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Deep learning-based object detection algorithms enable the simultaneous classification and localization of any number of objects in image data. Many of these algorithms are capable of operating in real-time on high resolution images, attributing to their widespread usage across many fields. We present an end-to-end object detection pipeline designed for rare event searches for the Migdal effect, at real-time speeds, using high-resolution image data from the scientific CMOS camera readout of the MIGDAL experiment. The Migdal effect in nuclear scattering, critical for sub-GeV dark matter searches, has yet to be experimentally confirmed, making its detection a primary goal of the MIGDAL experiment. The Migdal effect forms a composite rare event signal topology consisting of an electronic and nuclear recoil sharing the same vertex. Crucially, both recoil species are commonly observed in isolation in the MIGDAL experiment, enabling us to train YOLOv8, a state-of-the-art object detection algorithm, on real data. Topologies indicative of the Migdal effect can then be identified in science data via pairs of neighboring or overlapping electron and nuclear recoils. Applying selections to real data that retain 99.7% signal acceptance in simulations, we demonstrate our pipeline to reduce a sample of 20 million recorded images to fewer than 1000 frames, thereby transforming a rare search into a much more manageable search. More broadly, we discuss the applicability of using object detection to enable data-driven machine learning training for other rare event search applications such as neutrinoless double beta decay searches and experiments imaging exotic nuclear decays. Published by the American Physical Society2025 

Abstract Baryon number conservation is not guaranteed by any fundamental symmetry within the standard model, and therefore has been a subject of experimental and theoretical scrutiny for decades. So far, no evidence for baryon number violation has been observed. Large underground detectors have long been used for both neutrino detection and searches for baryon number violating processes. The next generation of large neutrino detectors will seek to improve upon the limits set by past and current experiments and will cover a range of lifetimes predicted by several Grand Unified Theories. In this White Paper, we summarize theoretical motivations and experimental aspects of searches for baryon number violation in neutrino experiments. 

Abstract SBND is the near detector of the Short-Baseline Neutrino program at Fermilab. Its location near to the Booster Neutrino Beam source and relatively large mass will allow the study of neutrino interactions on argon with unprecedented statistics. This paper describes the expected performance of the SBND photon detection system, using a simulated sample of beam neutrinos and cosmogenic particles. Its design is a dual readout concept combining a system of 120 photomultiplier tubes, used for triggering, with a system of 192 X-ARAPUCA devices, located behind the anode wire planes. Furthermore, covering the cathode plane with highly-reflective panels coated with a wavelength-shifting compound recovers part of the light emitted towards the cathode, where no optical detectors exist. We show how this new design provides a high light yield and a more uniform detection efficiency, an excellent timing resolution and an independent 3D-position reconstruction using only the scintillation light. Finally, the whole reconstruction chain is applied to recover the temporal structure of the beam spill, which is resolved with a resolution on the order of nanoseconds. 

Abstract The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60–80 t capable of probing the remaining weakly interacting massive particle-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in¹³⁶Xe using a natural-abundance xenon target. XLZD can reach a 3σdiscovery potential half-life of 5.7 × 10²⁷years (and a 90% CL exclusion of 1.3 × 10²⁸years) with 10 years of data taking, corresponding to a Majorana mass range of 7.3–31.3 meV (4.8–20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community. 

Abstract The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours. 

The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on ${}^{40}\mathrm{Ar}$and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called “brems flipping,” as well as the burst direction from an ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton, and 6.6 degrees for a 10 kton fiducial mass respectively. Assuming a 4% rate of charged-current interactions being misidentified as elastic scattering, DUNE’s burst pointing resolution is found to be 4.3 degrees (8.7 degrees) at 68% coverage. Published by the American Physical Society2025 

ProtoDUNE Single-Phase (ProtoDUNE-SP) is a 770-ton liquid argon time projection chamber that operated in a hadron test beam at the CERN Neutrino Platform in 2018. We present a measurement of the total inelastic cross section of charged kaons on argon as a function of kaon energy using 6 and $7\mathrm{GeV}/c$beam momentum settings. The flux-weighted average of the extracted inelastic cross section at each beam momentum setting was measured to be $380\pm 26\mathrm{mbarns}$for the $6\mathrm{GeV}/c$setting and $379\pm 35\mathrm{mbarns}$for the $7\mathrm{GeV}/c$setting. Published by the American Physical Society2024 

The Module-0 Demonstrator is a single-phase 600 kg liquid argon time projection chamber operated as a prototype for the DUNE liquid argon near detector. Based on the ArgonCube design concept, Module-0 features a novel 80k-channel pixelated charge readout and advanced high-coverage photon detection system. In this paper, we present an analysis of an eight-day data set consisting of 25 million cosmic ray events collected in the spring of 2021. We use this sample to demonstrate the imaging performance of the charge and light readout systems as well as the signal correlations between the two. We also report argon purity and detector uniformity measurements and provide comparisons to detector simulations. 

Abstract The nature of dark matter and properties of neutrinos are among the most pressing issues in contemporary particle physics. The dual-phase xenon time-projection chamber is the leading technology to cover the available parameter space for weakly interacting massive particles, while featuring extensive sensitivity to many alternative dark matter candidates. These detectors can also study neutrinos through neutrinoless double-beta decay and through a variety of astrophysical sources. A next-generation xenon-based detector will therefore be a true multi-purpose observatory to significantly advance particle physics, nuclear physics, astrophysics, solar physics, and cosmology. This review article presents the science cases for such a detector.