# Simulation study of energy resolution, position resolution and $\pi^0$-$\gamma$ separation of a sampling electromagnetic calorimeter at high energies

## TL;DR

This simulation study evaluates the energy and position resolution, and $$ separation capabilities of a sampling calorimeter, demonstrating the effectiveness of multivariate methods like boosted decision trees over traditional cut-based approaches.

## Contribution

The paper introduces a multivariate approach, specifically boosted decision trees, for improved $$-$$ separation in sampling calorimeters, adaptable to various geometries.

## Key findings

- Boosted decision trees outperform cut-based methods by a factor of three in $$-$$ separation.
- Energy resolution varies with different calorimeter layouts and absorber-scintillator combinations.
- The methods are applicable to the design geometry of the Shashlik calorimeter for high-energy physics experiments.

## Abstract

A simulation study of energy resolution, position resolution, and $\pi^0$-$\gamma$ separation using multivariate methods of a sampling calorimeter is presented. As a realistic example, the geometry of the calorimeter is taken from the design geometry of the Shashlik calorimeter which was considered as a candidate for CMS endcap for the phase II of LHC running. The methods proposed in this paper can be easily adapted to various geometrical layouts of a sampling calorimeter. Energy resolution is studied for different layouts and different absorber-scintillator combinations of the Shashlik detector. It is shown that a boosted decision tree using fine grained information of the calorimeter can perform three times better than a cut-based method for separation of $\pi^0$ from $\gamma$ over a large energy range of 20 GeV-200 GeV.