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1. Scintillating Bubble Chambers for Rare Event Searches

   https://doi.org/10.3390/universe9080346  

   Ernesto Alfonso-Pita, Edward Behnke (August 2023, Universe)

   Baracchini, Elisabetta (Ed.)

   The Scintillating Bubble Chamber (SBC) collaboration is developing liquid-noble bubble chambers for the detection of sub-keV nuclear recoils. These detectors benefit from the electron recoil rejection inherent in moderately-superheated bubble chambers with the addition of energy reconstruction provided from the scintillation signal. The ability to measure low-energy nuclear recoils allows the search for GeV-scale dark matter and the measurement of coherent elastic neutrino-nucleus scattering on argon from MeV-scale reactor antineutrinos. The first physics-scale detector, SBC-LAr10, is in the commissioning phase at Fermilab, where extensive engineering and calibration studies will be performed. In parallel, a functionally identical low-background version, SBC-SNOLAB, is being built for a dark matter search underground at SNOLAB. SBC-SNOLAB, with a 10 kg-yr exposure, will have sensitivity to a dark matter–nucleon cross section of 2×10−42 cm2 at 1 GeV/c2 dark matter mass, and future detectors could reach the boundary of the argon neutrino fog with a tonne-yr exposure. In addition, the deployment of an SBC detector at a nuclear reactor could enable neutrino physics investigations including measurements of the weak mixing angle and searches for sterile neutrinos, the neutrino magnetic moment, and the light Z’ gauge boson. 

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2. Exploring neutrino–nucleus interactions in the GeV regime using MINERvA

   https://doi.org/10.1140/epjs/s11734-021-00296-6  

   Lu, X.-G.; Dar, Z. Ahmad; Akbar, F.; Andrade, D. A.; Ascencio, M. V.; Barr, G. D.; Bashyal, A.; Bellantoni, L.; Bercellie, A.; Betancourt, M.; et al (December 2021, The European Physical Journal Special Topics)

   Abstract With the advance of particle accelerator and detector technologies, the neutrino physics landscape is rapidly expanding. As neutrino oscillation experiments enter the intensity and precision frontiers, neutrino–nucleus interaction measurements are providing crucial input. MINERvA is an experiment at Fermilab dedicated to the study of neutrino–nucleus interactions in the regime of incident neutrino energies from one to few GeV. The experiment recorded neutrino and antineutrino scattering data with the NuMI beamline from 2009 to 2019 using the Low-Energy and Medium-Energy beams that peak at 3GeV and 6GeV, respectively. This article reviews the broad spectrum of interesting nuclear and particle physics that MINERvA investigations have illuminated. The newfound, detailed knowledge of neutrino interactions with nuclear targets thereby obtained is proving essential to continued progress in the neutrino physics sector. 

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3. Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning

   https://doi.org/10.1140/epjc/s10052-025-14313-8  

   Abud, A Abed; Acciarri, R; Acero, M A; Adames, M R; Adamov, G; Adamowski, M; Adams, D; Adinolfi, M; Adriano, C; Aduszkiewicz, A; et al (June 2025, The European Physical Journal C)

   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. 

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4. Fast low energy reconstruction using Convolutional Neural Networks

   https://doi.org/10.1088/1748-0221/21/02/P02020  

   Abbasi, R; Ackermann, M; Adams, J; Agarwalla, SK; Aguilar, JA; Ahlers, M; Alameddine, JM; Amin, NM; Andeen, K; Argüelles, C; et al (February 2026, Journal of Instrumentation)

   Abstract IceCube is a Cherenkov detector instrumenting over a cubic kilometer of glacial ice deep under the surface of the South Pole. The DeepCore sub-detector lowers the detection energy threshold to a few GeV, enabling the precise measurements of neutrino oscillation parameters with atmospheric neutrinos. The reconstruction of neutrino interactions inside the detector is essential in studying neutrino oscillations. It is particularly challenging to reconstruct sub-100 GeV events with the IceCube detectors due to the relatively sparse detection units and detection medium. Convolutional neural networks (CNNs) are broadly used in physics experiments for both classification and regression purposes. This paper discusses the CNNs developed and employed for the latest IceCube-DeepCore oscillation measurements [1]. These CNNs estimate various properties of the detected neutrinos, such as their energy, direction of arrival, interaction vertex position, flavor-related signature, and are also used for background classification. 

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5. Physics potential of the IceCube Upgrade for atmospheric neutrino oscillations

   https://doi.org/10.1103/nnjw-jp1n  

   Abbasi, R; Ackermann, M; Adams, J; Agarwalla, S K; Aguilar, J A; Ahlers, M; Alameddine, J M; Ali, S; Amin, N M; Andeen, K; et al (April 2026, Physical Review D)

   The IceCube Upgrade is an extension of the existing IceCube Neutrino Observatory and will be deployed in the 2025–2026 austral summer. It will significantly improve the sensitivity of the detector to atmospheric neutrino oscillations. The existing 86-string IceCube array contains a dense in-fill known as DeepCore which is optimized to measure neutrinos with energies down to a few GeV. The IceCube Upgrade will consist of seven new densely instrumented strings placed within the DeepCore volume to further enhance the performance in the GeV energy range. The additional strings will feature new optical modules, each containing multiple photomultiplier tubes (PMTs), in contrast to the existing modules that each contain a single PMT. This will more than triple the number of PMT channels with respect to the current IceCube configuration, allowing for improved detection efficiency and reconstruction performance at GeV energies. We describe necessary updates to simulation, event selection, and reconstruction to accommodate the higher data rates observed by the upgraded detector and the addition of multi-PMT modules. We determine the expected sensitivity of the IceCube Upgrade to the atmospheric neutrino oscillation parameters ${\sin }^2{\theta }_{23}$and $\mathrm{\Delta }{m}_{32}^2$, the appearance of tau neutrinos and the neutrino mass ordering. The IceCube Upgrade will provide neutrino oscillation measurements that are of similar precision to those from accelerator experiments, while providing complementarity by probing higher energies and longer baselines, and with different sources of systematic uncertainties. 

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