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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Characterization of Proteoforms with Unknown Post-translational Modifications Using the MIScore

Various proteoforms may be generated from a single gene due to primary structure alterations (PSAs) such as genetic variations, alternative splicing, and post-translational modifications (PTMs). Top-down mass spectrometry is capable of analyzing intact proteins and identifying patterns of multiple PSAs, making it the method of choice for studying complex proteoforms. In top-down proteomics, proteoform identification is often performed by searching tandem mass spectra against a protein sequence database that contains only one reference protein sequence for each gene or transcript variant in a proteome. Because of the incompleteness of the protein database, an identified proteoform may contain unknown PSAs compared with the reference sequence. Proteoform characterization is to identify and localize PSAs in a proteoform. Although many software tools have been proposed for proteoform identification by top-down mass spectrometry, the characterization of proteoforms in identified proteoform-spectrum matches still relies mainly on manual annotation. We propose to use the Modification Identification Score (MIScore), which is based on Bayesian models, to automatically identify and localize PTMs in proteoforms. Experiments showed that the MIScore is accurate in identifying and localizing one or two modifications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A Swing of the Pendulum: The Chemodynamics of the Local Stellar Halo Indicate Contributions from Several Radial Merger Events

We find that the chemical abundances and dynamics of APOGEE and GALAH stars in the local stellar halo are inconsistent with a scenario in which the inner halo is primarily composed of debris from a single massive, ancient merger event, as has been proposed to explain the Gaia-Enceladus/Gaia Sausage (GSE) structure. The data contain trends of chemical composition with energy that are opposite to expectations for a single massive, ancient merger event, and multiple chemical evolution paths with distinct dynamics are present. We use a Bayesian Gaussian mixture model regression algorithm to characterize the local stellar halo, and find that the data are fit best by a model with four components. We interpret these components as the Virgo Radial Merger (VRM), Cronus, Nereus, and Thamnos; however, Nereus and Thamnos likely represent more than one accretion event because the chemical abundance distributions of their member stars contain many peaks. Although the Cronus and Thamnos components have different dynamics, their chemical abundances suggest they may be related. We show that the distinct low- and high-α halo populations from Nissen & Schuster are explained by VRM and Cronus stars, as well as some in situ stars. Because the local stellar halo contains multiple substructures, different popular methods of selecting GSE stars will actually select different mixtures of these substructures, which may change the apparent chemodynamic properties of the selected stars. We also find that the Splash stars in the Solar region are shifted to higher v $\phi$ and slightly lower [Fe/H] than previously reported.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Uncertainty quantification for equations of state: copper as an example

Equations of state are essential for providing a fundamental description of materials properties in thermodynamic equilibrium and are used to provide closure relations for hydrodynamics simulations. Generally, equations of state rely on simple physics-based parameterized materials models to inform on the free energy of a material through out a given thermodynamic state space. Historically the parameters of these models have been tuned by hand to fit various experimental data. However, modern optimization and uncertainty quantification techniques allow us to quickly test thousands of parameter combinations and obtain meaningful uncertainty estimates on the parameters, opening opportunities for assessing systematic uncertainties in experiments, assessing model adequacy, and more. In this report, we use Bayesian inference to fit the solid (fcc) equation of state of copper. We focus on fitting five different experimental datasets, including the isobaric density, isobaric heat capacity, room temperature isotherm, principal isentrope, and principal Hugoniot. We fit all five data types simultaneously, and then explore the extent to which combinations of 2 subsets of the 5 datasets can constrain the EOS parameters, as compared to the fit to all 5. This information is useful for investigating the extent to which different datasets can con strain EOS models and thereby help guide experimental investigations in order to best constrain the EOS. We also discuss ways that the methodologies can be used to investigate systematic discrepancies between experiments, as well as how the methods can be used to assess model uncertainty. The framework we develop is general, in that it can be used with a variety of optimization or uncertainty quantification techniques and with a variety of data sources, including both experimental and ab-inito data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Searches for New Physics With Muon Conversion at Fermilab and Triboson Production at the LHC

We report on several efforts to search for physics beyond the standard model of particle physics at broad energy scales. The Mu2e experiment at Fermilab will search for charged lepton flavor violation via the muon to electron conversion process, which is suppressed in the Standard Model. Mu2e will be operated at a low energy, yet can probe New Physics at very high mass scales (O(1e3 - 1e4 ) TeV). At high energies, the CMS experiment at the CERN LHC continues to deliver an impressive suite of Standard Model measurements and limits on a variety of New Physics signatures. Mu2e is under construction and slated to collect its first physics data in the coming years. This thesis describes work done during the construction phase of Mu2e and focuses on two critical areas: magnetic field modeling and statistical analysis. We describe a novel method for field modeling which we validate using a simulated dataset representing the expected magnetic field in the Detector Solenoid. This method blends a standard least-squares fitting technique that utilizes physically motivated analytical model functions with a novel physics informed network that is constructed to obey Maxwell’s equations. We show the technique can model the field with an accuracy of 10−7 despite the presence of injected noise in the pseudo-measurements at the 10−5 level. We then present preliminary results of the calibration of 3D Hall probes at the sub-10−4 level. These probes will be used to directly measure the Mu2e Detector Solenoid magnetic field on a sparse grid; these measurements serve as the input to the field model fitting. Finally, we describe the first implementation of both an unbinned shape analysis and a Bayesian interpretation applied to Mu2e pseudo-data. Up to 20% tighter limits can be set by the shape analysis compared to a standard cut & count analysis. The AlCap experiment collected data at PSI in 2015 to measure several important quantities related to nuclear muon capture on an aluminum target, which is a significant background process for Mu2e. The neutron emission from muon capture can introduce background hits in the Mu2e detectors and can increase radiation damage in various elements of the apparatus. We present measurements of the neutron group fluence and mean neutron multiplicity for muon capture on aluminum nuclei. Finally, we discuss an analysis of triboson production at CMS using an Effective Field Theory framework. Standard Model triboson production, which was first observed at CMS in 2020, has a relatively small cross section and provides direct access to both anomalous triple gauge couplings and quartic gauge couplings. These couplings, interpreted in the Standard Model Effective Field Theory, are studied in the present work. We target the boosted regime where the background rate is low and yields are enhanced when dimension-6 and dimension-8 Wilson coefficients are non-zero. We do not observe an excess in the data and therefore set bounds on the Wilson coefficients. For dimension-6 coefficients the tightest observed (expected) bounds are set on cW /Λ2 where Λ is the mass scale of new physics; the bounds are [−0.13, 0.12] TeV−2 ([−0.12, 0.12] TeV−2 ) at 95% CL. The tightest bounds in dimension-8 are set on fT,0 / Λ4 ; the observed (expected) bounds at 95% CL are [−0.63, 0.69] TeV−4 ([−0.54, 0.62] TeV−4 ). Additional results are presented which include scenarios where multiple Wilson coefficients are non-zero, the application of signal model clipping to address unitarity violation in Effective Field Theories, and a novel template fit developed for easier reinterpretation of our results.

Kampa, Cole Erik [Northwestern U. (main)] (ORCID:0↗
DOE OSTI2022

Bayesian Seismoacoustic Source Location: Acoustic Approach

Seismic waves and infrasound are key technologies in the International Monitoring System (IMS) to monitor explosive events in the solid Earth and atmosphere. Energetic man-made or natural events (e.g., chemical/nuclear explosions, volcanic eruptions, and earthquakes) near the Earth’s surface produce both ground motion and atmospheric pressure disturbances which propagate as seismic waves and infrasound, respectively. Seismic waves have been generally used to detect and identify underground and near-surface events (Myers, et. al., 2007), and infrasound are sensitive to events near the surface or in the atmosphere (Modrak et. al., 2010). Due to their different sensitivities to events, they can complement to each other to improve the event detection and discrimination. The framework of joint seismoacoustic event location has recently reviewed by theoretical research (Koch and Arrowsmith, 2019). Although the early applications showed promising results to improve the accuracy of event location, their application were still limited to a small set of events selected to prove the concepts, and practical capability of the method for operational purpose is not fully evaluated with data. Our final goal is to apply the method of seismoacoustic event location to a larger set of events and evaluate its applicability for operational event location in practice. To that end, we focus on developing and verifying acoustic source location method in this study.

58 GEOSCIENCES↗
DOE OSTI2024

Ground Heat Flux Reconstruction Using Bayesian Uncertainty Quantification Machinery and Surrogate Modeling

Ground heat flux (G 0 ) is a key component of the land-surface energy balance of high-latitude regions. Despite its crucial role in controlling permafrost degradation due to global warming, G 0 is sparsely measured and not well represented in the outputs of global scale model simulation. In this study, an analytical heat transfer model is tested to reconstruct G 0 across seasons using soil temperature series from field measurements, Global Climate Model, and climate reanalysis outputs. The probability density functions of ground heat flux and of model parameters are inferred using available G 0 data (measured or modeled) for snow-free period as a reference. When observed G 0 is not available, a numerical model is applied using estimates of surface heat flux (dependent on parameters) as the top boundary condition. These estimates (and thus the corresponding parameters) are verified by comparing the distributions of simulated and measured soil temperature at several depths. Aided by state-of-the-art uncertainty quantification methods, the developed G 0 reconstruction approach provides novel means for assessing the probabilistic structure of the ground heat flux for regional permafrost change studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials

Design of new microelectronic materials is characterized by several challenges such as high-dimensionality of the atomic structure-composition variable space, formidable cost of directly using high-fidelity simulations for design optimization, dispersity in literature-reported similar materials and synthesis methods, complex physical mechanisms, and mixed qualitative and quantitative design variables that lead to a disjointed design space. Even though machine learning (ML) techniques have been employed to expedite materials innovation, existing methods treat ML and design optimization as two separate processes, failing to resolve the fundamental challenges associated with high dimensionality and mixed-variable complexity. We have developed a ML enhanced mixed-variable material design optimization framework to efficiently extract useful information from existing data in literature and physics-based simulations to guide the autonomous search for optimal materials. Our proposed framework is composed of four computational modules: (1) a natural language processing (NLP) based virtual screening module, (2) classification based concept exploration module, (3) a density functional theory (DFT)-based high-fidelity evaluation model, and (4) a novel latent-variable Gaussian process (LVGP) ML model for mixed-variable problems with uncertainty quantification, which seamlessly integrates with Bayesian Optimization (BO) and achieves superb efficiency through embedded physics-based dimension reduction. Our approach is demonstrated and validated using the testbed of functional materials exhibiting metal-insulation transitions (MITs), with the targeted reversible resistivity changes (∼10^5) near room temperature. At the end of the 30-month project, we have developed a series of new ML techniques using NLP, conditional variational autoencoders, active learning, latent-variable Gaussian processes, integrated with Bayesian optimization. Our project has resulted in new predicted MITs compounds and improved understanding of MITs microscopic mechanisms, which in turn will revolutionize microelectronics science to provide energy-saving solutions. Our research has improved both creativity and efficiency in transforming rare-event discoveries of new functional materials to persistent innovations. In addition to open-sourcing the online MIT database and the classification model, the LVGP open source code has been downloaded more than 15,000 times within two years. More than 40 MIT compounds have been identified and many have been pursued experimentally via collaborators. The research results are published in close to 20 collaborative papers in high-impact journals, such as Chem. Mater., Appl. Phys. Rev., Sci. Rep., among others of design space.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Modeling the Radio Foreground for Detection of CMB Spectral Distortions from the Cosmic Dawn and the Epoch of Reionization

Cosmic baryon evolution during the Cosmic Dawn and Reionization results in redshifted 21-cm spectral distortions in the cosmic microwave background (CMB). These encode information about the nature and timing of first sources over redshifts 30–6 and appear at meter wavelengths as a tiny CMB distortion along with the Galactic and extragalactic radio sky, which is orders of magnitude brighter. Therefore, detection requires precise methods to model foregrounds. We present a method of foreground fitting using maximally smooth (MS) functions. We demonstrate the usefulness of MS functions over traditionally used polynomials to separate foregrounds from the Epoch of Reionization (EoR) signal. We also examine the level of spectral complexity in plausible foregrounds using GMOSS, a physically motivated model of the radio sky, and find that they are indeed smooth and can be modeled by MS functions to levels sufficient to discern the vanilla model of the EoR signal. We show that MS functions are loss resistant and robustly preserve EoR signal strength and turning points in the residuals. Finally, we demonstrate that in using a well-calibrated spectral radiometer and modeling foregrounds with MS functions, the global EoR signal can be detected with a Bayesian approach with 90% confidence in 10 minutes’ integration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Quantification of uncertainty in photon source spot size inference during laser-driven radiography experiments at TRIDENT

Images of the R2DTO resolution target were obtained during laser-driven-radiography experiments performed at the TRIDENT laser facility, and analysis of these images using the Bayesian Inference Engine (BIE) determines a most probable full-width half maximum (FWHM) spot size of 78 μm. However, significant uncertainty prevails due to variation in the measured detector blur. Propagating this uncertainty in detector blur through the forward model results in an interval of probabilistic ambiguity spanning approximately 35-195 μm when the laser energy impinges on a thick (1 mm) tantalum target. In other phases of the experiment, laser energy is deposited on a thin (~100 nm) aluminum target placed 250 μm ahead of the tantalum converter. When the energetic electron beam is generated in this manner, upstream from the bremsstrahlung converter, the inferred spot size shifts to a range of much larger values, approximately 270-600 μm FWHM. This report discusses methods applied to obtain these intervals as well as concepts necessary for interpreting the result within a context of probabilistic quantitative inference.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Recommended nuclear data for medical radioisotope production: diagnostic positron emitters

An IAEA coordinated research project that began in 2012 and ended in 2016 was primarily dedicated to the compilation, evaluation and recommendation of cross-section data for the production of medical radionuclides. One significant part of this work focused on diagnostic positron emitters. These particular studies consist of 69 reactions for direct and indirect or generator production of 44 Sc( 44 Ti), 52 mMn( 52 Fe), 52 gMn, 55 Co, 61 Cu, 62 Cu( 62 Zn), 66 Ga, 68 Ga( 68 Ge), 72 As( 72 Se), 73 Se, 76 Br, 82 Rb( 82 Sr), 82 mRb, 86 Y, 89 Zr, 90 Nb, 94 mTc, 110 mIn( 110 Sn), 118 Sb( 118 Te), 120 I, 122 I( 122 Xe), 128 Cs( 128 Ba), and 140 Pr( 140 Nd) medical radionuclides. The resulting reference cross-section data were obtained from Padé fits to selected and corrected experimental data, and integral thick target yields were subsequently deduced. Uncertainties in the fitted results were estimated via a Padé least-squares method with the addition of a 4% assessed systematic uncertainty. Experimental data were also compared with theoretical predictions available from the TENDL-2015 and TENDL-2017 libraries.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2019

Quantifying correlated truncation errors in effective field theory

Effective field theories (EFTs) organize the description of complex systems into an infinite sequence of decreasing importance. Predictions are made with a finite number of terms, which induces a truncation error that is often left unquantified. Here, we formalize the notion of EFT convergence and propose a Bayesian truncation error model for predictions that are correlated across the independent variables, e.g., energy or scattering angle. Central to our approach are Gaussian processes that encode both the naturalness and correlation structure of EFT coefficients. Our use of Gaussian processes permits efficient and accurate assessment of credible intervals, allows EFT fits to easily include correlated theory errors, and provides analytic posteriors for physical EFT-related quantities such as the expansion parameter. Furthermore, we demonstrate that model-checking diagnostics—applied to the case of multiple curves—are powerful tools for EFT validation. As an example, we assess a set of nucleon-nucleon scattering observables in chiral EFT. In an effort to be self-contained, appendices include thorough derivations of our statistical results. Our methods are packaged in Python code, called gsum, that is available for download on GitHub.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Dynamic Matrix Completion Based State Estimation in Distribution Grids

The power distribution network is undergoing tremendous transformation due to an increase in the penetration of renewable energy resources and electric vehicles. These changes have resulted in greater uncertainty and dynamics in the distribution grid states. Therefore, the ability to track and monitor system states has become a critical need for accurate and timely control actions. In this paper, we propose two dynamic sparsity-based state estimation approaches for distribution systems: (1) locally weighted matrix completion (LW-MC) and (2) Bayesian matrix completion with Kalman filter prediction (BMC-KF). The performance of the proposed dynamic state estimation strategies is compared with the classic/static matrix completion (static-MC) approach using the IEEE 37 and IEEE 123 bus test systems. Finally, results indicate that BMC-KF approach outperforms both LW-MC as well as static-MC even when 30% of the measurement data is available. Computational complexity associated with both approaches is quantified.

42 ENGINEERING↗
DOE OSTI2026

Supercharging simulation-based inference for Bayesian optimal experimental design

Abstract Bayesian optimal experimental design (BOED) seeks to maximize the expected information gain (EIG) of experiments. This requires a likelihood estimate, which in many settings is intractable. Simulation-based inference (SBI) provides powerful tools for this regime. However, existing work explicitly connecting SBI and BOED is restricted to a single contrastive EIG bound. We show that the EIG admits multiple formulations which can directly leverage modern SBI density estimators, encompassing neural posterior, likelihood, and ratio estimation. Building on this perspective, we define a novel EIG estimator using neural likelihood estimation. Further, we identify optimization as a key bottleneck of gradient based EIG maximization and show that a simple multi-start parallel gradient ascent procedure can substantially improve reliability and performance. With these innovations, our SBI-based BOED methods are able to match or outperform by up to 22% existing state-of-the-art approaches across standard BOED benchmarks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Efficient Optimization of Plasma Radiation Detector Configurations using Imperfect Inference Models

The configurations of instruments fielded on an experiment affect the amount of information captured and the quality of subsequent inference. Here, we investigate the problem of optimizing plasma x-ray radiation detectors in a magneto-inertial fusion experiment at Sandia National Laboratories. It is impossible to directly measure properties such as the temperature of the thermonuclear fusion plasma produced in these experiments because of the extreme environment and destructive nature of the experiment. Among other diagnostics, several detectors are placed with significant standoff from the fusion target to capture the x-rays emitted by the fusion plasma, which can be used to infer some of its properties. To optimize the configuration of these detectors, a high-fidelity model (HFM) is used for simulating outputs and a low-fidelity model (LFM) is used for inference. We develop methods based on A- and L-optimality criteria that are efficient to compute while explicitly accounting for the discrepancy between the HFM and the LFM. The method allows us to find detector configurations that perform similarly to or better than the configuration obtained using an existing sampling-based optimization method while decreasing computational time by a factor of 50. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Bayesian optimization↗
DOE OSTI2020

Quantifying uncertainties and correlations in the nuclear-matter equation of state

We perform statistically rigorous uncertainty quantification (UQ) for chiral effective field theory (χ EFT) applied to infinite nuclear matter up to twice nuclear saturation density. The equation of state (EOS) is based on high-order many-body perturbation theory calculations with nucleon-nucleon and three-nucleon interactions up to fourth order in the χ EFT expansion. From these calculations our newly developed Bayesian machine-learning approach extracts the size and smoothness properties of the correlated EFT truncation error. Furthermore, we then propose a novel extension that uses multitask machine learning to reveal correlations between the EOS at different proton fractions. The inferred in-medium χ EFT breakdown scale in pure neutron matter and symmetric nuclear matter is consistent with that from free-space nucleon-nucleon scattering. These significant advances allow us to provide posterior distributions for the nuclear saturation point and propagate theoretical uncertainties to derived quantities: the pressure and incompressibility of symmetric nuclear matter, the nuclear symmetry energy, and its derivative. Our results, which are validated by statistical diagnostics, demonstrate that an understanding of truncation-error correlations between different densities and different observables is crucial for reliable UQ. The methods developed here are publicly available as annotated Jupyter notebooks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Scalar bounded-from-below conditions from Bayesian active learning

We present a procedure leveraging Bayesian deep active learning to rapidly produce highly accurate approximate bounded-from-below conditions for arbitrary renormalizable scalar potentials, in the form of a neural network which may be saved and exported for use in arbitrary parameter space scans. We explore the performance of our procedure on three different scalar potentials with either highly nontrivial or unknown symbolic bounded-from-below conditions (the most general two-Higgs doublet model, the three-Higgs doublet model, and a version of the Georgi-Machacek model without custodial symmetry). We find that we can produce fast and highly accurate binary classifiers for all three potentials. Furthermore, for the potentials for which no known symbolic necessary and sufficient conditions on boundedness-from-below exist, our classifiers substantially outperform some common approximate analytical methods, such as producing tractable sufficient but not necessary conditions or evaluating boundedness-from-below conditions for scenarios in which only a subset of the theory’s fields achieve vacuum expectation values. Our methodology can be readily adapted to any renormalizable scalar field theory. For the community’s use, we have developed a package, BFBrain, which allows for the rapid implementation of our analysis procedure on user-specified scalar potentials with a high degree of customizability. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2018

A Likely Detection of a Two-planet System in a Low-magnification Microlensing Event

We report on the analysis of a microlensing event, OGLE-2014-BLG-1722, that showed two distinct short-term anomalies. The best-fit model to the observed light curves shows that the two anomalies are explained with two planetary mass ratio companions to the primary lens. Although a binary-source model is also able to explain the second anomaly, it is marginally ruled out by 3.1σ. The two-planet model indicates that the first anomaly was caused by planet “b” with a mass ratio of q=(4.5{sub −0.6}{sup +0.7})×10{sup −4} and projected separation in units of the Einstein radius, s = 0.753 ± 0.004. The second anomaly reveals planet “c” with a mass ratio of q{sub 2}=(7.0{sub −1.7}{sup +2.3})×10{sup −4} with Δχ {sup 2} ∼ 170 compared to the single-planet model. Its separation has two degenerated solutions: the separation of planet c is s {sub 2} = 0.84 ± 0.03 and 1.37 ± 0.04 for the close and wide models, respectively. Unfortunately, this event does not show clear finite-source and microlensing parallax effects; thus, we estimated the physical parameters of the lens system from Bayesian analysis. This gives the masses of planets b and c as m{sub b}=56{sub −33}{sup +51} and m{sub c}=85{sub −51}{sup +86} M{sub ⊕}, respectively, and they orbit a late-type star with a mass of M{sub host} =0.40{sub −0.24}{sup +0.36} M{sub ⊙} located at D{sub L}=6.4{sub −1.8}{sup +1.3} kpc from us. The projected distances between the host and planets are r{sub ⊥,b}=1.5±0.6 au for planet b and r{sub ⊥,c}=1.7{sub −0.6}{sup +0.7} au and r{sub ⊥,c}=2.7{sub −1.0}{sup +1.1} au for the close and wide models of planet c. If the two-planet model is true, then this is the third multiple-planet system detected using the microlensing method and the first multiple-planet system detected in low-magnification events, which are dominant in the microlensing survey data. The occurrence rate of multiple cold gas giant systems is estimated using the two such detections and a simple extrapolation of the survey sensitivity of the 6 yr MOA microlensing survey combined with the 4 yr μFUN detection efficiency. It is estimated that 6% ± 2% of stars host two cold giant planets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Measuring the Hubble Constant with Dark Neutron Star–Black Hole Mergers

Abstract Detection of gravitational waves (GWs) from neutron star-black hole (NSBH) standard sirens provides local measurements of the Hubble constant (H 0 ), regardless of the detection of an electromagnetic (EM) counterpart, given that matter effects can be exploited to break the redshift degeneracy of the GW waveforms. The distinctive merger morphology and the high-redshift detectability of tidally disrupted NSBH make them promising candidates for this method. Also, the detection prospects of an EM counterpart for these systems will be limited toz< 0.8 in the optical, in the era of future GW detectors. Using recent constraints on the equation of state of NSs from multi-messenger observations of NICER and LIGO/Virgo/KAGRA, we show the prospects of measuringH 0 solely from GW observation of NSBH systems, achievable by the Einstein telescope (ET) and Cosmic Explorer (CE) detectors. We first analyze individual events to quantify the effect of high-frequency (≥500 Hz) tidal distortions on the inference of NS tidal deformability parameter (Λ) and hence onH 0 . We find that disruptive mergers can constrain Λ up to  ( 60 % ) more precisely than nondisruptive ones. However, this precision is not sufficient to place stringent constraints on theH 0 from individual events. By performing Bayesian analysis on simulated NSBH data (up toN= 100 events, corresponding to a day of observation) in the ET+CE detectors, we find that NSBH systems enable unbiased 4%–13% precision on the estimate ofH 0 (68% credible interval). This is a similar measurement precision found in studies analyzing NSBH mergers with EM counterparts in the LVKC O5 era.

Astronomy & Astrophysics↗