arXiv subjects

Matthew McEneaney

Publications and source records attributed to Matthew McEneaney.

5 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

Direct Vertex Reconstruction of $Λ$ Baryons from Hits in CLAS12 using Graph Neural Networks

Machine learning techniques, including Graph Neural Networks (GNNs), have been used extensively for data analysis in high energy and nuclear physics. Here we report on the use of a GNN to reconstruct decay vertices of $Λ$ hyperons directly from hits in the tracking detector at the CLAS12 experiment at Jefferson Laboratory (JLab). We show that we can improve the vertex reconstruction in simulation compared to the standard, track based, algorithm. We believe this warrants further study. The current study is limited by available training resources but points to an interesting possibility to forgo vertex reconstruction by track fitting in a complicated magnetic field for a more direct approach where the hit to vertex mapping is encoded in a neural network.

hep-ex↗

arXiv2024

Improving $Λ$ Signal Extraction with Domain Adaptation via Normalizing Flows

The present study presents a novel application for normalizing flows for domain adaptation. The study investigates the ability of flow based neural networks to improve signal extraction of $Λ$ Hyperons at CLAS12. Normalizing Flows can help model complex probability density functions that describe physics processes, enabling uses such as event generation. $Λ$ signal extraction has been improved through the use of classifier networks, but differences in simulation and data domains limit classifier performance; this study utilizes the flows for domain adaptation between Monte Carlo simulation and data. We were successful in training a flow network to transform between the latent physics space and a normal distribution. We also found that applying the flows lessened the dependence of the figure of merit on the cut on the classifier output, meaning that there was a broader range where the cut results in a similar figure of merit.

hep-ex↗

arXiv2023

Domain-Adversarial Graph Neural Networks for $Λ$ Hyperon Identification with CLAS12

Machine learning methods and in particular Graph Neural Networks (GNNs) have revolutionized many tasks within the high energy physics community. We report on the novel use of GNNs and a domain-adversarial training method to identify $Λ$ hyperon events with the CLAS12 experiment at Jefferson Lab. The GNN method we have developed increases the purity of the $Λ$ yield by a factor of $1.95$ and by $1.82$ using the domain-adversarial training. This work also provides a good benchmark for developing event tagging machine learning methods for the $Λ$ and other channels at CLAS12 and other experiments, such as the planned Electron Ion Collider.

hep-ex↗

arXiv2022

Longitudinal Spin Transfer to $Λ$ Hyperons in CLAS12

Using the self-analyzing decay of the $Λ$, the longitudinal spin transfer $D_{LL'}$ to the hyperon from a polarized electron beam scattering off an unpolarized proton target can be determined. For $Λ$s produced in the current fragmentation region, this quantity is proportional to the helicity dependent fragmentation function $G_1^Λ$ and can provide insight into the spin structure of the $Λ$. Currently, limited experimental data on $D_{LL'}$ cannot discriminate between different models of $Λ$ spin structure. This contribution reports on the status of the ongoing analysis of the longitudinal spin transfer using data taken by the CLAS12 spectrometer at Jefferson Lab with a 10.6 GeV polarized electron beam. We also report on the novel use of Graph Neural Networks (GNNs) to identify signal events.

nucl-ex↗