Validation of a new background discrimination method for the TACTIC TeV $\gamma$-ray telescope with Markarian 421 data

TL;DR

This study validates a new Random Forest-based background discrimination method for gamma-ray data analysis from the TACTIC telescope, showing improved detection sensitivity and consistent spectral results for Markarian 421 observations.

Contribution

The paper introduces and validates a novel Random Forest technique for background discrimination in atmospheric Cherenkov telescope data analysis, demonstrating enhanced detection performance.

Findings

26% improvement in signal detection strength

18% increase in gamma-ray detection

Consistent spectral results with previous studies

Abstract

This paper describes the validation of a new background discrimination method based on Random Forest technique by re-analysing the Markarian 421 (Mrk 421) observations performed by the TACTIC (TeV Atmospheric Cherenkov Telescope with Imaging Camera) gamma-ray telescope. The Random Forest technique is a flexible multivariate method which combines Bagging and Random Split Selection to construct a large collection of decision trees and then combines them to construct a common classifier. Markarian 421 in a high state was observed by TACTIC during December 07, 2005 - April 30, 2006 for 202 h. Previous analysis of this data led to a detection of flaring activity from the source at Energy $ >$ 1 TeV. Within this data set, a spell of 97 h revealed strong detection of a gamma-ray signal with daily flux of > 1 Crab unit on several days. Here we re-analyze this spell as well as the data from the entire observation period with the Random Forest method. Application of this method led to an improvement in the signal detection strength by $\sim 26\%$ along with a $\sim 18\%$ increase in detected $\gamma$ rays compared to the conventional Dynamic Supercuts method. The resultant differential spectrum obtained is represented by a power law with an exponential cut off $\Gamma = -2.51 \pm 0.10$ and $E_{0} = 4.71 \pm 2.20$ TeV. Such a spectrum is consistent with previously reported results and justifies the use of Random Forest method for analyzing data from atmospheric Cherenkov telescopes.