GSI/FAIR AI Workshop

Real-time calibrations for future detectors at FAIR

The online data processing of the next generation of experiments conducted at FAIR requires a reliable reconstruction of event topologies and, therefore, will depend heavily on in-situ calibration procedures. In this study we present a neural network-based tool designed to provide real-time predictions of calibration constants, which rely...

The $\textbf{H}$igh $\textbf{A}$cceptance $\textbf{D}$i$\textbf{E}$lectron $\textbf{S}$pectrometer (HADES) is a versatile magnetic spectrometer aimed at studying dielectron production in pion, proton and heavy-ion induced collisions. The conventional reconstruction algorithm for the HADES RICH is based upon the Hough Transform (HT) method for ring finding. This method fails to efficiently...

In this contribution we present weak decay topology recognition utilizing an artifial Neural Network. This approach significantly improved the precision of the analysis of Λ hyperons and K0s mesons and enabled us to measure more rare probes like the Ξ hyperon or Hypernuclei for the first time in heavy-ion collisions with HADES. We highlight the choice of input parameters as well as the applied...

LISE quick safety setting simulation using ML

The LISE tool from MSU is widely use to prepare settings for nuclear physics spectrometers around the world. LISE is a very versatile and potent tool including all nuclear physics models needed for precise calculation. The main drawback is a standard setting calculation for a Super-FRS setting, used to check the total particle rate at each focal...