23 citations · 35 across the 8 of their papers we have counts for
10 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…
IR extremality and UV softness in string bootstrap
Shi-Lin Wan, Ji-Yang Wang, Shuang-Yong Zhou
Whether string amplitudes can be characterized by extremality within the space of consistent S-matrices is an intriguing question. We address this question within the framework of…
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…
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…
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…
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…
Improved Weak Lensing Photometric Redshift Calibration via StratLearn and Hierarchical Modeling
Maximilian Autenrieth, Angus H. Wright, Roberto Trotta +3
Discrepancies between cosmological parameter estimates from cosmic shear surveys and from recent Planck cosmic microwave background measurements challenge the ability of the highly…
Stratified Learning: A General-Purpose Statistical Method for Improved Learning under Covariate Shift
Maximilian Autenrieth, David A. van Dyk, Roberto Trotta +1
We propose a simple, statistically principled, and theoretically justified method to improve supervised learning when the training set is not representative, a situation known as c…
Uncertainty Based Detection and Relabeling of Noisy Image Labels
Jan M. Köhler, Maximilian Autenrieth, William H. Beluch
Deep neural networks (DNNs) are powerful tools in computer vision tasks. However, in many realistic scenarios label noise is prevalent in the training images, and overfitting to th…