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Abstract The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) was designed to reconstruct neutrino events from the Fermilab Booster Neutrino Beam (BNB) with the parallel goals of measuring neutron production in interactions with oxygen and serving as a testbed for new technology. The ANNIE detector consists of a 26-ton water Cherenkov target tank instrumented with conventional photomultiplier tubes (PMTs), a downstream tracking muon spectrometer, and an upstream double wall of plastic scintillator to serve to veto charged particles incoming from neutrino events that occur upstream of the experimental setup. ANNIE has also deployed multiple Large-Area Picosecond PhotoDetectors (LAPPDs) and a test vessel of water-based liquid scintillator (WbLS). This paper describes the event reconstruction performance of the detector before implementation of these novel technologies, which will serve as a baseline against which their impact can be measured. That said, even the techniques used for event reconstruction using only the conventional PMT array and muon spectrometer are significantly different than those used in other water Cherenkov detectors due to the small size of ANNIE (which makes nanosecond-scale timing not as useful as in a large detector) and the availability of reconstruction information from the tracking muon spectrometer. We demonstrate that combining the information from these two elements into a single fit using only pattern recognition yields a muon vertex uncertainty of 60 cm, a directional uncertainty of 13.2 degrees, and energy reconstruction uncertainty of about 10% for BNB muon neutrino Charged Current Zero Pion (CC0π) events. 

In recent years, many researchers have proposed new models for synaptic plasticity in the brain based on principles of machine learning. The central motivation has been the development of learning algorithms that are able to learn difficult tasks while qualifying as "biologically plausible". However, the concept of a biologically plausible learning algorithm is only heuristically defined as an algorithm that is potentially implementable by biological neural networks. Further, claims that neural circuits could implement any given algorithm typically rest on an amorphous concept of "locality" (both in space and time). As a result, it is unclear what many proposed local learning algorithms actually predict biologically, and which of these are consequently good candidates for experimental investigation. Here, we address this lack of clarity by proposing formal and operational definitions of locality. Specifically, we define different classes of locality, each of which makes clear what quantities cannot be included in a learning rule if an algorithm is to qualify as local with respect to a given (biological) constraint. We subsequently use this framework to distill testable predictions from various classes of biologically plausible synaptic plasticity models that are robust to arbitrary choices about neural network architecture. Therefore, our framework can be used to guide claims of biological plausibility and to identify potential means of experimentally falsifying a proposed learning algorithm for the brain. 

There is growing interest in understanding how real brains may approximate gradients and how gradients can be used to train neuromorphic chips. However, neither real brains nor neuromorphic chips can perfectly follow the loss gradient, so parameter updates would necessarily use gradient estimators that have some variance and/or bias. Therefore, there is a need to understand better how variance and bias in gradient estimators impact learning dependent on network and task properties. Here, we show that variance and bias can impair learning on the training data, but some degree of variance and bias in a gradient estimator can be beneficial for generalization. We find that the ideal amount of variance and bias in a gradient estimator are dependent on several properties of the network and task: the size and activity sparsity of the network, the norm of the gradient, and the curvature of the loss landscape. As such, whether considering biologically-plausible learning algorithms or algorithms for training neuromorphic chips, researchers can analyze these properties to determine whether their approximation to gradient descent will be effective for learning given their network and task properties. 

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is both a physics experiment and a technology testbed for next-generation light-based neutrino detection. In this paper, we report the first demonstration of a fully integrated Large Area Picosecond Photodetector (LAPPD) operating in a running neutrino beam experiment. Particular focus is given to the design, commissioning, and successful deployment of the Packaged ANNIE LAPPD (PAL), a waterproof, self-triggering module incorporating fast waveform digitization and precision timing synchronized to the ANNIE detector subsystems. We identify beam-correlated LAPPD data frames consistent with charged-current neutrino interactions observed in multiple detector subsystems, establishing the first detection of neutrino-induced Cherenkov light with an LAPPD. These results validate the system-level performance of LAPPDs under realistic experimental conditions — including long-term stability, timing synchronization, and event matching with conventional PMT and muon detector systems — marking a critical step toward their deployment in future large-scale neutrino and particle detectors. 

Super-Kamiokande (SK) has observed ${}^8\mathrm{B}$solar neutrino elastic scattering at recoil electron kinetic energies ( $E_{\mathrm{kin}}$) as low as 3.49 MeV to study neutrino flavor conversion within the Sun. At SK-observable energies, these conversions are dominated by the Mikheyev-Smirnov-Wolfenstein effect. An upturn in the electron neutrino survival probability in which vacuum neutrino oscillations become dominant is predicted to occur at lower energies, but radioactive background increases exponentially with decreasing energy. New machine learning approaches provide substantial background reduction below 3.49 MeV such that statistical extraction of solar neutrino interactions becomes feasible. This article presents an analysis of the solar neutrino interaction rate at $E_{\mathrm{kin}}<3.49\mathrm{MeV}$with the full SK-IV period, using data from a wideband intelligent trigger when available and with a boosted decision tree for event selection. A solar neutrino signal is observed between $2.99\mathrm{MeV}<E_{\mathrm{kin}}<3.49\mathrm{MeV}$with $2.76\sigma$significance and a data to unoscillated Monte Carlo ratio of ${0.307}_{-0.111}^{+0.112}$. These additional low-energy data have a negligible effect on the $1\sigma$intervals of the fits to the solar neutrino energy spectrum but have a noticeable effect on the best fit when using the exponential parametrization. 

Abstract The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton water Cherenkov neutrino detector installed on the Booster Neutrino Beam (BNB) at Fermilab. Its main physics goals are to perform a measurement of the neutron yield from neutrino-nucleus interactions, as well as a measurement of the charged-current cross section of muon neutrinos. An equally important focus is the research and development of new detector technologies and target media. Specifically, water-based liquid scintillator (WbLS) is of interest as a novel detector medium, as it allows for the simultaneous detection of Cherenkov light and scintillation. This paper presents the deployment of a 366 L WbLS vessel in ANNIE in March 2023 and the subsequent detection of both Cherenkov light and scintillation from the WbLS. This proof-of-concept allows for the future development of reconstruction and particle identification algorithms in ANNIE, as well as dedicated analyses within the WbLS volume, such as the search for neutral-current events and the hadronic scintillation component. 

We present the results of searches for nucleon decays via $p\to \nu {\pi }^{+}$and $n\to \nu {\pi }^0$using a $0.484\mathrm{Mt}·\mathrm{yr}$exposure of Super-Kamiokande I-V data covering the entire pure water phase of the experiment. Various improvements on the previous 2014 nucleon decay search [], which used an exposure of $0.173\mathrm{Mt}·\mathrm{yr}$, are incorporated. The physics models related to pion production and nuclear interaction are refined with external data, and a more comprehensive set of systematic uncertainties, now including those associated with the atmospheric neutrino flux and pion production channels, is considered. Also, the fiducial volume has been expanded by 21%. No significant indication of a nucleon decay signal is found beyond the expected background. Lower bounds on the nucleon partial lifetimes are determined to be $3.5\times {10}^{32}\mathrm{yr}$for $p\to \nu {\pi }^{+}$and $1.4\times {10}^{33}\mathrm{yr}$for $n\to \nu {\pi }^0$at 90% confidence level. 

In recent neutrino detectors, neutrons produced in neutrino reactions play an important role. Muon capture on oxygen nuclei is one of the processes that produce neutrons in water Cherenkov detectors. We measured neutron multiplicity in the process using cosmic ray muons that stop in the gadolinium-loaded Super-Kamiokande detector. For this measurement, neutron detection efficiency is obtained with the muon capture events followed by gamma rays to be ${50.2}_{-2.1}^{+2.0}\%$. By fitting the observed multiplicity considering the detection efficiency, we measure neutron multiplicity in muon capture as $P\left(0\right)=24\pm 3\%$, $P\left(1\right)=70_{-2}^{+3}\%$, $P\left(2\right)=6.1\pm 0.5\%$, $P\left(3\right)=0.38\pm 0.09\%$. This is the first measurement of the multiplicity of neutrons associated with muon capture on oxygen without neutron energy threshold.