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DOE OSTI2026

First Measurement of Charged Current Muon Neutrino-Induced Kaon Production on Argon using the MicroBooNE Detector

MicroBooNE is an 85-tonne active mass liquid argon time projection chamber (LArTPC) neutrino detector exposed to the Booster Neutrino Beamline (BNB) at Fermilab. One of the key physics goals is the precise measurement of neutrino interactions on argon in the 1 GeV energy regime. The study of strange and heavier meson production in neutrino interactions, in particular final states containing $K^{+}$, will help to improve the background estimates for future nucleon decay searches in experiments such as DUNE, provide valuable input for improving neutrino generator models, and will allow the development of techniques to enhance the particle identification capabilities of LArTPCs. In this work, we present the first-ever cross-section measurements of charged current muon neutrino-induced kaon production on argon at MicroBooNE.

Rodriguez Rondon, Jairo H. [South Dakota Sch. Mine↗

DOE OSTI2024

A More Precise Measurement of the Radius of PSR J0740+6620 Using Updated NICER Data

PSR J0740+6620 is the neutron star with the highest precisely determined mass, inferred from radio observations to be 2.08 ± 0.07 M ⊙ . Measurements of its radius therefore hold promise to constrain the properties of the cold, catalyzed, high-density matter in neutron star cores. Previously, Miller et al. and Riley et al. reported measurements of the radius of PSR J0740+6620 based on Neutron Star Interior Composition Explorer (NICER) observations accumulated through 2020 April 17, and an exploratory analysis utilizing NICER background estimates and a data set accumulated through 2021 December 28 was presented in Salmi et al. Here we report an updated radius measurement, derived by fitting models of X-ray emission from the neutron star surface to NICER data accumulated through 2022 April 21, totaling ~1.1 Ms additional exposure compared to the data set analyzed in Miller et al. and Riley et al., and to data from XMM-Newton observations. We find that the equatorial circumferential radius of PSR J0740+6620 is ${12.92}_{-1.13}^{+2.09}$ km (68% credibility), a fractional uncertainty ~83% the width of that reported in Miller et al., in line with statistical expectations given the additional data. If we were to require the radius to be less than 16 km, as was done in Salmi et al., then our 68% credible region would become $R={12.76}_{-1.02}^{+1.49}$ km, which is close to the headline result of Salmi et al. Our updated measurements, along with other laboratory and astrophysical constraints, imply a slightly softer equation of state than that inferred from our previous measurements.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

A data-driven method to estimate the antiproton background in Mu2e

The Mu2e experiment at Fermilab will search for the Charged Lepton Flavour Violating (CLFV) process of neutrinoless conversion of muon to electron in the field of an Al nucleus. The experimental signature is a monochromatic 104.97 MeV conversion electron. One of the expected backgrounds to the conversion electron search is antiprotons produced by the proton beam at the Production Target and annihilating in the Stopping Target (ST). The background expected from antiprotons is low but highly uncertain due to the uncertainty in the antiproton production cross section for the Mu2e beam energy in the relevant angular region. Antiprotons are significantly slower than the other beam particles, so they cannot be efficiently suppressed by the time window cut used to reduce the prompt background. At Mu2e energies, antiproton annihilation at rest in the ST is the only source of events with multiple, simultaneous particle trajectories. We utilized this unique feature and developed a novel way to reconstruct the multi-track events and estimate the antiproton background in situ.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

A data-driven method to estimate the antiproton background in Mu2e

The Mu2e experiment will search for the CLFV process of neutrinoless, coherent conversion of muon to electron in the field of an Al nucleus. One of the expected backgrounds is antiprotons produced by the proton beam at the Production Target and annihilating in the Stopping Target to produce signal-like electrons. Although not a dominant background, it has a large uncertainty and cannot be suppressed by the timing cuts used to reduce the prompt background. However, at Mu2e energies, $p\bar{p}$ annihilation is the only source of events with multiple, simultaneous particles coming from the Stopping Target. We utilized this unique feature and developed a novel approach to reconstruct multi-track events and estimate the antiproton background.

Chithirasreemadam, Namitha [Pisa U.; INFN, Pisa]↗

DOE OSTI2025

Dimuon production in the MicroBooNE detector

This analysis studies the production of the rare signal with two muons (dimuon) in the finalstate at µB using the Booster Neutrino Beam (BNB) as source of neutrino interactions. Themain background source for the dimuon signal are pions. Differentiating muons and pions hasbeen challenging at µB because the tracks left by these particles are almost identical. Thisanalysis faces this problem using a supervised classifier algorithm called boosted decisiontree (BDT). A Monte Carlo simulation (MC) enhanced dimuon signal sample was producedto train a BDT capable of recognizing this signal. A set of variables that show somediscrimination characteristics was used to perform the training. A MC neutrino interactionsimulation sample was used to estimate the background. Estimations of dimuon productionin real data samples were made for different cases. Additionally, an analysis of the systematicuncertainties, fake data studies, and BDT performance checks were obtained.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Measurement of the background in the CMS muon detector in ${p}{p}$-collisions at $\sqrt{s} = 13$ TeV

The CMS detector, including its muon system, has been operating at the CERN LHC in increasingly challenging conditions for about 15 years. The muon detector was designed to provide excellent triggering and track reconstruction for muons produced in proton–proton collisons at an instantaneous luminosity ($\mathcal{L}$) of 1 x 10 34 cm –2 s –1 . During the Run 2 data-taking period (2015–2018), the LHC achieved an instantaneous luminosity of twice its design value, resulting in larger background rates and making the efficient detection of muons more difficult. While some backgrounds result from natural radioactivity, cosmic rays, and interactions of the circulating protons with residual gas in the beam pipe, the dominant source of background hits in the muon system arises from proton–proton interactions themselves. Charged hadrons leaving the calorimeters produce energy deposits in the muon chambers. In addition, high-energy particles interacting in the hadron calorimeter and forward shielding elements generate thermal neutrons, which leak out of the calorimeter and shielding structures, filling the CMS cavern. We describe the method used to measure the background rates in the various muon subsystems. These rates, in conjunction with simulations, can be used to estimate the expected backgrounds in the High-Luminosity LHC. This machine will run for at least 10 years starting in 2029 reaching an instantaneous luminosity of $\mathcal{L}$ = 5 x 10 34 cm –2 s –1 and increasing ultimately to $\mathcal{L}$ = 7.5 x 10 34 cm –2 s –1 . These background estimates have been a key ingredient for the planning and design of the muon detector upgrade.

Tytgat, M. (ORCID:0000000239902074)↗

DOE OSTI2026

Estimating CO 2 fluxes through integrating spatial and temporal input layers via deep learning algorithms

Background Accurate estimation of net ecosystem exchange of CO 2 fluxes (Fc) is essential for understanding carbon cycle processes and assessing ecosystem carbon budgets. However, conventional modeling approaches often emphasize temporal dynamics while overlooking the pronounced spatial heterogeneity within the footprint of eddy covariance (EC) towers, potentially limiting predictive accuracy and interpretability of Fc estimates. To address this challenge, we developed a spatiotemporal model that integrates high-resolution footprint-weighted spatial information with sequential environmental drivers. Results The integrated model combines a deeper graph convolutional network to characterize fine-scale spatial variability within EC footprints and a gated recurrent unit network to capture temporal dependencies in biophysical conditions. Using multi-year flux tower observations, remote sensing vegetation indices and footprint modeling, we evaluate the proposed method across three land cover types. This spatiotemporal model consistently outperforms temporal-only and spatial-only baselines, achieving the highest overall accuracy (R 2 = 0.9569) and the lowest RMSE (1.8128 μmol m −2 s −1 ) and MAE (1.1939 μmol m −2 s −1 ). Performance gains are particularly evident in ecosystems with strong vegetation heterogeneity, where spatial structure substantially modulates Fc variability. Conclusions This study demonstrates the importance of joint modeling spatial heterogeneity and temporal dynamics for improving Fc estimation and provides a robust method for advancing footprint-based Fc estimates across diverse ecosystems, supporting refined assessments of terrestrial carbon fluxes, and enhancing scientific foundations for carbon studies.

CO2 flux estimate↗

DOE OSTI2026

HostSub_GP: Precise Galaxy Background Subtraction in Transient Long-slit Spectroscopy with Gaussian Processes

We present a novel host galaxy subtraction technique in long-slit spectroscopy for extragalactic transients. Unlike classic methods which generally estimate the background using simple interpolation of local galaxy flux in the 2D spectrum, our approach leverages multi-band archival images of the host galaxies to model the background emission from the galaxy in the 2D spectrum. Such imaging encodes the wavelength-dependent galaxy profile along the slit, and is readily accessible through wide-field imaging surveys. We construct a smooth prior for the 2D galaxy profile with a Gaussian process (GP) based on these reference images, and use another GP to model the correlated deviations from the prior in the observed spectrum. This enables accurate inference of the galaxy flux blended with the transient. On synthetic long-slit data of a spiral galaxy extracted from a Multi Unit Spectroscopic Explorer hyper-spectral cube, the GP method remains robust as long as the host galaxy is spatially resolved and consistently outperforms classic methods. We apply the method to archival Keck spectra of two real transients, SN 2019eix and AT 2019qiz, to further demonstrate how the method uniquely recovers weak spectral features amid strong galaxy contamination, enabling refined constraints on the properties of both transients. We have released the software implementation, HostSub_GP, a scalable toolkit that leverages JAX, with an MIT license.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Search For Low-Mass Quark-Antiquark Resonances Produced With an Initial State Photon at 13 TeV Using the CMS Detector

We present a search for low-mass narrow qq̅ resonances. This search uses data from LHC pp collisions at a center of mass of 13 TeV in Run 2, and corresponds to an integrated luminosity of 137 fb^{-1}, currently using 10\% of data. Utilizing full Run 2 data allows the use of a lower photon pT threshold trigger than a previous analysis performed with only 2016 data, allowing this analysis to be more sensitive to resonances in the low mass region. We require an initial state photon recoiling against the narrow resonance, leading to the resonance having a high transverse momentum. The high pT decay products of the resonance collimate and are reconstructed as a single large jet with an internal two-pronged substructure. A two-pronged dijet score based on the ParticleNet tagger is used to select jets with two-pronged substructure. The background is estimated via a data-driven method using a transfer factor between the distributions which fail and pass the two-pronged substructure requirement. The new physics signal is searched for as a narrow peak excess above the Standard Model backgrounds in the jet mass spectrum.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Search For Low-Mass Quark-Antiquark Resonances Produced With an Initial State Photon at 13 TeV Using the CMS Detector

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions

Neural posterior estimation (NPE), a type of amortized variational inference, is a computationally efficient means of constructing probabilistic catalogs of light sources from astronomical images. To date, NPE has not been used to perform inference in models with spatially varying covariates. However, ground-based astronomical images exhibit spatially varying sky backgrounds and point spread functions (PSFs), and accounting for this variation is essential for constructing accurate catalogs of imaged light sources. In this work, we introduce a novel NPE-based cataloging method that trains an inference network with semisynthetic astronomical images generated using PSFs and backgrounds sampled from the Sloan Digital Sky Survey. In experiments with semisynthetic images, we evaluate the method on key cataloging tasks: light source detection, star/galaxy separation, and flux measurement. A “generalist” inference network—trained with diverse PSFs and backgrounds—performs as well as a “specialist” network even when both are evaluated on the specialist’s particular PSF/background combination. This result suggests that a single NPE network can generalize across spatial variations, eliminating the need for retraining on each observational condition.

astronomy image processing↗

DOE OSTI2024

Search for pair-produced higgsinos decaying via Higgs or Z bosons to final states containing a pair of photons and a pair of b -jets with the ATLAS detector

A search is presented for the pair production of higgsinos $\tilde{X}$ in gauge-mediated supersymmetry models, where the lightest neutralinos $\tilde{X}^{0}_{1}$ decay into a light gravitino $\tilde{G}$ in association with either a Higgs h or a Z boson. The search is performed with the ATLAS detector at the Large Hadron Collider using 139 fb -1 of proton–proton collisions at a centre-of-mass energy of $\sqrt{s}$ = 13TeV. It targets final states in which a Higgs boson decays into a photon pair, while the other Higgs or Z boson decays into a $b\overline{b}$ pair, with missing transverse momentum associated with the two gravitinos. Search regions dependent on the amount of missing transverse momentum are defined by the requirements that the diphoton mass should be consistent with the mass of the Higgs boson, and the $b\overline{b}$ mass with the mass of the Higgs or Z boson. The main backgrounds are estimated with data-driven methods using the sidebands of the diphoton mass distribution. No excesses beyond Standard Model expectations are observed and higgsinos with masses up to 320 GeV are excluded, assuming a branching fraction of 100% for $\tilde{X}^{0}_{1}$ → h$\tilde{G}$. This analysis excludes higgsinos with masses of 130GeV for branching fractions to h$\tilde{G}$ as low as 36%, thus providing complementarity to previous ATLAS searches in final states with multiple leptons or multiple b-jets, targeting different decays of the electroweak bosons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Weakly supervised anomaly detection for resonant new physics in the dijet final state using proton-proton collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

An anomaly detection search for narrow-width resonances beyond the Standard Model that decay into a pair of jets is presented. The search is based on 139 fb −1 of proton-proton collisions at $\sqrt{s}$ = 13 TeV recorded during 2015–2018 with the ATLAS detector at the Large Hadron Collider. The analysis is optimized without a particular signal model and aims to be sensitive to a broad range of new physics. It uses two different machine learning strategies to estimate the background in different signal regions. In each region, a weakly supervised classifier is trained to distinguish this background model from data. The analysis focuses on events with high transverse momentum jets reconstructed as large-radius jets. The mass and substructure of these jets are used as inputs to the classifiers. After a classifier-based selection, the distribution of the invariant mass of the two jets is used to search for potential local excesses. The model-independent results of both the anomaly detection methods show no signs of significant local excesses. In addition to model-independent results, a representative set of signal models is injected into the data, and the sensitivity of the methods to these scenarios is reported.

Aad, G. [Aix-Marseille Université] (ORCID:00000002↗

DOE OSTI2026

Searches for BSM physics using challenging and long-lived signatures with the ATLAS detector

Various theories beyond the Standard Model predict new, long-lived particles with unique signatures which are difficult to reconstruct and for which estimating the background rates is also a challenge. Signatures from displaced and/or delayed decays anywhere from the inner detector to the muon spectrometer, as well as those of new particles with fractional or multiple values of the charge of the electron or high mass stable charged particles are all examples of experimentally demanding signatures. The talk will focus on the most recent results using 13 TeV and 13.6TeV pp collision data collected by the ATLAS detector.

Bruce, Laura [Harvard University]↗

DOE OSTI2026

Applications Of Machine Learning to Gas Plume Analysis In Longwave Infrared Hyperspectral Images

Longwave infrared hyperspectral images can be used for gas plume analysis, as many gases exhibit distinct absorption features in this portion of the electromagnetic spectrum. In practice, accurately identifying weak gas signatures is difficult because the observed radiance is dominated by background radiance, which varies with material, temperature, and viewing conditions. Many gas plume analysis pipelines operate on single images, limiting the ability to leverage spatial and multi-view information that could enhance the analysis. The goal of this dissertation is to explore how machine learning and deep learning methods can complement classical approaches to improve gas plume identification in longwave infrared hyperspectral imagery, and to investigate the use of neural radiance fields for hyperspectral scene reconstruction.

3D Scene Reconstruction↗

DOE OSTI2024

Red–green–blue Boolean image analysis of particulate debris laced with luminescent tracers

Abstract Particulate mass estimation from 3-pixel images is desirable in many fields. Red–green–blue (RGB) analysis and Boolean logic were shown to estimate the mass of luminescent tracers in microscopic images. With a controlled background intensity, an estimation error of 1.8 to 3.5% was achieved; in uncontrolled backgrounds, an error of about 18% was achieved. RGB analysis is a valuable tool for spatial location of particulates. This work shows it is possible to estimate the particulate mass in an image and gives RGB an extension into mass quantification that has far-reaching impacts in fields involving the fate and transport of particulate matter. Graphical abstract

36 MATERIALS SCIENCE↗

DOE OSTI2025

Search for light neutral particles decaying promptly into collimated pairs of electrons or muons in pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search for a dark photon, a new light neutral particle, which decays promptly into collimated pairs of electrons or muons is presented. The search targets dark photons resulting from the exotic decay of the Standard Model Higgs boson, assuming its production via the dominant gluon-gluon fusion mode. The analysis is based on 140 fb -1 of data collected with the ATLAS detector at the Large Hadron Collider from proton-proton collisions at a center-of-mass energy of 13 TeV. Events with collimated pairs of electrons or muons are analysed and background contributions are estimated using data-driven techniques. No significant excess in the data above the Standard Model background is observed. Upper limits are set at 95% confidence level on the branching ratio of the Higgs boson decay into dark photons between 0.001% and 5%, depending on the assumed dark photon mass and signal model.

Aad, G. [Aix-Marseille Université] (ORCID:00000002↗

DOE OSTI2024

Search for leptoquark pair production decaying into $te^- \bar{t}e^+$ or $t\mu ^- \bar{t}\mu ^+$ in multi-lepton final states in pp collisions at $\sqrt{s} = 13\,\textrm{TeV}$ with the ATLAS detector

A search for leptoquark pair production decaying into $te^- \bar{t}e^+$ or $t\mu ^- \bar{t}\mu ^+$ in final states with multiple leptons is presented. The search is based on a dataset of pp collisions at $\sqrt{s}=13~\text {TeV}$ recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb -1 . Four signal regions, with the requirement of at least three light leptons (electron or muon) and at least two jets out of which at least one jet is identified as coming from a b-hadron, are considered based on the number of leptons of a given flavour. The main background processes are estimated using dedicated control regions in a simultaneous fit with the signal regions to data. No excess above the Standard Model background prediction is observed and 95% confidence level limits on the production cross section times branching ratio are derived as a function of the leptoquark mass. Under the assumption of exclusive decays into $te^{-}$ ($t\mu ^{-}$ ), the corresponding lower limit on the scalar mixed-generation leptoquark mass $m_{\textrm{LQ}_{\textrm{mix}}^{\textrm{d}}}$ is at 1.58 (1.59) TeV and on the vector leptoquark mass $m_{{\tilde{U}}_1}$ at 1.67 (1.67) TeV in the minimal coupling scenario and at 1.95 (1.95) TeV in the Yang–Mills scenario.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗