Speaker
Description
The LHCf experiment aims to study forward neutral particle production at the LHC, providing crucial data for improving hadronic interaction models used in cosmic ray physics. A key challenge in this context is the reconstruction of events containing (K^0) mesons, which often involve multiple calorimetric hits.
To address this, we developed a machine learning pipeline that employs multiple neural networks to classify and reconstruct such events. The pipeline consists of four stages: (1) event selection, determining whether an event contains four particles, (2) photon/neutron discrimination, (3) event tagging into four specific topologies based on the distribution of photons between the two calorimeter towers, and (4) position and energy regression for each detected photon.
The model takes as input the energy deposits recorded by the two calorimetric towers of ARM2 and the energy deposits in the four pairs of silicon detectors oriented along the x and y axes. The network architecture is designed to process these heterogeneous data sources, allowing for a precise reconstruction of the event topology. Preliminary results, obtained with a dataset of 10k simulated events, show that the classification networks reach over 80% accuracy in selecting relevant events and distinguishing photon/neutron interactions. These promising results highlight the potential of deep learning techniques in enhancing event reconstruction at LHCf and lay the groundwork for further improvements with larger datasets and refined models.
| AI keywords | Neural networks, Event classification, Energy regression, Multi-modal learning, Manipulate complex graph topologies |
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