Estimation of Interaction Locations in Super Cryogenic Dark Matter Search Detectors Using Genetic Programming-Symbolic Regression Method
Created by W.Langdon from
gp-bibliography.bib Revision:1.8129
- @Article{Andelic:2023:applsci2,
-
author = "Nikola Andelic and Ivan Lorencin and
Sandi {Baressi Segota} and Zlatan Car",
-
title = "Estimation of Interaction Locations in Super Cryogenic
Dark Matter Search Detectors Using Genetic
Programming-Symbolic Regression Method",
-
journal = "Applied Sciences",
-
year = "2023",
-
volume = "13",
-
number = "4",
-
pages = "Article no 2059",
-
month = feb,
-
email = "nandelic@riteh.hr",
-
keywords = "genetic algorithms, genetic programming,
cross-validation, interaction location, SuperCDMS,
symbolic regression",
-
publisher = "MDPI",
-
ISSN = "2076-3417",
-
URL = "https://www.mdpi.com/2076-3417/13/4/2059",
-
DOI = "doi:10.3390/app13042059",
-
size = "23 pages",
-
abstract = "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
programming-symbolic 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
R2, 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 estimate the 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.",
- }
Genetic Programming entries for
Nikola Andelic
Ivan Lorencin
Sandi Baressi Segota
Zlatan Car
Citations