Deep learning (DL) models are increasingly used in astroparticle physics for tasks such as gamma–hadron separation, neutrino event reconstruction, and cosmic-ray classification. While these models achieve remarkable predictive accuracy, their opacity poses a challenge to the epistemic standards of discovery. Heatmap-based explainable AI (XAI) techniques—such as heatmaps—promise insight into...
My talk reevaluates the distinction between experiment and observation. I first argue that to get clear on what role observation plays in the generation of scientific knowledge, we need to distinguish “experiential observation” as a concept closely connected to experience from “observation” in a technical sense and from “field observation”, as a concept that reasonably contrasts with...
I propose a general methodological framework for astrophysical observations that can accommodate both single and multi-messenger observations. This is an eliminative inferential process aimed at identifying the source system from two directions, namely from date to phenomenon and from fundamental theory to phenomenon. That is, the process draws from both observational data and theoretical...
