https://doi.org/10.1140/epjc/s10052-024-12595-y
Regular Article - Experimental Physics
Improving the performance of cryogenic calorimeters with nonlinear multivariate noise cancellation algorithms
1
Department of Physics, University of California Berkeley, 94720, Berkeley, CA, USA
2
Nuclear Science Division, Lawrence Berkeley National Lab, 94720, Berkeley, CA, USA
3
Dipartimento di Fisica e Astronomia, Alma Mater Studiorum-Università di Bologna, 40127, Bologna, Italy
4
INFN–Sezione di Bologna, 40127, Bologna, Italy
5
Gran Sasso Science Institute, 67100, L’Aquila, Italy
6
INFN–Laboratori Nazionali del Gran Sasso, 67100, Assergi, L’Aquila, Italy
7
INFN–Sezione di Milano Bicocca, 20126, Milan, Italy
8
Dipartimento di Fisica, Università di Milano-Bicocca, 20126, Milan, Italy
9
Engineering Division, Lawrence Berkeley National Lab, 94720, Berkeley, CA, USA
10
Center for Neutrino Physics, Virginia Polytechnic Institute and State University, 24061, Blacksburg, Virginia, USA
Received:
3
November
2023
Accepted:
21
February
2024
Published online:
8
March
2024
State-of-the-art physics experiments require high-resolution, low-noise, and low-threshold detectors to achieve competitive scientific results. However, experimental environments invariably introduce sources of noise, such as electrical interference or microphonics. The sources of this environmental noise can often be monitored by adding specially designed “auxiliary devices” (e.g. microphones, accelerometers, seismometers, magnetometers, and antennae). A model can then be constructed to predict the detector noise based on the auxiliary device information, which can then be subtracted from the true detector signal. Here, we present a multivariate noise cancellation algorithm which can be used in a variety of settings to improve the performance of detectors using multiple auxiliary devices. To validate this approach, we apply it to simulated data to remove noise due to electromagnetic interference and microphonic vibrations. We then employ the algorithm to a cryogenic light detector in the laboratory and show an improvement in the detector performance. Finally, we motivate the use of nonlinear terms to better model vibrational contributions to the noise in thermal detectors. We show a further improvement in the performance of a particular channel of the CUORE detector when using the nonlinear algorithm in combination with optimal filtering techniques.
© The Author(s) 2024
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Funded by SCOAP3.
