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At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

SpeX spectroscopy of unresolved very low mass binaries. II. Identification of 14 candidate binaries with late-M/early-L and T dwarf components

Multiplicity is a key statistic for understanding the formation of very low mass (VLM) stars and brown dwarfs. Currently, the separation distribution of VLM binaries remains poorly constrained at small separations (≤1 AU), leading to uncertainty in the overall binary fraction. We approach this problem by searching for late-M/early-L plus T dwarf spectral binaries whose combined light spectra exhibit distinct peculiarities, allowing for separation-independent identification. We define a set of spectral indices designed to identify these systems, and we use a spectral template fitting method to confirm and characterize spectral binary candidates from a library of 815 spectra from the SpeX Prism Spectral Libraries. We present 11 new binary candidates, confirm 3 previously reported candidates, and rule out 2 previously identified candidates, all with primary and secondary spectral types in the range M7-L7 and T1-T8, respectively. We find that subdwarfs and blue L dwarfs are the primary contaminants in our sample and propose a method for segregating these sources. If confirmed by follow-up observations, these systems may add to the growing list of tight separation binaries, whose orbital properties may yield further insight into brown dwarf formation scenarios.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

THE SUB-mJy RADIO POPULATION OF THE E-CDFS: OPTICAL AND INFRARED COUNTERPART IDENTIFICATION

We study a sample of 883 sources detected in a deep Very Large Array survey at 1.4 GHz in the Extended Chandra Deep Field South. This paper focuses on the identification of their optical and infrared (IR) counterparts. We use a likelihood-ratio technique that is particularly useful when dealing with deep optical images to minimize the number of spurious associations. We find a reliable counterpart for 95% of our radio sources. Most of the counterparts (74%) are detected at optical wavelengths, but there is a significant fraction (21%) that are only detectable in the IR. Combining newly acquired optical spectra with data from the literature, we are able to assign a redshift to 81% of the identified radio sources (37% spectroscopic). We also investigate the X-ray properties of the radio sources using the Chandra 4 Ms and 250 ks observations. In particular, we use a stacking technique to derive the average properties of radio objects undetected in the Chandra images. The results of our analysis are collected in a new catalog containing the position of the optical/IR counterpart, the redshift information, and the X-ray fluxes. It is the deepest multi-wavelength catalog of radio sources, which will be used for future study of this galaxy population.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

AN ATLAS OF FAR-ULTRAVIOLET SPECTRA OF THE ZETA AURIGAE BINARY 31 CYGNI WITH LINE IDENTIFICATIONS

The ζ Aurigae system 31 Cygni (K4 Ib + B4 V) was observed by the FUSE satellite during total eclipse and at three phases during chromospheric eclipse. We present the coadded, calibrated spectra and atlases with line identifications. During total eclipse, emission from high ionization states (e.g., Fe III and Cr III) shows asymmetric profiles redshifted from the systemic velocity, while emission from lower ionization states (e.g., Fe II and O I) appears more symmetric and is centered closer to the systemic velocity. Absorption from neutral and singly ionized elements is detected during chromospheric eclipse. Late in chromospheric eclipse, absorption from the K star wind is detected at a terminal velocity of ∼80 km s{sup –1}. These atlases will be useful for interpreting the far-UV spectra of other ζ Aur systems, as the observed FUSE spectra of 32 Cyg, KQ Pup, and VV Cep during chromospheric eclipse resemble that of 31 Cyg.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Error field identification through torque balance on a saturated island in DIII-D

Measurement of the electromagnetic torque on a magnetic island could be an attractive method for error field identification in the early phase of ITER operation. Previous DIII-D experiments (Strait 2014 Nucl. Fusion 54 073004; Shiraki et al 2015 Plasma Phys. Control. Fusion 57 025016) have demonstrated the principle of this approach using a stationary or slowly rotating island, while recent developments in magnetic data analysis (Sweeney and Strait 2019 Phys. Plasmas 26 012509) allow the field of a rapidly rotating island to be readily distinguished from that of the wall currents induced by its rotation. In a recent experiment, a rotating n = 1 magnetic perturbation forced a saturated magnetic island to rotate, thus sampling all toroidal phases periodically in a single discharge. The phase and amplitude of the error field are inferred from analysis of the time-dependent torque balance on the island, including torques from the error field, the applied magnetic perturbation, and the wall currents induced by rotation of the applied perturbation and the island. Furthermore, results agree well with those from more conventional methods.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Quantum process identification: a method for characterizing non-markovian quantum dynamics

Established methods for characterizing quantum information processes do not capture non-Markovian (history-dependent) behaviors that occur in real systems. These methods model a quantum process as a fixed map on the state space of a predefined system of interest. Such a map averages over the system's environment, which may retain some effect of its past interactions with the system and thus have a history-dependent influence on the system. Although the theory of non-Markovian quantum dynamics is currently an active area of research, a systematic characterization method based on a general representation of non-Markovian dynamics has been lacking. In this article we present a systematic method for experimentally characterizing the dynamics of open quantum systems. Our method, which we call quantum process identification (QPI), is based on a general theoretical framework which relates the (non-Markovian) evolution of a system over an extended period of time to a time-local (Markovian) process involving the system and an effective environment. In practical terms, QPI uses time-resolved tomographic measurements of a quantum system to construct a dynamical model with as many dynamical variables as are necessary to reproduce the evolution of the system. Through numerical simulations, we demonstrate that QPI can be used to characterize qubit operations with non-Markovian errors arising from realistic dynamics including control drift, coherent leakage, and coherent interaction with material impurities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Identification of S VIII through S XIV emission lines between 17.5 and 50 nm in a magnetically confined plasma

43 spectral emission lines from F-like to Li-like sulphur ions have been identified in the wavelength range from 17.5 to 50 nm in spectra obtained following tracer injection into plasmas created in a magnetically confined plasma device, the stellarator TJ-II. Plasmas created and maintained in this heliac device with electron cyclotron resonance heating achieve central electron temperatures and densities up to 1.5 keV and 8 × 10 18 m –3 , respectively. Tracer injections were performed with ≤6 × 10 16 atoms of sulphur contained within ~300 μm diameter polystyrene capsules, termed tracer encapsulated solid pellets, using a gas propulsion system to achieve velocities between 250 and 450 m s –1 . Once ablation of the exterior polystyrene shell by plasma particles is completed, the sulphur is deposited in the plasma core where it is ionized up to S +13 and transported about the plasma. In order to aid line identification, which is made using a number of atomic line emission databases, spectra are collected before and after injection using a 1 m focal length normal incidence spectrometer equipped with a CCD camera. Furthermore, this work is motivated by the need to clearly identify sulphur emission lines in the vacuum ultraviolet range of magnetically confined plasmas, as sulphur x-ray emission lines are regularly observed in both tokamak and stellarator plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Identifications of RR Lyrae Stars and Quasars from the Simulated Data of Mephisto-W Survey

We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope (Mephisto) W Survey. Based on the variable sources light curve libraries from the Sloan Digital Sky Survey (SDSS) Stripe 82 data and the observation history simulation from the Mephisto-W Survey Scheduler, we have simulated the uvgriz multi-band light curves of RR Lyrae stars, quasars and other variable sources for the first-year observation of Mephisto W Survey. We have applied the ensemble machine learning algorithm Random Forest Classifier (RFC) to identify RR Lyrae stars and quasars, respectively. We build training and test samples and extract ~150 features from the simulated light curves and train two RFCs respectively for the RR Lyrae star and quasar classification. We find that, our RFCs are able to select the RR Lyrae stars and quasars with remarkably high precision and completeness, with purity = 95.4% and completeness = 96.9% for the RR Lyrae RFC and purity = 91.4% and completeness = 90.2% for the quasar RFC. In conclusion, we have also derived relative importances of the extracted features utilized to classify RR Lyrae stars and quasars.

(galaxies:) quasars: general↗
DOE OSTI2021

Identification of multiple eigenmode growth rates towards real time detection in DIII-D and KSTAR tokamak plasmas

Abstract The successful application of three-dimensional (3D) magnetohydrodynamic (MHD) spectroscopy enables us to identify the multi-mode eigenvalues in DIII-D and KSTAR tokamak experiments with stable plasmas. The temporal evolution of the multi-modes’ stabilities have been detected. The new method is numerically efficient allowing the real time detection of MHD modes’ stabilities during the discharge. The method performs active detection of the plasma stability by utilizing the upper and lower rows of internal non-axisymmetric coils to apply a wide variety of 3D fields. Multi-mode eigenvalues are extracted using subspace system identification of the plasma response measured by 3D-field magnetic sensors distributed at different poloidal locations. The equivalence of this new method with the one introduced by Wang (2019 Nucl. Fusion 59 024001) has been numerically corroborated. The more robust and efficient calculation developed here will enable real time monitoring of the plasma stability based on the extracted eigenvalues of stable modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Reconstruction and identification of $\tau$ lepton decays to hadrons and $\nu_\tau$ at CMS

This paper describes the algorithms used by the CMS experiment to reconstruct and identify τ→ hadrons + v t decays during Run 1 of the LHC. The performance of the algorithms is studied in proton-proton collisions recorded at a centre-of-mass energy of 8 TeV, corresponding to an integrated luminosity of 19.7 fb -1 . The algorithms achieve an identification efficiency of 50–60%, with misidentification rates for quark and gluon jets, electrons, and muons between per mille and per cent levels.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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 OSTI2013

Identification of b-quark jets with the CMS experiment

At the Large Hadron Collider, the identification of jets originating from b quarks is important for searches for new physics and for measurements of standard model processes. A variety of algorithms has been developed by CMS to select b-quark jets based on variables such as the impact parameters of charged-particle tracks, the properties of reconstructed decay vertices, and the presence or absence of a lepton, or combinations thereof. The performance of these algorithms has been measured using data from proton-proton collisions at the LHC and compared with expectations based on simulation. The data used in this study were recorded in 2011 at √s = 7 TeV for a total integrated luminosity of 5.0 fb(-1). The efficiency for tagging b-quark jets has been measured in events from multijet and t-quark pair production. CMS has achieved a b-jet tagging efficiency of 85% for a light-parton misidentification probability of 10% in multijet events. For analyses requiring higher purity, a misidentification probability of only 1.5% has been achieved, for a 70% b-jet tagging efficiency.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

IDENTIFICATION OF THE OPTICAL COUNTERPART OF FERMI BLACK WIDOW MILLISECOND PULSAR PSR J1544+4937

We report the optical identification of the companion to the Fermi black widow millisecond pulsar PSR J1544+4937. We find a highly variable source on Keck Low Resolution Imaging Spectrometer images at the nominal pulsar position, with 2 mag variations over orbital period in the B, g, R, and I bands. The nearly achromatic light curves are difficult to explain with a simply irradiated hemisphere model, and suggest that the optical emission is dominated by a nearly isothermal hot patch on the surface of the companion facing the pulsar. We roughly constrain the distance to PSR J1544+4937 to be between 2 and 5 kpc. A more reliable distance measurement is needed in order to constrain the composition of the companion.

79 ASTRONOMY AND ASTROPHYSICS↗
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↗