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DOE OSTI2023

Search for heavy, long-lived, charged particles with large ionisation energy loss in $pp$ collisions at $ \sqrt{s} $ = 13 TeV using the ATLAS experiment and the full Run 2 dataset

This paper presents a search for hypothetical massive, charged, long-lived particles with the ATLAS detector at the LHC using an integrated luminosity of 139 fb –1 of proton–proton collisions at √s =13 TeV. These particles are expected to move significantly slower than the speed of light and should be identifiable by their high transverse momenta and anomalously large specific ionisation losses, dE/dx. Trajectories reconstructed solely by the inner tracking system and a dE/dx measurement in the pixel detector layers provide sensitivity to particles with lifetimes down to O(1) ns with a mass, measured using the Bethe–Bloch relation, ranging from 100 GeV to 3 TeV. Interpretations for pair-production of R-hadrons, charginos and staus in scenarios of supersymmetry compatible with these particles being long-lived are presented, with mass limits extending considerably beyond those from previous searches in broad ranges of lifetime.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Search for exotic decays of the Higgs boson into long-lived particles in pp collisions at $\sqrt{s}$ = 13 TeV using displaced vertices in the ATLAS inner detector

A novel search for exotic decays of the Higgs boson into pairs of long-lived neutral particles, each decaying into a bottom quark pair, is performed using 139 fb -1 of $\sqrt{s}$ = 13 TeV proton-proton collision data collected with the ATLAS detector at the LHC. Events consistent with the production of a Higgs boson in association with a leptonically decaying Z boson are analysed. Long-lived particle (LLP) decays are reconstructed from inner-detector tracks as displaced vertices with high mass and track multiplicity relative to Standard Model processes. The analysis selection requires the presence of at least two displaced vertices, effectively suppressing Standard Model backgrounds. The residual background contribution is estimated using a data-driven technique. No excess over Standard Model predictions is observed, and upper limits are set on the branching ratio of the Higgs boson to LLPs. Branching ratios above 10% are excluded at 95% confidence level for LLP mean proper lifetimes cτ as small as 4 mm and as large as 100 mm. For LLP masses below 40 GeV, these results represent the most stringent constraint in this lifetime regime.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

How Water Attacks MXene

Two-dimensional (2D) transition metal carbides and nitrides (MXenes) have shown outstanding performances in electrochemical energy storage and many other applications. However, the stability of MXene remains a concern, especially its quick degradation in aqueous solutions under ambient conditions. Here, we report on the water/Ti 3 C 2 O 2 -MXene interfacial chemistry from first-principles molecular dynamics simulations at room temperature. Surprisingly, we find that the water molecules can attack the basal plane of Ti 3 C 2 O 2 and pull the surface Ti atoms out, thereby reconstructing the surface. Further, by tracking close encounters of water molecules and surface Ti atoms on the basal plane of Ti 3 C 2 O 2 , we show that the attack is initiated by the chemisorption of a water molecule on a surface Ti atom, followed by the breaking of Ti–C bonds and deprotonation of the water molecule, leading to the formation of Ti–OH on the Ti 3 C 2 O 2 surface and a hydronium ion in the aqueous phase. Our finding highlights the susceptibility of Ti 3 C 2 O 2 MXene to water attack, supporting recent experimental observations. Furthermore, we demonstrate that preventing close encounters of water molecules and the surface Ti atoms is key to the stability of the basal plane and can be realized by negatively charging the surface (thereby reorienting the O atoms of water away from the surface) or converting the surface O to -OH groups (thereby shifting the water layer further away from the surface). Our insights and approach highlight the importance of the reactivity of water when interfacing with 2D materials such as MXenes.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Machine learning for detection of 3D features using sparse x-ray tomographic reconstruction

In many inertial confinement fusion (ICF) experiments, the neutron yield and other parameters cannot be completely accounted for with one and two dimensional models. This discrepancy suggests that there are three dimensional effects that may be significant. Sources of these effects include defects in the shells and defects in shell interfaces, the fill tube of the capsule, and the joint feature in double shell targets. Due to their ability to penetrate materials, x rays are used to capture the internal structure of objects. Methods such as computational tomography use x-ray radiographs from hundreds of projections, in order to reconstruct a three dimensional model of the object. In experimental environments, such as the National Ignition Facility and Omega-60, the availability of these views is scarce, and in many cases only consists of a single line of sight. Mathematical reconstruction of a 3D object from sparse views is an ill-posed inverse problem. These types of problems are typically solved by utilizing prior information. Neural networks have been used for the task of 3D reconstruction as they are capable of encoding and leveraging this prior information. We utilize half a dozen, different convolutional neural networks to produce different 3D representations of ICF implosions from the experimental data. Deep supervision is utilized to train a neural network to produce high-resolution reconstructions. These representations are used to track 3D features of the capsules, such as the ablator, inner shell, and the joint between shell hemispheres. Machine learning, supplemented by different priors, is a promising method for 3D reconstructions in ICF and x-ray radiography, in general.

Wolfe, Bradley T. (ORCID:0000000268301614)↗

DOE OSTI2021

Performance of Michigan sMDT prototype chambers for the HL-LHC ATLAS muon detector upgrade

A new small-diameter Monitored Drift Tube (sMDT) chamber has been developed for the muon spectrometer of the ATLAS experiment to handle the higher collision rates expected at the CERN High Luminosity Large Hadron Collider (HL-LHC). This paper presents measurements of the tracking resolution and hit efficiency of two prototype sMDT chambers constructed at the University of Michigan. Using cosmic-ray muons the sMDT tracking resolution of 103.7 ± 8.1 μm was measured for one chamber and 101.8 ± 7.8 μm for the other, compared with a design resolution of 106 μm. A further tracking resolution improvement to 83.4 ± 7.8 μm was obtained by using new high-gain readout electronics which will be added for HL-LHC. An average tracking efficiency of (98.5 ± 0.2)% was found for both chambers. The methodology used to determine the detector tracking resolution and efficiency, including reconstruction of sMDT data and a Geant4 simulation of the test chamber, is presented in detail.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Measurement of inclusive jet cross section and substructure in 𝑝 + 𝑝 collisions at $\sqrt{s}$ = 200 GeV

The jet cross section and jet-substructure observables in 𝑝 + 𝑝 collisions at $\sqrt{s}$ =200 GeV were measured by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC). Jets are reconstructed from charged-particle tracks and electromagnetic-calorimeter clusters using the anti-𝑘 𝑡 algorithm with a jet radius of 𝑅 = 0.3 for jets with transverse momentum within 8.0 < 𝑝 𝑇 < 40.0 GeV/𝑐 and pseudorapidity |𝜂| < 0.15. Measurements include the jet cross section, as well as distributions of SoftDrop-groomed momentum fraction (𝑧 𝑔 ), charged-particle transverse momentum with respect to jet axis (𝑗 𝑇 ), and radial distributions of charged particles within jets (𝑟). Also measured was the distribution of 𝜉 = −ln⁡(𝑧), where 𝑧 is the fraction of the jet momentum carried by the charged particle. The measurements are compared to theoretical next-to and next-to-next-to-leading-order calculations, the PYTHIA and H erwig event generators, and to other existing experimental results. Indicated from these measurements is a lower particle multiplicity in jets at RHIC energies when compared to models. Also noted are implications for future jet measurements with sPHENIX at RHIC as well as at the future Electron-Ion Collider.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Near-constant retreat rate of a terrestrial margin of the Laurentide Ice Sheet during the last deglaciation

The Laurentide Ice Sheet (LIS) was the largest ice sheet during the last glacial period. An accurate representation of its behavior during the last deglaciation is critical to understanding its influence on and response to a changing climate. Here we use 10 Be dating and Bayesian modeling to track the recession of the southwest sector of the Labrador Dome of the LIS along an ~500-km-long transect west of Lake Superior during the last deglaciation. This transect reflects terrestrial ice-margin retreat and crosses multiple moraine sets, with the southwestern part of the transect deglaciated by ca. 19 ka and the northeastern part deglaciated by ca. 10 ka. The predominant behavior of the ice margin during this interval is near-constant retreat with retreat rates varying between ~59 m/a and 38 m/a. The moraine sets mark standstills and/or readvances that in total constitute only ~17% of the retreat interval. The spatial and temporal pattern of ice-margin retreat tracked here differs from existing reconstructions that are based on using isochrons to define ice-margin positions. Acknowledging the uncertainties associated with the modeled ages of ice-margin retreat, we suggest that the overall retreat pattern is consistent with forcing by a gradual increase in Northern Hemisphere, high-latitude summer insolation. The pattern of ice-margin retreat is inconsistent with Greenland ice-core temperature records, and thus these records may not be suitable to drive models of the LIS.

58 GEOSCIENCES↗

DOE OSTI2024

Addressing GPU memory limitations for Graph Neural Networks in High-Energy Physics applications

Introduction Reconstructing low-level particle tracks in neutrino physics can address some of the most fundamental questions about the universe. However, processing petabytes of raw data using deep learning techniques poses a challenging problem in the field of High Energy Physics (HEP). In the Exa.TrkX Project, an illustrative HEP application, preprocessed simulation data is fed into a state-of-art Graph Neural Network (GNN) model, accelerated by GPUs. However, limited GPU memory often leads to Out-of-Memory (OOM) exceptions during training, due to the large size of models and datasets. This problem is exacerbated when deploying models on High-Performance Computing (HPC) systems designed for large-scale applications. Methods We observe a high workload imbalance issue during GNN model training caused by the irregular sizes of input graph samples in HEP datasets, contributing to OOM exceptions. We aim to scale GNNs on HPC systems, by prioritizing workload balance in graph inputs while maintaining model accuracy. Our paper introduces diverse balancing strategies aimed at decreasing the maximum GPU memory footprint and avoiding the OOM exception, across various datasets. Results Our experiments showcase memory reduction of up to 32.14% compared to the baseline. We also demonstrate the proposed strategies can avoid OOM in application. Additionally, we create a distributed multi-GPU implementation using these samplers to demonstrate the scalability of these techniques on the HEP dataset. Discussion By assessing the performance of these strategies as data loading samplers across multiple datasets, we can gauge their effectiveness in both single-GPU and distributed environments. Our experiments, conducted on datasets of varying sizes and across multiple GPUs, broaden the applicability of our work to various GNN applications that handle input datasets with irregular graph sizes.

Lee, Claire Songhyun↗

DOE OSTI2023

Optimisation of the Search for CP-symmetry Violation at the Deep Underground Neutrino Experiment

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long baseline experiment, which will be situated in South Dakota. Its detectors will utilise liquid-argon time projection chamber technology, which is able to capture neutrino interactions with an incredible spatial and calorimetric resolution. With what will become the world’s most intense neutrino beam, a highly capable near detector, and four (10kt fducial mass) far detector modules, DUNE will be able to achieve an ambitious physics programme. Most notably, DUNE will determine whether charge-parity symmetry is broken in neutrino oscillations - a finding that would have significant implications for the understanding of the matter-antimatter asymmetry in our Universe.This thesis presents the optimisation of a CP-violation analysis at DUNE using thePandora pattern-recognition software. The analysis assumes a 3.5 year exposure (1.36 × 1023 protons on target) to a neutrino and an antineutrino beam (7 year total). Only the predicted data of the far detector modules is used; near detector samples are not included. The initial sensitivity to CP-violation is found to be 3.8σ+0.9σ−1.1σ in an estimate that includes oscillation parameter uncertainties, systematic uncertainties and statistical fluctuations, and assumes a normal-ordering of the neutrino mass hierarchy. The performance of the Pandora event reconstruction is linked to that of the analysis, which is found to be limited by the reconstruction of the initial track-like region of electrons and photons. The ShowerRefinement algorithm is developed in response to this and its implementation into the analysis workflow results in an improved sensitivity to CP-violation of 4.6σ+0.9σ−1.0σ. With a perfected neutrino interaction vertex placement, this is further increased to 5.1σ+1.0σ−1.1σ.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Using Boosted Decision Trees to Select High Quality Measurements in the Mu2e Experiment at Fermilab

This thesis presents the implementation and evaluation of a Boosted Decision Tree (BDT) model to improve the selection of high-quality track measurements in the Mu2e experiment at Fermilab. The Mu2e experiment is a high-energy physics experiments seeking to observe a rare theoretical physics process known as Charged Lepton Flavor Violation. A significant challenge faced by the Mu2e experiment are so-called background events, which are events whose data mimics that of the rare physics process the experiment seeks to observe. Without a mechanism to reduce background, it would be impossible to know whether Charged Lepton Flavor Violation occurred or not. To this end, high-quality track measurements must be distinguished from low-quality track measurements. A track can be conceived of as the reconstructed path of a particle that traveled through the Mu2e detector. In addition to other data, data about such tracks is stored using a C++-based framework, specific to the domain of high-energy physics, known as ROOT. A boosted decision tree model was trained using ROOT’s Toolkit For Multivariate Analysis by leveraging variables ancillary to track quality. In evaluation, the BDT achieves a ROC-AUC of 0.927 in discriminating good-quality tracks from poor-quality tracks. Such a score is indicative of both strong discrimination and strong generalization. Subsequently, it is shown that applying a BDT-based quality cut to the distribution of particle momenta significantly enhances the signal-to-background distinction for signal electrons, paving the way for improved sensitivity to Charged Lepton Flavor Violation.

Mullany, Brendan T. [Drew U.] (ORCID:0009000818888↗

DOE OSTI2024

Detection of MeV-Scale Gammas from Pion/Muon Nuclear Capture With the LArIAT Liquid Argon TPC

LArIAT (Liquid Argon In A Testbeam) is a LArTPC experiment at Fermilab which aims to understand and characterize interactions of neutrino final-state products with Argon. Tracks for pions and muons in LArTPCs are difficult to differentiate since both particles exhibit very similar ionization densities. We are exploring unique new particle discrimination capabilities for pions and muons by exploiting information from small, isolated ionization depositions, referred to as "blips", reconstructed near the endpoint of stopping tracks. These blips are formed by gammas emitted when an at-rest pion or muon captures on the argon nucleus. The relatively low beam energy provided by LArIAT makes it uniquely suited for performing this demonstration. In this talk, an overview of event candidate selection and reconstruct blips corresponding to our signal of interest, nuclear captures of pions and muons at rest inside LArIAT's TPC, and how we estimate and subtract backgrounds from these capture-at-rest blip signals.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Measurements of Pion and Muon Nuclear Capture at Rest on Argon in the LArIAT Test Beam Experiment

LArIAT is a liquid argon time projection chamber (LArTPC) experiment in a test beam at Fermilab from 2015 to 2017 to understand and characterize interactions of particles in LAr which are commonly observed in neutrino-Ar final-states. Tracks for pions and muons in LArTPCs are difficult to differentiate since both particles exhibit very similar ionization profiles for muon and pion that stop in the TPC. We are exploring unique new particle discrimination capabilities by exploiting information from small, isolated ionization depositions, referred to as "blips", reconstructed near the endpoint of stopping tracks. These blips are formed by gammas emitted when an at-rest pion or muon captures on the argon nucleus. The relatively low beam energy provided by LArIAT makes it uniquely suited for performing this demonstration. In this poster, we present an overview of event candidate selection, blip reconstruction, and background subtraction corresponding to our signal of interest, nuclear captures of pions and muons at rest inside LArIAT's TPC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

NMF-Based Anomaly Detection in CMS 2D Tracking Occupancy Histograms

The CMS experiment relies on Data Quality Monitoring (DQM) to ensure that recorded collision data are suitable for physics analysis. During LHC Run 3, each run contains many lumisections and tracking monitoring elements, making offline inspection challenging, especially for localized detector effects that may appear only for short periods of time. This poster presents an unsupervised machine-learning approach to identify anomalous lumisections in CMS tracking occupancy histograms using Non-Negative Matrix Factorization (NMF). The workflow uses offline CMS DQMIO tracking histograms retrieved with the CMS DIALS API and organized as two-dimensional occupancy maps for each lumisection. After selecting stable lumisections, the occupancy maps are normalized and arranged into a non-negative data matrix. The NMF model learns a compact set of basis patterns describing normal tracking occupancy. Each lumisection is then reconstructed from these learned components, and the reconstruction error is used as an anomaly score. Large residuals indicate occupancy patterns that deviate from normal detector behavior and are flagged for further inspection. This NMF-based approach provides a fast and interpretable way to flag lumisections whose tracking occupancy patterns differ from normal detector behavior. Preliminary studies show sensitivity to known tracking anomalies, and ongoing work is focused on validating the method across additional Run 3 Pixel and Strip detector issues.

Rodríguez Ramos, Iliomar [Puerto Rico U., Mayaguez↗

DOE OSTI2026

Search for Higgsinos in final states with low-momentum lepton-track pairs at 13 TeV

We present a search for the pair production of Higgsinos in final states with large missing transverse momentum and either two reconstructed muons or a reconstructed lepton (muon or electron) and an isolated track. The analyzed data correspond to proton-proton collisions with an integrated luminosity of 137 fb −1 , collected by the CMS experiment at $\sqrt{𝑠}$ =13 TeV in 2016, 2017, and 2018. The signal scenario assumes four nearly mass degenerate Higgsino mass eigenstates: two neutralino states $\tilde{𝜒}^0_2$ and $\tilde{𝜒}^0_1$ with a small mass difference in the range 1–10 GeV and two chargino states $\tilde{𝜒}^±_1$ with an intermediate mass. The analysis focuses on the decay of the heavier neutralino into the lighter one and a virtual 𝑍 boson, which decays into two same-flavor leptons. The leptons have small transverse momentum and/or a small opening angle between the identified muons. An isolated track is used to recover events in which only one of the two leptons is identified. Multivariate discriminants are used to enhance the sensitivity by efficiently rejecting backgrounds from SM processes or misreconstructed tracks and/or leptons. The search explores a unique phase space and probes a previously unexplored region of the signal model parameter space. Mass differences between the two neutralinos are probed down to 1.5 GeV, assuming a Higgsino mass of 100 GeV. The maximum excluded Higgsino mass is 115 GeV.

Hayrapetyan, A. [Yerevan Physics Institute]↗

DOE OSTI2024

Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks

MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Quantum pattern recognition algorithms for charged particle tracking

High-energy physics is facing a daunting computing challenge with the large datasets expected from the upcoming High-Luminosity Large Hadron Collider in the next decade and even more so at future colliders. A key challenge in the reconstruction of events of simulated data and collision data is the pattern recognition algorithms used to determine the trajectories of charged particles. The field of quantum computing shows promise for transformative capabilities and is going through a cycle of rapid development and hence might provide a solution to this challenge. This article reviews current studies of quantum computers for charged particle pattern recognition in high-energy physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Single nucleotide variants drive evolutionary phage-host arms race in anaerobic carbon dioxide-converting microbiome

Microbial bioconversions are shaped by environmental perturbations and the adaptation of resident microbiomes. Prokaryotes coexist with bacteriophages, yet their coevolutionary trajectories remain underexplored. Here, we investigate the effects of a cultivation vessel leak on an anaerobic consortium performing carbon dioxide reduction. Using time-series shotgun metagenomic sequencing, we reconstruct microbial and viral genomes to track community shifts. We further apply single-nucleotide variant profiling and CRISPR array analysis to monitor viral microdiversity and host defense mechanisms. After bioaugmentation restores bioconversion efficiency, the consortium undergoes pronounced restructuring, with new dominant taxa emerging from the rare biosphere. We identify patterns consistent with phage predation selectively removing certain species, while others exhibit resilience to infection. This shift aligns with a widespread viral outbreak and a transient increased frequency of single nucleotide variants in bacterial CRISPR–Cas defense genes. Expansion of CRISPR spacers further supports that CRISPR-mediated processes influence microbial resilience. Concurrently, phages infecting resilient hosts exhibited adaptive evolution, marked by high genetic heterogeneity. Selective pressure varies across their genomes, targeting infectivity genes and protospacer-adjacent motifs. These findings highlight a dynamic evolutionary arms race driven by the selection of beneficial genetic variants, providing a mechanistic framework for multi-omics investigations, and informing biotechnological applications, including phage-based microbiome manipulation.

Ghiotto, G↗