5 papers
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…
Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip Statistics
Sebastian Springer, Andre Scaffidi, Maximilian Autenrieth +4
Detecting localized differences between two samples is a central task in scientific data analysis, required for the identification of signal events, regime changes, or model mismat…
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…
Using fractional derivatives to derive marginal densities
Si-Yang Li, David A. van Dyk, Maximilian Autenrieth
This paper presents a novel method for analytical derivations of marginal densities using the fractional derivatives of moment-generating functions. Although the method requires li…
StratLearn-z: Improved photo- estimation from spectroscopic data subject to selection effects
Chiara Moretti, Maximilian Autenrieth, Riccardo Serra +3
A precise measurement of photometric redshifts (photo-z) is key for the success of modern photometric galaxy surveys. Machine learning (ML) methods show great promise in this conte…