83 citations · 258 across the 30 of their papers we have counts for
34 papers
Unsupervised Tool Evolution for Coding Agents in Data-Lake Analytics
Yuqing Yang, Yurong Liu, Deqing Fu +5
Coding agents can answer analytical questions over a data lake by discovering, cleaning, and joining the relevant data. However, they discard this work after each question and redo…
jax-SNANA: A GPU-accelerated Supernova Framework for the SBI Era
Matthew Grayling
We present jax-SNANA, a JAX reimplementation of the supernova analysis framework SNANA. jax-SNANA enables GPU-accelerated transient simulations and light curve fitting, supernova s…
Extreme Stellar Death and Galaxy Feedback at z = 2: A Rest-frame Ultraviolet Characterization of the Strongly Lensed Superluminous Supernova 2025wny
Willem B. Hoogendam, David O. Jones, Christopher J. Storfer +26
We present photometric and spectroscopic follow-up observations of the strongly-lensed H-poor superluminous supernova (SLSN) 2025wny at . We use integral-field Keck-II/…
A Simulation Based Inference Approach to Modelling of Type Ia Supernova Populations
B. Popovic, M. Grayling, M. O'Callaghan +10
Type Ia Supernovae (SNe Ia) are prominent cosmological probes, utilising a standardisation process to reduce their observed scatter to mag. A growing number of models se…
BayeSN Dovekie: Joint Photometric Cross-calibration and SED Modelling of Type Ia Supernovae
M. Grayling, B. Popovic, M. Ginolin +2
We present a new framework for BayeSN, the hierarchical Bayesian SED model for type Ia supernovae (SNe Ia), incorporating cross-calibration of samples observed across heterogeneous…
On the origin of the environmental dependence of SN Ia magnitudes: A BayeSN view of the ZTF SN Ia DR2
Madeleine Ginolin, Matthew Grayling, Kaisey S. Mandel +5
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 m…
FlowSN: Neural Simulation-Based Inference under Realistic Selection Effects applied to Supernova Cosmology
Benjamin M. Boyd, Kaisey S. Mandel, Matthew Grayling +10
We present FlowSN, a statistical framework using simulation-based inference (SBI) with normalising flows to account for selection effects in observational astronomy. Failure to acc…
Lightcurve Modelling of 2,205 ZTF DR2 Type~Ia Supernovae: Implications for SN Ia Physics and Cosmology
Nikhil Sarin, Ellen Lindsjö, Lisa Kelsey +11
We fit the multi-band light curves of 2,205 Type Ia supernovae (SNe~Ia) from the Zwicky Transient Facility DR2 with a one-zone radioactive decay model with a phenomenological addit…
Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…
A Fully Photometric Approach to Type Ia Supernova Cosmology in the LSST Era: Host Galaxy Redshifts and Supernova Classification
Ayan Mitra, Richard Kessler, Rebecca C. Chen +9
The upcoming Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) is expected to discover nearly a million Type Ia supernovae (SNeIa), offering an unprecedented oppor…
BayeSN-TD: Time Delay and Estimation for Lensed SN H0pe
M. Grayling, S. Thorp, K. S. Mandel +6
We present BayeSN-TD, an enhanced implementation of the probabilistic type Ia supernova (SN Ia) BayeSN SED model, designed for fitting multiply-imaged, gravitationally lensed type…
Testing Lens Models of PLCK G165.7+67.0 Using Lensed SN H0pe
Aadya Agrawal, J. D. R. Pierel, Gautham Narayan +9
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 $…
A Reassessment of the Pantheon+ and DES 5YR Calibration Uncertainties: Dovekie
B. Popovic, W. D. Kenworthy, M. Ginolin +21
Type Ia Supernovae (SNe Ia) are crucial tools to measure the accelerating expansion of the universe, comprising thousands of SNe across multiple telescopes. Accurate measurements o…
Characterising the Standardisation Properties of Type Ia Supernovae in the z band with Hierarchical Bayesian Modelling
Erin E. Hayes, Suhail Dhawan, Kaisey S. Mandel +21
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 e…
Large Cold Dust Reservoir Revealed in Transitional SN Ib 2014C by James Webb Space Telescope Mid-Infrared Spectroscopy
Samaporn Tinyanont, Ori D. Fox, Melissa Shahbandeh +32
Supernova (SN) 2014C is a rare transitional event that exploded as a hydrogen-poor, helium-rich Type Ib SN and subsequently interacted with a hydrogen-rich circumstellar medium (CS…
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
The Multimodal Universe Collaboration, Jeroen Audenaert, Micah Bowles +26
We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MU…
DAmodel: Hierarchical Bayesian Modelling of DA White Dwarfs for Spectrophotometric Calibration
Benjamin M. Boyd, Gautham Narayan, Kaisey S. Mandel +17
We use hierarchical Bayesian modelling to calibrate a network of 32 all-sky faint DA white dwarf (DA WD) spectrophotometric standards () alongside three CALSPEC st…
BayeSN and SALT: A Comparison of Dust Inference Across SN Ia Light-curve Models with DES5YR
Matthew Grayling, Brodie Popovic
In recent years there has been significant debate around the impact of dust on SNe Ia, a major source of uncertainty in cosmological analyses. We perform the first validation of th…
Accounting for Selection Effects in Supernova Cosmology with Simulation-Based Inference and Hierarchical Bayesian Modelling
Benjamin M. Boyd, Matthew Grayling, Stephen Thorp +1
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…
Variational Inference for Acceleration of SN Ia Photometric Distance Estimation with BayeSN
Ana Sofía M. Uzsoy, Stephen Thorp, Matthew Grayling +1
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 q…
Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams
P. D. Aleo, A. W. Engel, G. Narayan +29
We present LAISS (Lightcurve Anomaly Identification and Similarity Search), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy…
JWST Photometric Time-Delay and Magnification Measurements for the Triply-Imaged Type Ia "Supernova H0pe" at z = 1.78
J. D. R. Pierel, B. L. Frye, M. Pascale +60
Supernova (SN) H0pe is a gravitationally lensed, triply-imaged, Type Ia SN (SN Ia) discovered in James Webb Space Telescope imaging of the PLCK G165.7+67.0 cluster of galaxies. Wel…
SIDE-real: Supernova Ia Dust Extinction with truncated marginal neural ratio estimation applied to real data
Konstantin Karchev, Matthew Grayling, Benjamin M. Boyd +3
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…
Scalable hierarchical BayeSN inference: Investigating dependence of SN Ia host galaxy dust properties on stellar mass and redshift
Matthew Grayling, Stephen Thorp, Kaisey S. Mandel +5
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…