Mon·May 19, 2025Monday·May 19, 2025

1. 01*

   Distribution Functions of $\Lambda$ and ${\Sigma }^0$ Baryons

   Yang Yu🇨🇳 · Peng Cheng🇨🇳 · Hui-Yu Xing🇨🇳 · Daniele Binosi🇮🇹 · Craig D. Roberts🇨🇳

   Treating baryons as quark + interacting-diquark bound states, a symmetry-preserving formulation of a vector$\times$vector contact interaction (SCI) is used to deliver an extensive, coherent set of predictions for $\Lambda ,{\Sigma }^0$ baryon unpolarised and polarised distribution functions (DFs) -- valence, glue, and four-flavour separated sea -- and compare them with those of a like-structured nucleon. $\Lambda ,{\Sigma }^0$ baryons are strangeness negative-one isospin partners within the SU$(3)$-flavour baryon octet. This makes such structural comparisons significant. The study reveals impacts of diquark correlations and SU$(3)$-flavour symmetry breaking on $\Lambda$, ${\Sigma }^0$ structure functions, some of which are significant. For instance, were it not for the presence of axialvector diquarks in the ${\Sigma }^0$ at the hadron scale, the $s$ quark could carry none of the ${\Sigma }^0$ spin. The discussion canvasses issues that include helicity retention in hard scattering processes; the sign and size of polarised gluon DFs; and the origin and decomposition of baryon spins. Interpreted judiciously, the SCI analysis delivers an insightful explanation of baryon structure as expressed in DFs.

   ↳ hep-phhep-exhep-latnucl-ex+1EPJA(2025)·8 citations
2. 02*

   Particle identification in the GlueX detector with machine learning

   Eric Habjan🇺🇸 · Richard Dube🇺🇸 · James McIntyre🇺🇸 · Mezmur Edo🇺🇸 · Richard Jones🇺🇸

   In particle physics experiments, identifying the types of particles registered in a detector is essential for the accurate reconstruction of particle collisions. At Thomas Jefferson National Accelerator Facility (Jefferson Lab), the GlueX experiment performs particle identification (PID) by setting specific thresholds, known as cuts, on the kinematic properties of tracks and showers obtained from detector hits. Our research aims to enhance this cut-based method by employing machine-learning algorithms based on multi-layer perceptrons and boosted decision trees. Similar approaches have been applied in other particle physics experiments and offer an opportunity to increase PID accuracies using reconstructed kinematic data. Our study illustrates that both multilayered perceptrons and boosted decision trees can identify charged and neutral particles in Monte Carlo simulated GlueX data with significantly improved accuracy over the current cuts-based PID method.

   ↳ physics.ins-dethep-exnucl-exphysics.data-anJINST(2025)·0 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.