Multi-channel, multi-template event reconstruction for SuperCDMS data using machine learning
Document Type
Article
Source of Publication
Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment
Publication Date
7-1-2026
Abstract
SuperCDMS SNOLAB uses kilogram-scale germanium and silicon detectors to search for dark matter. Each detector has Transition Edge Sensors (TESs) patterned on the top and bottom faces of a large crystal substrate, with the TESs electrically grouped into six phonon readout channels per face. Noise correlations are expected among a detector's readout channels, in part because the channels and their readout electronics are located in close proximity to one another. Moreover, owing to the large size of the detectors, energy deposits can produce vastly different phonon propagation patterns depending on their location in the substrate, resulting in a strong position dependence in the readout-channel pulse shapes. Both of these effects can degrade the energy resolution and consequently diminish the dark matter search sensitivity of the experiment if not accounted for properly. We present a new algorithm for pulse reconstruction, mathematically formulated to take into account correlated noise and pulse shape variations. This new algorithm fits N readout channels with a superposition of M pulse templates simultaneously — hence termed the N×M filter. We describe a method to derive the pulse templates using principal component analysis (PCA) and to extract energy and position information using a gradient boosted decision tree (GBDT). We show that these new N×M and GBDT analysis tools can reduce the impact from correlated noise sources while improving the reconstructed energy resolution. This improvement amounts to more than a factor of three for simulated mono-energetic events. For the 71Ge K-shell electron-capture peak recoils measured in a previous version of SuperCDMS called CDMSlite, the energy resolution is reduced to less than 50 eV, compared with the previously published value of ∼100eV. These results lay the groundwork for position reconstruction in SuperCDMS with the N×M outputs.
DOI Link
ISSN
Publisher
Elsevier BV
Volume
1087
Disciplines
Computer Sciences
Keywords
Correlated noise, Event reconstruction, Machine learning, Optimal filter
Scopus ID
Recommended Citation
Albakry, M. F.; Alkhatib, I.; Alonso-González, D.; Anczarski, J.; Aralis, T.; Aramaki, T.; Langroudy, I. Ataee; Bathurst, C.; Bhattacharyya, R.; Biffl, A. J.; Brink, P. L.; Buchanan, M.; Bunker, R.; Cabrera, B.; Calkins, R.; Cameron, R. A.; Cartaro, C.; Cerdeño, D. G.; Chang, Y. Y.; Chaudhuri, M.; Chen, J. H.; Chen, R.; Chott, N.; Cooley, J.; Coombes, H.; Cushman, P.; Cyna, R.; and Das, S., "Multi-channel, multi-template event reconstruction for SuperCDMS data using machine learning" (2026). All Works. 7844.
https://zuscholars.zu.ac.ae/works/7844
Indexed in Scopus
yes
Open Access
no