From Equations to Discovery: AI-Driven Parameter Space Navigation in Materials Science & Process Engineering
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Propagation and application of waves in the ionosphere.
This review deals with the propagation of waves, especially radio waves in the ionosphere. In the macroscopic electromagnetic theory, the mathematical structure of wave propagation problems depends entirely on the properties of the dielectric operator in a magnetically nonpermeable medium. These properties can be deduced from general discussions of symmetry and considerations of physical principles. When the medium is specifically the ionosphere, various physical phenomena may occur. Because of a large number of parameters, it is desirable to define a parameter space. A point in the parameter space corresponds to a specific plasma. The parameter space is subdivided into regions whose boundaries correspond to conditions of resonance and cutoff. As the point crosses these boundaries, the refractive index surface transforms continuously.
Yeh, K. C.↗
Progressive Response Surfaces
Response surface functions are often used as simple and inexpensive replacements for computationally expensive computer models that simulate the behavior of a complex system over some parameter space. Progressive response surfaces are ones that are built up progressively as global information is added from new sample points in the parameter space. As the response surfaces are globally upgraded based on new information, heuristic indications of the convergence of the response surface approximation to the exact (fitted) function can be inferred. Sampling points can be incrementally added in a structured fashion, or in an unstructured fashion. Whatever the approach, at least in early stages of sampling it is usually desirable to sample the entire parameter space uniformly. At later stages of sampling, depending on the nature of the quantity being resolved, it may be desirable to continue sampling uniformly over the entire parameter space (Progressive response surfaces), or to switch to a focusing/economizing strategy of preferentially sampling certain regions of the parameter space based on information gained in early stages of sampling (Adaptive response surfaces). Here we consider Progressive response surfaces where a balanced indication of global response over the parameter space is desired.We use a variant of Moving Least Squares to fit and interpolate structured and unstructured point sets over the parameter space. On a 2-D test problem we compare response surface accuracy for three incremental sampling methods: Progressive Lattice Sampling; Simple-Random Monte Carlo; and Halton Quasi-Monte-Carlo sequences. We are ultimately after a system for constructing efficiently upgradable response surface approximations with reliable error estimates.
Romero, V. J.↗
A set of uniform variational parameters for space trajectory analysis
Set of uniform variational orbit parameters for space trajectory analysis
ORBIT CALCULATION↗
The effects of simulated space environmental parameters on six commercially available composite materials
The effects of simulated space environmental parameters on microdamage induced by the environment in a series of commercially available graphite-fiber-reinforced composite materials were determined. Composites with both thermoset and thermoplastic resin systems were studied. Low-Earth-Orbit (LEO) exposures were simulated by thermal cycling; geosynchronous-orbit (GEO) exposures were simulated by electron irradiation plus thermal cycling. The thermal cycling temperature range was -250 F to either 200 F or 150 F. The upper limits of the thermal cycles were different to ensure that an individual composite material was not cycled above its glass transition temperature. Material response was characterized through assessment of the induced microcracking and its influence on mechanical property changes at both room temperature and -250 F. Microdamage was induced in both thermoset and thermoplastic advanced composite materials exposed to the simulated LEO environment. However, a 350 F cure single-phase toughened epoxy composite was not damaged during exposure to the LEO environment. The simuated GEO environment produced microdamage in all materials tested.
Funk, Joan G.↗
SOC Microstructural Property Estimator
This pre-trained ML model is a tool that uses basic compositional parameters for porous solid oxide cell (SOC) electrodes - the phase fractions and mean particle/pore diameters – as inputs and uses them to estimate additional electrochemical performance parameters: active (i.e., connected) TPB density, all tortuosity factors, and phase pair specific interfacial areas. The electrode is assumed to be composed of two solid phases and a pore phase. The property calculations are performed using neural network regression models trained on a large bank of synthetic electrode microstructural data that NETL has generated using the program DREAM3D (that bank is also hosted on EDX: https://edx.netl.doe.gov/dataset/soc-synthetic-microstructure-bank). This means the generated parameters are based on training from actual measured properties from 3D microstructures, not estimated from geometric simplifications. This tool was developed and is intended to replace percolation theory calculations in models that use hypothetical electrode properties. An example use case would be running SOC performance simulations across a parametric sweep of electrode designs (e.g., varying phase fractions and particle sizes) and assessing how it impacts the electrochemical performance of the SOC. Within the parameter space of the training data (statistics of that parameter space is provided in the readme file), this model achieves sub-5% mean absolute percent errors, an order of magnitude less error than percolation theory across the same parameter space. However, be aware that this tool was developed with parametric simulations in mind, and users are encouraged to assess accuracy for their own specific use case rather than taking accuracy metrics at face value. More info, including a usage guide, is in the included readme file. This tool should be cited with the DOI number provided.
Electrode Microstructure↗
Derivation of Delaware Bay tidal parameters from Space Shuttle photography
The tide-related parameters of the Delaware Bay are derived from Space Shuttle time-series photographs. The water areas in the bay are measured from interpretation maps of the photographs with a CALCOMP 9100 digitizer and ERDAS Image Processing System. The corresponding tidal levels are calculated using the exposure time annotated on the photographs. From these data, an approximate function relating the water area to the tidal level at a reference point is determined. Based on the function, the water areas of the Delaware Bay at mean high water (MHW) and mean low water (MLW), below 0 m, and for the tidal zone are inferred. With MHW and MLW areas and the mean tidal range, we calculate the tidal influx of the Delaware Bay, which is 2.76 x 10 exp 9 cu m. The velocity of flood tide at the bay mouth is determined using the tidal flux and an integral of the velocity distribution function at the cross section between Cape Henlopen and Cape May. The result is 132 cm/s, which compares well with the data on tidal current charts.
Zheng, Quanan↗
Current status of the light neutralino thermal dark matter in the phenomenological MSSM
In a previous publication, we studied the parameter space of the phenomenological minimal Supersymmetric standard model with a light neutralino thermal dark matter ( M χ ˜ 1 0 ≤ M h / 2 ) and observed that the recent results from the dark matter and collider experiments put strong constraints on this scenario. In this work, we present in detail the arguments behind the robustness of this result against scanning over the large number of parameters in phenomenological minimal Supersymmetric standard model. The Run 3 of LHC will be crucial in probing the surviving regions of the parameter space. We further investigate the impact of light staus on our parameter space and also provide benchmarks that can be interesting for Run 3 of LHC. We analyze these benchmarks at the LHC using the machine learning framework of . Finally, we also discuss the effect of nonstandard cosmology on the parameter space. Published by the American Physical Society 2025
Barman, Rahool Kumar↗
NOvA's Current and Future Sterile Neutrino Searches
The NOvA experiment's most recent search for eV-scale sterile neutrinos under a 3+1 model simultaneously analyses muon neutrino and neutral current datasets from the NuMI beam at its Near ($\sim$\qty1{km} baseline) and Far (\qty{810}{km} baseline) detectors to look for oscillations consistent with a sterile neutrino. The analysis is systematically limited in the region of parameter space where $Δm^2_{41} \gtrsim 1~\mathrm{eV}^2$. This region of parameter space is preferred by sterile neutrino interpretations of current experimental anomalies and so improving sensitivity here is high-priority. These proceedings present our current search strategy, and discusses future plans to include data from a second beamline, the Booster Neutrino Beam, to improve our sensitivity in systematics-dominated regions of parameter space.
Lister, Adam [Wisconsin U., Madison; Virginia Tech↗
Modal parameters of space structures in 1 G and 0 G
Analytic and experimental results are presented from a study of the changes in the modal parameters of space structural test articles from one- to zero-gravity. Deployable, erectable, and rotary modules was assembled to form three one- and two-dimensional structures, in which variations in bracing wire and rotary joint preload could be introduced. The structures were modeled as if hanging from a suspension system in one gravity, and unconstrained, as if free floating in zero-gravity. The analysis is compared with ground experimental measurements, which were made on a spring-wire suspension system with a nominal plunge frequency of one Hertz, and with measurements made on the Shuttle middeck. The degree of change in linear modal parameters as well as the change in nonlinear nature of the response is examined. Trends in modal parameters are presented as a function of force amplitude, joint preload, reassembly, shipset, suspension, and ambient gravity level.
Bicos, Andrew S.↗
Simulation budgeting for hybrid effective field theories
In this work, we forecast the number of, and requirements on, N-body simulations needed to train hybrid effective field theory (HEFT) emulators for a range of use cases, using a hybrid of HMcode and perturbation theory as a surrogate model. Our accuracy goals, determined with careful consideration of statistical and systematic uncertainties, are 1% accurate in the high-likelihood range of cosmological parameters, and 2% accurate over a broader parameter space volume for k < 1 h Mpc -1 and z < 3. Focusing in part on the 8-parameter w 0 w a CDM+ m ν cosmological model, we find that < 225 simulations are required to meet our error goals over our wide parameter space, including models with rapidly evolving dark energy, given our simulation and emulator recommendations. For a more restricted parameter space volume, as few as 80 simulations are sufficient. We additionally present simulation forecasts for example use cases, and make the code used in our analyses publicly available. These results offer practical guidance for efficient emulator design and simulation budgeting in future cosmological analyses.
cosmological parameters from LSS↗
Astrometric Detection of Extrasolar Planets: Results of a Feasibility Study with the Palomar 5 Meter Telescope
The detection of extrasolar planets around stars like the Sun remains an important goal of astronomy. We present results from Palomar 5 m observations of the open cluster NGC 2420 in which we measure some of the sources of noise that will be present in an astrometric search for extrasolar planets. This is the first time that such a large aperture has been used for high-precision astrometry. We find that the atmospheric noise is 150 micro-arcsec hr(exp 1/2) across a 90 sec field of view and that differential chromatic refraction (DCR) can be calibrated to 128 micro-arcsec for observations within 1 hr of the meridian and 45 deg of zenith. These results confirm that a model for astrometric measurements can be extrapolated to large apertures. We demonstrate, based upon these results, that a large telescope achieves the sensitivity required to perform a statistically significant search for extra solar planets. We describe an astrometric technique to detect planets, the astrometric signals expected, the role of reference stars, and the sources of measurement noise: photometric noise, atmospheric motion between stars, sky background, instrumental noise, and DCR. For the latter, we discuss a method to reduce the noise further to 66 micro-arcsecond for observations within 1 hr of the meridian and 45 deg of zenith. We discuss optimal lists of target stars taken from the latest Gliese & Jahreiss catalog of nearby stars with the largest potential astrometric signals, declination limits for both telescope accessibility and reduced DCR, and galactic latitude limits for a sufficiant number of reference stars. Two samples are described from which one can perform statistically significant searches for gas giant planets around nearby stars. One sample contains 100 "solar class" stars with an average stellar mass of 0.82 solar mass; the other maximizes the number of stars, 574, by searching mainly low-mass M stars. We perform Monte Carlo simulations of the statistical significance of the expected results by using measured and estimated noise quantities. We show the semimajor axis parameter spaces that are searched for each star and how an increase in the length of the observing program expands these spaces. The search over semimajor axis parameter space relates to the theory of gas giant planet formation.
Pravdo, Steven H.↗
Physical Parameters of Space Mission Asteroid Targets
Ground-based characterization of asteroids that are planned or potential targets of space missions provides important data on their physical parameters and properties. Knowledge of the properties of mission targets is important especially during mission preparation and planning, as it serves to select best or suitable targets for specific purpose of a given mission, to prepare mission plans, to constrain possible mission scenarios, and to design mission experiments. Such characterization efforts may be particularly critical for flyby missions that take only limited data during the high-speed flybys of their target asteroids, but they also provide very crucial data for targets of rendezvous missions. Moreover, long-term observations taken from Earth also allows the modelling of, or constraining, processes acting on the asteroids over extended time scales. Over the past years and decades we have obtained rich data on physical parameters of 82 asteroids that are planned or potential targets of space missions. Our primary observing technique is time-resolved (lightcurve) photometry, but we use also data obtained with other techniques, such as spectroscopy, thermal or radar observations. 15 of the 82 characterized asteroids are planned or possible targets of several space missions that are in flight or preparation, such as ESA’s Hera, RAMSES and PRIAMOS, NASA’s OSIRIS-APEX, JAXA’s Hayabusa2#, DESTINY+ and Next Generation Sample Return (NGSR), the Emirates Mission to Asteroids (EMA), and Karman+’s High Frontier, but we have also characterized 67 asteroids that are potential targets of space missions for their low delta-V’s and were announced as being “of interest to NASA” in the Small-Bodies-Observations- NASA mailing list or listed on the NHATS page of “Accessible NEAs”. The sample of asteroid targets span 3 orders of magnitude in size, with absolute magnitudes H from 12.57 to 26.8, corresponding to diameters from about 10 m to about 10 km. The sample contains a variety of taxonomic types and physically or dynamically interesting objects. Among them, we have identified 5 binary asteroids and 20 tumblers (i.e., asteroids in excited, non-principal axis rotation states). Rotation periods of the characterized asteroids range from 1.45 min to 280 h, reflecting diversity of their properties and formation/evolution paths. We will present an overview of the data set and highlight several representative cases.
Petr Pravec↗
Generalized fiducial inference on differentiable manifolds
We introduce a novel approach to inference on parameters that take values in a Riemannian manifold embedded in a Euclidean space. Parameter spaces of this form are ubiquitous across many fields, including chemistry, physics, computer graphics, and geology. Here, this new approach uses generalized fiducial inference (GFI) to obtain a posterior-like distribution on the manifold, without needing to know local parameterizations that map to the constrained space from an unconstrained Euclidean space. Using mathematical tools from Riemannian geometry, we construct a constrained generalized fiducial distribution (CGFD). A Bernstein-von Mises-type result for the CGFD, which provides intuition for how the desirable asymptotic qualities of the unconstrained generalized fiducial distribution are inherited by the CGFD, is provided. To illustrate the practical use of the CGFD, we provide a proof-of-concept example in the context of a linear logspline density estimation problem, and demonstrate that CGFD-based confidence sets exhibit desirable coverage properties via simulation. As an application, we fit a CGFD to COVID-19 case count data from North Carolina, USA.
97 MATHEMATICS AND COMPUTING↗
Extreme Meteorological Parameters During Space Shuttle Pad Exposure Periods
During the 113 missions of the Space Transportation System (STS), the Space Shuttle fleet has been exposed to the elements on the launch pad for a total of 4195 days. This paper provides a summary of the historical record of the meteorological extremes encountered by the Space Shuttle fleet during the pad exposure period. Parameters included are temperature, dew point, relative humidity, wind speed, sea level pressure and precipitation. All the data presented are archived by the Marshall Space Flight Center Environments Group, and were obtained from a combination of surface observations and meteorological towers at Kennedy Space Center (KSC), Florida. Data are provided from the first launch of the STS in 1981 through the launch of STS-107 in 2003.
B. Glenn Overbey↗
Hitting the Thermal Target for Leptophilic Dark Matter at Future Lepton Colliders
We study future lepton collider prospects for testing predictive models of leptophilic dark matter (DM) candidates with a thermal origin. We calculate experimental milestones for testing the parameter space compatible with freeze-out and the associated collider signals at past, present, and future facilities. This analysis places new limits on such models by leveraging the utility of lepton colliders. At 𝑒+𝑒− machines, we make projections using precision 𝑍 -pole observables from 𝑒+𝑒−→ℓ+ℓ−+𝐸 signatures at large electron-positron collider and future projections for future circular collider (ee) in these channels. Additionally, a muon collider could also probe new thermal relic parameter space in this scenario via 𝜇+𝜇−→𝑋+𝐸 , where 𝑋 is any easily identifiable standard model object. Collectively, these processes can probe much of the parameter space for which DM direct annihilation to ℓ+ℓ− yields the observed relic density in Higgs-like models with mass-proportional couplings to charged leptons.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Experimental targets for dark photon dark matter
Ultralight dark photon dark matter features distinctive cosmological and astrophysical signatures and is also supported by a burgeoning direct-detection program searching for its kinetic mixing with the ordinary photon over a wide mass range. Dark photons, however, cannot necessarily constitute the dark matter in all of this parameter space. In minimal models where the dark photon mass arises from a dark Higgs mechanism, early-Universe dynamics can easily breach the regime of validity of the low-energy effective theory for a massive vector field. In the process, the dark sector can collapse into a cosmic string network, precluding dark photons as viable dark matter. We establish the general conditions under which dark photon production avoids significant backreaction on the dark Higgs and identify regions of parameter space that naturally circumvent these constraints. After surveying implications for known dark photon production mechanisms, we propose novel models that set well-motivated experimental targets across much of the accessible parameter space. We also discuss complementary cosmological and astrophysical signatures that can probe the dark sector physics responsible for dark photon production.
Dark matter↗
Machine Learning for Mapping Multipactor Susceptibility in RF Systems: Capabilities and Generalization Constraints
Multipactor is a surface-driven electron avalanche phenomenon that degrades the performance and reliability of radio-frequency (RF) systems in particle accelerator and vacuum electronics applications. Multipactor behavior in a given device structure is conventionally assessed through susceptibility charts, which provide a parameter-space characterization of the instability. In this work, we assess the capabilities of machine-learning (ML) models to learn and predict such susceptibility charts and analyze the constraints governing their generalization across materials. Using a simulation-derived dataset spanning six distinct secondary-electron-yield material profiles in a canonical two-surface planar geometry, we train supervised regression models and artificial neural networks to predict the time-averaged electron growth rate, δavg, across the relevant parameter space. Model performance is evaluated using metrics that explicitly probe the structure of susceptibility charts, including Intersection over Union, Structural Similarity Index, and correlation analysis. Tree-based ensemble models outperform neural-network models in reconstructing susceptibility regions and in generalizing across material domains. Principal-component analysis reveals disjoint material feature distributions, indicating that the piecewise mode structure of multipactor susceptibility is difficult to represent with a single global model and that generalization is constrained by data coverage rather than by model complexity. An exhaustive reduced-coverage study further shows that sparse material-space coverage can yield mean performance in the same general range but producing large variability in the susceptibility-region overlap. These results clarify the capabilities of ML-based surrogate models for parameter-space characterization of multipactor discharge. They also provide guidance for their appropriate use in RF system design.
43 PARTICLE ACCELERATORS↗