Speaker
Description
Astroparticle physics measures cosmic rays using particle detector systems that function as telescopes for detecting subatomic particles of extraterrestrial origin. As part of current multi-messenger astronomy, its models and methods bridge the gap between astrophysics, cosmology, and particle physics. My talk focuses on the causal models and probabilistic AI methods used to analyse the data taken by the ICECUBE neutrino observatory at the South Pole. Machine learning is used to extract the few relevant cosmic neutrino signals from a vast background of other neutrino signals (e.g., resulting from cosmic rays scattered in the atmosphere). Based on current ICECUBE results, my talk will focus on three questions: (1) What kind of causal modelling underlies the probabilistic data analysis? (2) To what kind of representation do the probabilistic quality standards implemented in the AI models of data analysis give rise? (3) Is the probabilistic representation of data by AI models compatible with scientific realism regarding the results of data processing, i.e., with neutrino signals originating from a specific cosmic source?