Energy reconstruction with the LHCf Arm2 silicon microstrip detector

The Large Hadron Collider-forward (LHCf) experiment plays a fundamental role in the field of Ultra High Energy Cosmic Ray (UHECR) physics, since it provides calibration data for the tuning of the hadronic interaction models used in the simulations of Extensive Air Showers (EASs). The LHCf experiment detects neutral particles produced in the very forward region of LHC collisions, thanks to its two detectors, called Arm1 and Arm2, located 140 m away from the interaction point IP1. Both Arm1 and Arm2 consist of two imaging and sampling calorimetric towers. These calorimetric towers alternate passive tungsten layers with active GSO scintillator layers, which are used to reconstruct the energy of incident particles. Position sensitive layers are inserted at different depths in the calorimetric towers. They are used to reconstruct the impact point of the incident particle by detecting the transverse profile of the showers generated by the incident particles. In particular, in the case of the Arm2 detector, the position-sensitive layers consist of eight layers of silicon microstrip sensors. After LHC Run I, the positioning of the silicon microstrip layers along the Arm2 calorimetric towers was modified to enable the reconstruction of the incident particle energy using the silicon detector independently of the energy deposited in the GSO layers. This is a very useful information for future analyses, as it provides independent control over the calibration of the GSO absolute energy scale and improves the overall energy reconstruction performance for LHCf events. In this paper, preliminary studies related to incident energy reconstruction using the Arm2 silicon detector will be illustrated, with a focus on the definition of the energy deposit, the estimation of the conversion function and the determination of position dependent correction factors. The performances of the algorithm developed for this purpose will be compared with the traditional method using Arm2 GSO layers, illustrating the advantages of using this additional information to improve the reconstruction performances.