Novel technique for monitoring the performance of the LAT instrument on board the GLAST satellite

TL;DR

This paper introduces a novel classification tree-based technique for monitoring and diagnosing the performance of the LAT instrument on the GLAST satellite, enhancing real-time problem detection and performance evaluation.

Contribution

The paper presents a new method using classification trees to quickly identify and assess potential issues affecting the LAT instrument's performance.

Findings

Effective in pinpointing potential problems in LAT data

Allows rapid evaluation of instrumental issues

Improves monitoring accuracy for high-energy astrophysics

Abstract

The Gamma-ray Large Area Space Telescope (GLAST) is an observatory designed to perform gamma-ray astronomy in the energy range 20 MeV to 300 GeV, with supporting measurements for gamma-ray bursts from 10 keV to 25 MeV. GLAST will be launched at the end of 2007, opening a new and important window on a wide variety of high energy astrophysical phenomena . The main instrument of GLAST is the Large Area Telescope (LAT), which provides break-through high-energy measurements using techniques typically used in particle detectors for collider experiments. The LAT consists of 16 identical towers in a four-by-four grid, each one containing a pair conversion tracker and a hodoscopic crystal calorimeter, all covered by a segmented plastic scintillator anti-coincidence shield. The scientific return of the instrument depends very much on how accurately we know its performance, and how well we can monitor it and correct potential problems promptly. We report on a novel technique that we are developing to help in the characterization and monitoring of LAT by using the power of classification trees to pinpoint in a short time potential problems in the recorded data. The same technique could also be used to evaluate the effect on the overall LAT performance produced by potential instrumental problems.