6 papers
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…
Hawai`i Supernova Flows: Bulk Flow Measurements using SNe Ia in the Optical and NIR
Aaron Do, Kaisey S. Mandel, Benjamin J. Shappee +9
The present day peculiar velocity-field was sourced by primordial density fluctuations and sculpted over the lifespan of the Universe. Cosmological models such as CDM make pred…
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…
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…
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…
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…