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We present the strongest limits to date on the mixing angle, $\theta$, with which a new scalar particle, $S$, mixes with the Higgs field in the mass range $100$ $MeV

This Letter presents an investigation of low-energy electron-neutrino interactions in the Fermilab Booster Neutrino Beam by the MicroBooNE experiment, motivated by the excess of electron-neutrino-like events observed by the MiniBooNE experiment. This is the first measurement to use data from all five years of operation of the MicroBooNE experiment, corresponding to an exposure of $1.11\times 10^{21}$ protons on target, a $70\%$ increase on past results. Two samples of electron neutrino interactions without visible pions are used, one with visible protons and one without any visible protons. MicroBooNE data is compared to two empirical models that modify the predicted rate of electron-neutrino interactions in different variables in the simulation to match the unfolded MiniBooNE low energy excess. In the first model, this unfolding is performed as a function of electron neutrino energy, while the second model aims to match the observed shower energy and angle distributions of the MiniBooNE excess. This measurement excludes an electron-like interpretation of the MiniBooNE excess based on these models at $> 99\%$ CL$_\mathrm{s}$ in all kinematic variables., Comment: 8 pages, 5 figures, 1 table

Can computer vision help us explore the ocean? The ultimate challenge for computer vision is to recognize any visual phenomena, more than only the objects and animals humans encounter in their terrestrial lives. Previous datasets have explored everyday objects and fine-grained categories humans see frequently. We present the FathomVerse v0 detection dataset to push the limits of our field by exploring animals that rarely come in contact with people in the deep sea. These animals present a novel vision challenge. The FathomVerse v0 dataset consists of 3843 images with 8092 bounding boxes from 12 distinct morphological groups recorded at two locations on the deep seafloor that are new to computer vision. It features visually perplexing scenarios such as an octopus intertwined with a sea star, and confounding categories like vampire squids and sea spiders. This dataset can push forward research on topics like fine-grained transfer learning, novel category discovery, species distribution modeling, and carbon cycle analysis, all of which are important to the care and husbandry of our planet., Comment: 10 pages, 14 figures

We have evaluated the imaging capabilities of the Timepix4 hybrid silicon pixel detector for 100 keV and 200 keV electrons in a Transmission Electron Microscope (TEM). Using the knife-edge method, we have measured the Modulation Transfer Function (MTF) at both energies. Our results show a decrease in MTF response at Nyquist (spatial) frequency, dropping from approximately 0.16 at 100 keV to 0.0046 at 200 keV. However, by using the temporal structure of the detected events, including the arrival time and amplitude provided by the Timepix4, we enhanced the spatial discrimination of electron arrival. This approach improved the MTF at Nyquist by factors of 2.12 for 100 keV and 3.16 for 200 keV. These findings demonstrate that the blurring effects caused by extended electron trajectories within the sensing layer can be effectively corrected in the image data., Comment: 20 pages, 11 figures; submitted to NIMA; corresponding author: N. Dimova

Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mis-modeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling.

Explanations in machine learning are critical for trust, transparency, and fairness. Yet, complex disagreements among these explanations limit the reliability and applicability of machine learning models, especially in high-stakes environments. We formalize four fundamental ranking-based explanation disagreement problems and introduce a novel framework, EXplanation AGREEment (EXAGREE), to bridge diverse interpretations in explainable machine learning, particularly from stakeholder-centered perspectives. Our approach leverages a Rashomon set for attribution predictions and then optimizes within this set to identify Stakeholder-Aligned Explanation Models (SAEMs) that minimize disagreement with diverse stakeholder needs while maintaining predictive performance. Rigorous empirical analysis on synthetic and real-world datasets demonstrates that EXAGREE reduces explanation disagreement and improves fairness across subgroups in various domains. EXAGREE not only provides researchers with a new direction for studying explanation disagreement problems but also offers data scientists a tool for making better-informed decisions in practical applications.

Large neutrino liquid argon time projection chamber (LArTPC) experiments can broaden their physics reach by reconstructing and interpreting MeV-scale energy depositions, or blips, present in their data. We demonstrate new calorimetric and particle discrimination capabilities at the MeV energy scale using reconstructed blips in data from the MicroBooNE LArTPC at Fermilab. We observe a concentration of low energy ($<$3 MeV) blips around fiberglass mechanical support struts along the TPC edges with energy spectrum features consistent with the Compton edge of 2.614 MeV $^{208}$Tl decay $\gamma$ rays. These features are used to verify proper calibration of electron energy scales in MicroBooNE's data to few percent precision and to measure the specific activity of $^{208}$Tl in the fiberglass composing these struts, $(11.7 \pm 0.2 ~\text{(stat)} \pm 2.8~\text{(syst)})~\text{Bq/kg}$. Cosmogenically-produced blips above 3 MeV in reconstructed energy are used to showcase the ability of large LArTPCs to distinguish between low-energy proton and electron energy depositions. An enriched sample of low-energy protons selected using this new particle discrimination technique is found to be smaller in data than in dedicated CORSIKA cosmic ray simulations, suggesting either incorrect CORSIKA modeling of incident cosmic fluxes or particle transport modeling issues in Geant4., Comment: 19 pages, 15 figures total including the supplementary material section, 1 table. CC BY license

To determine the causal effect of a treatment using observational data, it is important to balance the covariate distributions between treated and control groups. However, achieving balance can be difficult when treated and control groups lack overlap. In the presence of limited overlap, researchers typically choose between two types of methods: 1) methods (e.g., inverse propensity score weighting) that imply traditional estimands (e.g., ATE) but whose estimators are at risk of variance inflation and considerable statistical bias; and 2) methods (e.g., overlap weighting) which imply a different estimand, thereby changing the target population to reduce variance. In this work, we introduce a framework for characterizing estimands by their target populations and the statistical performance of their estimators. We introduce a bias decomposition that encapsulates bias due to 1) the statistical bias of the estimator; and 2) estimand mismatch, i.e., deviation from the population of interest. We propose a design-based estimand selection procedure that helps navigate the tradeoff between these two sources of bias and variance of the resulting estimators. Our procedure allows the analyst to incorporate their domain-specific preference for preservation of the original population versus reduction of statistical bias. We demonstrate how to select an estimand based on these preferences by applying our framework to a right heart catheterization study., Comment: 34 pages, 7 main text figures, 9 supplementary figures

In the past utilities relied on in-field inspections to identify asset defects. Recently, utilities have started using drone-based inspections to enhance the field-inspection process. We consider a vast repository of drone images, providing a wealth of information about asset health and potential issues. However, making the collected imagery data useful for automated defect detection requires significant manual labeling effort. We propose a novel solution that combines synthetic asset defect images with manually labeled drone images. This solution has several benefits: improves performance of defect detection, reduces the number of hours spent on manual labeling, and enables the capability to generate realistic images of rare defects where not enough real-world data is available. We employ a workflow that combines 3D modeling tools such as Maya and Unreal Engine to create photorealistic 3D models and 2D renderings of defective assets and their surroundings. These synthetic images are then integrated into our training pipeline augmenting the real data. This study implements an end-to-end Artificial Intelligence solution to detect assets and asset defects from the combined imagery repository. The unique contribution of this research lies in the application of advanced computer vision models and the generation of photorealistic 3D renderings of defective assets, aiming to transform the asset inspection process. Our asset detection model has achieved an accuracy of 92 percent, we achieved a performance lift of 67 percent when introducing approximately 2,000 synthetic images of 2k resolution. In our tests, the defect detection model achieved an accuracy of 73 percent across two batches of images. Our analysis demonstrated that synthetic data can be successfully used in place of real-world manually labeled data to train defect detection model.

Two-dimensional (2D) materials are poised to revolutionize current solid-state technology with their extraordinary properties. Yet, the primary challenge remains their scalable production. While there have been significant advancements, much of the scientific progress has depended on the exfoliation of materials, a method that poses severe challenges for large-scale applications. With the advent of artificial intelligence (AI) in materials science, innovative synthesis methodologies are now on the horizon. This study explores the forefront of autonomous materials synthesis using an artificial neural network (ANN) trained by evolutionary methods, focusing on the efficient production of graphene. Our approach demonstrates that a neural network can iteratively and autonomously learn a time-dependent protocol for the efficient growth of graphene, without requiring pretraining on what constitutes an effective recipe. Evaluation criteria are based on the proximity of the Raman signature to that of monolayer graphene: higher scores are granted to outcomes whose spectrum more closely resembles that of an ideal continuous monolayer structure. This feedback mechanism allows for iterative refinement of the ANN's time-dependent synthesis protocols, progressively improving sample quality. Through the advancement and application of AI methodologies, this work makes a substantial contribution to the field of materials engineering, fostering a new era of innovation and efficiency in the synthesis process.

We study maximal almost rigid modules over a gentle algebra $A$. We prove that the number of indecomposable direct summands of every maximal almost rigid $A$-module is equal to the sum of the number of vertices and the number of arrows of the Gabriel quiver of $A$. Moreover, the algebra $A$, considered as an $A$-module, can be completed to a maximal almost rigid module in a unique way. Gentle algebras are precisely the tiling algebras of surfaces with marked points. We show that the (permissible) triangulations of the surface of $A$ are in bijection with the maximal almost rigid $A$-modules. Furthermore, we study the endomorphism algebra $C=\text{End}_A T$ of a maximal almost rigid module $T$. We construct a fully faithful functor $G\colon \text{mod}\,A\to \text{mod}\, \overline{A}$ into the module category of a bigger gentle algebra $\overline{A}$ and show that $G$ maps maximal almost rigid $A$-modules to tilting $\overline{A}$-modules. In particular, $C$ and $\overline{A}$ are derived equivalent and $C$ is gentle. After giving a geometric realization of the functor $G$, we obtain a tiling $G(\mathbf{T})$ of the surface of $\overline{A}$ as the image of the triangulation $\mathbf{T}$ corresponding to $T$. We then show that the tiling algebra of $G(\mathbf{T})$ is $C$. Moreover, the tiling algebra of $\mathbf{T}$ is obtained algebraically from $C$ as the tensor algebra with respect to the $C$-bimodule $\text{Ext}_C^2(DC,C)$, which also is fundamental in cluster-tilting theory., Comment: 50 pages, 35 figures. Comments are welcome!

Different prediction models might perform equally well (Rashomon set) in the same task, but offer conflicting interpretations and conclusions about the data. The Rashomon effect in the context of Explainable AI (XAI) has been recognized as a critical factor. Although the Rashomon set has been introduced and studied in various contexts, its practical application is at its infancy stage and lacks adequate guidance and evaluation. We study the problem of the Rashomon set sampling from a practical viewpoint and identify two fundamental axioms - generalizability and implementation sparsity that exploring methods ought to satisfy in practical usage. These two axioms are not satisfied by most known attribution methods, which we consider to be a fundamental weakness. We use the norms to guide the design of an $\epsilon$-subgradient-based sampling method. We apply this method to a fundamental mathematical problem as a proof of concept and to a set of practical datasets to demonstrate its ability compared with existing sampling methods.

At optical/ultraviolet energies, blazars display an underlying thermal (unpolarized) contribution from the accretion disc, torus and line emitting regions, diluting the polarized emission from the jet-component. Optical polarimetry can be used to disentangle the thermal and non-thermal components, and place constraints on the particle populations and acceleration mechanisms responsible for the non-thermal emission. We present the results of a linear optical spectropolarimetric observing campaign of 18 blazars (6 BLLs and 12 FSRQs) undertaken with the Southern African Large Telescope between 2016 and 2022. This was done to observe these systems during flaring states, as well as long term monitoring of PKS1510-089, AP Lib and PKS 1034-293. The observations traced the frequency dependence of the degree and angle of polarization, as well as changes in the spectral line strengths. We investigated possible correlations between the polarization and other observed characteristics for the sources. While an indication of correlation was found between the frequency dependence and the average level of polarization for some sources, a correlation was not found for the population as a whole. These results highlight that continuous observations and in-depth modelling of polarization and its frequency dependence is required to obtain a more holistic view of TeV blazars., Comment: 17 pages, 21 figures, 3 tables, accepted for publication in MNRAS

A significant challenge in measurements of neutrino oscillations is reconstructing the incoming neutrino energies. While modern fully-active tracking calorimeters such as liquid argon time projection chambers in principle allow the measurement of all final state particles above some detection threshold, undetected neutrons remain a considerable source of missing energy with little to no data constraining their production rates and kinematics. We present the first demonstration of tagging neutrino-induced neutrons in liquid argon time projection chambers using secondary protons emitted from neutron-argon interactions in the MicroBooNE detector. We describe the method developed to identify neutrino-induced neutrons and demonstrate its performance using neutrons produced in muon-neutrino charged current interactions. The method is validated using a small subset of MicroBooNE's total dataset. The selection yields a sample with $60\%$ of selected tracks corresponding to neutron-induced secondary protons.

We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture for neutrino energy estimation in the MicroBooNE experiment, utilizing liquid argon time projection chamber (LArTPC) detector technology. Traditional energy estimation approaches in LArTPCs, which largely rely on reconstructing and summing visible energies, often experience sizable biases and resolution smearing because of the complex nature of neutrino interactions and the detector response. The estimation of neutrino energy can be improved after considering the kinematics information of reconstructed final-state particles. Utilizing kinematic information of reconstructed particles, the deep learning-based approach shows improved resolution and reduced bias for the muon neutrino Monte Carlo simulation sample compared to the traditional approach. In order to address the common concern about the effectiveness of this method on experimental data, the RNN-based energy estimator is further examined and validated with dedicated data-simulation consistency tests using MicroBooNE data. We also assess its potential impact on a neutrino oscillation study after accounting for all statistical and systematic uncertainties and show that it enhances physics sensitivity. This method has good potential to improve the performance of other physics analyses.

Transcranial low-intensity focused ultrasound can deliver energy to the brain in a minimally invasive manner for neuromodulation applications. However, long-pulsed sonication through the skull introduces significant wave interactions, complicating precise energy delivery to the target. We present a comprehensive examination of intracranial acoustic fields generated by focused ultrasound transducers and assess the characteristics of cranial bone that affect acoustic transmission. Acoustic field maps were generated at 88 regions of interest across 10 historical and 2 Thiel-embalmed human skull specimens with sonication at frequencies of 220 kHz, 650 kHz, and 1000 kHz. The average peak pressure insertion loss was 3.1$\pm$1.4 dB, 9.0$\pm$1.7 dB, and 14.6$\pm$4.1 dB, and the average power insertion loss was 5.0$\pm$2.4 dB, 14.9$\pm$3.2 dB, and 24.1$\pm$6.3 dB, respectively. The effect of skull thickness, skull density ratio, and skull curvature on intracranial peak pressure and power was investigated and linear fits produced. Our results demonstrate that Thiel-embalmed samples fall within the confidence intervals of fits for historical samples. The effects of angulation and spacing between the transducer and the skull were also investigated. Results indicate that wave superposition resulting from skull and transducer spacing could lead to a 30-40% uncertainty in peak recorded intracranial pressure., Comment: 38 pages, 8 figures

We report the first double-differential cross section measurement of neutral-current neutral pion (NC$\pi^0$) production in neutrino-argon scattering, as well as single-differential measurements of the same channel in terms of final states with and without protons. The kinematic variables of interest for these measurements are the $\pi^0$ momentum and the $\pi^0$ scattering angle with respect to the neutrino beam. A total of 4971 candidate NC$\pi^0$ events fully-contained within the MicroBooNE detector are selected using data collected at a mean neutrino energy of $\sim 0.8$~GeV from $6.4\times10^{20}$ protons on target from the Booster Neutrino Beam at the Fermi National Accelerator Laboratory. After extensive data-driven model validation to ensure unbiased unfolding, the Wiener-SVD method is used to extract nominal flux-averaged cross sections. The results are compared to predictions from commonly used neutrino event generators, which tend to overpredict the measured NC$\pi^0$ cross section, especially in the 0.2-0.5~GeV/c $\pi^0$ momentum range and at forward scattering angles. Events with at least one proton present in the final state are also underestimated. This data will help improve the modeling of NC$\pi^0$ production, which represents a major background in measurements of charge-parity violation in the neutrino sector and in searches for new physics beyond the Standard Model.

HESS J1813$-$178 is a very-high-energy $\gamma$-ray source spatially coincident with the young and energetic pulsar PSR J1813$-$1749 and thought to be associated with its pulsar wind nebula (PWN). Recently, evidence for extended high-energy emission in the vicinity of the pulsar has been revealed in the Fermi Large Area Telescope (LAT) data. This motivates revisiting the HESS J1813$-$178 region, taking advantage of improved analysis methods and an extended data set. Using data taken by the High Energy Stereoscopic System (H.E.S.S.) experiment and the Fermi-LAT, we aim to describe the $\gamma$-ray emission in the region with a consistent model, to provide insights into its origin. We performed a likelihood-based analysis on 32 hours of H.E.S.S. data and 12 years of Fermi-LAT data and fit a spectro-morphological model to the combined datasets. These results allowed us to develop a physical model for the origin of the observed $\gamma$-ray emission in the region. In addition to the compact very-high-energy $\gamma$-ray emission centered on the pulsar, we find a significant yet previously undetected component along the Galactic plane. With Fermi-LAT data, we confirm extended high-energy emission consistent with the position and elongation of the extended emission observed with H.E.S.S. These results establish a consistent description of the emission in the region from GeV energies to several tens of TeV. This study suggests that HESS J1813$-$178 is associated with a $\gamma$-ray PWN powered by PSR J1813$-$1749. A possible origin of the extended emission component is inverse Compton emission from electrons and positrons that have escaped the confines of the pulsar and form a halo around the PWN., Comment: 13+5 pages, 13+11 figures. Accepted for publication in A&A. Corresponding authors: T.Wach, A.Mitchell, V.Joshi, P.Chamb\'ery

Exposure of nitrogen doped diamond to high energy, heavy ions induces formation of vacancy related color centers aligned along the trajectories of the ions. Quasi 1D chains of coupled NV centers with lengths of a few tens of microns can be building blocks for quantum information processing and they provide insights into harsh radiation-matter interactions. Here, we report on color center formation in diamond (1 ppm nitrogen) with 1 GeV gold and uranium ions. Using depth-resolved photoluminescence, we observe direct formation of single vacancy related color centers (GR1 centers) along the ion tracks. Mobile vacancies can form NV-centers with native nitrogen atoms during thermal annealing. Molecular dynamics simulations indicate that both isolated vacancies and defect clusters form along ion trajectory through electronic stopping processes, leading to broad color center profiles that range from the sample surface to a depth of about 25 microns. We quantify the spin properties of NV-centers formed by swift heavy ions through optical detection of magnetic resonance (ODMR) and validate the feasibility of using swift-heavy-ion-generated NV$^{-}$ along quasi 1D chains (for isolated tracks from low fluence irradiations) or in thin sheets of coupled 1D spin chains (formed with higher ion fluences) for NV-based magnetometry and for the exploration of quasi 1D and 2D spin textures in diamond.

How the solar wind influences the magnetospheres of the outer planets is a fundamentally important question, but is difficult to answer in the absence of consistent, simultaneous monitoring of the upstream solar wind and the large-scale dynamics internal to the magnetosphere. To compensate for the relative lack of in-situ data, propagation models are often used to estimate the ambient solar wind conditions at the outer planets for comparison to remote observations or in-situ measurements. This introduces another complication: the propagation of near-Earth solar wind measurements introduces difficult-to-assess uncertainties. Here, we present the Multi-Model Ensemble System for the outer Heliosphere (MMESH) to begin to address these issues, along with the resultant multi-model ensemble (MME) of the solar wind conditions near Jupiter. MMESH accepts as input any number of solar wind models together with contemporaneous in-situ spacecraft data. From these, the system characterizes typical uncertainties in model timing, quantifies how these uncertainties vary under different conditions, attempts to correct for systematic biases in the input model timing, and composes a MME with uncertainties from the results. For the case of the Jupiter-MME presented here, three solar wind propagation models were compared to in-situ measurements from the near-Jupiter spacecraft Ulysses and Juno which span diverse geometries and phases of the solar cycle, amounting to more than 14,000 hours of data over 2.5 decades. The MME gives the most-probable near-Jupiter solar wind conditions for times within the tested epoch, outperforming the input models and returning quantified estimates of uncertainty., Comment: 31 pages, 10 figures

While ethical challenges are widely discussed in HCI, far less is reported about the ethical processes that researchers routinely navigate. We reflect on a multispecies project that negotiated an especially complex ethical approval process. Cat Royale was an artist-led exploration of creating an artwork to engage audiences in exploring trust in autonomous systems. The artwork took the form of a robot that played with three cats. Gaining ethical approval required an extensive dialogue with three Institutional Review Boards (IRBs) covering computer science, veterinary science and animal welfare, raising tensions around the welfare of the cats, perceived benefits and appropriate methods, and reputational risk to the University. To reveal these tensions we introduce beneficiary-epistemology space, that makes explicit who benefits from research (humans or animals) and underlying epistemologies. Positioning projects and IRBs in this space can help clarify tensions and highlight opportunities to recruit additional expertise., Comment: Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), May 11--16, 2024, Honolulu, HI, USA

Accurately extracting clinical information from speech is critical to the diagnosis and treatment of many neurological conditions. As such, there is interest in leveraging AI for automatic, objective assessments of clinical speech to facilitate diagnosis and treatment of speech disorders. We explore transfer learning using foundation models, focusing on the impact of layer selection for the downstream task of predicting pathological speech features. We find that selecting an optimal layer can greatly improve performance (~15.8% increase in balanced accuracy per feature as compared to worst layer, ~13.6% increase as compared to final layer), though the best layer varies by predicted feature and does not always generalize well to unseen data. A learned weighted sum offers comparable performance to the average best layer in-distribution (only ~1.2% lower) and had strong generalization for out-of-distribution data (only 1.5% lower than the average best layer)., Comment: Accepted to INTERSPEECH 2024

Machine learning methods have been remarkably successful in material science, providing novel scientific insights, guiding future laboratory experiments, and accelerating materials discovery. Despite the promising performance of these models, understanding the decisions they make is also essential to ensure the scientific value of their outcomes. However, there is a recent and ongoing debate about the diversity of explanations, which potentially leads to scientific inconsistency. This Perspective explores the sources and implications of these diverse explanations in ML applications for physical sciences. Through three case studies in materials science and molecular property prediction, we examine how different models, explanation methods, levels of feature attribution, and stakeholder needs can result in varying interpretations of ML outputs. Our analysis underscores the importance of considering multiple perspectives when interpreting ML models in scientific contexts and highlights the critical need for scientists to maintain control over the interpretation process, balancing data-driven insights with domain expertise to meet specific scientific needs. By fostering a comprehensive understanding of these inconsistencies, we aim to contribute to the responsible integration of eXplainable Artificial Intelligence (XAI) into physical sciences and improve the trustworthiness of ML applications in scientific discovery.

The fractal dimension of a surface allows its degree of roughness to be characterized quantitatively. However, limited effort is attempted to calculate the fractal dimension of surfaces computed from precisely known atomic coordinates from computational biomolecular and nanomaterial studies. This work proposes methods to estimate the fractal dimension of the surface of any 3D object composed of spheres, by representing the surface as either a voxelized point cloud or a mathematically exact surface, and computing its box-counting dimension. Sphractal is published as a Python package that provides these functionalities, and its utility is demonstrated on a set of simulated palladium nanoparticle data., Comment: 18 pages, 13 figures