Development of data processing and analysis pipeline for the RICOCHET experiment

TL;DR

This paper introduces a new Python-based data processing pipeline for the RICOCHET experiment, enabling precise simulation, analysis, and optimization of CE{ u}NS signals to achieve a 70 eV energy threshold and effective recoil discrimination.

Contribution

The paper presents a novel data processing pipeline with a simulation tool that enhances the analysis and optimization of the RICOCHET experiment's detection capabilities.

Findings

Achieved a 70 eV energy threshold for the CryoCube detector.

Enabled electronic/nuclear recoil discrimination down to ~100 eV.

Validated the pipeline's effectiveness through simulated data analysis.

Abstract

Achieving a percentage-level precision measurement of the Coherent Elastic Neutrino Nucleus Scattering (CE{\nu}NS) spectrum requires a robust data processing pipeline which can be characterised with great precision. To fulfil this goal we present hereafter a new Python-based data processing pipeline specifically designed for temporal data analysis and pulse amplitude estimation. This pipeline features a data generator allowing to accurately simulate the expected data stream from the RICOCHET experiment at the Institut Laue Langevin (ILL) nuclear reactor, including both background and CE{\nu}NS signals. This data generator is pivotal to fully understand and characterise the data processing overall efficiency, its reconstruction biases, and to properly optimise its configuration parameters. We show that thanks to this optimized data processing pipeline, the CryoCube detector array will be able to achieve a 70 eV energy threshold combined with electronic/nuclear recoil discrimination down to {\sim}100 eV, hence fulfilling the RICOCHET targeted performance.