Speaker
Description
The LEGEND experiment is dedicated to the search for neutrinoless double beta decay using $^{76}Ge$-enriched High Purity Germanium detectors. While LEGEND has excellent energy resolution and ultra-low background levels, noise from readout electronics can make identifying events of interest more challenging. An efficient signal denoising algorithm can further enhance LEGEND’s energy resolution, background rejection techniques, and help identify low-energy events where the signal-to-noise ratio is small. In this talk, I will present several promising machine-learning based approaches, such as Noise2Noise with autoencoder, to effectively remove electronic noise from LEGEND detectors signals without the need for simulated data (or ground truth). Such a denoising algorithm can also be extended beyond the LEGEND experiment and is broadly applicable to other detector technologies.
This work is supported by the U.S. DOE and the NSF, the LANL, ORNL and LBNL LDRD programs; the European ERC and Horizon programs; the German DFG, BMBF, and MPG; the Italian INFN; the Polish NCN and MNiSW; the Czech MEYS; the Slovak SRDA; the Swiss SNF; the UK STFC; the Russian RFBR; the Canadian NSERC and CFI; the LNGS, SNOLAB, and SURF facilities.
| Type of contribution | Talk: 15 minutes. |
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