SEARCH · Search NASA
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.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
We demonstrate that enhanced early galaxy formation can generically arise in axion-like particle (ALP) dark matter (DM) models with a delayed onset of axion field oscillation. In these models, the formation of localized massive objects enhances structure formation, potentially addressing the excess recently observed by the James Webb Space Telescope (JWST), while remaining consistent with existing constraints. We identify viable parameter space with the ALP mass in the range of 10 −22 eV m a < 10 –19 eV. In addition, we show that the ALP parameter regions of interest can lead to intriguing complementary signatures in the small scale structure of DM halos and existing experimental searches for ALPs.
We present a search for the pair production of Higgsinos in final states with large missing transverse momentum and either two reconstructed muons or a reconstructed lepton (muon or electron) and an isolated track. The analyzed data correspond to proton-proton collisions with an integrated luminosity of 137 fb −1 , collected by the CMS experiment at $\sqrt{𝑠}$ =13 TeV in 2016, 2017, and 2018. The signal scenario assumes four nearly mass degenerate Higgsino mass eigenstates: two neutralino states $\tilde{𝜒}^0_2$ and $\tilde{𝜒}^0_1$ with a small mass difference in the range 1–10 GeV and two chargino states $\tilde{𝜒}^±_1$ with an intermediate mass. The analysis focuses on the decay of the heavier neutralino into the lighter one and a virtual 𝑍 boson, which decays into two same-flavor leptons. The leptons have small transverse momentum and/or a small opening angle between the identified muons. An isolated track is used to recover events in which only one of the two leptons is identified. Multivariate discriminants are used to enhance the sensitivity by efficiently rejecting backgrounds from SM processes or misreconstructed tracks and/or leptons. The search explores a unique phase space and probes a previously unexplored region of the signal model parameter space. Mass differences between the two neutralinos are probed down to 1.5 GeV, assuming a Higgsino mass of 100 GeV. The maximum excluded Higgsino mass is 115 GeV.
CRAFT (Calibration and Rapid-Adoption Forecasting Techniques) CRAFT is a Python-based project for processing, analyzing, and modeling atmospheric or environmental data. It uses machine learning techniques, specifically Random Forest Regression, to create emulators for various environmental variables such as gross primary production and soil water content. It then uses these emulators to robustly test the parameter space of mechanistic models to provide posterior estimations of the free parameters.
Different astrophysical circumstances under which fractal structures have been identified so far, or are likely to be identified in the future, are reviewed. The observed fractals can be classified into 2 main groups: (1) fractal configurations in space-time, materializing as fractals defined over the time axis at a given position in space, or over the physical configuration space at a fixed instant in time; and (2) fractals in parameter spaces. The theoretical interpretation of the origin of the spatial fractal geometry of the most conspicuous 'irregular' astronomical bodies is still wanting in the context of standard continuum models. In contrast, the less conventional discrete models (cellular automata) naturally produce such spatially fractal structures.
NASA’s Advanced Composites Project has established the goal of achieving a 30 percent reduction in the timeline for certification of primary composite structures for application on commercial aircraft. Prepreg tack is one of several critical parameters affecting composite manufacturing by automated fiber placement (AFP). Tack plays a central role in the prevention of wrinkles and puckers that can occur during AFP, thus knowledge of tack variation arising from a myriad of manufacturing and environmental conditions is imperative for the prediction of defects during AFP. A full design of experiments was performed to experimentally characterize tack on 0.25-inch slit-tape tow IM7/8552-1 prepreg using probe tack testing. Several process parameters (contact force, contact time, retraction speed, and probe diameter) as well as environmental parameters (temperature and humidity) were varied such that the entire parameter space could be efficiently evaluated. Mid-point experimental conditions (i.e., parameters not at either extrema) were included to enable prediction of curvature in relationships and repeat measurements were performed to characterize experimental error. Collectively, these experiments enable determination of primary dependencies as well as multi-parameter relationships. Slit-tape tow samples were mounted to the bottom plate of a rheometer parallel plate fixture using a jig to prevent modification of the active area to be interrogated with the top plate, a polished stainless steel probe, during tack testing. The probe surface was slowly brought into contact with the pre-preg surface until a pre-determined normal force was achieved (2-30 newtons). After a specified dwell time (0.02-10 seconds), during which the probe substrate interaction was maintained under displacement control, the probe was retracted from the surface (0.1-50 millimeters per second). Initial results indicated a clear dependence of tack strength on several parameters, with a particularly strong dependence on temperature and humidity. Although an increase in either of these parameters reduces tack strength, a maximum in tack was predicted to occur under conditions of low temperature and moderate humidity.
We present progress in utilizing a machine learning (ML) assisted optimization framework to study the trends in a parameter space defined by spectrally shaped, high-intensity, petawatt-class (8 J, 45 fs) laser pulses interacting with solid targets and give the first simulation-based overview of predicted trends. A neural network (NN) incorporating uncertainty quantification is trained to predict the number of hot electrons generated by the laser–target interaction as a function of pulse shaping parameters. The predictions of this NN serve as the basis function for a Bayesian optimization framework to navigate this space. For post-experimental evaluation, we compare two separate neural network (NN) models. One is based solely on data from experiments, and the other is trained only on ensemble particle-in-cell simulations. Reviewing the predicted and observed trends across the experiment-capable laser parameter search space, we find that both ML models predict a maximal increase in hot electron generation at a level of approximately 12%–18%; however, no statistically significant enhancement was observed in experiments. On direct comparison of the NN models, the average discrepancy is 8.5%, with a maximum of 30%. Since shot-to-shot fluctuations in experiments affect the observations, we evaluate the behavior of our optimization framework by performing virtual experiments that vary the number of repeated observations and the noise levels. Here, we discuss the implications of such a framework for future autonomous exploration platforms in high-repetition-rate experiments.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Hybrid DC-AC power systems integrating diverse inverter-based resources (IBRs) and multi-terminal high-voltage direct current (MTDC) networks represent a promising architecture for future power grids, while introducing challenges for modeling, stability analysis, and control design. This paper develops a hybrid symbolic-numerical modeling framework and tool to characterize the parametric small-signal stability of DC-AC coupled power systems. The proposed approach constructs parametric state-space models to enable efficient representation of system dynamics under varying control parameters and network configurations, with target parameters retained as symbolic variables and the remainder treated numerically. The stability analysis framework covers eigenvalue, sensitivity, and stability boundary and region characterization. Enhanced linear matrix inequality (LMI) techniques are proposed to directly certify small-signal stability over regions of parameter space while also reducing the conservativeness and computational burden. The resulting tools and frameworks enable rapid parametric model construction across diverse grid conditions, thereby facilitating stability-informed control and operation in complex DC–AC power systems.
An asymmetric lens made of a spherically symmetric galaxy and a quadrupole tidal part allows a very simple pictorial examination of the multiplicity and positions of the images, for any mass density profile. The lens equations can be reduced to one equation in one variable. In the special case of a singular isothermal sphere with a tide (SIST), the lens properties are described by five parameters. Two parameters determine the angular scale and orientation, while three determine the configuration of the images. A comprehensive investigation of the SIST lens is performed, including the variation of the configurations with the parameter values, the critical surfaces in the parameter space, and the extremal solutions. Analytic expressions are derived for the SIST parameters, and additional image positions, in the case when the intensity ratio and positions relative to the galaxy for two of the images are given as input. Quadrupole lenses with a point mass and a pseudoisothermal sphere as the main part are also described in terms of direction diagrams. A direct relation between deflection profile and rotational velocity is derived.
Plasma expansion into the wake of a large rectangular plate immersed in a collisionless, supersonic plasma was investigated in laboratory experiments. The experimental conditions address both single ion and binary ion plasma flows for the case of a body whose size is large in comparison with the Debye length, when the potential difference between the body and the plasma is relatively small. A new plasma source was developed to generate equi-velocity, binary ion plasma flows, which allows access to new parameter space that have previously been unavailable for laboratory studies. Specifically, the new parameters are the ionic mass ratio and the ionic component density ratio. In a series of experiments, a krypton-neon plasma is employed where the ambient density ratio of neon to krypton is varied more than an order of magnitude. The expansion in both the single ion and binary ion plasma cases is limited to early times, i.e., a few ion plasma periods, by the combination of plasma density, plasma drift speed, and vacuum chamber size, which prevented detailed comparison with self-similar theory.
A central goal of extragalactic observational astronomy is to understand how normal galaxies evolve with redshift, and particularly when galaxies formed their stars. While optical and rest-frame UV observations have begun to address these issues, the interpretation of such data is particularly challenging because of the sensitivity to dust obscuration (at optical and UV wavelengths). The absorbed light is re-radiated at IR wavelengths, hence the optimal indicators of the star formation rate (SFR) is at a rest-frame wavelength of approx. 60 microns. The SIRTF mission will revolutionize the study of the global evolution of the SFR by providing mass-selected, complete samples of galaxies and far-IR estimators of the SFR. SIRTF will be two orders of magnitude more sensitive than previous IR space missions, and therefore will open up a completely new region of parameter space in sensitivity and angular resolution. This research program will study the SFR using statistical samples of galaxies in the local universe, at intermediate redshifts, and set the stage for continuing studies up to z = 5. The overall research program is divided into three main investigations: a Mid-IR Hubble Atlas and SFR estimators in the local universe, evolution of the SFR at 0 less than z less that 1 using pencil beam redshift surveys, and Galaxy formation and evolution at 1 less than z less than 5.
We present a methodology for performing scans of beyond the standard model (BSM) parameter spaces with reinforcement learning. We identify a novel procedure using graph neural networks that is capable of exploring spaces of models without the user specifying a fixed particle content, allowing broad classes of BSM models to be explored—in theory, the technique is applicable to nearly any model space with a prespecified gauge group. We provide a generic procedure by which a suitable graph grammar can be developed for any BSM model that features user-specified symmetry groups and a finite number of different possible particle species, the use of which is applicable to a variety of machine learning tasks over the actions of BSM theories beyond our particular reinforcement learning use case. As a proof of concept, we construct the graph grammar for theories with vectorlike leptons that may or may not be charged under a dark U ( 1 ) group, inspired by portal matter extensions of the sub-GeV vector portal/kinetic mixing simplified dark matter models. We then use this graph grammar to create a reinforcement learning environment tasked with creating models with these vectorlike leptons that are consistent with a list of a variety of precision observables. The reinforcement learning agent succeeds in developing models that can address the observed muon anomalous magnetic moment discrepancy while remaining consistent with flavor violation and electroweak precision observables, including both constructions that have previously been studied as well as new models that have not, to our knowledge, previously been identified. By inspecting the resulting ensembles of models that the agent produces and experimenting with different configurations for our reinforcement learning environment and graph grammar, we also infer various lessons about the development of these environments that can be transferable to reinforcement learning scans of more complicated model spaces and comment on future directions for the development of this technique into a more mature tool. Published by the American Physical Society 2025
It is conventionally assumed that the physics underlying the Peccei-Quinn (PQ) mechanism for addressing the strong C P problem is at very high energies, orders of magnitude above the weak scale. However, this may not be the case in general and the associated PQ boson ϕ , besides the signature state, i.e., the ultralight axion a , may emerge well below the weak scale. We consider this possibility and examine some of the conditions for its viability. The example model proposed here may also provide the requisite Standard Model Higgs mass parameter, without invoking new scalars above the giga-electron-volt (GeV) scale. The corresponding parameter space can maintain against quantum corrections. This scenario, depending on the choice of parameters, can potentially be constrained by flavor data. We point out that the current mild excess in B + → K + ν ν ¯ , reported by the Belle II experiment, could in principle be explained in this setup as B + → K + ϕ and B + → K + a , with both ϕ and a escaping the detector as missing energy. For a sufficiently heavy PQ boson, in the GeV regime (as the preferred explanation of the Belle II excess), one can separate these two contributions, due to the difference in K + momenta. In this case, the axion may also affect lighter meson, e.g., kaon, decays while ϕ would not be a kinematically allowed final state. Published by the American Physical Society 2024
Compact binary systems with neutron stars or black holes are one of the most promising sources for ground-based gravitational-wave detectors. Gravitational radiation encodes rich information about source physics; thus parameter estimation and model selection are crucial analysis steps for any detection candidate events. Detailed models of the anticipated waveforms enable inference on several parameters, such as component masses, spins, sky location and distance, that are essential for new astrophysical studies of these sources. However, accurate measurements of these parameters and discrimination of models describing the underlying physics are complicated by artifacts in the data, uncertainties in the waveform models and in the calibration of the detectors. Here we report such measurements on a selection of simulated signals added either in hardware or software to the data collected by the two LIGO instruments and the Virgo detector during their most recent joint science run, including a blind injection where the signal was not initially revealed to the collaboration. We exemplify the ability to extract information about the source physics on signals that cover the neutron-star and black-hole binary parameter space over the component mass range 1M25M and the full range of spin parameters. The cases reported in this study provide a snapshot of the status of parameter estimation in preparation for the operation of advanced detectors.