arXiv subjects

Gregory Matousek

Publications and source records attributed to Gregory Matousek.

4 recordsLinked to original sources

arXiv2026

Impact of Future Dihadron Production Measurements on the Transversity Distributions and Tensor Charges of the Nucleon

We assess the impact of future measurements of dihadron production in semi-inclusive deep-inelastic scattering from the CLAS12 and proposed SoLID experiments at Jefferson Lab, as well as from the ePIC experiment at the future Electron-Ion Collider (EIC), on the transversity parton distribution functions (PDFs) and the corresponding tensor charges of the nucleon. To this end, we generate pseudo-data for these experiments for a proton target (CLAS12 and ePIC) and a $^3$He target (SoLID and ePIC), and we include these pseudo-data in the JAMDiFF global analysis of existing experimental dihadron data. We find that future data from Jefferson Lab will significantly reduce uncertainties in the transversity PDFs in the region of intermediate-to-large quark momentum fractions $x$, while the EIC will provide strong constraints across the entire range of $x$, allowing for the first experimental test of the predicted small-$x$ behavior of the transversity PDFs. In discussing the reduction of uncertainties in the tensor charges, we also compare the results from the data analyses with those from lattice QCD, highlighting scenarios in which compatibility or tension between the two would arise.

hep-ph↗

arXiv2025

AI-Assisted Object Condensation Clustering for Calorimeter Shower Reconstruction at CLAS12

Several nuclear physics studies using the CLAS12 detector rely on the accurate reconstruction of neutrons and photons from its forward angle calorimeter system. These studies often place restrictive cuts when measuring neutral particles due to an overabundance of false clusters created by the existing calorimeter reconstruction software. In this work, we present a new AI approach to clustering CLAS12 calorimeter hits based on the object condensation framework. The model learns a latent representation of the full detector topology using GravNet layers, serving as the positional encoding for an event's calorimeter hits which are processed by a Transformer encoder. This unique structure allows the model to contextualize local and long range information, improving its performance. Evaluated on one million simulated $e+p$ collision events, our method significantly improves cluster trustworthiness: the fraction of reliable neutron clusters, increasing from 8.88\% to 30.73\%, and photon clusters, increasing from 51.07\% to 64.73\%. Our study also marks the first application of AI clustering techniques for hodoscopic detectors, showing potential for usage in many other experiments.

physics.ins-det↗

arXiv2024

Photon Classification with Gradient Boosted Trees at CLAS12

Dihadron semi-inclusive deep inelastic scattering (SIDIS) of 10.6 GeV longitudinally polarized electrons off the proton has been measured using the CLAS12 detector at Jefferson Lab. Two separate channels, $\pi^+\pi^0$ and $\pi^-\pi^0$, were analyzed, requiring the reconstruction of diphoton pairs. In this analysis, we addressed the problem of false neutral particles being reconstructed by CLAS12's event builder, polluting the otherwise physical combinatorial background underneath the $\pi^0$ peak. A photon classifier using a Gradient Boosted Trees (GBTs) architecture was trained with Monte Carlo simulations to reduce the amount of background $\pi^0$'s. We show that the nearest-neighbor features learned by the model lead to a substantial increase in signal vs. background discrimination compared to previous CLAS12 $\pi^0$ analyses. The machine learning approach recovers several times more dihadron statistics for the dataset.

hep-ex↗

arXiv2022

Scaling properties of exclusive vector meson production cross section from gluon saturation

It is already known from phenomenological studies that in exclusive deep-inelastic scattering off nuclei there appears to be a scaling behavior of vector meson production cross section in both nuclear mass number, $A$, and photon virtuality, $Q^{2}$, which is strongly modified due to gluon saturation effects. In this work we continue those studies in a realistic setup based upon using the Monte Carlo event generator Sar{\em t}re. We make quantitative predictions for the kinematics of the Electron-Ion Collider, focusing on this $A$ and $Q^{2}$ scaling picture, along with establishing a small region of squared momentum transfer, $t$, where there are signs of this scaling that may potentially be observed at the EIC. Our results are represented as pseudo-data of vector meson production diffractive cross section and/or their ratios, which are obtained by parsing data collected by the event generator through smearing functions, emulating the proposed detector resolutions for the future EIC.

nucl-th↗