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This commit introduces a complete pipeline for cosmic ray detection in QUBIC datasets, including: - TES data validation and mask handling - Candidate detection based on rising + exponential decay patterns - Fitting with time constant estimation and quality checks - Multi-dataset and multi-strategy aggregation and reporting - Plot generation and export of results - Optional atmospheric drift removal and signal deconvolution
This commit introduces a complete pipeline for cosmic ray detection in QUBIC datasets, including: - TES data validation and mask handling - Candidate detection based on rising + exponential decay patterns - Fitting with time constant estimation and quality checks - Multi-dataset and multi-strategy aggregation and reporting - Plot generation and export of results - Optional atmospheric drift removal and signal deconvolution
This commit introduces a complete pipeline for cosmic ray detection in QUBIC datasets, including: - TES data validation and mask handling - Candidate detection based on rising + exponential decay patterns - Fitting with time constant estimation and quality checks - Multi-dataset and multi-strategy aggregation and reporting - Plot generation and export of results - Optional atmospheric drift removal and signal deconvolution - Config files and iv masks added
…: an algorithm for detecting cosmic rays - Frontiers)
…nt in src/cosmic_rays
… the raw TOD presented in the paper.
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This PR includes:
• the preprocessing steps applied to TODs before the detection stage, including removal of baseline and azimuth/elevation-related effects;
• the use of IV-curve information to determine the TES sign consistently across detectors;
• the setup of the Poetry environment and installation of dependencies;
• the main command to run the cosmic ray detection analysis from a TOML configuration file;
• an example configuration file with the main mandatory and optional parameters;
• the parameter exploration workflow using parameters_script.py;