9 citations · 23 across the 10 of their papers we have counts for
12 papers
2Flow2SN: Dusty Supernova Cosmology with Photometric Redshifts and Contamination using Neural Simulation-Based Inference
Benjamin M. Boyd, Maximilian Autenrieth, Kaisey S. Mandel +4
Photometric Type Ia supernova (SN Ia) cosmology in the era of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) demands statistical frameworks that simultaneou…
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…
Exploiting weight-space symmetries for approximating curvature
Artem Artemev, Rui Xia, Benjamin M. Boyd +4
Many machine learning techniques rely on approximating a loss function's curvature, but this is notoriously hard to do at the scale of modern deep networks. Surprisingly, no previo…
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…
Attaining Spectral Energy Distributions With Sub-Percent Uncertainties: All-Sky DA White Dwarf Spectrophotometric Standard Stars For Large Telescopes And Surveys
Abhijit Saha, Edward W. Olszewski, Benjamin M. Boyd +14
We present a synopsis of the project to establish thirty-two new faint () DA white dwarfs as spectrophotometric standards distributed over the whole sky. O…
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…
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…
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…
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…
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…