Monte Carlo computer simulations and methods have been widely used in physics for many decades, particularly in data analysis. Despite that, the growing philosophical literature about computer simulations has largely bracketed them. This talk aims to fill in this lacuna. I provide an overview of various Monte Carlo techniques as they are applied in physics, covering Monte Carlo integration,...
Physicists aim to reconstruct the distribution of physical quantities from the vast amounts of data collected by telescopes, as a means to better understand the physical processes of the Universe. This
reconstruction involves solving an inverse problem, specifically the Fredholm integral equation highlighted in the overview of this workshop. Methods for finding such a solution are not only...
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...
