Particle identification and analysis in the SciCRT using machine learning tools

Garcı́a, Rocı́o; Anzorena, M.; Valdés-Galicia, J. F.; Matsubara, Y.; Sako, T. K.; Ortiz, E.; Hurtado, A.; Taylor, R.; Musalém, O.; González, L. X.; Itow, Y.; Kawabata, Takeshi; Munakata, K.; Kato, C.; Kihara, W.; Ko, Y.; Shibata, S.; Takamaru, Hiroyuki; Oshima, A.; Koi, T.; Kojima, Hiroshi; Tsuchiya, H.; Watanabe, Kyoko; Kozai, M.; Nakamura, Yuya

doi:10.1016/j.nima.2021.165326

Search citation statements

Order By: Relevance

Paper Sections

Select...

Features and Regressors1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. Three different regressors are studied in this paper, labeled as "regressor A-C" [21][22][23], shown in Table 1. These regressors are selected from a full range of regression models included in the scikit-learn toolkit.…”

Section: Features and Regressorsmentioning

confidence: 99%

Improvement of $q^{2}$ Resolution in Semileptonic Decays Based on Machine Learning

Xiao

Saur

et al. 2023

Advances in High Energy Physics

View full text Add to dashboard Cite

The neutrino closure method is often used to obtain kinematics of semileptonic decays with one unreconstructed particle in hadron collider experiments. The kinematics of decays can be deducted by a twofold ambiguity with a quadratic equation. To resolve the twofold ambiguity, a novel method based on machine learning (ML) is proposed. We study the effect of different sets of features and regressors on the improvement of reconstructed invariant mass squared of ℓ ν system ( q 2 ). The result shows that the best performance is obtained by using the flight vector as the features and the multilayer perceptron (MLP) model as the regressor. Compared with the random choice, the MLP model improves the resolution of reconstructed q 2 by ~40%. Furthermore, the possibility of using this method on various semileptonic decays is shown.

show abstract

Section: Features and Regressorsmentioning

confidence: 99%