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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

High-accuracy emulators for observables in ΛCDM, N eff, Σ m ν, and w cosmologies

ABSTRACT We use the emulation framework CosmoPower to construct and publicly release neural network emulators of cosmological observables, including the cosmic microwave background (CMB) temperature and polarization power spectra, matter power spectrum, distance-redshift relation, baryon acoustic oscillation (BAO) and redshift-space distortion (RSD) observables, and derived parameters. We train our emulators on Einstein–Boltzmann calculations obtained with high-precision numerical convergence settings, for a wide range of cosmological models including ΛCDM, wCDM, ΛCDM + Neff, and ΛCDM + Σmν. Our CMB emulators are accurate to better than 0.5 per cent out to ℓ = 104, which is sufficient for Stage-IV data analysis, and our P(k) emulators reach the same accuracy level out to $k=50 \, \, \mathrm{Mpc}^{-1}$, which is sufficient for Stage-III data analysis. We release the emulators via an online repository (CosmoPower Organisation), which will be continually updated with additional extended cosmological models. Our emulators accelerate cosmological data analysis by orders of magnitude, enabling cosmological parameter extraction analyses, using current survey data, to be performed on a laptop. We validate our emulators by comparing them to class and camb and by reproducing cosmological parameter constraints derived from Planck TT, TE, EE, and CMB lensing data, as well as from the Atacama Cosmology Telescope Data Release 4 CMB data, Dark Energy Survey Year-1 galaxy lensing and clustering data, and Baryon Oscillation Spectroscopic Survey Data Release 12 BAO and RSD data.

Astronomy & Astrophysics↗
DOE OSTI2024

Testing for coherence and nonstandard neutrino interactions in COHERENT data

We analyze data from the CsI, liquid Ar and Ge detectors of the COHERENT experiment and confirm within 1.5 σ that the measured elastic neutrino-nucleus scattering cross section is proportional to the square of the number of neutrons in the nucleus, as expected for coherent scattering in the standard model. We also show how various degeneracies involving nonstandard neutrino interaction parameters are broken in a combined analysis of the three datasets. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2024

Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based Inference

In this work, we present a scalable approach for inferring the dark energy equation-of-state parameter ($w$) from a population of strong gravitational lens images using Simulation-Based Inference (SBI). Strong gravitational lensing offers crucial insights into cosmology, but traditional Monte Carlo methods for cosmological inference are computationally prohibitive and inadequate for processing the thousands of lenses anticipated from future cosmic surveys. New tools for inference, such as SBI using Neural Ratio Estimation (NRE), address this challenge effectively. By training a machine learning model on simulated data of strong lenses, we can learn the likelihood-to-evidence ratio for robust inference. Our scalable approach enables more constrained population-level inference of $w$ compared to individual lens analysis, constraining $w$ to within $1\sigma$. Our model can be used to provide cosmological constraints from forthcoming strong lens surveys, such as the 4MOST Strong Lensing Spectroscopic Legacy Survey (4SLSLS), which is expected to observe 10,000 strong lenses.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Robustness of the Galactic Center excess morphology against masking

The Galactic Center excess (GCE) remains an enduring mystery, with leading explanations being annihilating dark matter or an unresolved population of millisecond pulsars. Analyzing the morphology of the GCE provides critical clues to identify its exact origin. We investigate the robustness of the inferred GCE morphology against the effects of masking, an important step in the analysis where the gamma-ray emission from point sources and the galactic disk are excluded. Using different masks constructed from Fermi point source catalogs and a wavelet method, we find that the GCE morphology, particularly its ellipticity and cuspiness, is relatively independent of the choice of mask for energies above 2–3 GeV. The GCE morphology systematically favors an approximately spherical shape, as expected for dark matter annihilation. Compared to various stellar bulge profiles, a spherical dark matter annihilation profile better fits the data across different masks and galactic diffuse emission backgrounds, except for the stellar bulge profile which provides a similar fit to the data. Modeling the GCE with two components, one from dark matter annihilation and one tracing the Coleman bulge, we find this two-component model outperforms any single component or combinations of dark matter annihilation and other stellar bulge profiles. Uncertainty remains about the exact fraction contributed by each component across different background models and masks. Furthermore, when the Coleman bulge dominates, its corresponding spectrum lacks characteristics typically associated with millisecond pulsars, suggesting that it mostly models the emission from other sources instead of the GCE that is still present and spherically symmetric.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Quantifying the Role of EMIC Wave Scattering During the 27 February 2014 Storm by RAM–SCB Simulations

Electromagnetic Ion Cyclotron (EMIC) wave scattering has been proved to be responsible for the fast loss of both radiation belt (RB) electrons and ring current (RC) protons. However, its role in the concurrent dropout of these two co–located populations remains to be quantified. In this work, we study the effect of EMIC wave scattering on both populations during the 27 February 2014 storm by employing the global physics–based RAM–SCB model. Throughout this storm event, MeV RB electrons and 100s keV RC protons experienced simultaneous dropout following the occurrence of intense EMIC waves. By implementing data–driven initial and boundary conditions, we perform simulations for both populations through the interplay with EMIC waves and compare them against Van Allen Probes observations. Notably, the results indicate that by including EMIC wave scattering loss, especially by the He–band EMIC waves, the model aligns closely with data for both populations. Additionally, we investigate the simulated pitch angle distributions (PADs) for both populations. Including EMIC wave scattering in our model predicts a 90° peaked PAD for electrons with stronger losses at lower pitch angles, while protons exhibit an isotropic PAD with enhanced losses at pitch angles above 40°. Furthermore, our model predicts considerable precipitation of both particle populations, predominantly confined to the afternoon to midnight sector (12 hr < MLT < 24 hr) during the storm's main phase, corresponding closely with the presence of EMIC waves.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

AIR Framework for Physics-Inspired Artificial Intelligence in High Energy Physics (Final Report)

The FAIR4HEP project was a collaboration between Argonne National Laboratory, University of Illinois Urbana-Champaign, Massachusetts Institute of Technology, University of Minnesota, and the University of California San Diego funded by the US Department of Energy, Office of Science, Office of Advanced Scientific Research (ASCR) from 2020 to 2023. The primary focus of the FAIR4HEP project was to advance our understanding of the relationship between data and artificial intelligence (AI) models by exploring relationships among them through the development of findable, accessible, interoperable, and reusable (FAIR) frameworks. Using high-energy physics (HEP) as the science driver, this project developed a FAIR framework to advance our understanding of AI, provide new insights to apply AI techniques and provide an environment where novel approaches to AI can be explored. This final report summarizes the accomplishments of the MIT group.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection

The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The key contributions of the study include exploring TL’s potential and limitations within the context of encoder and decoder networks, revealing insights into model initialization and training configurations that enhance performance while substantially reducing trainable parameters and mitigating data contamination effects.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Applying simulation-based inference to spectral and spatial information from the Galactic Center gamma-ray excess

Abstract The two most favored explanations of theFermiGalactic Center gamma-ray excess (GCE) are millisecond pulsars and self annihilation of the smooth dark matter halo of the galaxy. In order to distinguish between these possibilities, we would like to optimally use all information in the available data, including photon direction and energy information.To date, analyses of the GCE have generally treated directional and energy information separately, or have ignored one or the other completely.Here, we develop a method for analyzing the GCE that relies on simulation-basedinference with neural posterior models to jointly analyze photon directional and spectral information while correctly accounting for the spatial and energy resolution of the telescope, here assumed to be theFermiLarge Area Telescope (LAT).Our results also have implications for analyses of the diffuse gamma-ray background, which we discuss.

Astronomy & Astrophysics↗
DOE OSTI2024

CTA and SWGO can discover Higgsino dark matter annihilation

Thermal Higgsino dark matter (DM), with a mass near 1.1 TeV, is one of the most well-motivated and untested DM candidates. Leveraging recent hydrodynamic cosmological simulations that give DM density profiles in Milky Way analog galaxies we show that the linelike gamma-ray signal predicted from Higgsino annihilation in the Galactic Center could be detected at high significance with the upcoming Cherenkov Telescope Array (CTA) and Southern Wide-field Gamma-ray Observatory (SWGO) for all but the most pessimistic DM profiles. We perform the most sensitive search to-date for the linelike signal using 15 years of data from the Fermi Large Area Telescope, coming within an order one factor of the necessary sensitivity to detect the Higgsino for some Milky Way analog DM density profiles. We show that H.E.S.S. has subleading sensitivity relative to Fermi for the Higgsino at present. In contrast, we analyze H.E.S.S. inner Galaxy data for the thermal wino model with a mass near 2.8 TeV; we find no evidence for a DM signal and exclude the wino by over a factor of two in cross section for all DM profiles considered. In the process, we identify and attempt to correct what appears to be an inconsistency in previous H.E.S.S. inner Galaxy analyses for DM annihilation related to the analysis effective area, which may weaken the DM cross-section sensitivity claimed in those works by around an order of magnitude.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Probing jet base emission of M87* with the 2021 Event Horizon Telescope observations

We investigate the presence and spatial characteristics of the jet base emission in M87* at 230 GHz, enabled by the significantly enhanced (u,v) coverage in the 2021 Event Horizon Telescope (EHT) observations. The integration of the 12−m Kitt Peak Telescope (USA) and NOEMA (France) stations into the array introduces two critical intermediate-length baselines to SMT (USA) and IRAM 30−m (Spain), providing sensitivity to emission structures at spatial scales of ∼250 μas and ∼2500 μas (∼ 0.02 pc and ∼ 0.02 pc). Without these new baselines, previous EHT observations of the source in 2017 and 2018 lacked the capability to constrain emission on large scales, where a “missing flux” of order ∼1 Jy is expected to reside. To probe these scales, we analyzed closure phases–robust against station-based gain calibration errors–and model the jet base emission using a simple Gaussian component offset from the compact ring emission at spatial separations > 100 μas. Our analysis revealed a Gaussian feature centered at (ΔRA ≈ 320 μ as, ΔDec. ≈ 60 μ as), projected separation of ≈ 5500 AU, with an estimated flux density of only ∼60 mJy, implying that most of the missing flux identified in previous EHT studies had to originate from different, larger scales. Brighter emission at the relevant spatial scales is firmly ruled out, and the data do not favor more complex models. This component aligns with the inferred position of the large-scale jet and is therefore physically consistent with the emission of the jet base. While our findings point to detectable jet base emission at 230 GHz, the limited coverage provided by only two intermediate baselines limits our ability to robustly reconstruct its morphology. Consequently, we treated the recovered Gaussian as an upper limit on the jet base flux density. Future EHT observations with expanded intermediate baseline coverage will be essential to constrain the structure and nature of this component with higher precision.

accretion, accretion disks↗
DOE OSTI2024

Direct ab initio calculation of the 4 He nuclear electric dipole polarizability

The calculation of nuclear electromagnetic sum rules by directly diagonalizing the nuclear Hamiltonian in a large basis is numerically challenging and has not been performed for A>2 nuclei. With the significant progress of high performance computing, we show that calculating sum rules using numerous discretized continuum states obtained by directly diagonalizing the ab initio no-core shell model Hamiltonian is achievable numerically. Specifically, we calculate the 4 He electric dipole (E1) polarizability, that is an inverse energy weighted sum rule, employing the Daejeon16 NN interaction. We demonstrate that the calculations are numerically tractable as the dimension of the basis increases and are convergent. Our results for the 4 He electric dipole polarizability are consistent with the most recent experimental data and are compared with those of other theoretical studies employing different techniques and various interactions.

Astronomy & Astrophysics↗
DOE OSTI2025

Direct nonparametric multimessenger constraints on the equation of state of cold dense nuclear matter

We utilize the now substantial amount of astrophysical observations of neutron stars (NSs), along with perturbative quantum chromodynamics (pQCD) calculations at high density, to directly constrain the NS equation of state (EOS). To this end, we construct nonparametric EOS priors by using Gaussian processes trained on 75 EOSs, which include models with either hadrons, hyperons, or quarks at high densities. We create a prior using the full EOS sample (model agnostic), and one prior for each EOS family to test model discrimination. We introduce a novel inference approach, which allows the simultaneous sampling of intrinsic and extrinsic parameters of binary NS mergers, as well as a nonparametric equation of state. We showcase this method in a Bayesian updating scheme by first performing a complete analysis of the binary NS merger event GW170817 with minimal assumptions, and sequentially adding information from x-ray and radio NS observations, along with pQCD calculations. Besides providing standard constraints, such as the pressure at twice nuclear saturation density 𝑝⁡(2⁢𝜌 sat ) = 4.3$^{+0.6}_{−0.6}$ × 10 34 dyne/cm 2 , at 95% confidence level, for the model agnostic prior, our methodology shows how the choice of EOS families used in conditioning changes the inferred astrophysical properties of the EOS, namely tidal deformability and maximum supported NS mass. We find hyperonic priors predicting higher tidal deformabilities for a 1.4⁢𝑀 ⊙ NS, and hadronic priors being preferred by the considered astrophysical data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

FAIR Framework for Physics-Inspired Artificial Intelligence in High Energy Physics (Final Report)

The FAIR4HEP project was a collaboration between Argonne National Laboratory, University of Illinois Urbana-Champaign, Massachusetts Institute of Technology, University of Minnesota, and University of California San Diego funded by the US Department of Energy, Office of Science, Office of Advanced Scientific Research (ASCR) from 2020 to 2024. The primary focus of the FAIR4HEP project was to advance our understanding of the relationship between data and artificial intelligence (AI) models by exploring relationships among them through the development of findable, accessible, interoperable, and reusable (FAIR) frameworks. Using high-energy physics (HEP) as the science driver, this project developed a FAIR framework to advance our understanding of AI, provide new insights to apply AI techniques, and provide an environment where novel approaches to AI can be explored. This final report summarizes the accomplishments of the University of Illinois group.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Discovering Dark Matter Clumps and Primordial Particles with Galaxies

Cosmological observations and galaxy dynamics seem to imply that five out of six parts in mass of all matter in the universe is composed of dark matter, which is not accounted for by the Standard Model of particles. The cold dark matter (CDM) paradigm has been extremely successful at describing observations on large-cosmological scale. However, many different dark matter candidates have the same observable effects as CDM on large-length scales. One possible avenue to distinguish between these different models is to look on much smaller-length scales, where usually dark matter models become distinguishable. This research developed the theoretical framework and statistical tools needed to map the detailed distribution of dark matter on subgalactic scales using strong gravitational lensing. A second goal of this research was to build new statistical techniques that efficiently exploit the full power of cosmic-structure data from next-generation surveys. Galaxy clustering on large scales provides significant cosmological information through the power spectrum. Additional information can be gained from higher-order statistics. This research provided new ways to understand the initial conditions of the universe from current and upcoming cosmological surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The Galaxy–Galaxy Strong Lensing Cross Section and the Internal Distribution of Matter in ΛCDM Substructure

Abstract Strong gravitational lensing offers a powerful probe of the detailed distribution of matter in lenses, while magnifying and bringing faint background sources into view. Observed strong lensing by massive galaxy clusters, which are often in complex dynamical states, has also been used to map their dark matter (DM) substructures on smaller scales. Deep high-resolution imaging has revealed the presence of strong lensing events associated with these substructures, namely galaxy-scale sub-halos. However, an inventory of these observed galaxy–galaxy strong lensing (GGSL) events is noted to be discrepant with state-of-the-art ΛCDM simulations. Cluster sub-halos appear to be over-concentrated compared to their simulated counterparts yielding an order-of-magnitude higher value of GGSL. In this paper, we explore the possibility of resolving this observed discrepancy by redistributing the mass within observed cluster sub-halos in ways that are consistent within the ΛCDM paradigm of structure formation. Lensing mass reconstructions from data provide constraints on the mass enclosed within apertures and are agnostic to the detailed mass profile within them. Therefore, as the detailed density profile within cluster sub-halos currently remains unconstrained by data, we are afforded the freedom to redistribute the enclosed mass. We investigate if rearranging the mass to a more centrally concentrated density profile helps alleviate the GGSL discrepancy. We report that refitting cluster sub-halos to the ubiquitous ΛCDM-motivated Navarro–Frenk–White profile, and further modifying them to include significant baryonic components, does not resolve this tension. A resolution to this persisting GGSL discrepancy may require more careful exploration of alternative DM models.

Astronomy & Astrophysics↗
DOE OSTI2026

Searching for synchrotron emission from the geminga TeV halo using the planck satellite

Pulsars convert a significant fraction of their total spin-down power into very high-energy electrons, leading to the formation of TeV halos. While these halos are well characterized at TeV energies, it remains unclear whether pulsars also accelerate electrons efficiently at lower energies and how these particles propagate through their surrounding environments. We aim to test whether synchrotron emission from $\sim 50$–$300 \, \textrm{GeV}$ electrons around the Geminga pulsar can be detected in the frequency range observed by the Planck satellite. This would help constrain low-energy particle acceleration and diffusion in the vicinity of pulsars. We model the expected synchrotron emission from Geminga’s TeV halo based on various diffusion and injection spectrum scenarios and compare these predictions to publicly available multi-frequency Planck data. We find no conclusive evidence of spatially extended synchrotron emission associated with Geminga in any of Planck’s frequency bands. Our calculations show that even under favorable diffusion and injection conditions, the predicted synchrotron flux lies well below Planck’s measured background levels.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Modest set of simulations of local-type primordial non-Gaussianity in the DESI era

A measurement of a primordial non-Gaussianity (PNG) signal through late- or early-Universe probes has the potential to transform our understanding of the physics of the primordial Universe. While large-scale structure observables in principle contain vital information, interpreting these measurements is challenging due to poorly understood astrophysical effects. Luckily, N-body simulations, such as the abacuspng set presented in this study, consisting of 9 boxes, each of size L box = 2 Gpc/h and particle mass of 1.01 × 10 10 M ⊙ /h, provide a viable path forward. As validation, we find good agreement between the simulations and our expectations from one-loop perturbation theory (PT) and the "separate universe" method for the matter bispectrum, matter power spectrum and the halo bias parameter associated with PNG, b φ . As a science application, here we investigate the link between halo assembly bias and b φ for halo properties known to play a vital role in accurately predicting galaxy clustering: concentration, shear (environment), and accretion rate. We find a strong response for all three parameters, suggesting that the connection between b φ and the assembly history of halos needs to be taken into account by future PNG analyses. We further perform the first study of the b φ parameter from fits to early DESI data of the luminous red galaxy (LRG) and quasistellar object (QSO) samples and comment on the effect on f NL constraints for the allowed galaxy-halo models (note that σ[f NL ]∝ $\frac{σ[b_φ]}{b_φ}$). We find that the error on f NL is 15, 8, 7 for the LRGs at z = 0.5 and z = 0.8 and QSOs at z = 1.4, respectively, suggesting that a thorough understanding of galaxy assembly bias is warranted so as to perform robust high-precision analysis of local-type PNG with future surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers

We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers whose waveforms include the higher order gravitational wave modes ($\ell$, |m|) = {(2,2), (2,1), (3,3), (3,2), (4,4)}, and mode mixing effects in the $\ell$ = 3, |m| = 2 harmonics. These AI models combine hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational waves; and graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a three detector network encompassing the Advanced LIGO and Virgo detectors. We first trained these spatiotemporal-graph AI models using synthetic noise, using 1.2 million modeled waveforms to densely sample this signal manifold, within 1.7 h using 256 NVIDIA A100 GPUs in the Polaris supercomputer at the Argonne Leadership Computing Facility. This distributed training approach exhibited optimal classification performance, and strong scaling up to 512 NVIDIA A100 GPUs. With these AI ensembles we processed data from a three detector network, and found that an ensemble of 4 AI models achieves state-of-the-art performance for signal detection, and reports two misclassifications for every decade of searched data. We distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, and completed the processing of a decade of gravitational wave data from a three detector network within 3.5 h. Finally, we fine-tuned these AI ensembles to process the entire month of February 2020, which is part of the O3b LIGO/Virgo observation run, and found 6 gravitational waves, concurrently identified in Advanced LIGO and Advanced Virgo data, and zero false positives. This analysis was completed in one hour using one NVIDIA A100 GPU.

79 ASTRONOMY AND ASTROPHYSICS↗