Mon·Jan 8, 2024Monday·January 8, 2024

1. 01*

   Femtoscopy analysis in small systems at NA61/SHINE

   Barnabas Porfy (for the NA61/SHINE Collaboration)🇭🇺

   Recent measurements of femtoscopic correlations at NA61/SHINE unravel that the shape of the particle emitting source is not Gaussian. The measurements are based on Lévy-stable symmetric sources, and we discuss the average pair transverse mass dependence of the source parameters. One of the parameters, the Lévy exponent $\alpha$, is of particular importance. It describes the shape of the source, which, in the vicinity of the critical point of the QCD phase diagram, may be related to the critical exponent $\eta$. Its measurement hence may contribute to the search for and characterization of the critical point of the phase diagram.

   nucl-exhep-exEPJ Web Conf.(2024)·3 citations
2. 02*

   Deep Learning Based Event Reconstruction for Cyclotron Radiation Emission Spectroscopy

   A. Ashtari Esfahani🇺🇸 · S. Böser🇩🇪 · N. Buzinsky🇺🇸 · M. C. Carmona-Benitez🇺🇸 · R. Cervantes🇺🇸 · C. Claessens🇺🇸 · L. de Viveiros🇺🇸 · M. Fertl🇩🇪 · J. A. Formaggio🇺🇸 · J. K. Gaison🇺🇸 · L. Gladstone🇺🇸 · M. Grando🇺🇸 and 39 other authors

   The objective of the Cyclotron Radiation Emission Spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time-frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization - may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment - a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium ${\beta }^{-}$-decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.

   ↳ physics.ins-detnucl-exMach.Learn.Sci.Tech.(2024)·3 citations

* Reconstructed cohort: no mailing for this day survives in the archive. Papers are grouped by their submission times and arXiv's announcement cut-off, assuming announcement without delay; positions follow identifier order. Validated at ~91% exact-day agreement against the archived era.