# An evolutionary strategy for DeltaE - E identification

## TL;DR

This paper introduces an automatic charge and mass identification method for nuclear fragments in heavy ion collisions, combining a generative DeltaE-E model with CMA-ES optimization, validated on simulated data.

## Contribution

It presents a novel combination of a generative model with CMA-ES for charge and mass identification in nuclear physics experiments.

## Key findings

- Effective on simulated data
- Automates charge and mass identification
- Uses CMA-ES for parameter optimization

## Abstract

In this article we present an automatic method for charge and mass identification of charged nuclear fragments produced in heavy ion collisions at intermediate energies. The algorithm combines a generative model of DeltaE - E relation and a Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). The CMA-ES is a stochastic and derivative-free method employed to search parameter space of the model by means of a fitness function. The article describes details of the method along with results of an application on simulated labeled data.