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Dustin Keller

Publications and source records attributed to Dustin Keller.

16 recordsLinked to original sources

Scheme-invariant stratified factorization algebras for inclusive deep inelastic scattering

Inclusive deep inelastic scattering factorization combines two features that are often treated separately: an asymptotic reconstruction of the current-current matrix element from hard and long-distance data, and an invariance under finite changes of collinear scheme or operator basis. We formulate these two features as a single proof object. The construction packages the leading-region analysis, overlap subtraction, Wilson-line reduction, finite scheme kernels and physical measurement into a typed, filtered structure on a compactified space of asymptotic regimes. Its central carrier is the balanced hard-collinear core over the interface algebra of finite scheme transformations. The hard QCD input is the construction of a scheme-balanced comparison map from this core to the collinear collar of the regime algebra. Once this comparison is an equivalence through the chosen power accuracy and the measurement descends to convolution, the standard DIS convolution formula follows formally and independently of the chosen scheme presentation. We separate this formal implication from the analytic QCD obligations needed to construct the collar equivalence, describe Collins-style subtraction as descent and M\"obius inversion on the region poset, and give a finite check relating $\overline{\mathrm{MS}}$ and DIS presentations. The framework is intended as proof infrastructure rather than as a new calculation of DIS coefficient functions. It supplies diagnostics for missing regions, nonclosed operator sectors, nonbalanced measurements and failed collar equivalences, and it gives a typed interface for future proof-assistant and machine-learning implementations of factorization workflows.

hep-ph

A Core Representation Theorem for Scheme-Invariant Collinear Factorization in QCD

Collinear factorization and the leading-twist operator product expansion (OPE) in perturbative QCD express suitably inclusive observables in scale-separated kinematics as composites of perturbative short-distance coefficients with universal long-distance non-perturbative correlators such as parton distribution functions (PDFs), up to controlled power corrections. A persistent structural feature is \emph{presentation non-uniqueness}: coefficients and correlators are not individually physical, but are defined only up to finite factorization-scheme redefinitions induced by collinear subtractions and renormalized-operator mixing. We formalize this redundancy categorically by introducing an \emph{interface algebra object} encoding admissible finite collinear counterterms/mixing kernels and by organizing coefficient data and hadronic data as right/left modules over this algebra in a symmetric monoidal category encoding the chosen recomposition calculus. Our main result, the \emph{Core Representation Theorem}, identifies the universal scheme-invariant carrier: the functor of balanced (scheme-invariant) pairings is represented by the relative tensor product $C\otimes_A f$, which is terminal among all quotients of the naive composite $C\otimes f$ that preserve scheme-invariant semantics. Finally, we show how standard physics inputs (symmetry constraints, locality/OPE, and a stated accuracy truncation) canonically induce the interface algebra and module structures, and we prove a minimal closure principle for completing a generating set of long-distance operators/correlators to an $A$-stable sector.

hep-ph

The SpinQuest Microwave System for Dynamic Nuclear Polarization

The SpinQuest experiment at Fermilab employs a dynamically polarized solid ammonia target to probe the spin structure of the proton, requiring stable, optimized microwave-driven Dynamic Nuclear Polarization (DNP) under high radiation conditions. We present the design, operation, and automation of a 140 GHz microwave system based on an extended interaction oscillator (EIO), integrated with real-time polarization feedback from a continuous-wave NMR system and cryogenic diagnostics. The system enables fine frequency control through motorized cavity tuning and is operated remotely to mitigate radiation exposure. To continuously optimize target polarization, we develop an automation framework supported by a Monte Carlo (digital twin) of the DNP process. The simulation incorporates rate-equation dynamics, frequency-dependent steady-state behavior, dose-induced frequency drift, beam-induced depolarization, and realistic NMR noise. This framework is used to design and benchmark control strategies, including a heuristic feedback algorithm, reinforcement learning (RL), and unsupervised RL approaches. These methods enable autonomous frequency tuning, improve ramp-up efficiency, and maintain near-optimal polarization under evolving conditions. We also demonstrate integration of EIO power-supply control into the feedback loop via anode voltage modulation, providing an additional degree of freedom for simultaneous control of microwave frequency and RF power. This combined control of cavity tuning and anode voltage allows the system to avoid frequency-dependent power nonuniformities and to better match broad Larmor distributions in irradiated targets. The results establish a scalable framework for AI-driven control of complex microwave systems in polarized-target experiments, with implications for future spin-physics measurements and other cryogenic, high-field applications.

physics.ins-det

Polarized Target Nuclear Magnetic Resonance Measurements with Deep Neural Networks

Continuous-wave Nuclear Magnetic Resonance (CW-NMR) operated in constant-current mode has served as a foundational technique for polarization measurement in solid-state dynamically polarized targets within nuclear and high-energy physics experiments for several decades, and it remains an essential tool. Conventional Q-meter-based phase-sensitive detection is critical for precise real-time determination of target polarization during scattering runs. However, the accuracy and reliability of these measurements are frequently compromised by elevated noise levels, baseline drift, and systematic uncertainties arising from signal isolation and fitting, ultimately degrading the overall experimental figure of merit. In this work, we report the first successful application of neural network architectures to continuous-wave NMR polarization metrology. By leveraging advanced machine learning techniques for signal extraction and denoising, we achieve a substantial reduction of fitting uncertainties under a variety of realistic simulated and experimental conditions. These improvements translate directly into more robust real-time (online) polarization monitoring and higher precision in subsequent offline analysis. By reducing analysis-induced uncertainty, the resulting methodology can improve the effective figure of merit for scattering experiments employing dynamically polarized targets and provides a new toolset for NMR-based polarimetry in high-energy and nuclear physics.

physics.ins-det

Long Range Outlook for Short-Range Correlations

Short range correlated (SRC) N N pairs are pairs of nucleons with high relative momentum (prel > kF where kF ~ 250 MeV/c is the Fermi momentum in medium to heavy nuclei) and lower center of mass momentum. The motivation for studying SRC pairs ranges from a desire to achieve a more comprehensive understanding of the many-body nuclear wave-function at high-resolution to searching for explicit QCD-dynamics effects within the nuclear medium, not to mention connections to many other open problems in nuclear physics. Exploring short-range correlations was one of the physics motivations for building CEBAF (now Jefferson Lab). Scientists used the high luminosity and high energy of this cutting-edge machine to find kinematics that cleanly showed the signals of short-range correlations. This paved the way in the last two decades for tremendous progress understanding these correlations. This paper reviews recent progress and highlights outstanding questions and areas that need further study.

nucl-ex

Differentiable Principal-Value Inversion for Neural-Network Extraction of Generalized Parton Distributions

We present a machine-learning method for the nonparametric extraction of generalized parton distributions (GPDs) from Compton form factors (CFFs) constrained by experimental data. The method addresses the longstanding inverse problem posed by the principal-value (PV) linear integral transform with a singular kernel that relates the charge-conjugation-even (C-even) quark GPD $H^{(+)}$ to the real part of the deeply virtual Compton scattering (DVCS) amplitude. Our approach constructs a differentiable representation of the Quantum Chromodynamics (QCD) PV kernel and embeds it as a fixed, physics-preserving layer inside a neural network that parameterizes the GPD $H^{(+)}(x,\xi,t,Q^{2})$ itself. The model enforces exact oddness in $x$, implements endpoint suppression, and includes curvature-based regularization that stabilizes the inversion in kinematically ill-conditioned regions. A Monte Carlo ensemble of CFFs, obtained from a global neural-network fit to unpolarized DVCS measurements with propagated experimental uncertainties, serves as input to a replica ensemble of GPD networks, yielding a fully probabilistic extraction of $H^{(+)}$ over the phase space. We demonstrate the method using a global determination of $\mathrm{Re}\,\mathcal{H}$ for Jefferson Lab measurements, and present a direct neural-network reconstruction of three-dimensional GPD surfaces $H^{(+)}(x_{0},\xi,t,Q_{0}^{2})$ obtained from experimental CFF inputs. This work establishes a flexible, scalable, and model-independent strategy for extracting multidimensional hadronic structure from current and future DVCS data and other GPD-related processes.

hep-ph

An ASME-Compliant Helium-4 Evaporation Refrigerator for the SpinQuest Experiment

This paper presents the design, safety basis, and commissioning results of a 1 K liquid helium-4 (4He) evaporation refrigerator developed for the Fermilab SpinQuest Experiment (E1039). The system represents the first high power helium evaporation refrigerator operated in a fixed target scattering experiment at Fermilab and was engineered to comply with the Fermilab ES\&H Manual (FESHM) requirements governing pressure vessels, piping, cryogenic systems, and vacuum vessels. The design is mapped to ASME B31.3 (Process Piping) and the ASME Boiler and Pressure Vessel Code (BPVC) for pressure boundary integrity and overpressure protection, with documented compliance to FESHM Chapters 5031 (Pressure Vessels), 5031.1 (Piping Systems), and 5033 (Vacuum Vessels). This work documents the methodology used to reach compliance and approval for the 4He evaporation refrigerator at Fermilab which the field lacks. Design considerations specific to the high radiation target-cave environment including remotely located instrumentation approximately 20 m from the cryostat are summarized, together with the relief-system sizing methodology used to accommodate transient heat loads from dynamic nuclear polarization microwaves and the high-intensity proton beam. Commissioning data from July 2024 confirms that the system satisfies all thermal performance and safety objectives.

physics.ins-det

Experimental Uncertainty Propagation in Neural Network Extraction in Hadronic Physics

Obtaining Compton Form Factors (CFFs) and Transverse Momentum Dependent parton distribution functions (TMDs) from experimental data using neural network-based information extraction requires the precise propagation of experimental errors. Accurate representation of uncertainties and detailed experimental covariance matrices, accounting for both statistical and systematic uncertainties, are essential for high-quality extractions. This paper explores instrumental and analytical contributions to fit and model uncertainties, along with methods for integrating these uncertainties into quantifiable results, ensuring robust extraction of physical observables across local and global datasets. Using pseudodata we demonstrate the critical role of accurate uncertainty propagation in producing meaningful results and advancing our understanding of partonic structure and dynamics inside of hardrons. \keywords{Deep neural networks \and Hadronic Physics \and Transverse momentum dependent parton distributions functions \and Compton form factors \and Uncertainty Analysis

hep-ph

Compton Form Factor Extraction using Quantum Deep Neural Networks

We extract Compton form factors (CFFs) from deeply virtual Compton scattering measurements at the Thomas Jefferson National Accelerator Facility (JLab) using quantum-inspired deep neural networks (QDNNs). The analysis implements the twist-2 Belitsky-Kirchner-M\"uller formalism and employs a fitting strategy that emulates standard local fits. Using pseudodata, we benchmark QDNNs against classical deep neural networks (CDNNs) and find that QDNNs often deliver higher predictive accuracy and tighter uncertainties at comparable model complexity. Guided by these results, we introduce a quantitative selection metric that indicates when QDNNs or CDNNs are optimal for a given experimental fit. After obtaining local extractions from the JLab data, we perform a standard neural-network global CFF fit and compare with previous global analyses. The results support QDNNs as an efficient and complementary tool to CDNNs for CFF determination and for future multidimensional studies of parton distributions and hadronic structure.

cs.LG

Spin 1 Transverse Momentum Dependent Tensor Structure Functions in CLAS12

We propose to analyze CLAS12 RG-C data to study the tensor transverse-momentum-dependent parton distribution functions (TMDs) on deuteron data. The deuteron is the lightest nucleus with spin-1, in essence a weakly bound system of two spin-1/2 nucleons. However, one of the most intriguing characteristics of the deuteron is that the tensor polarized structure provides direct access to the quark and gluon distribution of light nuclear system, which cannot be naively constructed from the proton and neutron. We will study the tensor polarized structure functions with the Semi-inclusive Deep Inelastic Scattering (SIDIS) $eD \arrow eP_{h}X$ and Inclusive processes in the available data on deuterated ammonia (ND3) target. We will perform the first ever SIDIS analysis extraction of the tensor structure functions, which can be interpreted in term of completely unexplored tensor polarized TMDs. Our analysis will focus on the extraction of the tensor structure functions b1 from inclusive process, and $F_{U(LL),T}$ and $F^{cos 2\phi_{h}}_{U(LL)}$ from SIDIS. These last two structure functions carry information related to two tensor-polarized TMDs, $f_{1LL}$ and $h^{\perp}_{1LL}$. These initial exploratory measurements of tensor-polarized structure functions will enable the first extraction of spin-1 TMDs and motivate more precise future measurements.

hep-ph

Neutrino Scattering Measurements on Hydrogen and Deuterium: A Snowmass White Paper

Neutrino interaction uncertainties are a limiting factor in current and next-generation experiments probing the fundamental physics of neutrinos, a unique window on physics beyond the Standard Model. Neutrino-nucleon scattering amplitudes are an important part of the neutrino interaction program. However, since all modern neutrino detectors are composed primarily of heavy nuclei, knowledge of elementary neutrino-nucleon amplitudes relies heavily on experiments performed in the 1970s and 1980s, whose statistical and systematic precision are insufficient for current needs. In this white paper, we outline the motivation for attempting measurements on hydrogen and deuterium that would improve this knowledge, and we discuss options for making these measurements either with the DUNE near detector or with a dedicated facility.

hep-ex

Bubble Chamber Detectors with Light Nuclear Targets: A Snowmass 2021 White Paper

Neutrino cross sections are a critical ingredient in experiments that depend on neutrino scattering to reconstruct event kinematics and infer neutrino characteristics, like NOvA and T2K. An opportunity exists to reduce the 5-10% broad uncertainty on neutrino cross sections by producing more measurements of neutrino scattering from light nuclear targets at the relevant energies. Bubble chambers with light nuclear targets would be ideal for these measurements but the most recent device designed for use with an accelerator neutrino source is at least fifty years old. A new bubble chamber with light nuclear targets could be designed by observing how the technology has progressed for use in dark matter experiments and producing smaller modular devices that use more efficient cooling systems. A smaller modular device could also be designed for deployment to all functioning neutrino beams, but an investigation of the proper operating characteristics is necessary to adapt newer detectors to the structure of contemporary neutrino beams.

physics.ins-det

DarkQuest: A dark sector upgrade to SpinQuest at the 120 GeV Fermilab Main Injector

Expanding the mass range and techniques by which we search for dark matter is an important part of the worldwide particle physics program. Accelerator-based searches for dark matter and dark sector particles are a uniquely compelling part of this program as a way to both create and detect dark matter in the laboratory and explore the dark sector by searching for mediators and excited dark matter particles. This paper focuses on developing the DarkQuest experimental concept and gives an outlook on related enhancements collectively referred to as LongQuest. DarkQuest is a proton fixed-target experiment with leading sensitivity to an array of visible dark sector signatures in the MeV-GeV mass range. Because it builds off of existing accelerator and detector infrastructure, it offers a powerful but modest-cost experimental initiative that can be realized on a short timescale.

hep-ex

Enhanced Tensor Polarization in Solid-State Targets

We report measurements of enhanced tensor polarization on solid-state targets. The results here represent an increase in tensor polarization over that previously achieved in high energy and nuclear scattering experiments that focused on the measurement of tensor polarized observables. Enhancement techniques are used which require RF produced close to the Larmor frequency of the target spins and use selective semi-saturation resulting from two sources of irradiation, microwave for the DNP process and the additional RF used to manipulate the population of the energy levels in the target material. The spin dynamics of the solid target are used to align the spins enhancing the ensemble average to improve the figure of merit of the scattering experiment. Target rotation at an optimized rate can lead to additional enhancement by applying selective semi-saturation in polycrystalline materials that possess a Pake doublet in their NMR signal.

physics.ins-det

Extraction of Generalized Parton Distribution Observables from Deeply Virtual Electron Proton Scattering Experiments

We provide the general expression of the cross section for exclusive deeply virtual photon electroproduction from a spin 1/2 target using current parameterizations of the off-forward correlation function in a nucleon for different beam and target polarization configurations up to twist three accuracy. All contributions to the cross section including deeply virtual Compton scattering, the Bethe-Heitler process, and their interference, are described within a helicity amplitude based framework which is also relativistically covariant and readily applicable to both the laboratory frame and in a collider kinematic setting. Our formalism renders a clear physical interpretation of the various components of the cross section by making a connection with the known characteristic structure of the electron scattering coincidence reactions. In particular, we focus on the total angular momentum, $J_z$, and on the orbital angular momentum, $L_z$. On one side, we uncover an avenue to a precise extraction of $J_z$, given by the combination of generalized parton distributions, $H+E$, through a generalization of the Rosenbluth separation method used in elastic electron proton scattering. On the other, we single out for the first time, the twist three angular modulations of the cross section that are sensitive to $L_z$. The proposed generalized Rosenbluth technique adds an important constraint for mapping the 3D structure of the nucleon.

hep-ph

Modeling alignment enhancement for solid polarized targets

A model of dynamic orientation using optimized radiofrequency (RF) irradiation produced perpendicular to the holding field is developed for the spin-1 system required for tensor-polarized fixed-target experiments. The derivation applies to RF produced close to the Larmor frequency of the nucleus and requires the electron spin-resonance linewidth to be much smaller than the nuclear magnetic resonance frequency. The rate equations are solved numerically to study a semi-saturated steady-state resulting from the two sources of irradiation: microwave from the DNP process and the additional RF used to manipulate the tensor polarization. The steady-state condition and continuous-wave NMR lineshape are found that optimize the spin-1 alignment in the polycrystalline materials used as solid polarized targets in charged-beam nuclear and particle physics experiments.

nucl-th