Cait: analysis toolkit for cryogenic particle detectors in Python

TL;DR

Cait is an open-source Python toolkit designed for analyzing cryogenic particle detector data, offering methods for event triggering, particle identification, energy reconstruction, and integration with machine learning for improved automation.

Contribution

It introduces a comprehensive, open-source analysis toolkit for cryogenic detectors, integrating traditional methods with machine learning for enhanced data processing.

Findings

Effective event triggering from continuous data streams

Improved particle recoil identification in low signal-to-noise environments

Enhanced automation in data cleaning and background rejection

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.