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Results for “parameter space exploration”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Dark Matter searches with photons at the LHC

We unveil blind spot regions in dark matter (DM) direct detection (DMDD), for weakly interacting massive particles with a mass around a few hundred GeV that may reveal interesting photon signals at the LHC. We explore a scenario where the DM primarily originates from the singlet sector within the Z 3 -symmetric Next-to-Minimal Supersymmetric Standard Model (NMSSM). A novel DMDD spin-independent blind spot condition is revealed for singlino-dominated DM, in cases where the mass parameters of the higgsino and the singlino-dominated lightest supersymmetric particle (LSP) exhibit opposite relative signs (i.e., κ < 0), emphasizing the role of nearby bino and higgsino-like states in tempering the singlino-dominated LSP. Additionally, proximate bino and/or higgsino states can act as co-annihilation partner(s) for singlino-dominated DM, ensuring agreement with the observed relic abundance of DM. Remarkably, in scenarios involving singlino-higgsino co-annihilation, higgsino-like neutralinos can distinctly favor radiative decay modes into the singlino-dominated LSP and a photon, as opposed to decays into leptons/hadrons. In exploring this region of parameter space within the singlino-higgsino compressed scenario, we study the signal associated with at least one relatively soft photon alongside a lepton, accompanied by substantial missing transverse energy (E T ) and a hard initial state radiation jet at the LHC. In the context of singlino-bino co-annihilation, the bino state, as the next-to-LSP, exhibits significant radiative decay into a soft photon and the LSP, enabling the possible exploration at the LHC through the triggering of this soft photon alongside large E T and relatively hard leptons/jets resulting from the decay of heavier higgsino-like states.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

SANE: strategic autonomous non-smooth exploration for multiple optima discovery in multi-modal and non-differentiable black-box functions

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and multimodal parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, material structure image spaces, and molecular embedding spaces. Often these systems are black-boxes and time-consuming to evaluate, which resulted in strong interest towards active learning methods such as Bayesian optimization (BO). However, these systems are often noisy which make the black box function severely multi-modal and non-differentiable, where a vanilla BO can get overly focused near a single or faux optimum, deviating from the broader goal of scientific discovery. To address these limitations, here we developed Strategic Autonomous Non-Smooth Exploration (SANE) to facilitate an intelligent Bayesian optimized navigation with a proposed cost-driven probabilistic acquisition function to find multiple global and local optimal regions, avoiding the tendency to becoming trapped in a single optimum. To distinguish between a true and false optimal region due to noisy experimental measurements, a human (domain) knowledge driven dynamic surrogate gate is integrated with SANE. We implemented the gate-SANE into pre-acquired piezoresponse spectroscopy data of a ferroelectric combinatorial library with high noise levels in specific regions, and piezoresponse force microscopy (PFM) hyperspectral data. SANE demonstrated better performance than classical BO to facilitate the exploration of multiple optimal regions and thereby prioritized learning with higher coverage of scientific values in autonomous experiments. Our work showcases the potential application of this method to real-world experiments, where such combined strategic and human intervening approaches can be critical to unlocking new discoveries in autonomous research.

Biswas, Arpan [University of Tennessee, Knoxville,↗
DOE OSTI2024

Gravitational Waves from Nnaturalness

Abstract We study the prospects for probing the Nnaturalness solution to the electroweak hierarchy problem with future gravitational wave observatories. Nnaturalness, in its simplest incarnation, predictsNcopies of the Standard Model with varying Higgs mass parameters. We show that in certain parameter regions the scalar reheaton transfers a substantial energy density to the sector with the smallest positive Higgs squared mass while remaining consistent with bounds on additional effective relativistic species. In this sector, all six quarks are much lighter than the corresponding QCD confinement scale, allowing for the possibility of a first-order chiral symmetry-breaking phase transition and an associated stochastic gravitational wave signal. We consider several scenarios characterizing the strongly-coupled phase transition dynamics and estimate the gravitational wave spectrum for each. Pulsar timing arrays (SKA), spaced-based interferometers (BBO, Ultimate-DECIGO,μAres, asteroid ranging), and astrometric measurements (THEIA) all have the potential to explore new regions of Nnaturalness parameter space, complementing probes from next generation cosmic microwave background radiation experiments.

Physics↗
DOE OSTI2023

New physics in multi-electron muon decays

Abstract We study the exotic muon decays with five charged tracks in the final state. First, we investigate the Standard Model rate forμ + → 3e + 2e − 2ν($$ \mathcal{B} $$ B = 4.0 × 10 −10 ) and find that the Mu3e experiment should have tens to hundreds of signal events per 10 15 μ + decays, depending on the signal selection strategy. We then turn to a neutrinolessμ + → 3e + 2e − decay that may arise in new-physics models with lepton-flavor-violating effective operators involving a dark Higgsh d . Following its production inμ + →e + h d decays, the dark Higgs can undergo a decay cascade to twoe + e − pairs through two dark photons,h d → γ d γ d →2(e + e − ). We show that aμ + →3e + 2e − search at the Mu3e experiment, with potential sensitivity to the branching ratio at the$$ \mathcal{O} $$ O (10 −12 ) level or below, can explore new regions of parameter space and new physics scales as high as Λ ∼ 10 15 GeV.

Physics↗
DOE OSTI2025

A machine-learning approach to measure 3D sample properties from 2D Transmission Electron Microscopy images

Transmission Electron Microscopy (TEM) is a powerful tool for the characterization of materials at the nanoscale; however, its inherent two-dimensional (2D) nature poses significant challenges to accurately measure three-dimensional (3D) properties. We introduce a supervised machine-learning model that predicts 3D structural information, such as sample thickness and curvature, from a series of conventional 2D TEM images. The model, a U-Net convolutional neural network, is trained on a large synthetic dataset generated from dynamical diffraction simulations that model TEM’s complex, nonlinear image formation, accounting for sample thickness and curvature. This physically realistic framework enables exploration of a broad parameter space impractical to sample experimentally. We demonstrate that the trained model has accurate predictions for experimental single-crystal silicon samples, achieving performance comparable to established measurement techniques. This work highlights the critical role of robust, simulation-based training in overcoming the limitations of real-world imaging artifacts and inconsistent sample geometries. By integrating machine learning with numerical simulations, we offer an efficient and scalable framework for quantitative TEM analysis, paving the way for more sophisticated 3D characterization of complex materials.

Dynamical diffraction↗
DOE OSTI2026

Hierarchical Reinforcement Learning of a Short-Range Bond-Order Potential for Silica: Analytic Embedding of Coordination with Classical Efficiency

Reinforcement learning (RL) has recently emerged as a data-efficient strategy to parametrize short-range interatomic potentials. Building on our past RL optimization of pairwise silica models, we extend the framework to a bond-order (Tersoff-type) potential that provides an analytic embedding of local coordination through a three-body term. A hierarchical RL workflow combining continuous-action Monte Carlo Tree Search and property-based rewards efficiently explores the 26-dimensional parameter space, sequentially optimizing lattice parameters, densities, angles, and cohesive energies of 21 silica polymorphs. The resulting models, Q-Tersoff and ML-Tersoff, reproduce the energetic ordering of low-energy phases and capture the angular correlations and amorphous structure factors of silica with improved fidelity over pairwise force fields, while remaining orders of magnitude faster than high-dimensional machine-learned potentials. Both models underperform for elastic constants and high-energy frameworks, delineating the limits of the current analytic form. The approach establishes a general and interpretable route to angle-aware, short-range potentials that bridge physics-based and machine-learned descriptions of silicate materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

MISPR : an open-source package for high-throughput multiscale molecular simulations

Computational tools provide a unique opportunity to study and design optimal materials by enhancing our ability to comprehend the connections between their atomistic structure and functional properties. However, designing materials with tailored functionalities is complicated due to the necessity to integrate various computational-chemistry software (not necessarily compatible with one another), the heterogeneous nature of the generated data, and the need to explore vast chemical and parameter spaces. The latter is especially important to avoid bias in scattered data points-based models and derive statistical trends only accessible by systematic datasets. Here, we introduce a robust high-throughput multi-scale computational infrastructure coined MISPR (Materials Informatics for Structure–Property Relationships) that seamlessly integrates classical molecular dynamics (MD) simulations with density functional theory (DFT). By enabling high-performance data analytics and coupling between different methods and scales, MISPR addresses critical challenges arising from the needs of automated workflow management and data provenance recording. The major features of MISPR include automated DFT and MD simulations, error handling, derivation of molecular and ensemble properties, and creation of output databases that organize results from individual calculations to enable reproducibility and transparency. In this work, we describe fully automated DFT workflows implemented in MISPR to compute various properties such as nuclear magnetic resonance chemical shift, binding energy, bond dissociation energy, and redox potential with support for multiple methods such as electron transfer and proton-coupled electron transfer reactions. The infrastructure also enables the characterization of large-scale ensemble properties by providing MD workflows that calculate a wide range of structural and dynamical properties in liquid solutions. MISPR employs the methodologies of materials informatics to facilitate understanding and prediction of phenomenological structure–property relationships, which are crucial to designing novel optimal materials for numerous scientific applications and engineering technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High-volume tunable resonator for axion searches above 7 GHz

In this research, we present results from an experimental demonstration of a tunable thin-shell axion haloscope whose geometry decouples its overall volume from its resonant frequency, thereby evading the steep sensitivity degradation at high frequencies. An aluminum 2.6-l (41⁢λ 3 ) prototype, which tunes from 7.1 to 8.0 GHz, was fabricated and characterized at room temperature. An axion-sensitive, straightforwardly tunable TM 010 mode is clearly identified with a room-temperature quality factor, Q, of approximately 5000. The on-resonance E-field distribution is mapped and found to agree with numerical calculations. Anticipating future cryogenic operation, we develop an alignment protocol relying only on rf measurements of the cavity, maintaining a form factor of 0.57 across the full tuning range. These measurements demonstrate the feasibility of cavity-based haloscopes with operating volume V $\gg$ λ 3 . We discuss plans for future development and the parameters required for a thin-shell haloscope exploring the postinflationary axion parameter space (approximately 4 to 30 GHz) at Dine-Fischler-Srednicki-Zhitnitsky sensitivity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Temperature dependence of magnetic anisotropy and magnetoelasticity from classical spin-lattice calculations

Here we present a classical molecular-spin dynamics (MSD) methodology that enables accurate computations of the temperature dependence of the magnetocrystalline anisotropy as well as magnetoelastic properties of magnetic materials. The nonmagnetic interactions are accounted for by a spectral neighbor analysis potential (SNAP) machine-learned interatomic potential, whereas the magnetoelastic contributions are accounted for using a combination of an extended Heisenberg Hamiltonian and a Néel pair interaction model, representing both the exchange interaction and spin-orbit-coupling effects, respectively. All magnetoelastic potential components are parameterized using a combination of first-principles and experimental data. Our framework is applied to the α phase of iron. Initial testing of our MSD model is done using a 0 K parametrization of the Néel interaction model. After this, we examine how individual Néel parameters impact the $B$ 1 and $B$ 2 magnetostrictive coefficients using a moment-independent δ sensitivity analysis. The results from this study are then used to initialize a genetic algorithm optimization which explores the Néel parameter phase space and tries to minimize the error in the B 1 and B 2 magnetostrictive coefficients in the range of 0–1200 K. Our results show that while both the 0 K and genetic algorithm optimized parametrization provide good experimental agreement for $B$ 1 and $B$ 2 , only the genetic algorithm optimized results can capture the second peak in the $B$ 1 magnetostrictive coefficient which occurs near approximately 800 K.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Shifted μ -hybrid inflation, gravitino dark matter, and observable gravity waves

We investigate supersymmetric hybrid inflation in a realistic model based on the gauge symmetry SU(4) c × SU(2) L × SU(2) R . The minimal supersymmetric standard model (MSSM) μ term arises, following Dvali, Lazarides, and Shafi, from the coupling of the MSSM electroweak doublets to a gauge singlet superfield which plays an essential role in inflation. The primordial monopoles are inflated away by arranging that the SU(4) c × SU(2) L × SU(2) R symmetry is broken along the inflationary trajectory. The interplay between the (above) μ coupling, the gravitino mass, and the reheating following inflation is discussed in detail. We explore regions of the parameter space that yield gravitino dark matter and observable gravity waves with the tensor-to-scalar ratio r ~ 10 –4 –10 –3 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Shedding light on the MiniBooNE excess with searches at the LHC

The origin of the excess of low-energy events observed by the MiniBooNE experiment remains a mystery, despite exhaustive investigations of backgrounds and a series of null measurements from complementary experiments. One intriguing explanation is the production of beyond-the-Standard-Model particles that could mimic the experimental signature of additional ν e appearance seen in MiniBooNE. In one proposed mechanism, muon neutrinos up-scatter to produce a new “dark neutrino” state that decays by emitting highly collimated electron-positron pairs. We propose high-energy neutrinos produced from W boson decays at the Large Hadron Collider as an ideal laboratory to study such models. Simple searches for a low-mass, boosted dilepton resonance produced in association with a high- p T muon from the W decay with run 2 data would already provide unique sensitivity to a range of dark neutrino scenarios, with prompt and displaced searches providing complementarity. Looking farther ahead, we show how the unprecedented sample of W boson decays anticipated at the HL-LHC, together with improved lepton acceptance would explore much of the parameter space most compatible with the MiniBooNE excess. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Leptonic probes of alternative left-right symmetric models

We explore constraints on the parameter space of the alternative left-right model originating from the leptonic sector. Our analyses focuses on both lepton-flavor-conserving observables, particularly the anomalous magnetic moment of the muon, and lepton-flavor-violating processes like μ → e γ decay and μ − e conversions in nuclei. While contributions to the anomalous magnetic moment fall below the measured values at 2 σ , current and future experimental sensitivities to flavor-violating branching rations of the Standard Model leptons are expected to impose lower bounds on the mass of the peculiar S U ( 2 ) R gauge boson of the model. This provides complementary constraints relative to existing limits, which are indirect and derived from collider bounds on the mass of the associated neutral gauge boson Z ′ . Published by the American Physical Society 2025

Frank, Mariana (ORCID:0000000322684821)↗
DOE OSTI2024

GALILEO: Galactic Axion Laser Interferometer Leveraging Electro-Optics

We propose a novel experimental method for probing light dark matter candidates. We show that an electro-optical material’s refractive index is modified in the presence of a coherently oscillating dark matter background. A high-precision resonant Michelson interferometer can be used to read out this signal. The proposed detection scheme allows for the exploration of an uncharted parameter space of dark matter candidates over a wide range of masses—including masses exceeding a few tens of microelectronvolts, which is a challenging parameter space for microwave cavity haloscopes. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

HPC Network Simulation Tuning via Automatic Extraction of Hardware Parameters

Popular HPC network interconnection simulators such as SST/macro provide a variety of configurable parameters to explore the design space of hardware components such as network interface cards (NIC), switches, and links among them. While such knobs provide flexibility to explore design trade-offs for novel hardware, manually configuring simulations for matching configurations of the existing hardware to focus on topology exploration can be cumbersome and error-prone, leading to widely inaccurate simulations. This challenge is compounded when specifications of various (proprietary) technologies are not readily available or intentionally omitted. In this work, we propose a framework to autotune the multiple network models’ simulation configurations within SST/macro using Tree-structured Parzen Estimator-based Bayesian optimization to observe the effect on simulation accuracy across different message regimes. These regimes consist of small to large message sizes and latency to bandwidth-bound messages. We provide a detailed analysis of the simulation error for four representative HPC systems. Our Bayesian optimization based autotuning framework for network models achieves a maximum of 5x improvement in accuracy over best-effort manual configurations based on available hardware specifications.

Simulation, autotuning↗
DOE OSTI2024

Automatic Extraction of Network Configurations for Realistic Simulation and Validation

Popular HPC network interconnection simulators such as SST Macro provide a variety of configurable parameters to explore the design space of hardware components such as network links and switches. While such knobs provide flexibility to explore design trade-offs for novel hardware, manually configuring simulations for existing hardware to focus on topology exploration can be cumbersome and error-prone, leading to widely inaccurate simulations. This challenge is compounded when specifications of various (proprietary) technologies are not readily available or are intentionally omitted. In this work, we provide a methodology to automatically tune the simulation configuration of the multiple network models running within SST Macro using Bayesian optimization. We perform this optimization in the context of multiple messaging regimes (i.e., small to large and latency to bandwidth-bound messages) and provide a detailed analysis of the simulation error for four systems. With our automated framework, we achieve a 5x improvement in accuracy over best-effort configurations based on available hardware specifications.

Suetterlein, Joshua D.↗
DOE OSTI2025

Phenomenology of bubble size distributions in a first-order phase transition

In a cosmological first-order phase transition (FOPT), the true and false vacuum bubble radius distributions are not expected to be monochromatic, as is usually assumed. Consequently, Fermi balls (FBs) and primordial black holes (PBHs) produced in a dark FOPT will have extended mass distributions. We show how gravitational wave (GW), microlensing and Hawking evaporation signals for extended bubble radius/mass distributions deviate from the case of monochromatic distributions. The peak of the GW spectrum is shifted to lower frequencies, and the spectrum is broadened at frequencies below the peak frequency. Thus, the radius distribution of true vacuum bubbles introduces another uncertainty in the evaluation of the GW spectrum from a FOPT. The extragalactic gamma-ray signal at AMEGO-X/e-ASTROGAM from PBH evaporation may evince a break in the power-law spectrum between 5 MeV and 10 MeV for an extended PBH mass distribution. Optical microlensing surveys may observe PBH mass distributions with average masses below 10 -10 M ⊙ , which is not possible for monochromatic mass distributions. This expands the FOPT parameter space that can be explored with microlensing.

Marfatia, Danny [Univ. of Hawaii at Manoa, Honolul↗
DOE OSTI2020

Vibration and EMF Backgrounds at NEXUS

NEXUS (Northwestern EXperimental Underground Site) is a dark matter detector prototyping and calibration facility at Fermilab. It is part of the SuperCDMS collaboration, which is focused on exploring light-WIMP parameter space. The NEXUS cryostat, typically operated at temperatures of about 10 mK, is located 107 meters underground in the MINOS Near Detector Hall to reduce cosmic ray backgrounds. We characterized the vibrational and electro-magnetic frequency (EMF) backgrounds surrounding the cryostat and investigated how these backgrounds transfer into the detector itself.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Self-assembly of cocontinuous nanostructured copolymer templates with compositional and architectural dispersity. Final Report

Cocontinuous nanostructured materials in which multiple domains of different materials simultaneously span three dimensional space offer opportunities to achieve combined properties not possible with a single homogeneous material. These architectures have importance in a broad range of energy-relevant technologies including batteries, supercapacitors, fuel cells, separation membranes, and catalysts. Achieving such structures in polymeric materials has been of long-standing interest in the field, due to both the inherently attractive properties of cocontinuous polymer morphologies as well as their ability to serve as templates for other functional nanostructured materials. Our work on this project has established that randomly-linked polymer architectures constructed from two immiscible polymer strands provide robust and highly tunable approaches to disordered cocontinuous nanostructures. In particular, we developed a detailed understanding of how the parameters (linker functionality and strand length, asymmetry, and dispersity) of randomly-linked networks controlled the breadth of the cocontinuous window over which both phases remain percolated. We further characterized how these nanostructures undergo orientation, while retaining cocontinuity, under mechanical deformation. We also compared their behavior to that of random multi-block polymers of linear architecture, which show similar propensity to form disordered nanostructures, albeit over narrower ranges of parameter space. Finally, we have explored the development of functional polymer nanostructures and composites based in part on the fundamental understanding obtained via this project.

36 MATERIALS SCIENCE↗