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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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59 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Time-Domain Astronomy with the Fermi GBM

The Fermi Gamma-ray Burst Monitor (GBM) is an all-sky monitoring instrument sensitive to energies from 8 kiloelectronvolts to 40 megaelectronvolts. Over the past 8 years of operation, the GBM has detected over 240 gamma-ray bursts per year and provided timely GCN (Gamma-ray Coordinates Network) notices with localization to few-degree accuracy for follow-up observations. In addition to GRBs, galactic transients, solar flares, and terrestrial gamma-ray flashes have also been observed. In recent years we have also been searching the continuous GBM data for electromagnetic counterpart to astrophysical neutrinos and gravitational wave events, as these are believed to be associated with gamma-ray bursts. With continuous data downlink every few hours and a temporal resolution of 2 microseconds, GBM is well suited for observing transients and supporting EM followup in the era of multi-messenger astronomy.

gamma-ray burst↗
NASA NTRS2021

GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run

We report on gravitational wave discoveries from compact binary coalescences detected by Advanced LIGO and Advanced Virgo in the first half of the third observing run (O3a) between 1 April 2019 15:00 UTC and 1 October 2019 15:00 UTC. By imposing a false-alarm-rate threshold of two per year in each of the four search pipelines that constitute our search, we present 39 candidate gravitational wave events. At this threshold, we expect a contamination fraction of less than 10%. Of these, 26 candidate events were reported previously in near real-time through GCN Notices and Circulars; 13 are reported here for the first time. The catalog contains events whose sources are black hole binary mergers up to a redshift of ~ 0.8, as well as events whose components could not be unambiguously identified as black holes or neutron stars. For the latter group, we are unable to determine the nature based on estimates of the component masses and spins from gravitational wave data alone. The range of candidate event masses which are unambiguously identified as binary black holes (both objects ≥ 3 M⨀) is increased compared to GWTC-1, with total masses from ∼ 14M⨀ for GW190924 021846 to ∼ 150M⨀ for GW190521. For the first time, this catalog includes binary systems with significantly asymmetric mass ratios, which had not been observed in data taken before April 2019. We also find that 11 of the 39 events detected since April 2019 have positive effective inspiral spins under our default prior (at 90% credibility), while none exhibit negative effective inspiral spin. Given the increased sensitivity of Advanced LIGO and Advanced Virgo, the detection of 39 candidate events in ∼26 weeks of data (∼1.5 per week) is consistent with GWTC-1.

Gravitational wave detection↗
DOE OSTI2025

Towards Content Authenticity: Multimodal Fake News Detection and AI-Generated Text Identification

In today’s digital world, the spread of fake news and the rise of AI-generated text have become major threats to content authenticity and public trust. This thesis addresses both challenges through two complementary research directions: detecting fake news using multimodal features, and identifying AI-generated text using semantic and structural reasoning. The first part of the work focuses on fake news detection by introducing a novel model that combines text and image features through a unique rotational attention mechanism. Unlike traditional attention methods, this approach rotates the roles of query, key, and value across modalities to capture deeper interactions. Additionally, the model incorporates external domain information by linking news posts to top-ranked websites from Google search results, which helps assess the credibility of content based on its broader web context. This results in a more reliable and accurate fake news detection system that outperforms existing state-of-the-art methods. The second part presents SGG-ATD, a new framework for detecting AI-generated text. It uses masked language modeling to measure sentence coherence, followed by constructing a graph where keywords—both original and predicted—are connected based on semantic and contextual similarity. A Graph Convolutional Network (GCN) is then used to learn structural relationships within the text for final classification. Experimental results demonstrate that SGG-ATD achieves high F1-scores and consistently outperforms strong baselines. This method contributes to robust AI text detection, supporting accountability and resilience against AI-driven misinformation.

Gupta, Nidhi↗
DOE OSTI2022

Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows

We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show how different design choices (e.g., event representations, anomaly score definitions, network architectures) affect the result on specific benchmark new physics models. Once a baseline is established, we discuss how to improve the anomaly detection accuracy by exploiting normalizing flow layers in the latent space of the variational autoencoder.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗