Universität zu Köln

Mayer, Jan ORCID: 0000-0002-8781-5338, Boretzky, Konstanze, Douma, Christiaan, Hoemann, Elena and Zilges, Andreas ORCID: 0000-0002-9328-799X (2021). Classical and machine learning methods for event reconstruction in NeuLAND. Nucl. Instrum. Methods Phys. Res. Sect. A-Accel. Spectrom. Dect. Assoc. Equip., 1013. AMSTERDAM: ELSEVIER. ISSN 1872-9576

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Abstract

NeuLAND, the New Large Area Neutron Detector, is a key component to investigate the origin of matter in the universe with experimental nuclear physics. It is a core component of the Reactions with Relativistic Radioactive Beams setup at the Facility for Antiproton and Ion Research, Germany. Neutrons emitted from these reactions create a wide range of patterns in NeuLAND. From these patterns, the number of neutrons (multiplicity) and their first interaction points must be reconstructed to determine the neutrons' four momenta. In this paper, we detail the challenges involved in this reconstruction and present a range of possible solutions. Scikit-Learn classification models and simple Keras-based neural networks were trained on a wide range of input-scaler combinations and compared to classical models. While the improvement in multiplicity reconstruction is limited due to the overlap between features, the machine learning methods achieve a significantly better first interaction point selection, which directly improves the resolution of physical quantities.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Mayer, Jan UNSPECIFIED orcid.org/0000-0002-8781-5338 UNSPECIFIED Boretzky, Konstanze UNSPECIFIED UNSPECIFIED UNSPECIFIED Douma, Christiaan UNSPECIFIED UNSPECIFIED UNSPECIFIED Hoemann, Elena UNSPECIFIED UNSPECIFIED UNSPECIFIED Zilges, Andreas UNSPECIFIED orcid.org/0000-0002-9328-799X UNSPECIFIED
URN:	urn:nbn:de:hbz:38-584345
DOI:	10.1016/j.nima.2021.165666
Journal or Publication Title:	Nucl. Instrum. Methods Phys. Res. Sect. A-Accel. Spectrom. Dect. Assoc. Equip.
Volume:	1013
Date:	2021
Publisher:	ELSEVIER
Place of Publication:	AMSTERDAM
ISSN:	1872-9576
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language Instruments & Instrumentation; Nuclear Science & Technology; Physics, Nuclear; Physics, Particles & Fields Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/58434