arXiv subjects

Anselm Vossen

Publications and source records attributed to Anselm Vossen.

At least 19 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↗

arXiv2026

ML for the hKLM at the 2nd Detector

The present research applies Graph Neural-Networks (GNNs) for energy measurement and particle identification tasks for a proposed second detector at the future Electron Ion Collider (EIC). In particular, an iron-scintillator sampling calorimeter would provide neutral hadron ($K_L$ and neutron) energy measurements and identification, as well as separation of muons from hadrons. Using detector simulations, particle hits in the detector are represented as graphs, and a GNN is trained for either classification or prediction. Furthermore, we developed a parameterization of the scintillator optical photon simulation that yields a 20-fold speed up compared to the default simulation. We find that the GNN method outperforms classical methods at the same tasks, and we report projections for the energy and timing resolution, and identification accuracy of the calorimeter. We also present an integration of the GNN method into a Multi-Objective Optimization framework, enabled by an automated pipeline of data generation, GNN training, and detector performance evaluation. We utilize the optimization to quantify the tradeoffs between different performance metrics at high and low energies when changing the detector design parameters, such as the iron/scintillator thickness.

physics.ins-det↗

arXiv2026

Scalable AI-assisted Workflow Management for Detector Design Optimization Using Distributed Computing

The Production and Distributed Analysis (PanDA) system, originally developed for the ATLAS experiment at the CERN Large Hadron Collider (LHC), has evolved into a robust platform for orchestrating large-scale workflows across distributed computing resources. Coupled with its intelligent Distributed Dispatch and Scheduling (iDDS) component, PanDA supports AI/ML-driven workflows through a scalable and flexible workflow engine. We present an AI-assisted framework for detector design optimization that integrates multi-objective Bayesian optimization with the PanDA--iDDS workflow engine to coordinate iterative simulations across heterogeneous resources. The framework addresses the challenge of exploring high-dimensional parameter spaces inherent in modern detector design. We demonstrate the framework using benchmark problems and realistic studies of the ePIC and dRICH detectors for the Electron-Ion Collider (EIC). Results show improved automation, scalability, and efficiency in multi-objective optimization. This work establishes a flexible and extensible paradigm for AI-driven detector design and other computationally intensive scientific applications.

cs.DC↗

arXiv2025

Design and Performance Studies of a Granular Thin HCAL-MuID Detector for the EIC Optimized for AI-Based Reconstruction

We describe the design concept and estimated performance of an iron-scintillator sampling calorimeter for the future Electron Ion Collider. The novel aspect of this detector is a multi-dimensional readout coupled with foreseen excellent timing resolution, enabling time-of-flight capabilities as well as a more compact overall assembly. Machine learning has been integrated into the detector design process from the ground up. Detector design objectives are defined using Machine Learning based reconstruction and Machine Learning is used to optimize the detector design. The highly segmented readout is implemented with Machine Learning algorithms in mind to reach performance levels usually reserved for much more expensive detector systems. The primary physics objective is to serve as a muon detector/ID system and a neutral hadron calorimeter. In EIC kinematics, charged particles are best measured through tracking rather than calorimetry, but the hKLM can identify and measure the momentum of neutral hadrons. The latter are mainly $K_L$'s and neutrons: for lower energies, excellent relative momentum measurements of a few 10\% are achieved using time of flight, while for higher particle momenta, the energy can be measured calorimetrically with a resolution significantly better than that demonstrated for similar calorimeters read out with less granularity.

physics.ins-det↗

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↗

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, $π^+π^0$ and $π^-π^0$, 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 $π^0$ peak. A photon classifier using a Gradient Boosted Trees (GBTs) architecture was trained with Monte Carlo simulations to reduce the amount of background $π^0$'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 $π^0$ analyses. The machine learning approach recovers several times more dihadron statistics for the dataset.

hep-ex↗

arXiv2024

Normalizing Flows for Domain Adaptation when Identifying $Λ$ Hyperon Events

This study focuses on the novel application of a normalizing flow as a method of domain adaptation. Normalizing flows offer a way to transform data points between two different distributions. The present study investigates a method of transforming latent representations of physics data to a normal distribution and then to a physics distribution again. The final distribution models a simulated distribution. Following the transformation process, the data can be classified by a neural network trained on labeled simulation data. The present study succeeds in training two normalizing flows that can transform between data (or simulation) and a Gaussian distribution.

physics.data-an↗

arXiv2024

Perspectives of Semi-Inclusive Deep-Inelastic Scattering

This contribution highlights some topics addressed by current and future experiments in Semi-Inclusive Deep-Inelastic Scattering. We concentrate on the programs at 12 and 22 GeV at Jefferson Lab using the CLAS detector, and at the future Electron-Ion Collider.

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

Reconstruction of event kinematics in semi-inclusive deep-inelastic scattering using the hadronic final state and Machine Learning

Semi-inclusive deep-inelastic scattering (SIDIS) at the Electron-Ion Collider will allow for precise mapping of the 3D momentum and spin structure of nucleons and nuclei over a large kinematic region. In this contribution, we demonstrate methods utilizing the hadronic final state and scattered electron, as well as machine learning, to more reliably reconstruct the virtual photon four momentum and SIDIS kinematics across the inclusive DIS coverage at the EIC.

hep-ph↗

arXiv2022

Snowmass White Paper: Belle II physics reach and plans for the next decade and beyond

Belle II is an experiment operating at the intensity frontier. Over the next decades, it will record the decay of billions of bottom mesons, charm hadrons, and tau leptons produced in 10 GeV electron-positron collisions at the SuperKEKB high-luminosity collider at KEK. These data, collected in low-background and kinematically known conditions, will allow us to measure hundreds of parameters that test the standard model (SM) and probe for the existence of new particles, at mass scales orders of magnitudes higher than those studied at the energy frontier. We project our sensitivities for measurements that are of primary relevance and where Belle II will be unique or world leading for data corresponding to 1 to 50 ab$^{-1}$. Belle II will uniquely probe non-SM contributions in sensitive $b \to q\bar q s$ decays and charmless $b \to q\bar q d(u)$ decays, semileptonic $b \to s ν\barν$ and $s τ^+ τ^-$ decays, fully leptonic $b \to \ell ν$ decays, and select $c \to u$ processes. Belle II will lead exploration of non-SM physics in $b \to c τν$ and $b \to s γ$ decays and will most precisely determine the quark-mixing parameters $|V_{ub}|$ and $|V_{cb}|$. Belle II will measure many parameters in $τ$ physics to precisions that will be world leading for the foreseeable future, including the electric and magnetic dipole moments, branching fractions for charged-lepton-flavor-violating decays, and quantities that test lepton-flavor universality. Belle II will perform unique searches for dark-sector particles with masses in the MeV-GeV range. We will also pursue a broad spectroscopy program for conventional and multiquark $c \bar c$ and $b \bar b$ states and provide essential inputs to sharpen the interpretation of muon magnetic-anomaly results. Our exploration of uncharted regions of non-SM parameter space with high precision will reveal non-SM particles or set stringent constraints on their existence, guiding future endeavors.

hep-ex↗

arXiv2022

Transverse Lambda production at the future Electron-Ion Collider

We provide a comprehensive overview of transversely polarized $Λ$ production at the future Electron-Ion Collider (EIC). In particular, we study both spontaneous transverse $Λ$ polarization as well as the transverse spin transfer within the Transverse Momentum Dependent (TMD) factorization region. To describe spontaneous $Λ$ polarization, we consider the contribution from the TMD Polarizing Fragmentation Function (TMD PFF). Similarly, we study the contribution of the transverse spin transfer originating from the transversity TMD Fragmentation Function (TMD FF). We provide projections for the statistical uncertainties in the corresponding spin observables at the future EIC. Using these statistical uncertainties, we characterize the role that the future EIC will play in constraining these distributions. We perform an impact study in the semi-inclusive deep inelastic scattering process for spontaneous $Λ$ polarization with a proton beam. We find that the projected experimental data leads to a significant decrease in the uncertainties for the $u$ and sea TMD PFFs. Furthermore, to access the impact of the EIC on the transversity TMD FF, we perform the first extraction of the transversity TMD FF from the recent COMPASS data. We compare the statistical uncertainties of the future EIC with the theoretical uncertainties from our extraction and find that the EIC could have a significant role in constraining this distribution. Finally, we also provide projections for both spontaneous $Λ$ polarization as well as the transverse spin transfer inside the jets in back-to-back electron-jet production at the EIC.

hep-ph↗

arXiv2020

Transverse spin effects in hard semi-inclusive collisions

The nucleons (protons and neutrons) are by far the most abundant form of matter in our visible Universe; they are composite particles made of quarks and gluons, the fundamental quanta of Quantum Chromo Dynamics (QCD). The usual interpretation of the nucleon dynamics in high energy interactions is often limited to a simple one-dimensional picture of a fast moving nucleon as a collection of co-linearly moving quarks and gluons (partons), interacting accordingly to perturbative QCD rules. However, massive experimental evidence shows that, in particular when transverse spin dependent observables are involved, such a simple picture is not adequate. The intrinsic transverse motion of partons has to be taken into account; this opens the way to a new, truly 3-dimensional (3D) study of the nucleon structure. A review of the main experimental data, their interpretation and understanding in terms of new transverse momentum dependent partonic distributions, and the progress in building a 3D imaging of the nucleon is presented.

hep-ph↗

arXiv2020

Probing Nucleons and Nuclei in High Energy Collisions

This volume is a collection of contributions for the 7-week program "Probing Nucleons and Nuclei in High Energy Collisions" that was held at the Institute for Nuclear Theory in Seattle, WA, USA, from October 1 until November 16, 2018. The program was dedicated to the physics of the Electron Ion Collider (EIC), the world's first polarized electron-nucleon (ep) and electron-nucleus (eA) collider to be constructed in the USA. These proceedings are organized by chapters, corresponding to the weeks of the program: Week I, Generalized parton distributions; Week II, Transverse spin and TMDs; Week III, Longitudinal spin; Week IV, Symposium week; Weeks V & VI, eA collisions; Week VII, pA and AA collisions. We hope these proceedings will be useful to readers as a compilation of EIC-related science at the end of the second decade of the XXI century.

hep-ph↗

arXiv2018

Novel experimental probes of QCD in SIDIS and $e^+e^-$ annihilation

Semi-inclusive deep inelastic Scattering (SIDIS) has been a very successful tool to investigate the partonic structure of the nucleon over the last decade. Compared to inclusive DIS, information about the quantum numbers of the struck quark can be inferred from the identity, distribution and polarization of the final state hadrons. Up to now, virtually all knowledge about the quark-gluon structure of the nucleon from SIDIS has been gained from distributions of independently observed scalar hadrons. However, given the amount of data current and future experiments at JLab, RHIC, KEK and the EIC will collect, new paradigms have to be explored to leverage the statistical power of the data. Similar to other felds in nuclear and particle physics, it is natural to move towards the exploration of more complex correlations in the observed fi nal state. This contribution will discuss recent results and future prospects of using di-hadron correlations and polarized hyperon probes to study QCD in SIDIS, pp and e+e- annihilation. Both of these probes exploit additional degrees of freedom in the final state, given by the relative momentum of the di-hadron pair and the hyperon polarization, respectively. This contribution will focus on recent results and opportunities opened by these probes to study nucleon structure, hadronization and QCD in novel ways. The focus will be on planned SIDIS measurements at CLAS12 at JLab and e+e- at Belle II.

hep-ex↗

arXiv2016

Parton Fragmentation Functions

The field of fragmentation functions of light quarks and gluons is reviewed. In addition to integrated fragmentation functions, attention is paid to the dependence of fragmentation functions on transverse momenta and on polarization degrees of freedom. Higher-twist and di-hadron fragmentation functions are considered as well. Moreover, the review covers both theoretical and experimental developments in single-inclusive hadron production in electron-positron annihilation, deep-inelastic lepton-nucleon scattering, and proton-proton collisions.

hep-ex↗