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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

DELVE 6: An Ancient, Ultra-faint Star Cluster on the Outskirts of the Magellanic Clouds

Abstract We present the discovery of DELVE 6, an ultra-faint stellar system identified in the second data release of the DECam Local Volume Exploration (DELVE) survey. Based on a maximum-likelihood fit to its structure and stellar population, we find that DELVE 6 is an old ( τ > 9.8 Gyr at 95% confidence) and metal-poor ([Fe/H] < −1.17 dex at 95% confidence) stellar system with an absolute magnitude of M V = − 1.5 − 0.6 + 0.4 mag and an azimuthally averaged half-light radius of r 1 / 2 = 10 − 3 + 4 pc. These properties are consistent with the population of ultra-faint star clusters uncovered by recent surveys. Interestingly, DELVE 6 is located at an angular separation of ∼10° from the center of the Small Magellanic Cloud (SMC), corresponding to a 3D physical separation of ∼20 kpc given the system’s observed distance ( D ⊙ = 80 kpc). This also places the system ∼35 kpc from the center of the Large Magellanic Cloud (LMC), lying within recent constraints on the size of the LMC’s dark matter halo. We tentatively measure the proper motion of DELVE 6 using data from Gaia, which we find supports a potential association between the system and the LMC/SMC. Although future kinematic measurements will be necessary to determine its origins, we highlight that DELVE 6 may represent only the second or third ancient ( τ > 9 Gyr) star cluster associated with the SMC, or one of fewer than two dozen ancient clusters associated with the LMC. Nonetheless, we cannot currently rule out the possibility that the system is a distant Milky Way halo star cluster.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Cardinality-Regularized Hawkes-Granger Model

We propose a new sparse Granger-causal learning framework for temporal event data. We focus on a specific class of point processes called the Hawkes process. We begin by pointing out that most of the existing sparse causal learning algorithms for the Hawkes process suffer from a singularity in maximum likelihood estimation. As a result, their sparse solutions can appear only as numerical artifacts. In this paper, we propose a mathematically well-defined sparse causal learning framework based on a cardinality-regularized Hawkes process, which remedies the pathological issues of existing approaches. We leverage the proposed algorithm for the task of instance-wise causal event analysis, where sparsity plays a critical role. We validate the proposed framework with two real use-cases, one from the power grid and the other from the cloud data center management domain.

Ide, Tsuyoshi↗
DOE OSTI2024

High-precision measurement of the W boson mass with the CMS experiment at the LHC

In the standard model of particle physics, the masses of the carriers of the weak interaction, the W and Z bosons, are uniquely related. Physics beyond the standard model could change this relationship through the effects of quantum loops of virtual particles, thus making it of great importance to measure these masses with the highest possible precision. Although the mass of the Z boson is known to the remarkable precision of 22 parts per million (2.0 MeV), the W boson mass is known much less precisely, given the difficulty of the measurement. A global fit to electroweak data, used to predict the W boson mass in the standard model, yields an uncertainty of 6 MeV. Reaching a comparable experimental precision would be a sensitive and fundamental test of the standard model. Furthermore, a precision measurement of the W boson mass performed by the CDF Collaboration at the Fermilab Tevatron has challenged the standard model by significantly disagreeing with the prediction of the global electroweak fit and the average of other $m_\mathrm{W}$ measurements. We report the first W boson mass measurement by the CMS Collaboration at the CERN LHC, based on a data sample collected in 2016 at the proton-proton collision energy of 13 TeV. The W boson mass is measured using a large sample of W$\toμν$ events via a highly granular binned maximum likelihood fit to the kinematic properties of the muons produced in the W$^{+}$ and W$^{-}$ boson decays. The significant in situ constraints of theoretical inputs and their corresponding uncertainties, together with an accurate determination of the experimental effects, lead to a precise W boson mass measurement, $m_\mathrm{W} =$ 80$\,$360.2 $\pm$ 9.9 MeV, in agreement with the standard model prediction.

FOS: Physical sciences↗
DOE OSTI2023

Performance of methods for SARS-CoV-2 variant detection and abundance estimation within mixed population samples

The accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (e.g., air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja). We simulated DNA sequence datasets of known variant composition from both Illumina and Oxford Nanopore Technologies (ONT) platforms and assessed the performance of each VCE. We also evaluated VCEs performance using publicly available empirical wastewater samples collected for SC2 surveillance efforts. Bioinformatic analyses were performed with a custom NextFlow workflow (C-WAP, CFSAN Wastewater Analysis Pipeline). Relative root mean squared error (RRMSE) was used as a measure of performance with respect to the known abundance and concordance correlation coefficient (CCC) was used to measure agreement between pairs of estimators. Based on our results from simulated data, Kallisto was the most accurate estimator as it had the lowest RRMSE, followed by Freyja. Kallisto and Freyja had the most similar predictions, reflected by the highest CCC metrics. We also found that accuracy was platform and amplicon panel dependent. For example, the accuracy of Freyja was significantly higher with Illumina data compared to ONT data; performance of Kallisto was best with ARTICv4. However, when analyzing empirical data there was poor agreement among methods and variations in the number of variants detected (e.g., Freyja ARTICv4 had a mean of 2.2 variants while Kallisto ARTICv4 had a mean of 10.1 variants). This work provides an understanding of the differences in performance of a number of VCEs and how accurate they are in capturing the relative abundance of SC2 variants within a mixed sample (e.g., wastewater). Such information should help officials gauge the confidence they can have in such data for informing public health decisions.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1960

Tracking and Orbit-Determination Program of the Jet Propulsion Laboratory

The lunar-probe tracking program at the Jet Propulsion Laboratory has two prime objectives: (1) provide real-time predictions of the direction of the probe from various observation stations; (2) establish a reliable trajectory corresponding to the actual flight path of the probe. The tracking program, although developed for use with lunar probes, can be used for interplanetary probes if certain modifications are made. The program, as developed for the IBM 704 digital computer, has two distinct phases. First, the equations of motion and the variational equations are integrated to each observation time where the elements of the equation A (sub u) equals b [linearization of the maximum likelihood equations] are computed. The second phase is concerned with the solution of a specified subset of A (sub u) equals b. Flexibility and ease of operation have been major objectives in writing the 704 program. The number of data points and tracking stations that may be used is limited only by computing time and core storage. Input formats and operating instructions are presented for utilizing the various computational options available in the program.

Carr, Russell E.↗
NASA NTRS1967

Computer program reduces calculation time of normal response functions

FORTRAN 2 computer program rapidly calculates parameters of maximum likelihood estimates from sensitivity experiment data populations. The program uses the Newton-Raphson iterative procedure to calculate the mean and standard deviation of portions of the cumulative normal response function.

Alexander, M. J.↗
NASA NTRS1968

Concept for simplified serial digital decoder

Modular decoder, which lends itself best to special purpose digital equipment using sequential access memories, decodes the first order Reed-Muller codes. It functions as a maximum-likelihood exhaustive-search decoder and is a modular implementation to accommodate codes of any length.

Green, R. R.↗