Development of Data Processing and Analysis Pipeline for the RICOCHET Experiment

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 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 optimised 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 similar to 100 eV, hence fulfilling the Ricochet targeted performance.

Automated summary

This article presents a Python-based data processing pipeline for precise measurement of the coherent elastic neutrino nucleus scattering spectrum, and shows the optimized performance of the pipeline, meeting the targeted performance of Ricochet.