia, we employ nested sampling to fit the model to the data and analyse the posterior over stellar parameters for both synthetic and real data. Charting low variations in extinction is complex due to systematic errors and degeneracies between extinction and other stellar parameters. The systematic errors can be minimised by restricting our data to a region of the HR diagram where the stellar models are most accurate. Moreover, the degeneracies can be reduced by including astrophysical priors and spectroscopic constraints. We show that accounting for the measurement error of the data and the assumed inaccuracies of the stellar models are critical in accurately recovering small variations in extinction. We compare the posterior distribution for individual stars with spectroscopic stellar parameter estimates from LAMOST and Gaia ESO and illustrate the importance of estimating extinction and effective temperature as a joint solution.

astro-ph.GA↗

arXiv2024

Six Maxims of Statistical Acumen for Astronomical Data Analysis

The production of complex astronomical data is accelerating, especially with newer telescopes producing ever more large-scale surveys. The increased quantity, complexity, and variety of astronomical data demand a parallel increase in skill and sophistication in developing, deciding, and deploying statistical methods. Understanding limitations and appreciating nuances in statistical and machine learning methods and the reasoning behind them is essential for improving data-analytic proficiency and acumen. Aiming to facilitate such improvement in astronomy, we delineate cautionary tales in statistics via six maxims, with examples drawn from the astronomical literature. Inspired by the significant quality improvement in business and manufacturing processes by the routine adoption of Six Sigma, we hope the routine reflection on these Six Maxims will improve the quality of both data analysis and scientific findings in astronomy.

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

Accounting for Selection Effects in Supernova Cosmology with Simulation-Based Inference and Hierarchical Bayesian Modelling

Type Ia supernovae (SNe Ia) are thermonuclear exploding stars that can be used to put constraints on the nature of our universe. One challenge with population analyses of SNe Ia is Malmquist bias, where we preferentially observe the brighter SNe due to limitations of our telescopes. If untreated, this bias can propagate through to our posteriors on cosmological parameters. In this paper, we develop a novel technique of using a normalising flow to learn the non-analytical likelihood of observing a SN Ia for a given survey from simulations, that is independent of any cosmological model. The learnt likelihood is then used in a hierarchical Bayesian model with Hamiltonian Monte Carlo sampling to put constraints on different sets of cosmological parameters conditioned on the observed data. We verify this technique on toy model simulations finding excellent agreement with analytically-derived posteriors to within $1 σ$.

astro-ph.CO↗

arXiv2024

SIDE-real: Supernova Ia Dust Extinction with truncated marginal neural ratio estimation applied to real data

We present the first fully simulation-based hierarchical analysis of the light curves of a population of low-redshift type Ia supernovae (SNae Ia). Our hardware-accelerated forward model, released in the Python package slicsim, includes stochastic variations of each SN's spectral flux distribution (based on the pre-trained BayeSN model), extinction from dust in the host and in the Milky Way, redshift, and realistic instrumental noise. By utilising truncated marginal neural ratio estimation (TMNRE), a neural network-enabled simulation-based inference technique, we implicitly marginalise over 4000 latent variables (for a set of $\approx 100$ SNae Ia) to efficiently infer SN Ia absolute magnitudes and host-galaxy dust properties at the population level while also constraining the parameters of individual objects. Amortisation of the inference procedure allows us to obtain coverage guarantees for our results through Bayesian validation and frequentist calibration. Furthermore, we show a detailed comparison to full likelihood-based inference, implemented through Hamiltonian Monte Carlo, on simulated data and then apply TMNRE to the light curves of 86 SNae Ia from the Carnegie Supernova Project, deriving marginal posteriors in excellent agreement with previous work. Given its ability to accommodate arbitrarily complex extensions to the forward model -- e.g. different populations based on host properties, redshift evolution, complicated photometric redshift estimates, selection effects, and non-Ia contamination -- without significant modifications to the inference procedure, TMNRE has the potential to become the tool of choice for cosmological parameter inference from future, large SN Ia samples.

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.

astro-ph.CO↗