Gauge Theory on Projective Surfaces and Anti-self-dual Einstein Metrics in Dimension Four

Given a projective structure on a surface (Formula presented.), we show how to canonically construct a neutral signature Einstein metric with non-zero scalar curvature as well as a symplectic form on the total space M of a certain rank 2 affine bundle (Formula presented.). The Einstein metric has anti-self-dual conformal curvature and admits a parallel field of anti-self-dual planes. We show that locally every such metric arises from our construction unless it is conformally flat. The homogeneous Einstein metric corresponding to the flat projective structure on (Formula presented.) is the non-compact real form of the Fubini–Study metric on (Formula presented.). We also show how our construction relates to a certain gauge-theoretic equation introduced by Calderbank.The authors wish to thank Andreas Čap, Andrzej Derdziński, Nigel Hitchin and Claude LeBrun for helpful discussions regarding the contents of this paper. TM is grateful for travel support via the grant SNF 200020_144438 of the Swiss National Science Foundation. MD has been partially supported by STFC consolidated grant ST/P000681/1

Health professionals' perspective on the promotion of e-mental health apps in the context of maternal depression.

Our study focuses on exploring (1) the intention of health professionals to use and recommend e-mental health applications, (2) how this intention of health professionals might be influenced, (3) which group of health professionals might be most accessible to promote e-mental health applications for maternal depression, and (4) for which tasks they rate them to be most useful. Based on a questionnaire informed by the theory of planned behavior, we collected 131 responses of U.S., Spanish, and Swiss health professionals in the field of pregnancy and maternal care (including psychologists, psychiatrists, midwives, and doctors) by means of an online survey. We analyzed the gathered data applying a structured equation model. Our study reveals that health professionals would in general intend to recommend and use e-mental health applications. However, their attitude towards e-mental health applications varies regarding the respective use cases and also differs among health professions. We offer three alternative propositions for private or public organizations, associations, or any other entity whose purpose is service to the community for introducing e-mental health applications into practice

Charges of twisted branes: the exceptional cases

The charges of the twisted D-branes for the two exceptional cases (SO(8) with the triality automorphism and E_6 with charge conjugation) are determined. To this end the corresponding NIM-reps are expressed in terms of the fusion rules of the invariant subalgebras. As expected the charge groups are found to agree with those characterising the untwisted branes.Comment: 15 page

ArCLight - a Compact Dielectric Large-Area Photon Detector

ArCLight is a novel device for detecting scintillation light over large areas with Photon Detection Efficiency (PDE) of the order of a few percent. Its robust technological design allows for efficient use in large-volume particle detectors, such as Liquid Argon Time Projection Chambers (LArTPCs) or liquid scintillator detectors. Due to its dielectric structure it can be placed inside volumes with high electric field. It could potentially replace vacuum PhotoMultiplier Tubes (PMTs) in applications where high PDE is not required. The photon detection efficiency for a 10x10cm2 detector prototype was measured to be in the range of 0.8% to 2.2% across the active area

Search for an anomalous excess of charged-current quasielastic νe interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction

We present a measurement of the νe-interaction rate in the MicroBooNE detector that addresses the observed MiniBooNE anomalous low-energy excess (LEE). The approach taken isolates neutrino interactions consistent with the kinematics of charged-current quasielastic (CCQE) events. The topology of such signal events has a final state with one electron, one proton, and zero mesons (1e1p). Multiple novel techniques are employed to identify a 1e1p final state, including particle identification that use two methods of Deep-Learning-based image identification and event isolation using a boosted decision-tree ensemble trained to recognize two-body scattering kinematics. This analysis selects 25 νe-candidate events in the reconstructed neutrino energy range of 200–1200 MeV, while 29.0±1.9(sys)±5.4(stat) are predicted when using νμ CCQE interactions as a constraint. We use a simplified model to translate the MiniBooNE LEE observation into a prediction for a νe signal in MicroBooNE. A Δχ2 test statistic, based on the combined Neyman–Pearson χ2 formalism, is used to define frequentist confidence intervals for the LEE signal strength. Using this technique, in the case of no LEE signal, we expect this analysis to exclude a normalization factor of 0.75 (0.98) times the median MiniBooNE LEE signal strength at 90% (2σ) confidence level, while the MicroBooNE data yield an exclusion of 0.25 (0.38) times the median MiniBooNE LEE signal strength at 90% (2σ) confidence level

Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation

Wire-Cell is a 3D event reconstruction package for liquid argon time projection chambers. Through geometry, time, and drifted charge from multiple readout wire planes, 3D space points with associated charge are reconstructed prior to the pattern recognition stage. Pattern recognition techniques, including track trajectory and dQ/dx (ionization charge per unit length) fitting, 3D neutrino vertex fitting, track and shower separation, particle-level clustering, and particle identification are then applied on these 3D space points as well as the original 2D projection measurements. A deep neural network is developed to enhance the reconstruction of the neutrino interaction vertex. Compared to traditional algorithms, the deep neural network boosts the vertex efficiency by a relative 30% for charged-current νe interactions. This pattern recognition achieves 80–90% reconstruction efficiencies for primary leptons, after a 65.8% (72.9%) vertex efficiency for charged-current νe (νμ) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20% energy reconstruction resolutions for charged-current neutrino interactions

New CC0π GENIE model tune for MicroBooNE

Obtaining a high-quality interaction model with associated uncertainties is essential for neutrino experiments studying oscillations, nuclear scattering processes, or both. As a primary input to the MicroBooNE experiment’s next generation of neutrino cross section measurements and its flagship investigation of the MiniBooNE low-energy excess, we present a new tune of the charged-current pionless (CC0π) interaction cross section via the two major contributing processes—charged-current quasielastic and multinucleon interaction models—within version 3.0.6 of the GENIE neutrino event generator. Parameters in these models are tuned to muon neutrino CC0π cross section data obtained by the T2K experiment, which provides an independent set of neutrino interactions with a neutrino flux in a similar energy range to MicroBooNE’s neutrino beam. Although the fit is to muon neutrino data, the information carries over to electron neutrino simulation because the same underlying models are used in GENIE. A number of novel fit parameters were developed for this work, and the optimal parameters were chosen from existing and new sets. We choose to fit four parameters that have not previously been constrained by theory or data. Thus, this will be called a theory-driven tune. The result is an improved match to the T2K CC0π data with more well-motivated uncertainties based on the fit

First measurement of inclusive electron-neutrino and antineutrino charged current differential cross sections in charged lepton energy on argon in MicroBooNE

We present the first measurement of the single-differential νe+¯νe charged-current inclusive cross sections on argon in electron or positron energy and in electron or positron scattering angle over the full range. Data were collected using the MicroBooNE liquid argon time projection chamber located off axis from the Fermilab neutrinos at the main injector beam over an exposure of 2.0×1020 protons on target. The signal definition includes a 60 MeV threshold on the νe or ¯νe energy and a 120 MeV threshold on the electron or positron energy. The measured total and differential cross sections are found to be in agreement with the genie, nuwro, and gibuu neutrino generators

Novel approach for evaluating detector-related uncertainties in a LArTPC using MicroBooNE data

Primary challenges for current and future precision neutrino experiments using liquid argon time projection chambers (LArTPCs) include understanding detector effects and quantifying the associated systematic uncertainties. This paper presents a novel technique for assessing and propagating LArTPC detector-related systematic uncertainties. The technique makes modifications to simulation waveforms based on a parameterization of observed differences in ionization signals from the TPC between data and simulation, while remaining insensitive to the details of the detector model. The modifications are then used to quantify the systematic differences in low- and high-level reconstructed quantities. This approach could be applied to future LArTPC detectors, such as those used in SBN and DUNE