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DOE OSTI2025

Comparison of ACHILLES and GENIE Observables for DUNE-PRISM and the SBN Program, with New 2p2h Predictions

The DUNE near detector LAr-TPC can be moved to different off-axis positions to sample different neutrino fluxes, which in turn will yield different cross section distributions for observables. Several DUNE ND off axis locations were simulated using GENIE and ACHILLES, and generator differences and FSI contributions were examined for various observables. Additionally, the predictions were compared with those of existing experiments, and it is shown that the DUNE ND 20m off axis predictions are similar to that of MicroBooNE for various observables. Finally, the 2p2h interaction mode was simulated for ACHILLES for the first time for ICARUS, and a preliminary comparison is made to MicroBooNE data and simulations from different event generators.

Dumont, Daniel [Fermilab]↗

DOE OSTI2025

Summer 2025 SULI: Nucleus ID, TinyTPC, and Scientific Communication

This paper summarizes my work during the Summer 2025 SULI internship, which focused on two main projects and broader scientific development. The first project involved improving the particle identification (PID) of protons, deuterons, and tritons using PIDA distributions and template fitting, with the goal of modeling nuclear final-state interactions (FSI) and testing the robustness of the method against systematic uncertainties. These techniques pave the way for future application to LArTPC data from the ICARUS detector. The second project centered on the optimization and data-taking of the TinyTPC detector, a compact LArTPC used for high-resolution low-energy measurements. I adjusted gain and threshold parameters, performed hardware validation tests, and developed analysis strategies to extract meaningful physics from collected data. Throughout the summer, I also enhanced my scientific communication and mentorship skills through presentations, collaborative analysis, and peer guidance.

McCright, Hannah [Maryland U.]↗

DOE OSTI2026

Enhanced Ionization Charge Identification in the Short-Baseline Neutrino Program Neutrino Detectors with Deep Neural Networks

We present a deep neural net-based region of interest detection method (DNN ROI) for signal processing in the liquid argon time projection chambers of the Short-Baseline Neutrino (SBN) Program, SBND and ICARUS. DNN ROI addresses limitations of the traditional wire-by-wire thresholding algorithm by leveraging the full two-dimensional detector readout and cross-plane matching information. To account for detector performance variations, we explore training with augmented samples. We find that DNN ROI outperforms the traditional method in both low-level ROI identification performance and high-level reconstruction metrics for high-energy cosmic and accelerator neutrino interaction products, while also being more robust against detector variations, with or without sample augmentation.

Abratenko, P. [Tufts U.]↗

DOE OSTI2026

Multiple Coulomb scattering for muon energy reconstruction in SBND Monte Carlo simulations

The Short Baseline Near Detector (SBND) is a liquid argon time projection chamber (LArTPC) located 110 meters downstream of the Booster Neutrino Beam target at Fermilab \cite{SBN}. The LArTPC is a detector technology with unprecedented position and calorimetric resolution for precise neutrino studies. The most dominant neutrino interaction topology in SBND is charged-current quasi-elastic muon neutrino-argon interactions, where the final state consists of a muon and a proton. Here, muons often travel long distances, sometimes exiting the detector before stopping. This loss of information creates a challenge for accurate energy estimation. There have been efforts in the MicroBooNE and ICARUS collaborations to improve energy estimation using a phenomenon known as Multiple Coulomb Scattering (MCS), which describes the jagged path taken by muons through a detector due to continuous Coulomb interactions with argon atoms. Building on these efforts, my project aims to improve an MCS-based approach to estimating the energies of exiting muons in SBND.

Puig, Xilonem Angeles [Mt. Holyoke Coll.; Fermilab↗

DOE OSTI2025

Early Career: Constraining the Electromagnetic Shower Energy Scale at LArTPC Neutrino Detectors Near and Far (Final Scientific/Technical Report)

This is the final scientific/technical report associated with the award "Early Career: Constraining the Electromagnetic Shower Energy Scale at LArTPC Neutrino Detectors Near and Far"; the primary research topics associated with the award are neutrino oscillation physics, reconstruction of neutrino interaction events using machine learning techniques, and detector calibration.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

NNSS R&D 100 awards winner and finalist have roots in SDRD

Newsletter article to be included in the 1Q FY21 LDRD Quarterly Highlights online. Website: https://www.lanl.gov/projects/ldrd-tri-lab/quarterly-highlights.php. The text of this article was excerpted from inSite article "NNSS honored as R&D 100 winner and finalist," dated October 1, 2020.

99 GENERAL AND MISCELLANEOUS↗