<div class="csl-bib-body">
<div class="csl-entry">Wagner, F., Bartolot, D., Rizvanovic, D., Reindl, F., Schieck, J., & Waltenberger, W. (2022). Cait: Analysis Toolkit for Cryogenic Particle Detectors in Python. <i>Computing and Software for Big Science</i>, <i>6</i>, Article 19. https://doi.org/10.1007/s41781-022-00092-4</div>
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dc.identifier.issn
2510-2036
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/139703
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dc.description.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.
en
dc.language.iso
en
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dc.publisher
Springer International Publishing
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dc.relation.ispartof
Computing and Software for Big Science
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dc.subject
Cryogenic particle detectors
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dc.subject
Data analysis
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dc.subject
Machine learning
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dc.subject
Transition edge sensor
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dc.title
Cait: Analysis Toolkit for Cryogenic Particle Detectors in Python