Search NASA⌕ Search

SEARCH · Search NASA

Results for “BOM”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

21 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Streams, Shells, and Substructures in the Accretion-Built Stellar Halo of NGC 300

We present deep optical observations of the stellar halo of NGC 300, an LMC-mass galaxy, acquired with the DEEP sub-component of the DECam Local Volume Exploration survey (DELVE) using the 4 m Blanco Telescope. Our resolved star analysis reveals a large, low surface brightness stellar stream ($M_{V}\sim-8.5$; [Fe/H] $= -1.4\pm0.15$) extending more than 40 kpc north from the galaxy's center. We also find other halo structures, including potentially an additional stream wrap to the south, which may be associated with the main stream. The morphology and derived low metallicities of the streams and shells discovered surrounding NGC 300 are highly suggestive of a past accretion event. Assuming a single progenitor, the accreted system is approximately Fornax-like in luminosity, with an inferred mass ratio to NGC 300 of approximately $1:15$. We also present the discovery of a metal-poor globular cluster ($R_{\rm{proj}}=23.3$~kpc; $M_{V}=-8.99\pm0.16$; [Fe/H] $\approx-1.6\pm0.6$) in the halo of NGC 300, the furthest identified globular cluster associated with NGC 300. The stellar structures around NGC 300 represent the richest features observed in a Magellanic Cloud analog to date, strongly supporting the idea that accretion and subsequent disruption is an important mechanism in the assembly of dwarf galaxy stellar halos.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

EP 250108a/SN 2025kg: Observations of the Most Nearby Broad-line Type Ic Supernova Following an Einstein Probe Fast X-Ray Transient

With a small sample of fast X-ray transients (FXTs) with multiwavelength counterparts discovered to date, their progenitors and connections to γ-ray bursts (GRBs) and supernovae (SNe) remain ambiguous. Here, we present photometric and spectroscopic observations of SN 2025kg, the SN counterpart to the FXT EP 250108a. At z = 0.17641, this is the closest known SN discovered following an Einstein Probe (EP) FXT. We show that SN 2025kg’s optical spectra reveal the hallmark features of a broad-lined Type Ic SN. Its light-curve evolution and expansion velocities are comparable to those of GRB-SNe, including SN 1998bw, and two past FXT-SNe. We present JWST/NIRSpec spectroscopy taken around SN 2025kg’s maximum light, and find weak absorption due to He I 1.0830 μm and 2.0581 μm and a broad, unidentified emission feature at ∼4–4.5 μm. Further, we observe broadened Hα in optical data at 42.5 days that is not detected at other epochs, indicating interaction with H-rich material. From its light curve, we derive a 56 Ni mass of 0.2–0.6 M ⊙ . Together with our companion Letter, our broadband data are consistent with a trapped or low-energy (≲10 51 erg) jet-driven explosion from a collapsar with a zero-age main-sequence mass of 15–30 M ⊙ . Finally, we show that the sample of EP FXT-SNe supports past estimates that low-luminosity jets seen through FXTs are more common than successful (GRB) jets, and that similar FXT-like signatures are likely present in at least a few percent of the brightest Type Ic-BL SNe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

Aubourg, Eric [APC, Paris] (ORCID:000000025592023X↗