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Cait: Analysis Toolkit for Cryogenic Particle Detectors in Python

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Abstract

Cryogenic solid state detectors are widely used in dark matter and neutrino experiments, and require a sensible raw data analysis. For this purpose, we present Cait, an open source Python package with all essential methods for the analysis of detector modules fully integrable with the Python ecosystem for scientific computing and machine learning. It comes with methods for triggering of events from continuously sampled streams, identification of particle recoils and artifacts in a low signal-to-noise ratio environment, the reconstruction of deposited energies, and the simulation of a variety of typical event types. Furthermore, by connecting Cait with existing machine learning frameworks we introduce novel methods for better automation in data cleaning and background rejection.

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Data Availability Statement

This manuscript has associated data in a data repository. [Authors’ comment: 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

We thank the CRESST and COSINUS collaborations for many discussions, especially Franz Pröbst, Martin Stahlberg, Nahuel Ferreiro Iachellini, Daniel Schmiedmayer and Christian Strandhagen. We are grateful for all early users, among them especially Rituparna Maji, who provided crucial feedback to the project. The computational results presented were obtained using the Vienna CLIP cluster. FW was supported by the Austrian Research Promotion Agency (FFG), project ML4CPD.

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Authors and Affiliations

  1. Institut für Hochenergiephysik, Österreichischen Akademie der Wissenschaften, Nikolsdorfer Gasse 18, 1050, Vienna, Austria

    Felix Wagner, Daniel Bartolot, Damir Rizvanovic, Florian Reindl, Jochen Schieck & Wolfgang Waltenberger

  2. Atominstitut, Technische Universität Wien, Stadionallee 2, 1020, Vienna, Austria

    Daniel Bartolot, Damir Rizvanovic, Florian Reindl & Jochen Schieck

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  1. Felix Wagner
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  3. Damir Rizvanovic
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Correspondence to Felix Wagner.

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Daniel Bartolot, Damir Rizvanovic, Florian Reindl, Jochen Schieck, and Wolfgang Waltenberger are contributing authors.

Appendix A: Code to simulate a mock data stream

Appendix A: Code to simulate a mock data stream

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Wagner, F., Bartolot, D., Rizvanovic, D. et al. Cait: Analysis Toolkit for Cryogenic Particle Detectors in Python. Comput Softw Big Sci 6, 19 (2022). https://doi.org/10.1007/s41781-022-00092-4

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