A Study of the Cosmic Ray Rate in the CHIPS-M Prototype Detector

Following the discovery of neutrino mass and neutrino oscillations, the next big question is whether or not neutrinos violate charge-parity symmetry. To achieve the precision in electron neutrino appearance necessary to make measurements of charge parity symmetry violation, we need a detector with a very large fiducial mass. These large detectors are beyond our budgetary reach and take an incredible amount of time to build. The CHIPS collaboration is building a series of prototype detectors with the aim to lower the cost of these massive detectors and develop an incremental approach so that the physics measurements can be made in all phases of the program. To lower the costs, the detectors will be deployed under the water in an existing mine pit. An underwater detector design relieves the need to build a site to house the detector, provides an overburden of water to block many cosmic rays, and structurally supports the detector

Benchmarks for a Global Extraction of Information from Deeply Virtual Exclusive Scattering

We develop a framework to establish benchmarks for machine learning and deep neural networks analyses of exclusive scattering cross sections (FemtoNet). Within this framework we present an extraction of Compton form factors for deeply virtual Compton scattering from an unpolarized proton target. Critical to this effort is a study of the effects of physics constraint built into machine learning (ML) algorithms. We use the Bethe-Heitler process, which is the QED radiative background to deeply virtual Compton scattering, to test our ML models and, in particular, their ability to generalize information extracted from data. We then use these techniques on the full cross section and compare the results to analytic model calculations. We propose a quantification technique, the random targets method, to begin understanding the separation of aleatoric and epistemic uncertainties as they are manifest in exclusive scattering analyses. We propose a set of both physics driven and machine learning based benchmarks providing a stepping stone towards applying explainable machine learning techniques with controllable uncertainties in a wide range of deeply virtual exclusive processes.Comment: 16 pages, 12 figure

Physics-Informed Neural Networks (PINNs) For DVCS Cross Sections

We present a physics informed deep learning technique for Deeply Virtual Compton Scattering (DVCS) cross sections from an unpolarized proton target using both an unpolarized and polarized electron beam. Training a deep learning model typically requires a large size of data that might not always be available or possible to obtain. Alternatively, a deep learning model can be trained using additional knowledge gained by enforcing some physics constraints such as angular symmetries for better accuracy and generalization. By incorporating physics knowledge to our deep learning model, our framework shows precise predictions on the DVCS cross sections and better extrapolation on unseen kinematics compared to the basic deep learning approaches.https://digitalcommons.odu.edu/gradposters2022_sciences/1001/thumbnail.jp

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.Comment: 62 pages, 7 figures, 3 table

The case for an EIC Theory Alliance: Theoretical Challenges of the EIC

44 pages, ReVTeX, White Paper on EIC Theory AllianceWe outline the physics opportunities provided by the Electron Ion Collider (EIC). These include the study of the parton structure of the nucleon and nuclei, the onset of gluon saturation, the production of jets and heavy flavor, hadron spectroscopy and tests of fundamental symmetries. We review the present status and future challenges in EIC theory that have to be addressed in order to realize this ambitious and impactful physics program, including how to engage a diverse and inclusive workforce. In order to address these many-fold challenges, we propose a coordinated effort involving theory groups with differing expertise is needed. We discuss the scientific goals and scope of such an EIC Theory Alliance