Speaker
Description
Monte Carlo simulations (MCS) are the method of choice for the simulation chain in (astro-)particle physics. Even though there exists an extensive philosophical debate on the epistemic nature of computer simulations, the reasons for preferring MCSs have not been addressed. In my talk I claim that analyzing these reasons sheds light on the debate on the epistemic nature.
In (astro-)particle physics, several MCSs are combined to a simulation chain to simulate the entire process from the first interaction of the primary particle to the registration event inside the detector. This produces synthetic data with complete information on the physical properties of the original particle and the involved secondary particles. The synthetic data can thus serve as labelled training and test data for machine learning methods that are among other things used for signal-background-separation in the raw data.
Furthermore, the Monte Carlo simulation chain can be used for an inverse analysis. With a sufficient amount of simulated data, probabilistic conclusions can be drawn about the physical processes that have resulted in a particular detector image. This twofold function enables MCSs to serve as both surrogates for experiments and theoretical inference.
