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Recently, Approximate Policy Iteration (API) algorithms have achieved superhuman proficiency in two-player zero-sum games such as Go, Chess, and Shogi without human data. These API algorithms iterate between two policies: a slow policy (tree search), and a fast policy (a neural network). In these two-player games, a reward is always received at the end of the game. However, the Rubik’s Cube has only a single solved state, and episodes are not guaranteed to terminate. This poses a major problem for these API algorithms since they rely on the reward received at the end of the game. We introduce Autodidactic Iteration: an API algorithm that overcomes the problem of sparse rewards by training on a distribution of states that allows the reward to propagate from the goal state to states farther away. Autodidactic Iteration is able to learn how to solve the Rubik’s Cube without relying on human data. Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves — less than or equal to solvers that employ human domain knowledge. 

Double- and single-differential cross sections for inclusive charged-current ${\nu }_{\mu }$-nucleus scattering are reported for the kinematic domain 0 to $2\mathrm{GeV}/c$in three-momentum transfer and 0 to 2 GeV in available energy, at a mean ${\nu }_{\mu }$energy of 1.86 GeV. The measurements are based on an estimated 995,760 ${\nu }_{\mu }$charged-current (CC) interactions in the scintillator medium of the NOvA Near Detector. The subdomain populated by 2-particle-2-hole (2p2h) reactions is identified by the cross section excess relative to predictions for ${\nu }_{\mu }$-nucleus scattering that are constrained by a data control sample. Models for 2-particle-2-hole processes are rated by ${\chi }^2$comparisons of the predicted-versus-measured ${\nu }_{\mu }$CC inclusive cross section over the full phase space and in the restricted subdomain. Shortfalls are observed in neutrino generator predictions obtained using the theory-based València and SuSAv2 2p2h models. Published by the American Physical Society2025 

We report a search for neutrino oscillations to sterile neutrinos under a model with three active and one sterile neutrinos ( $3+1$model). This analysis uses the NOvA detectors exposed to the NuMI beam, running in neutrino mode. The data exposure, $13.6\times {10}^{20}$protons on target, doubles that previously analyzed by NOvA, and the analysis is the first to use ${\nu }_{\mu }$charged-current interactions in conjunction with neutral-current interactions. Neutrino samples in the near and far detectors are fitted simultaneously, enabling the search to be carried out over a $\mathrm{\Delta }{m}_{41}^2$range extending 2 (3) orders of magnitude above (below) $1{\mathrm{eV}}^2$. NOvA finds no evidence for active-to-sterile neutrino oscillations under the $3+1$model at 90% confidence level. New limits are reported in multiple regions of parameter space, excluding some regions currently allowed by IceCube at 90% confidence level. We additionally set the most stringent limits for anomalous ${\nu }_{\tau }$appearance for $\mathrm{\Delta }{m}_{41}^2\le 3{\mathrm{eV}}^2$. Published by the American Physical Society2025 

This Letter reports a search for charge-parity ( $CP$) symmetry violating nonstandard interactions (NSI) of neutrinos with matter using the NOvA Experiment, and examines their effects on the determination of the standard oscillation parameters. Data from ${\nu }_{\mu }\left({\overline{\nu }}_{\mu }\right)\to {\nu }_{\mu }\left({\overline{\nu }}_{\mu }\right)$and ${\nu }_{\mu }\left({\overline{\nu }}_{\mu }\right)\to {\nu }_e\left({\overline{\nu }}_e\right)$oscillation channels are used to measure the effect of the NSI parameters ${ϵ}_{e\mu }$and ${ϵ}_{e\tau }$. With 90% CL the magnitudes of the NSI couplings are constrained to be $\left|{ϵ}_{e\mu }\right|\lesssim 0.3$and $\left|{ϵ}_{e\tau }\right|\lesssim 0.4$. A degeneracy at $\left|{ϵ}_{e\tau }\right|\approx 1.8$is reported, and we observe that the presence of NSI limits sensitivity to the standard $CP$phase ${\delta }_{CP}$. Published by the American Physical Society2024 

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.