7 papers
A Simulation Based Inference Approach to Modelling of Type Ia Supernova Populations
B. Popovic, M. Grayling, M. O'Callaghan +12
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…
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…
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 step: 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…
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…