arXiv subjects

R. Tyson

Publications and source records attributed to R. Tyson.

8 recordsLinked to original sources

arXiv2026

First Measurement of Near-Threshold J/{\psi} Photoproduction on the Neutron

We report the first measurement of the total and t-differential cross sections for near-threshold J/$\psi$ photoproduction on the neutron, obtained with the CLAS12 detector at the Thomas Jefferson National Accelerator Facility. The measurements, performed using a liquid-deuterium target, also provide the incoherent total and t-differential cross sections on the bound proton, enabling the first direct comparison of near-threshold J/$\psi$ photoproduction on bound protons and neutrons. Interpreted within Vector Meson Dominance, holographic QCD, and GPD-based frameworks, the data allow for the investigation of the gluonic structure of bound nucleons. A comparison with free proton results reveals hints of modifications to the gluon structure of nucleons in the nuclear medium, within the assumptions of the extraction and on the underlying production mechanism. The comparison of bound neutron and bound proton cross sections provides new constraints on the near-threshold J/$\psi$ production mechanism, which is crucial in order to establish J/$\psi$ photoproduction as a probe of the nucleon's gluonic structure.

nucl-ex↗

arXiv2026

Electro- and photoproduction of muon pairs with $\mu$CLAS12: Double Deeply Virtual Compton Scattering, Timelike Compton Scattering, and $J/\psi$ production

The CEBAF Large Acceptance Spectrometer for operation at 12 GeV (CLAS12) at the Thomas Jefferson National Accelerator Facility has played a central role in advancing the understanding of nucleon and nuclear structure. As increasingly precise data become available, new physics opportunities emerge that extend beyond the current capabilities of CLAS12. In this article, a program to explore the quark and gluon structure of the nucleon through di-muon electro- and photoproduction is presented. Its primary focus is the measurement of beam-spin asymmetries in Double Deeply Virtual Compton Scattering, $ep \rightarrow e^\prime \mu^+ \mu^-p^\prime $. By independently varying the incoming and outgoing photon virtualities and momentum transfer, the DDVCS measurement provides access to the Generalized Parton Distributions over their full three-dimensional phase space, extending beyond the kinematic constraints of Deeply Virtual Compton Scattering and Timelike Compton Scattering. In addition, the large acceptance and high luminosity of the $\mu$CLAS12 experiment will enable precision measurements of near-threshold $J/\psi$ production and high-statistics studies of Timelike Compton Scattering.

hep-ex↗

arXiv2026

Measurement of the near-threshold J$/\psi$ photoproduction cross section with the CLAS12 experiment

We present measurements of the total and differential cross sections for near-threshold J/$\psi$ photoproduction obtained with the CLAS12 detector at the Thomas Jefferson National Accelerator Facility. The results are based on data collected during the Fall 2018 and Spring 2019 running periods, using electron beams with energies of 10.6 and 10.2 GeV, respectively, scattered off a liquid-hydrogen target. Near-threshold J$/\psi$ photoproduction offers a unique sensitivity to the strong interaction in the non-perturbative regime of Quantum Chromodynamics (QCD). The energy dependence of the cross section constrains the underlying J$/\psi$ production mechanisms, including multi-gluon exchange and potential baryonic excitations. Additionally, the $t$-dependence of the differential cross section can be related to the transverse spatial distribution of gluons in the proton, providing critical input for theoretical descriptions of the gluonic structure of the proton. An interpretation of the results in terms of the gluon content of the proton is presented, providing new experimental constraints on QCD-inspired models of the proton structure and the role of gluonic degrees of freedom in hadronic mass generation.

hep-ex↗

arXiv2025

Hadron Physics Opportunities at FAIR

This White Paper outlines a coordinated, decade-spanning programme of hadron and QCD studies anchored at the GSI/FAIR accelerator complex. Profiting from intense deuteron, proton and pion beams coupled with high-rate capable detectors and an international theory effort, the initiative addresses fundamental questions related to the strong interaction featuring confinement and dynamical mass generation. This includes our understanding of hadron-hadron interactions and the composition of hadrons through mapping the baryon and meson spectra, including exotic states, and quantifying hadron structure. This interdisciplinary research connects topics in the fields of nuclear, heavy-ion, and (nuclear) astro (particle) physics, linking, for example, terrestrial data to constraints on neutron star structure. A phased roadmap with SIS100 accelerator start-up and envisaged detector upgrades will yield precision cross sections, transition form factors, in-medium spectral functions, and validated theory inputs. Synergies with external programmes at international accelerator facilities worldwide are anticipated. The programme is expected to deliver decisive advances in our understanding of non-perturbative (strong) QCD and astrophysics, and high-rate detector and data-science technology.

hep-ex↗

arXiv2025

Threshold $J/\psi$ Photoproduction as a Probe of Nuclear Gluon Structure

The nuclear EMC effect is the observation that quark distributions in bound nucleons experience significant modification at large $x$ relative to free nucleons. Despite decades of measurements verifying the presence of this effect in quarks across a wide range of nuclei, behavior of large-$x$ gluons in nuclei remains almost completely unknown. As the nuclear physics community seeks out new observables to try to elucidate the mechanisms behind the EMC effect, it becomes striking that we remain ignorant regarding the impact of nuclear effects on gluonic behavior. Recent photonuclear data using the Hall D photon beam have enabled the first measurement of $J/\psi$ photoproduction from nuclei near and below the energy threshold, with the results highlighted in Physical Review Letters as an Editors' Suggestion. These data have placed the first, and currently only, constraints on the behavior of large-$x$ gluons within bound nucleons. However, compared to the quantity of data which currently informs our knowledge of the quark-sector EMC effect, these data are extremely limited, and remain unable to conclusively observe or exclude large modification of gluon distributions. A high-luminosity photonuclear experiment will enable a precision measurement of incoherent $J/\psi$ photoproduction at and below the threshold region. This data will provide the first stringent constraints on nuclear modification of gluon structure or other exotic effects which could impact the production of $J/\psi$ from nuclei. We request 85 PAC days at Hall D using the GlueX detector with a 12 GeV electron beam energy and a coherent photon peak energy of $8$ GeV, split into 80 days using a $^4$He target and 5 calibration days using a $^2$H target.

nucl-ex↗

arXiv2024

Converting sWeights to Probabilities with Density Ratios

The use of machine learning approaches continues to have many benefits in experimental nuclear and particle physics. One common issue is generating training data which is sufficiently realistic to give reliable results. Here we advocate using real experimental data as the source of training data and demonstrate how one might subtract background contributions through the use of probabilistic weights which can be readily applied to training data. The sPlot formalism is a common tool used to isolate distributions from different sources. However, the negative sWeights produced by the sPlot technique can cause training problems and poor predictive power. This article demonstrates how density ratio estimation can be applied to convert sWeights to event probabilities, which we call drWeights. The drWeights can then be applied to produce the distributions of interest and are consistent with direct use of the sWeights. This article will also show how decision trees are particularly well suited to convert sWeights, with the benefit of fast prediction rates and adaptability to aspects of experimental data such as the data sample size and proportions of different event sources. We also show that a density ratio product approach in which the initial drWeights are reweighted by an additional converter gives substantially better results.

physics.data-an↗

arXiv2023

Artificial Intelligence for the Electron Ion Collider (AI4EIC)

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.

physics.acc-ph↗

arXiv2022

Machine Learned Particle Detector Simulations

The use of machine learning algorithms is an attractive way to produce very fast detector simulations for scattering reactions that can otherwise be computationally expensive. Here we develop a factorised approach where we deal with each particle produced in a reaction individually: first determine if it was detected (acceptance) and second determine its reconstructed variables such as four momentum (reconstruction). For the acceptance we propose using a probability classification density ratio technique to determine the probability the particle was detected as a function of many variables. Neural Network and Boosted Decision Tree classifiers were tested for this purpose and we found using a combination of both, through a reweighting stage, provided the most reliable results. For reconstruction a simple method of synthetic data generation, based on nearest neighbour or decision trees was developed. Using a toy parameterised detector we demonstrate that such a method can reliably and accurately reproduce kinematic distributions from a physics reaction. The relatively simple algorithms allow for small training overheads whilst producing reliable results. Possible applications for such fast simulated data include Toy-MC studies of parameter extraction, preprocessing expensive simulations or generating templates for background distributions shapes.

physics.data-an↗