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At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at $\sqrt{s}$ = 13TeV, corresponding to an integrated luminosity of 35.9 fb -1 . Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC

The performance is presented of the reconstruction and identification algorithms for electrons and photons with the CMS experiment at the LHC. The reported results are based on proton-proton collision data collected at a center-of-mass energy of 13 TeV and recorded in 2016–2018, corresponding to an integrated luminosity of 136 fb^-1. Results obtained from lead-lead collision data collected at √(sNN)=5.02 TeV are also presented. Innovative techniques are used to reconstruct the electron and photon signals in the detector and to optimize the energy resolution. Events with electrons and photons in the final state are used to measure the energy resolution and energy scale uncertainty in the recorded events. The measured energy resolution for electrons produced in Z boson decays in proton-proton collision data ranges from 2 to 5%, depending on electron pseudorapidity and energy loss through bremsstrahlung in the detector material. The energy scale in the same range of energies is measured with an uncertainty smaller than 0.1 (0.3)% in the barrel (endcap) region in proton-proton collisions and better than 1 (3)% in the barrel (endcap) region in heavy ion collisions. The timing resolution for electrons from Z boson decays with the full 2016–2018 proton-proton collision data set is measured to be 200 ps.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Identification of charm jets at LHCb

The identification of charm jets is achieved at LHCb for data collected in 2015–2018 using a method based on the properties of displaced vertices reconstructed and matched with jets. The performance of this method is determined using a dijet calibration dataset recorded by the LHCb detector and selected such that the jets are unbiased in quantities used in the tagging algorithm. The charm-tagging efficiency is reported as a function of the transverse momentum of the jet. The measured efficiencies are compared to those obtained from simulation and found to be in good agreement.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV

“Soft” muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously suppressing backgrounds from long-lived light-flavor hadron decays, is therefore important for the success of the CMS physics program. Multivariate techniques have been shown to deliver better muon identification performance than traditional selection techniques. To take full advantage of the large data set currently being collected during Run 3 of the CERN LHC, a new multivariate classifier based on a gradient-boosted decision tree has been developed. It offers a significantly improved separation of signal and background muons compared to a similar classifier used for the analysis of the Run 2 data. The performance of the new classifier is evaluated on a data set collected with the CMS detector in 2022 and 2023, corresponding to an integrated luminosity of 62 fb -1 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Line identification of boron and nitrogen emissions in EUV and VUV wavelength ranges in the impurity powder dropping experiments of LHD and its application to spectroscopic diagnostics

An impurity powder dropper was installed in the 21st campaign of the Large Helical Device experiment (Oct. 2019–Feb. 2020) under a collaboration between the National Institute for Fusion Science and the Princeton Plasma Physics Laboratory for the purposes of real-time wall conditioning and edge plasma control. In order to assess the effective injection of the impurity powders, spectroscopic diagnostics were applied to observe line emission from the injected impurity. Therefore, extreme-ultraviolet (EUV) and vacuum-ultraviolet (VUV) emission spectra were analyzed to summarize observable impurity lines with B and BN powder injection. Emission lines released from B and N ions were identified in the EUV wavelength range of 5–300 Å measured using two grazing incidence flat-field EUV spectrometers and in the VUV wavelength range of 300–2400 Å measured using three normal incidence 20 cm VUV spectrometers. BI–BV and NIII–NVII emission lines were identified in the discharges with the B and BN powder injection, respectively. Useful B and N emission lines which have large intensities and are isolated from other lines were successfully identified as follows: BI (1825.89, 1826.40) Å (blended), BII 1362.46 Å, BIII (677.00, 677.14, 677.16) Å (blended), BIV 60.31 Å, BV 48.59 Å, NIII (989.79, 991.51, 991.58) Å (blended), NIV 765.15 Å, NV (209.27, 209.31) Å (blended), NVI 1896.80 Å, and NVII 24.78 Å. Applications of the line identifications to the advanced spectroscopic diagnostics were demonstrated, such as the vertical profile measurements for the BV and NVII lines using a space-resolved EUV spectrometer and the ion temperature measurement for the BII line using a normal incidence 3 m VUV spectrometer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Lightweight jet reconstruction and identification as an object detection task

We apply object detection techniques based on deep convolutional blocks to end-to-end jet identification and reconstruction tasks encountered at the CERN large hadron collider (LHC). Collision events produced at the LHC and represented as an image composed of calorimeter and tracker cells are given as an input to a Single Shot Detection network. The algorithm, named PFJet-SSD performs simultaneous localization, classification and regression tasks to cluster jets and reconstruct their features. This all-in-one single feed-forward pass gives advantages in terms of execution time and an improved accuracy w.r.t. traditional rule-based methods. A further gain is obtained from network slimming, homogeneous quantization, and optimized runtime for meeting memory and latency constraints of a typical real-time processing environment. We experiment with 8-bit and ternary quantization, benchmarking their accuracy and inference latency against a single-precision floating-point. We show that the ternary network closely matches the performance of its full-precision equivalent and outperforms the state-of-the-art rule-based algorithm. Finally, we report the inference latency on different hardware platforms and discuss future applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Deep graph representations embed network information for robust disease marker identification

We report that the accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and progression. In this article, we present GCNCC, a Graph Convolutional Network-based approach for Clustering and Classification, that can identify highly effective and robust network-based disease markers. Based on a geometric deep learning framework, GCNCC learns deep network representations by integrating gene expression data with protein interaction data to identify highly reproducible markers with consistently accurate prediction performance across independent datasets possibly from different platforms. GCNCC identifies these markers by clustering the nodes in the protein interaction network based on latent similarity measures learned by the deep architecture of a graph convolutional network, followed by a supervised feature selection procedure that extracts clusters that are highly predictive of the disease state. By benchmarking GCNCC based on independent datasets from different diseases (psychiatric disorder and cancer) and different platforms (microarray and RNA-seq), we show that GCNCC outperforms other state-of-the-art methods in terms of accuracy and reproducibility.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

MINE 2.0: enhanced biochemical coverage for peak identification in untargeted metabolomics

Abstract Summary Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource. Availability and implementation The MINE 2.0 website can be accessed at https://minedatabase.ci.northwestern.edu. The MINE 2.0 web API documentation can be accessed at https://mine-api.readthedocs.io/en/latest/. The data and code underlying this article are available in the MINE-2.0-Paper repository at https://github.com/tyo-nu/MINE-2.0-Paper. MINE 2.0 source code can be accessed at https://github.com/tyo-nu/MINE-Database (MINE construction), https://github.com/tyo-nu/MINE-Server (backend web API) and https://github.com/tyo-nu/MINE-app (web app). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Dark Energy Survey Identification of A Low-Mass Active Galactic Nucleus at Redshift 0.823 from Optical Variability

We report the identification of a low-mass active galactic nucleus (AGN), DES J0218−0430, in a redshift z = 0.823 galaxy in the Dark Energy Survey (DES) Supernova field. We select DES J0218−0430 as an AGN candidate by characterizing its long-term optical variability alone based on DES optical broad-band light curves spanning over 6 yr. An archival optical spectrum from the fourth phase of the Sloan Digital Sky Survey shows both broad Mg ii and broad H β lines, confirming its nature as a broad-line AGN. Archival XMM–Newton X-ray observations suggest an intrinsic hard X-ray luminosity of |$L_{{\rm 2-12\, keV}}\approx 7.6\pm 0.4\times 10^{43}$| erg s^−1, which exceeds those of the most X-ray luminous starburst galaxies, in support of an AGN driving the optical variability. Based on the broad H β from SDSS spectrum, we estimate a virial black hole (BH) mass of M_• ≈ 10^6.43–10^6.72 M_⊙ (with the error denoting the systematic uncertainty from different calibrations), consistent with the estimation from OzDES, making it the lowest mass AGN with redshift > 0.4 detected in optical. We estimate the host galaxy stellar mass to be M_* ≈ 10^10.5 ± 0.3 M_⊙ based on modelling the multiwavelength spectral energy distribution. DES J0218−0430 extends the M_•–M_* relation observed in luminous AGNs at z ∼ 1 to masses lower than being probed by previous work. Our work demonstrates the feasibility of using optical variability to identify low-mass AGNs at higher redshift in deeper synoptic surveys with direct implications for the upcoming Legacy Survey of Space and Time at Vera C. Rubin Observatory.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

El-CID: a filter for gravitational-wave electromagnetic counterpart identification

ABSTRACT As gravitational-wave (GW) interferometers become more sensitive and probe ever more distant reaches, the number of detected binary neutron star mergers will increase. However, detecting more events farther away with GWs does not guarantee corresponding increase in the number of electromagnetic counterparts of these events. Current and upcoming wide-field surveys that participate in GW follow-up operations will have to contend with distinguishing the kilonova (KN) from the ever increasing number of transients they detect, many of which will be consistent with the GW sky-localization. We have developed a novel tool based on a temporal convolutional neural network architecture, trained on sparse early-time photometry and contextual information for Electromagnetic Counterpart Identification (El-CID). The overarching goal for El-CID is to slice through list of new transient candidates that are consistent with the GW sky localization, and determine which sources are consistent with KNe, allowing limited target-of-opportunity resources to be used judiciously. In addition to verifying the performance of our algorithm on an extensive testing sample, we validate it on AT2017gfo – the only EM counterpart of a binary neutron star merger discovered to date – and AT2019npv – a supernova that was initially suspected as a counterpart of the GW event, GW190814, but was later ruled out after further analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

SDSS-IV MaNGA: Identification and multiwavelength properties of Type-1 AGN in the DR15 sample

ABSTRACT We present a method to identify type-1 active galactic nuclei (AGNs) in the central 3 arcsec integrated spectra of galaxies in the MaNGA DR15 sample. It is based on flux ratios estimates in spectral bands flanking the expected h α broad component h αBC. The high signal-to-noise ratio obtained (mean S/N = 84) permits the identification of h αBC without prior subtraction of the host galaxy (HG) stellar component. A final sample of 47 type-1 AGN is reported out of 4700 galaxies at z < 0.15. The results were compared with those from other methods based on the SDSS DR7 and MaNGA data. Detection of type-1 AGN in those works compared to our method goes from 26 per cent to 81 per cent. Spectral indexes were used to classify the type-1 AGN spectra according to different levels of AGN-HG contribution, finding 9 AGN-dominated, 14 intermediate, and 24 HG-dominated objects. Complementary data in NIR-MIR allowed us to identify type I AGN-dominated objects as blue and HG-dominated as red in the WISE colours. From NVSS and FIRST radio continuum data, we identify 5 HERGs (high-excitation radio galaxies) and 4 LERGs (low-excitation radio galaxies), three showing evidence of radio-jets in the FIRST maps. Additional X-ray data from ROSAT allowed us to build [O iii] and h αBC versus X-ray, NIR-MIR, and radio continuum diagrams, showing that L(h αBC) and L([O iii]) provide good correlations. The range in h αBC luminosity is wide 38 < logL(h αBC) < 44, with log FWHM(h αBC) ∼ 3–4, covering a range of Eddington ratios of −5.15 < log Lbol/LEdd < 0.70. Finally, we also identify and report ten possible changing-look AGN candidates.

Cortes-Suárez, Edgar↗
DOE OSTI2022

Identification and characterization of the WYL BrxR protein and its gene as separable regulatory elements of a BREX phage restriction system

Bacteriophage exclusion (‘BREX’) phage restriction systems are found in a wide range of bacteria. Various BREX systems encode unique combinations of proteins that usually include a site-specific methyltransferase; none appear to contain a nuclease. Here we describe the identification and characterization of a Type I BREX system from Acinetobacter and the effect of deleting each BREX ORF on growth, methylation, and restriction. We identified a previously uncharacterized gene in the BREX operon that is dispensable for methylation but involved in restriction. Biochemical and crystallographic analyses of this factor, which we term BrxR (‘BREX Regulator’), demonstrate that it forms a homodimer and specifically binds a DNA target site upstream of its transcription start site. Deletion of the BrxR gene causes cell toxicity, reduces restriction, and significantly increases the expression of BrxC. In contrast, the introduction of a premature stop codon into the BrxR gene, or a point mutation blocking its DNA binding ability, has little effect on restriction, implying that the BrxR coding sequence and BrxR protein play independent functional roles. We speculate that elements within the BrxR coding sequence are involved in cis regulation of anti-phage activity, while the BrxR protein itself plays an additional regulatory role, perhaps during horizontal transfer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Metabolic Source Isotopic Pair Labeling and Genome-Wide Association Are Complementary Tools for the Identification of Metabolite-Gene Associations in Plants

The optimal extraction of information from untargeted metabolomics analyses is a continuing challenge. Here, we describe an approach that combines stable isotope labeling, liquid chromatography– mass spectrometry (LC–MS), and a computational pipeline to automatically identify metabolites produced from a selected metabolic precursor. Here, we identified the subset of the soluble metabolome generated from phenylalanine (Phe) in Arabidopsis thaliana, which we refer to as the Phe-derived metabolome (FDM) In addition to identifying Phe-derived metabolites present in a single wild-type reference accession, the FDM was established in nine enzymatic and regulatory mutants in the phenylpropanoid pathway. To identify genes associated with variation in Phe-derived metabolites in Arabidopsis, MS features collected by untargeted metabolite profiling of an Arabidopsis diversity panel were retrospectively annotated to the FDM and natural genetic variants responsible for differences in accumulation of FDM features were identified by genome-wide association. Large differences in Phe-derived metabolite accumulation and presence/absence variation of abundant metabolites were observed in the nine mutants as well as between accessions from the diversity panel. Many Phe-derived metabolites that accumulated in mutants also accumulated in non-Col-0 accessions and was associated to genes with known or suspected functions in the phenylpropanoid pathway as well as genes with no known functions. Overall, we show that cataloguing a biochemical pathway’s products through isotopic labeling across genetic variants can substantially contribute to the identification of metabolites and genes associated with their biosynthesis.

09 BIOMASS FUELS↗
DOE OSTI2020

Bayesian differential programming for robust systems identification under uncertainty

This paper presents a machine learning framework for Bayesian systems identification from noisy, sparse and irregular observations of nonlinear dynamical systems. The proposed method takes advantage of recent developments in differentiable programming to propagate gradient information through ordinary differential equation solvers and perform Bayesian inference with respect to unknown model parameters using Hamiltonian Monte Carlo sampling. This allows an efficient inference of the posterior distributions over plausible models with quantified uncertainty, while the use of sparsity-promoting priors enables the discovery of interpretable and parsimonious representations for the underlying latent dynamics. A series of numerical studies is presented to demonstrate the effectiveness of the proposed methods, including nonlinear oscillators, predator–prey systems and examples from systems biology. Taken together, our findings put forth a flexible and robust workflow for data-driven model discovery under uncertainty. All codes and data accompanying this article are available at https://bit.ly/34FOJMj .

Science & Technology - Other Topics↗
DOE OSTI2025

Genomic reconstruction of Bacillus anthracis from complex environmental samples enables high-throughput identification and lineage assignment in Pakistan

Bacillus anthracis, the causative agent of anthrax, is a highly virulent zoonotic pathogen primarily affecting domesticated and wild herbivores. Human exposure to B. anthracis is primarily through contact with infected animals or contaminated animal products. In Pakistan, where livestock vaccines are largely unavailable and infected carcasses are often disposed of improperly, the risk to humans, wildlife and livestock is significant. Currently, the diagnosis of anthrax infections and outbreak tracing necessitates the isolation and culturing of B. anthracis, a process that requires BSL-3 facilities. In this study, we show that positive identification, genome reconstruction and lineage assignment can be accomplished using bioinformatic analysis of DNA extracted directly from environmental samples that would otherwise provide the starting material for isolation and culturing. This approach does not require laboratory target enrichment as is necessary for other pathogens, due in part to the extremely high bacterial load in the bloodstream in the deceased animals. Using these methods, we greatly expand the knowledge of endemic B. anthracis in Pakistan. We provide the first reference B. anthracis genomes from Pakistan since the 1970s and identify A.Br.014 Aust94 as a minor circulating sublineage alongside the dominant A.Br.047 Vollum. Future work will focus on the limits of detection and will determine if this bioinformatic method can be expanded more broadly for B. anthracis or other pathogens to replace typical culture-based methods.

A.Br.047 Vollum↗
DOE OSTI2026

Highly boosted dielectron identification in proton-proton collisions at $\sqrt{s}$ = 13 TeV

A new technique is developed to identify dielectrons (e$^+$e$^-$) with Lorentz boost $γ_\mathrm{L}$$\gt$ 20 that produce one single merged cluster in the electromagnetic calorimeter of the CMS detector. The identification uses two multivariate models: one for the case where both electron tracks are reconstructed, and another where only one of the tracks is reconstructed. The efficiency is determined using proton-proton collision data collected at a center-of-mass energy of 13 TeV. Boosted J/$ψ$ mesons decaying into e$^+$e$^-$ pairs are used to estimate the efficiency of the model with two tracks, yielding an overall efficiency of 80%. The Z $\to$ $μ^+μ^-γ$ events, where the photon converts into a collimated dielectron, are used for the model with a single track, yielding an efficiency of about 60%. A dedicated energy correction for dielectron candidates is also developed using B$^\pm$ $\to$ J/$ψ$K$^\pm$ $\to$ e$^+$e$^-$K$^\pm$ data.

Hayrapetyan, A. [Yerevan Phys. Inst.]↗
DOE OSTI2020

Interaction networks for the identification of boosted H → $b\overline b$ decays

We develop an algorithm based on an interaction network to identify high-transverse-momentum Higgs bosons decaying to bottom quark-antiquark pairs and distinguish them from ordinary jets that reflect the configurations of quarks and gluons at short distances. The algorithm's inputs are features of the reconstructed charged particles in a jet and the secondary vertices associated with them. Describing the jet shower as a combination of particle-to-particle and particle-to-vertex interactions, the model is trained to learn a jet representation on which the classification problem is optimized. The algorithm is trained on simulated samples of realistic LHC collisions, released by the CMS Collaboration on the CERN Open Data Portal. The interaction network achieves a drastic improvement in the identification performance with respect to state-of-the-art algorithms.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Novel method for the identification of the production flavor of neutral charmed mesons

We propose a new algorithm for the identification of the production flavor of neutral D mesons in the Belle II experiment. The algorithm exploits the correlation between the flavor of a reconstructed neutral D meson (signal D meson) and the electric charges of particles reconstructed in the rest of the e + e – →$c\bar{c}$ event. These include those originating from the decay of the other charm hadron produced in the event, as well as those possibly produced in association with the signal D meson. We develop the algorithm using simulation and calibrate it in data using decay modes that identify the flavor of the decaying neutral D meson. We use a data sample of e + e – collisions, corresponding to 362 fb –1 of integrated luminosity, collected by Belle II at center-of-mass energies near the Υ(4S) mass. The effective tagging efficiency in data is (47.91±0.07 (stat) ±0.51 (syst)) %, independent of the neutral-D-meson decay mode. This charm flavor tagger will approximately double the effective sample size of many CP-violation and charm-mixing measurements that so far have exclusively relied on neutral D mesons originating from D* ± decays. While developed for Belle II, the basic principles underlying the charm flavor tagger can be used in other experiments, including those at hadron colliders.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗