Speaker
Description
Alpha particle clustering is thought to be widespread throughout the nuclear chart, and light nuclei provide an ideal testing ground for state-of-the-art theoretical calculations, such as the Algebraic Cluster Model (ACM) [1]. The predicted 2+ rotational excitation of the Hoyle State was first unambiguously measured using gamma beams and an Optical TPC detector [2]. To explore further rotational states built on the Hoyle state in carbon-12, a similar experiment was performed using the HIγS gamma-beam facility at Duke University in 2022. Gamma beams from 8.6 to 13.9 MeV were incident on a CO2 active target contained within a new electronic TPC, built by the university of Warsaw [3]. By examining the photo-dissociation of 12C through an intermediate state in 8Be, 12C(α,γ)8Be, we search for new states predicted by the ACM.
Three-alpha-particle events, corresponding to 12C photodissociation, form a fraction of all measured events. This presents a significant data analysis challenge, since typical experimental signatures, such as total charge deposition and track lengths, strongly overlap with those of competing reaction channels, e.g. 16O(α,γ)12C. This paper discusses the use of convolutional neural networks for event classification – ResNet-18/ResNet-50 [4] and ResNeXt [5] – alongside more traditional data analysis techniques. The performance of these neural networks is discussed and preliminary results such as angular distributions and Dalitz plots are presented.
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[4] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. (2016) "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778.
[5] S. Xie, R. Girshick, P. Dollar, Z. Tu, and K. He. (2017) “Aggregated residual transformations for deep neural networks”. Proceedings of the IEEE conference on computer vision and pattern recognition, 1492-1500.
