Speaker
Description
The Compton Spectrometer and Imager (COSI) is a NASA Small Explorer (SMEX) satellite mission planned to fly in 2027. It has the participation of institutions in the US, Europe and Asia and aims at the construction of a gamma-ray telescope for observations in the 0.2-5 MeV energy range. COSI consists of an array of germanium strip detectors cooled to cryogenic temperatures with millimeter position resolution for gamma-ray interactions, surrounded by an active BGO shield to reduce background contamination. Goals of COSI are: the study of the electron-positron 511 keV annihilation emission in the Galaxy, mapping the emission from galactic element formation, exploring the polarization in the gamma rays and the detection of transient sources in multimessenger campaigns. The COSI reconstruction pipeline is based on a decade-long heritage
and is being further developed in view of the forthcoming launch. In this contribution we present the efforts to improve the background identification performance of the traditional algorithms with a Machine Learning approach. The expected morphology of the events will be discussed and the algorithms will be presented together with the needed data preprocessing. A preliminary comparison with the traditional methods will be performed.
Significance
We explore the advantages of the ML methods to reconstruct the events. We improve the traditional methods, which have been developed in many years of efforts. We develop algorithms to maximize the scientific ouput of a future mission like COSI. ML methods could be applied to future missions, with a great impact on astrophysics.
| Experiment context, if any | COSI, gamma-ray astrophysics, space missions, Compton telescopes. |
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