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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Performance of heavy-flavour jet identification in Lorentz-boosted topologies in proton-proton collisions at √(s) = 13 TeV

Measurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to bb̅ or cc̅, have been developed and deployed across a range of physics analyses. This paper highlights their performance on simulated events, and summarizes novel calibration techniques using proton-proton collision data collected at √(s) = 13 TeV during the 2016–2018 LHC data-taking period. Three dedicated methods are used for the calibration in multijet events, leveraging either machine learning techniques, the presence of muons within energetic boosted jets, or the reconstruction of hadronically decaying high-energy Z bosons. The calibration results, obtained through a combination of these approaches, are presented and discussed.

Pattern recognition↗
DOE OSTI2024

A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks

Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, have greatly motivated the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, we can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we provide a fair and comprehensive comparison of classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their area under the curve (AUC) scores, the quantum networks were found to outperform the classical networks. However, seeing the computational advantage of quantum networks in practice may have to wait for the further development of quantum technology and its associated application programming interfaces (APIs).

Forestano, Roy T. (ORCID:0000000203552076)↗
DOE OSTI2023

Measurement of the production of a $W$ boson in association with a charmed hadron in $pp$ collisions at $\sqrt{s}$ = $\mathrm{13 TeV}$ with the ATLAS detector

The production of a $W$ boson in association with a single charm quark is studied using 140 fb –1 of $\sqrt{s}$ = $13$ $\mathrm{TeV}$ proton-proton collision data collected with the ATLAS detector at the Large Hadron Collider. The charm quark is tagged by the presence of a charmed hadron reconstructed with a secondary-vertex fit. The $W$ boson is reconstructed from the decay to either an electron or a muon and the missing transverse momentum present in the event. The charmed mesons reconstructed are D + → K – π + π + and D *+ → D 0 π + → (K – π + ) π + and the charge conjugate decays in the fiducial regions where $p$ T ($e,μ$) > 30 GeV, |$η(e,μ)$| < 2.5, $p$ T (D (*) ) > 8 GeV, and |$η$(D (*) )| < 2.2. The integrated and normalized differential cross sections as a function of the pseudorapidity of the lepton from the $W$ boson decay, and of the transverse momentum of the charmed hadron, are extracted from the data using a profile likelihood fit. The measured total fiducial cross sections are ${σ}_{fid}^{OS – SS}$ (W – + D + ) = 50.2 ± 0.2 ${(stat)}_{–2.3}^{+2.4}$(syst) pb, ${σ}_{fid}^{OS – SS}$ (W + + D – ) = 48.5 ± 0.2 ${(stat)}_{–2.2}^{+2.3}$(syst) pb, ${σ}_{fid}^{OS – SS}$ (W – + D *+ ) = 51.1 ± 0.4 ${(stat)}_{–1.8}^{+1.9}$(syst) pb, ${σ}_{fid}^{OS – SS}$ (W + + D *– ) = 50.0 ± 0.4 ${(stat)}_{–1.8}^{+1.9}$(syst) pb. Results are compared with the predictions of next-to-leading-order quantum chromodynamics calculations performed using state-of-the-art parton distribution functions. Additionally, the ratio of charm to anticharm production cross sections is studied to probe the $s$-$\bar{s}$ quark asymmetry. The ratio is found to be ${R}_{c}^{±}$= 0.971 ± 0.006 (stat) ± 0.011 (syst). The ratio and cross-section measurements are consistent with the predictions obtained with parton distribution function sets that have a symmetric $s$-$\bar{s}$ sea, indicating that any $s$-$\bar{s}$ asymmetry in the Bjorken-x region relevant for this measurement is small.

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

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 OSTI2023

Target jet substructure and correlation

We discuss the reconstruction of target jet and the framework of quantifying its internal substructure. Due to momentum and charge conservation, target and current correlation can be exploited which significantly constrains the event-wide particle distributions. We demonstrate this method using Pythia simulations of electron-proton collisions in the context of determining the flavor and substructure of the struck quark jet. Extensions to electron-ion collisions and target tagging using BeAGLE simulations will be discussed. This study will provide novel physics cases for forward detector designs and promote the synergy with nuclear physics.

Chien, Yang-Ting↗
DOE OSTI2022

Studies of BONuS12 Radial GEM Detector and TCS Beam Spin Asymmetry in CLAS12

The Barely Offshell Nucleon Structure (BONuS12) experiment adopted the concept of spectator tagging technique to study the nearly-free neutron structure function Fn2 in the CLAS12 of Jefferson Lab. A novel Radial Time Projection Chamber (RTPC) detector was built, tested and integrated into the CLAS12 system to detect a back-moving low momentum tagged proton in d(e,ep)X deep-inelastic scattering. It was a 40 cm long gaseous detector consisting of 3 layers of cylindrical GEM foils for the charge amplification, with the data readout directly from the surrounding padboard. The RTPC detected the recoiling spectator proton, in coincidence with the scattered electron in the CLAS12. Nucleon structure functions are directly related to the partonic functions, quarks momentum distribution in one dimension. A Generalized Parton Distribution (GPD) came to the lime-light as it encodes the information of both longitudinal momentum and transverse position of partons inside the nucleons. Factorization of hard process such as DVCS allows to access GPDs. Timelike Compton Scattering (TCS), ?p ? ??p, is another process that allows to access the GPDs. TCS is studied experimentally in the CLAS12 of Jefferson lab using the quasi-real photoproduction of time-like photon which eventually decays to lepton pair. This dissertation presents the concept of spectator tagging in BONuS12, and the research and development efforts during the BONuS12 preparation leading up to the successful data-taking during spring and summer 2020. In addition, analysis framework to extract the beam spin asymmetry of TCS events through the CLAS12 Run group A data is presented.

Poudel, Jiwan↗
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

Observation of Antiproton Annihilation at Rest in LArIAT

Antiproton annihilation at-rest can provide a unique probe into the intra-nuclear structure of nuclei. This process was first observed in the 1950’s using photographic emulsion and has since been observed and studied on a variety of nuclei. We present here the first observation and reconstruction of antiproton annihilation at-rest interactions on argon nuclei using data from the LArIAT experiment, a liquid argon time projection chamber (LArTPC). LArIAT was exposed to a charged particle test-beam at Fermilab from 2015-2017. Antiprotons tagged using LArIAT’s beamline instrumentation were reconstructed in the LArTPC, and the multiplicities of final-state particles emerging from the identified annihilation at-rest vertex were measured. These results will inform searches for neutron-antineutron oscillation events in the future LArTPCs like DUNE due to their similar topological signature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Arabidopsis myosin XIK interacts with the exocyst complex to facilitate vesicle tethering during exocytosis

Myosin motors are essential players in secretory vesicle trafficking and exocytosis in yeast and mammalian cells; however, similar roles in plants remain a matter for debate, at least for diffusely growing cells. Here, we demonstrate that Arabidopsis (Arabidopsis thaliana) myosin XIK, via its globular tail domain (GTD), participates in the vesicle tethering step of exocytosis through direct interactions with the exocyst complex. Specifically, myosin XIK GTD bound directly to several exocyst subunits in vitro and functional fluorescently tagged XIK colocalized with multiple exocyst subunits at plasma membrane (PM)-associated stationary foci. Moreover, genetic and pharmacological inhibition of myosin XI activity reduced the rate of appearance and lifetime of stationary exocyst complexes at the PM. By tracking single exocytosis events of cellulose synthase (CESA) complexes with high spatiotemporal resolution imaging and pair-wise colocalization of myosin XIK, exocyst subunits, and CESA6, we demonstrated that XIK associates with secretory vesicles earlier than exocyst and is required for the efficient localization and normal dynamic behavior of exocyst complex at the PM tethering site. This study reveals an important functional role for myosin XI in secretion and provides insights about the dynamic regulation of exocytosis in plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Proton reconstruction with the TOTEM Roman pot detectors for high- β * LHC data

The TOTEM Roman pot detectors are used to reconstruct the transverse momentum of scattered protons and to estimate the transverse location of the primary interaction. This paper presents new methods of track reconstruction, measurements of strip-level detection efficiencies, cross-checks of the LHC beam optics, and detector alignment techniques, along with their application in the selection of signal collision events. The track reconstruction is performed by exploiting hit cluster information through a novel method using a common polygonal area in the intercept-slope plane. The technique is applied in the relative alignment of detector layers with μm precision. A tag-and-probe method is used to extract strip-level detection efficiencies. The alignment of the Roman pot system is performed through time-dependent adjustments, resulting in a position accuracy of 3 μm in the horizontal and 60 μm in the vertical directions. The goal is to provide an optimal reconstruction tool for central exclusive physics analyses based on the high-β* data-taking period at $\sqrt({s})$ = 13 TeV in 2018.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Search for $ {\textrm{B}}_{\textrm{s}}^0 $ → ℓ ∓ τ ± with the semi-leptonic tagging method at Belle

We present a search for the lepton-flavor-violating decays ${B}_s^0$ → ℓ ∓ τ ± , where ℓ = e, μ, using the full data sample of 121 fb -1 collected at the Υ(5S) resonance with the Belle detector at the KEKB asymmetric-energy e + e - collider. We use ${B}_s^0{\overline{B}}_s^0$ events in which one ${B}_s^0$ meson is reconstructed in a semileptonic decay mode and the other in the signal mode. We find no evidence for (${B}_s^0$ → ℓ ∓ τ ± decays and set upper limits on their branching fractions at 90% confidence level as $\mathcal{B}$(${B}_s^0$ → e ∓ τ ± ) < 14 × 10 -4 and $\mathcal{B}$ (${B}_s^0$ → μ ∓ τ ± ) < 7.3 × 10 -4 . Our result represents the first upper limit on the ${B}_s^0$ → e ∓ τ ± decay rate.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Label-based Virtual Directories In dCache

Traditional filesystems organize data in directories. These directories are typically a collection of files whose grouping is based on a single criterion, e.g., the starting date of an experiment, experiment name, beamline ID, measurement device, or instrument. However, each file in a directory can belong to several logical groups, such as a special event type, experiment condition, or a part of a selected dataset. dCache is a storage system developed to store large amounts of scientific data, used by many HEP and Photon Science experiments. With recent developments in dCache, we have introduced a concept of file tagging, which dynamically groups files with the same label into virtual directories. The file labels can be added, removed, renamed, and deleted through the admin interface or via REST API. The files in virtual directories are exposed through all protocols supported by dCache. This contribution will describe the details of the implementation for file tagging in dCache and present our future development plans on automatic metadata extractions, a feature that will significantly simplify data management. Additionally, we are exploring the future use of virtual directories as a way to translate scientific data catalogs into filesystem views for direct data analysis.

Sahakyan, Marina [DESY]↗
DOE OSTI2020

A deep neural network to search for new long-lived particles decaying to jets

A tagging algorithm to identify jets that are significantly displaced from the proton-proton (pp) collision region in the CMS detector at the LHC is presented. Displaced jets can arise from the decays of long-lived particles (LLPs), which are predicted by several theoretical extensions of the standard model. The tagger is a multiclass classifier based on a deep neural network, which is parameterised according to the proper decay length $\mathrm{c}\tau_0$ of the LLP. A novel scheme is defined to reliably label jets from LLP decays for supervised learning. Samples of pp collision data, recorded by the CMS detector at a centre-of-mass energy of 13 TeV, and simulated events are used to train the neural network. Domain adaptation by backward propagation is performed to improve the simulation modelling of the jet class probability distributions observed in pp collision data. The potential performance of the tagger is demonstrated with a search for long-lived gluinos, a manifestation of split supersymmetric models. The tagger provides a rejection factor of 10 000 for jets from standard model processes, while maintaining an LLP jet tagging efficiency of 30-80% for gluinos with 1 mm $\leq$ $c\tau_0$ $\leq$ 10 m. The expected coverage of the parameter space for split supersymmetry is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

A Membrane-Associated Light-Harvesting Model is Enabled by Functionalized Assemblies of Gene-Doubled TMV Proteins

Photosynthetic light harvesting requires efficient energy transfer within dynamic networks of light-harvesting complexes embedded within phospholipid membranes. Artificial light-harvesting models are valuable tools for understanding the structural features underpinning energy absorption and transfer within chromophore arrays. Here, a method for attaching a protein-based light-harvesting model to a planar, fluid supported lipid bilayer (SLB) is developed. The protein model consists of the tobacco mosaic viral capsid proteins that are gene-doubled to create a tandem dimer (dTMV). Assemblies of dTMV break the facial symmetry of the double disk to allow for differentiation between the disk faces. A single reactive lysine residue is incorporated into the dTMV assemblies for the site-selective attachment of chromophores for light absorption. Further, on the opposing dTMV face, a cysteine residue is incorporated for the bioconjugation of a peptide containing a polyhistidine tag for association with SLBs. The dual-modified dTMV complexes show significant association with SLBs and exhibit mobility on the bilayer. The techniques used herein offer a new method for protein-surface attachment and provide a platform for evaluating excited state energy transfer events in a dynamic, fully synthetic artificial light-harvesting system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Dark Matter Search Results from 4.2 Tonne−Years of Exposure of the LUX-ZEPLIN (LZ) Experiment

We report results of a search for nuclear recoils induced by weakly interacting massive particle (WIMP) dark matter using the LUX-ZEPLIN (LZ) two-phase xenon time projection chamber. This analysis uses a total exposure of 4.2 ±0.1 tonne-years from 280 live days of LZ operation, of which 3.3 ± 0.1 tonne-years and 220 live days are new. A technique to actively tag background electronic recoils from 214 Pb 𝛽 decays is featured for the first time. Enhanced electron-ion recombination is observed in two-neutrino double electron capture decays of 124 Xe, representing a noteworthy new background. After removal of artificial signal-like events injected into the dataset to mitigate analyzer bias, we find no evidence for an excess over expected backgrounds. World-leading constraints are placed on spin-independent (SI) and spin-dependent WIMP-nucleon cross sections for masses ≥9 GeV/𝑐 2 . The strongest SI exclusion set is 2.2×10 −48 cm 2 at the 90% confidence level and the best SI median sensitivity achieved is 5.1 ×10 −48 cm 2 , both for a mass of 40 GeV/𝑐 2 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Study of High-Transverse-Momentum Higgs Boson Production in Association with a Vector Boson in the qqbb Final State with the ATLAS Detector

This Letter presents the first study of Higgs boson production in association with a vector boson (V=W or Z) in the fully hadronic qqbb final state using data recorded by the ATLAS detector at the LHC in proton-proton collisions at $\sqrt{s}$=13 TeV and corresponding to an integrated luminosity of 137 fb –1 . The vector bosons and Higgs bosons are each reconstructed as large-radius jets and tagged using jet substructure techniques. Dedicated tagging algorithms exploiting b-tagging properties are used to identify jets consistent with Higgs bosons decaying into $b\bar{b}$. Dominant backgrounds from multijet production are determined directly from the data, and a likelihood fit to the jet mass distribution of Higgs boson candidates is used to extract the number of signal events. The VH production cross section is measured inclusively and differentially in several ranges of Higgs boson transverse momentum: 250–450, 450–650, and greater than 650 GeV. The inclusive signal yield relative to the standard model expectation is observed to be μ=1.4$^{+1.0}_{–0.9}$ and the corresponding cross section is 3.1±1.3⁢(stat)$^{+1.8}_{–1.4}$(syst.) pb.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Measurement of the CP-violating phase ϕs in the B s 0 → J / ψ ϕ ( 1020 ) → μ + μ − K + K − channel in proton-proton collisions at s = 13 TeV

The CP -violating weak phase ϕ s and the decay width difference Δ Γ s between the light and heavy B s 0 mass eigenstates are measured with the CMS detector at the LHC in a sample of 48 500 reconstructed B s 0 → J / ψ ϕ ( 1020 ) → μ + μ − K + K − events. The measurement is based on a data sample corresponding to an integrated luminosity of 96.4 fb − 1 , collected in proton-proton collisions at s = 13 TeV in 2017–2018. To extract the values of ϕ s and Δ Γ s , a time-dependent and flavor-tagged angular analysis of the μ + μ − K + K − final state is performed. The analysis employs a dedicated tagging trigger and a novel opposite-side muon flavor tagger based on machine learning techniques. The measurement yields ϕ s = − 11 ± 50 (stat) ± 10 (syst) mrad and Δ Γ s = 0.114 ± 0.014 (stat) ± 0.007 (syst) ps − 1 , in agreement with the standard model predictions. When combined with the previous CMS measurement at s = 8 TeV , the following values are obtained: ϕ s = − 21 ± 44 (stat) ± 10 (syst) mrad , Δ Γ s = 0.1032 ± 0.0095 (stat) ± 0.0048 (syst) ps − 1 , a significant improvement over the 8 TeV result.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

First Search for Exclusive Diphoton Production at High Mass with Tagged Protons in Proton-Proton Collisions at $\sqrt{s} = 13$

A search for exclusive two-photon production via photon exchange in proton-proton collisions, pp $\to$ p$\gamma\gamma$p with intact protons, is presented. The data correspond to an integrated luminosity of 9.4 fb$^{-1}$ collected in 2016 using the CMS and TOTEM detectors at a center-of-mass energy of 13 TeV at the LHC. Events are selected with a diphoton invariant mass above 350 GeV and with both protons intact in the final state, to reduce backgrounds from strong interactions. The events of interest are those where the invariant mass and rapidity calculated from the momentum losses of the forward-moving protons matches the mass and rapidity of the central, two-photon system. No events are found that satisfy this condition. Interpreting this result in an effective dimension-8 extension of the standard model, the first limits are set on the two anomalous four-photon coupling parameters. If the other parameter is constrained to its standard model value, the limits at 95% CL are $\lvert\zeta_1\rvert$ $\lt$ 2.9 $\times$ 10$^{-13}$ GeV$^{-4}$ and $\lvert\zeta_2\rvert$ $\lt$ 6.0 $\times$ 10$^{-13}$ GeV$^{-4}$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗