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Development of Data Processing and Analysis Pipeline for the Ricochet Experiment

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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 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 \(\sim\)100 eV, hence fulfilling the Ricochet targeted performance.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under Grant Agreement ERC-StG-CENNS 803079. We are grateful to J.-B. Filippini and H. Lattaud for their feedbacks on the use of this processing pipeline.

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Authors and Affiliations

  1. CNRS/IN2P3, IP2I, Univ. Lyon, Univ. Lyon 1, 69622, Villeurbanne, France

    J. Colas, J. Billard, S. Ferriol, J. Gascon & T. Salagnac

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  1. J. Colas
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  2. J. Billard
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  3. S. Ferriol
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  4. J. Gascon
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  5. T. Salagnac
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Correspondence to J. Colas.

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Colas, J., Billard, J., Ferriol, S. et al. Development of Data Processing and Analysis Pipeline for the Ricochet Experiment. J Low Temp Phys 211, 310–319 (2023). https://doi.org/10.1007/s10909-022-02907-5

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  • DOI: https://doi.org/10.1007/s10909-022-02907-5

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