Ponente
Descripción
The Short-Baseline Near Detector (SBND) is a 112-ton liquid argon time projection chamber (LArTPC) located 110m from the Booster Neutrino Beam target at Fermilab, serving as the near detector of the Short-Baseline Neutrino program. It also incorporates a photon detection system (PDS) with a dual-readout design featuring 120 photomultiplier tubes (PMTs) and 192 X-ARAPUCA devices that distinguish between VUV and visible light components. This setup delivers a high light yield and a more uniform detection efficiency across the volume. The detector began its first physics run in December 2024 and has already obtained the world's highest-statistics neutrino-argon dataset.
In this talk, we will review the current performance of the SBND detector and present a machine learning (ML) algorithm developed for 3D vertex reconstruction using scintillation light patterns from the photon detection system. The algorithm achieves a spatial resolution of 5–8 cm in all three coordinates. This ML-based optical reconstruction provides an independent reconstruction that complements traditional TPC ones, highlighting the strong potential of machine learning approaches to enhance neutrino physics analyses in liquid argon detectors.
Abstract
The Short-Baseline Near Detector (SBND) is a 112-ton liquid argon time projection chamber (LArTPC) located 110m from the Booster Neutrino Beam target at Fermilab, serving as the near detector of the Short-Baseline Neutrino program. It also incorporates a photon detection system (PDS) with a dual-readout design featuring 120 photomultiplier tubes (PMTs) and 192 X-ARAPUCA devices that distinguish between VUV and visible light components. This setup delivers a high light yield and a more uniform detection efficiency across the volume. The detector began its first physics run in December 2024 and has already obtained the world's highest-statistics neutrino-argon dataset.
In this talk, we will review the current performance of the SBND detector and present a machine learning (ML) algorithm developed for 3D vertex reconstruction using scintillation light patterns from the photon detection system. The algorithm achieves a spatial resolution of 5–8 cm in all three coordinates. This ML-based optical reconstruction provides an independent reconstruction that complements traditional TPC ones, highlighting the strong potential of machine learning approaches to enhance neutrino physics analyses in liquid argon detectors.
