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We present multiwavelength observations of the first recorded low state of the intermediate polar BG CMi. Optical monitoring of the source by members of the American Association of Variable Star Observers reveals a decrease of ∼0.5 mag that lasted ∼50 days in early 2025. During the low state the optical timing properties imply that BG CMi underwent a change in accretion mode, as power at the spin frequency ω dramatically dropped. An XMM-Newton observation revealed a substantial decrease in intrinsic absorption and a slight increase in intrinsic X-ray luminosity, compared to archival Suzaku data. Timing analysis of the X-ray light curves shows that power shifted from the orbital frequency Ω (prominent in Suzaku data) to 2Ω in the low-state XMM-Newton data, along with strengthening of certain orbital sidebands. We suggest that BG CMi transitioned to disk-overflow accretion, where the white dwarf accreted matter via both a disk and a stream, the latter becoming more dominant during the low state due to a decrease in the mass and size of the disk.

The two-detector design of the NOvA neutrino oscillation experiment, in which two functionally identical detectors are exposed to an intense neutrino beam, aids in canceling leading order effects of cross-section uncertainties. However, limited knowledge of neutrino interaction cross sections still gives rise to some of the largest systematic uncertainties in current oscillation measurements. We show contemporary models of neutrino interactions to be discrepant with data from NOvA, consistent with discrepancies seen in other experiments. Adjustments to neutrino interaction models in GENIE are presented, creating an effective model that improves agreement with our data. We also describe systematic uncertainties on these models, including uncertainties on multi-nucleon interactions from a newly developed procedure using NOvA near detector data.

High-degree atrioventricular block (HAVB) is a frequent complication in the acute stages of a myocardial infarction associated with an increased rate of mortality. However, the incidence and clinical significance of HAVB in late convalescent phases of an AMI is largely unknown. The aim of this study was to assess the incidence and prognostic value of late HAVB documented by continuous electrocardiogram (ECG) monitoring in post-AMI patients with reduced left ventricular function. The study included 286 patients from the CARISMA study with AMI and left ventricular ejection fraction of 40% or less. An insertable loop recorder was implanted 5 to 21 days after AMI for incessant arrhythmia surveillance. Furthermore, ECG documentation was supplemented by a 24-hour Holter monitoring conducted at week 6 post-AMI. The clinical significance of HAVB occurring more than 21 days after AMI was examined with respect to development of major heart failure events and major ventricular tachyarrhythmic events. During a median follow-up of 1.9 years (interquartile range 0.9-2.0), late HAVB was documented in 30 patients. The risk of major heart failure events (hazard ratio [HR] 4.08 [1.38-12.09], P = .01) and major ventricular tachyarrhythmic events (HR = 5.41 [1.88-15.58], P = .002) were significantly increased in patients who developed late HAVB. High-degree atrioventricular block documented by continuous ECG monitoring occurring more than 3 weeks after AMI is a frequent complication in post-AMI patients with left ventricular dysfunction. Furthermore, HAVB is associated with ominous prognostic implications of both potentially lethal arrhythmias and heart failure.

The rate of high energy cosmic ray muons as measured underground is shown to be strongly correlated with upper‐air temperatures during short‐term atmospheric (10‐day) events. The effects are seen by correlating data from the MINOS underground detector and temperatures from the European Centre for Medium Range Weather Forecasts during the winter periods from 2003–2007. This effect provides an independent technique for the measurement of meteorological conditions and presents a unique opportunity to measure both short and long‐term changes in this important part of the atmosphere.

The sensitivity of the Deep Underground Neutrino Experiment (DUNE) to neutrino oscillation is determined, based on a full simulation, reconstruction, and event selection of the far detector and a full simulation and parameterized analysis of the near detector. Detailed uncertainties due to the flux prediction, neutrino interaction model, and detector effects are included. DUNE will resolve the neutrino mass ordering to a precision of 5 σ , for all δ CP values, after 2 years of running with the nominal detector design and beam configuration. It has the potential to observe charge-parity violation in the neutrino sector to a precision of 3 σ (5 σ ) after an exposure of 5 (10) years, for 50% of all δ CP values. It will also make precise measurements of other parameters governing long-baseline neutrino oscillation, and after an exposure of 15 years will achieve a similar sensitivity to sin 2 2 θ 13 to current reactor experiments.

We present time‐series observations, spectra, and archival outburst data of a newly discovered variable star in Hercules, Var Her 04. Its orbital period, mass ratio, and outburst amplitude resemble those of the ugwz‐type subclass of ugsu dwarf novae. However, its supercycle and outburst light curve defy classification as a clear ugwz. Var Her 04 is most similar to the small group of possible hydrogen‐burning “period bouncers,” dwarf novae that have passed beyond the period minimum and returned.

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 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 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.