Naslov
Estimation of Interaction Locations in Super Cryogenic Dark Matter Search Detectors Using Genetic Programming-Symbolic Regression Method

Autori
Anđelić, Nikola ; Baressi Šegota, Sandi ; Glučina, Matko ; Car, Zlatan

Izvornik
Applied sciences (Basel) (2076-3417) 13 (2023), 4; 1-23

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
cross-validation ; genetic programming ; interaction location ; SuperCDMS ; symbolic regression

Sažetak
The Super Cryogenic Dark Matter Search (SuperCDMS) experiment is used to search for Weakly Interacting Massive Particles (WIMPs)— candidates for dark matter particles. In this experiment, the WIMPs interact with nuclei in the detector ; however, there are many other interactions (background interactions). To separate background interactions from the signal, it is necessary to measure the interaction energy and to reconstruct the location of the interaction between WIMPs and the nuclei. In recent years, some research papers have been investigating the reconstruction of interaction locations using artificial intelligence (AI) methods. In this paper, a genetic programmingsymbolic regression (GPSR), with randomly tuned hyperparameters cross- validated via a five-fold procedure, was applied to the SuperCDMS experiment to estimate the interaction locations with high accuracy. To measure the estimation accuracy of obtaining the SEs, the mean and standard deviation (σ) values of R 2 , the root-mean-squared error (RMSE), and finally, the mean absolute error (MAE) were used. The investigation showed that using GPSR, SEs can be obtained that estimatethe interaction locations with high accuracy. To improve the solution, the five best SEs were combined from the three best cases. The results demonstrated that a very high estimation accuracy can be achieved with the proposed methodology

Izvorni jezik
Engleski

Znanstvena područja
Fizika, Elektrotehnika, Računarstvo, Strojarstvo, Interdisciplinarne tehničke znanosti

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