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DOE OSTI2024

Tackling the curse of dimensionality in fractional and tempered fractional PDEs with physics-informed neural networks

Fractional and tempered fractional partial differential equations (PDEs) are effective models of long-range interactions, anomalous diffusion, and non-local effects. Traditional numerical methods for these problems are mesh-based, thus struggling with the curse of dimensionality (CoD). Physics-informed neural networks (PINNs) offer a promising solution due to their universal approximation, generalization ability, and mesh-free training. In principle, Monte Carlo fractional PINN (MC-fPINN) estimates fractional derivatives using Monte Carlo methods and thus could lift CoD. However, this may cause significant variance and errors, hence affecting convergence; in addition, MC-fPINN is sensitive to hyperparameters. In general, numerical methods and specifically PINNs for tempered fractional PDEs are under-developed. Herein, we extend MC-fPINN to tempered fractional PDEs to address these issues, resulting in the Monte Carlo tempered fractional PINN (MC-tfPINN). To reduce possible high variance and errors from Monte Carlo sampling, we replace the one-dimensional (1D) Monte Carlo with 1D Gaussian quadrature, applicable to both MC-fPINN and MC-tfPINN. We validate our methods on various forward and inverse problems of fractional and tempered fractional PDEs, scaling up to 100,000 dimensions. Our improved MC-fPINN/MC-tfPINN using quadrature consistently outperforms the original versions in accuracy and convergence speed in very high dimensions.

42 ENGINEERING↗

DOE OSTI2025

Common femtoscopic hadron-emission source in pp collisions at the LHC

The femtoscopic study of pairs of identical pions is particularly suited to investigate the effective source function of particle emission, due to the resulting Bose–Einstein correlation signal. In small collision systems at the LHC, pp in particular, the majority of the pions are produced in resonance decays, which significantly affect the profile and size of the source. In this work, we explicitly model this effect in order to extract the primordial source in pp collisions at $\sqrt{s}$ = 13 TeV from charged π–π correlations measured by ALICE. We demonstrate that the assumption of a Gaussian primordial source is compatible with the data and that the effective source, resulting from modifications due to resonances, is approximately exponential, as found in previous measurements at the LHC. The universality of hadron emission in pp collisions is further investigated by applying the same methodology to characterize the primordial source of K–p pairs. The size of the primordial source is evaluated as a function of the transverse mass (m T ) of the pairs, leading to the observation of a common scaling for both π–π and K–p, suggesting a collective effect. Further, the present results are compatible with the m T scaling of the p–p and p–Λ primordial source measured by ALICE in high multiplicity pp collisions, providing additional evidence for the presence of a common emission source for all hadrons in small collision systems at the LHC. This will allow the determination of the source function for any hadron–hadron pairs with high precision, granting access to the properties of the possible final-state interaction among pairs of less abundantly produced hadrons, such as strange or charmed particles.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2021

LiDAR-Based Map Relative Localization Performance Analysis for Landing on Europa

This paper presents preliminary simulations andanalyses done to assess the feasibility of performing Map RelativeLocalization (MRL) with the Europa Lander LiDAR beingdeveloped for the Europa Lander Pre-Phase A concept. MapRelative Localization is the process of determining the horizontalposition of a lander with respect to an onboard, a-priori map,by comparing the map to sensor observations of the terrain duringdeorbit, descent, and landing (DDL). Although kilometerscaleposition knowledge is commonly available during DDL,landing in hazard-rich environments requires position errors of100 m or less. Prior knowledge in the case of Europa Landerwill be visual and topographic maps collected by the upcomingEuropa Clipper mission. The Mars 2020 Lander Vision System(LVS) uses images from a camera to localize with respect tovisual maps. This technology, as well as a 3D imaging LiDAR indevelopment for hazard detection, is currently baselined for theEuropa Lander Pre-Phase A concept. This paper investigatesthe potential use of the hazard detection LiDAR to performMRL with respect to a 3D digital elevation model (DEM)provided by the Europa Clipper mission, as an alternative orbackup solution to passive optical MRL. Compared to passiveoptical MRL, one advantage of LiDAR-based localization isthat it is insensitive to lighting conditions, potentially relaxingrequirements on synchronizing map acquisition and landingtime of day. To analyze LiDAR based MRL performance,six representative terrains are synthetically up-sampled fromGalileo-derived maps of Europa to a resolution of 0.5 m/pxand covering an area of 4 km by 4 km. These maps are usedas ground-truth to generate simulated noisy a-priori onboardtopographic maps expected from Europa Clipper as well assimulated LiDAR DEMs generated at an altitude of 5 km duringEuropa Lander DDL. The simulated LiDAR DEM is matchedagainst the simulated map via 2D normalized cross-correlation,exploiting the accurately known spacecraft attitude to avoidthe need for more computationally intensive algorithms such asIterative Closest Point (ICP). Two sources of measurement errorare identified for analysis: 1) additive Gaussian noise in therange measurements from the Europa Lander LiDAR and theEuropa Clipper derived maps and 2) errors in the LiDAR DEMinduced by errors in the Europa Lander state estimate which isused to de-warp the LiDAR scan data into a DEM format. Weassess the effect of each of these types of errors independently onmatching performance as well as the overall performance whenall types of error are introduced. Additionally, we present theresult of a sensitivity study to terrain frequency content.

Trawny, Nikolas↗

NASA NTRS2001

Probabilistic Structural Analysis and Reliability Using NESSUS With Implemented Material Strength Degradation Model

This project included both research and education objectives. The goal of this project was to advance innovative research and education objectives in theoretical and computational probabilistic structural analysis, reliability, and life prediction for improved reliability and safety of structural components of aerospace and aircraft propulsion systems. Research and education partners included Glenn Research Center (GRC) and Southwest Research Institute (SwRI) along with the University of Texas at San Antonio (UTSA). SwRI enhanced the NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) code and provided consulting support for NESSUS-related activities at UTSA. NASA funding supported three undergraduate students, two graduate students, a summer course instructor and the Principal Investigator. Matching funds from UTSA provided for the purchase of additional equipment for the enhancement of the Advanced Interactive Computational SGI Lab established during the first year of this Partnership Award to conduct the probabilistic finite element summer courses. The research portion of this report presents the cumulation of work performed through the use of the probabilistic finite element program, NESSUS, Numerical Evaluation and Structures Under Stress, and an embedded Material Strength Degradation (MSD) model. Probabilistic structural analysis provided for quantification of uncertainties associated with the design, thus enabling increased system performance and reliability. The structure examined was a Space Shuttle Main Engine (SSME) fuel turbopump blade. The blade material analyzed was Inconel 718, since the MSD model was previously calibrated for this material. Reliability analysis encompassing the effects of high temperature and high cycle fatigue, yielded a reliability value of 0.99978 using a fully correlated random field for the blade thickness. The reliability did not change significantly for a change in distribution type except for a change in distribution from Gaussian to Weibull for the centrifugal load. The sensitivity factors determined to be most dominant were the centrifugal loading and the initial strength of the material. These two sensitivity factors were influenced most by a change in distribution type from Gaussian to Weibull. The education portion of this report describes short-term and long-term educational objectives. Such objectives serve to integrate research and education components of this project resulting in opportunities for ethnic minority students, principally Hispanic. The primary vehicle to facilitate such integration was the teaching of two probabilistic finite element method courses to undergraduate engineering students in the summers of 1998 and 1999.

Bast, Callie C.↗

NASA NTRS1994

Constraints on Neural Mechanisms Underlying the Spatial Integration of Speed Information

We measured human ability to integrate speed information presented simultaneously at multiple locations in the visual field. Observers did a two-interval forced-choice speed discrimination task with n grating patches in each interval at 4' eccentricity from fixation. In the first (integration) paradigm, all gratings in each interval moved at the same speed and observers were asked to pick the interval containing the faster gratings. Speed discrimination improved as the number of gratings in each interval increased. The observed decrease in threshold is not due simply to an increase in the effective area because performance with a single grating two, four, or six times the area of the original grating showed no such improvement. Furthermore, the improvement with n was still observed even when the speeds of the individual gratings in each interval were independent samples from a Gaussian distribution and the grating directions were balanced (i.e. equal numbers of gratings moved to the left and right). These results indicate that the neural mechanisms responsible for the integration of speed information act as if each patch provides an independent sample of speed, independent of grating direction. The results are not consistent with simple summation across space by a directionally selective mechanism with a large receptive field. In the second (search) paradigm, only one of the gratings in an interval moved faster and observers were asked to pick this interval. In this case, thresholds increased with the number of distractor gratings. The decrease in performance with increased n is common in search tasks and is often attributed to a bottleneck in the information that observers can process simultaneously. However, the results in the integration paradigm show that subjects indeed have access to and can use additional information with increasing n, at least up to 4 patches. Finally, simple detection models predict the trends in both the search and integration paradigms without invoking an input bottleneck.

Verghese, Preeti↗

NASA NTRS1994

Constraints on Neural Mechanisms Underlying the Spatial Integration of Speed Information

We measured human ability to integrate speed information presented simultaneously at multiple locations in the visual field. Observers did a two-interval forced-choice speed discrimination task with n grating patches in each interval at 4deg eccentricity from fixation. In the first (integration) paradigm, all gratings in each interval moved at the same speed and observers were asked to pick the interval containing the faster gratings. Speed discrimination improved as the number of gratings in each interval increased. The observed decrease in threshold is not due simply to an increase in the effective area because performance with a single grating two, four, or six times the area of the original grating showed no such improvement. Furthermore, the improvement with n was still observed even when the speeds of the individual gratings in each interval were independent samples from a Gaussian distribution and the grating directions were balanced (i.e. equal numbers of gratings moved to the left and right). These results indicate that the neural mechanisms responsible for the integration of speed information act as if each patch provides an independent sample of speed, independent of grating direction. The results are not consistent with simple summation across space by a directionally selective mechanism with a large receptive field. in the second (search) paradigm, only one of the gratings in an interval moved faster and observers were asked to pick this interval. In this case, thresholds increased with the number of distractor gratings. The decrease in performance with increased n is common in search tasks and is often attributed to a bottleneck in the information that observers can process simultaneously. However, the results in the integration paradigm show that subjects indeed have access to and can use additional information with increasing n, at least up to 4 patches. Finally, simple detection models predict the trends in both the search and integration paradigms without invoking an input bottleneck.

Verghese, Preeti↗

NASA NTRS2011

Chance-Constrained Guidance With Non-Convex Constraints

Missions to small bodies, such as comets or asteroids, require autonomous guidance for descent to these small bodies. Such guidance is made challenging by uncertainty in the position and velocity of the spacecraft, as well as the uncertainty in the gravitational field around the small body. In addition, the requirement to avoid collision with the asteroid represents a non-convex constraint that means finding the optimal guidance trajectory, in general, is intractable. In this innovation, a new approach is proposed for chance-constrained optimal guidance with non-convex constraints. Chance-constrained guidance takes into account uncertainty so that the probability of collision is below a specified threshold. In this approach, a new bounding method has been developed to obtain a set of decomposed chance constraints that is a sufficient condition of the original chance constraint. The decomposition of the chance constraint enables its efficient evaluation, as well as the application of the branch and bound method. Branch and bound enables non-convex problems to be solved efficiently to global optimality. Considering the problem of finite-horizon robust optimal control of dynamic systems under Gaussian-distributed stochastic uncertainty, with state and control constraints, a discrete-time, continuous-state linear dynamics model is assumed. Gaussian-distributed stochastic uncertainty is a more natural model for exogenous disturbances such as wind gusts and turbulence than the previously studied set-bounded models. However, with stochastic uncertainty, it is often impossible to guarantee that state constraints are satisfied, because there is typically a non-zero probability of having a disturbance that is large enough to push the state out of the feasible region. An effective framework to address robustness with stochastic uncertainty is optimization with chance constraints. These require that the probability of violating the state constraints (i.e., the probability of failure) is below a user-specified bound known as the risk bound. An example problem is to drive a car to a destination as fast as possible while limiting the probability of an accident to 10(exp -7). This framework allows users to trade conservatism against performance by choosing the risk bound. The more risk the user accepts, the better performance they can expect.

FROM↗

NASA NTRS2012

Finite Element Models for Electron Beam Freeform Fabrication Process

Electron beam freeform fabrication (EBF3) is a member of an emerging class of direct manufacturing processes known as solid freeform fabrication (SFF); another member of the class is the laser deposition process. Successful application of the EBF3 process requires precise control of a number of process parameters such as the EB power, speed, and metal feed rate in order to ensure thermal management; good fusion between the substrate and the first layer and between successive layers; minimize part distortion and residual stresses; and control the microstructure of the finished product. This is the only effort thus far that has addressed computer simulation of the EBF3 process. The models developed in this effort can assist in reducing the number of trials in the laboratory or on the shop floor while making high-quality parts. With some modifications, their use can be further extended to the simulation of laser, TIG (tungsten inert gas), and other deposition processes. A solid mechanics-based finite element code, ABAQUS, was chosen as the primary engine in developing these models whereas a computational fluid dynamics (CFD) code, Fluent, was used in a support role. Several innovative concepts were developed, some of which are highlighted below. These concepts were implemented in a number of new computer models either in the form of stand-alone programs or as user subroutines for ABAQUS and Fluent codes. A database of thermo-physical, mechanical, fluid, and metallurgical properties of stainless steel 304 was developed. Computing models for Gaussian and raster modes of the electron beam heat input were developed. Also, new schemes were devised to account for the heat sink effect during the deposition process. These innovations, and others, lead to improved models for thermal management and prediction of transient/residual stresses and distortions. Two approaches for the prediction of microstructure were pursued. The first was an empirical approach involving the computation of thermal gradient, solidification rate, and velocity (G,R,V) coupled with the use of a solidification map that should be known a priori. The second approach relies completely on computer simulation. For this purpose a criterion for the prediction of morphology was proposed, which was combined with three alternative models for the prediction of microstructure; one based on solidification kinetics, the second on phase diagram, and the third on differential scanning calorimetry data. The last was found to be the simplest and the most versatile; it can be used with multicomponent alloys and rapid solidification without any additional difficulty. For the purpose of (limited) experimental validation, finite element models developed in this effort were applied to three different shapes made of stainless steel 304 material, designed expressly for this effort with an increasing level of complexity. These finite element models require large computation time, especially when applied to deposits with multiple adjacent beads and layers. This problem can be overcome, to some extent, by the use of fast, multi-core computers. Also, due to their numerical nature coupled with the fact that solid mechanics- based models are being used to represent the material behavior in liquid and vapor phases as well, the models have some inherent approximations that become more pronounced when dealing with multi-bead and multi-layer deposits.

Chandra, Umesh↗

DOE OSTI2025

Polarization-shape alignment of IllustrisTNG star-forming galaxies

In star-forming disk galaxies, the radio continuum emission (1–10GHz) powered by star formation has an integrated polarization direction imperfectly aligned with the apparent disk minor axis. This polarization-shape alignment effect was previously observed in a small sample of local spirals. If this is prevalent for disk galaxies out to cosmological redshifts, novel measurements of cosmic birefringence and cosmic shear will be enabled by leveraging radio continuum surveys such as the Square Kilometre Array synergized with galaxy shape measurements. We calculate the polarization-shape misalignment angle for star-forming galaxies in the IllustrisTNG50 simulation at 0 < z < 2, assuming that additional polarized radio emission from an active galactic nucleus is negligible in at least a sizable fraction of the star-forming galaxies. The alignment found for z = 0 is consistent with local spiral data, but significantly deteriorates as redshift increases. Moreover, it degrades toward lower frequencies due to internal Faraday depolarization. Thanks to cosmic redshifting, observing higher-z galaxies at a fixed frequency greatly mitigates degradation due to reduced Faraday depolarization at the source-frame frequency. We present analytic fits to the non-Gaussian misalignment angle distribution, and evaluate Fisher information per galaxy for measuring a polarization rotation angle induced by cosmic birefringence. For observation at 4.8 GHz, the effective root-mean-square misalignment angle σα,eff is 18°, 23° and 33° at z = 0, 1 and 2, respectively. Analyzing N independent galaxies reduces the uncertainty on an isotropic cosmic birefringence signal to σ α,eff /√(N), providing competitive sensitivity once large samples are available. As accurate observation-driven models are not yet available for cosmological galaxy samples, our results motivate pilot observations to empirically characterize polarization-shape alignment, and can facilitate forecasts of cosmology and fundamental physics applications that exploit this effect.

axions↗

DOE OSTI2025

Persistent Classification: Understanding Adversarial Attacks by Studying Decision Boundary Dynamics

ABSTRACT There are a number of hypotheses underlying the existence of adversarial examples for classification problems. These include the high‐dimensionality of the data, the high codimension in the ambient space of the data manifolds of interest, and that the structure of machine learning models may encourage classifiers to develop decision boundaries close to data points. This article proposes a new framework for studying adversarial examples that does not depend directly on the distance to the decision boundary. Similarly to the smoothed classifier literature, we define a (natural or adversarial) data point to be ( γ , σ)‐stable if the probability of the same classification is at least for points sampled in a Gaussian neighborhood of the point with a given standard deviation . We focus on studying the differences between persistence metrics along interpolants of natural and adversarial points. We show that adversarial examples have significantly lower persistence than natural examples for large neural networks in the context of the MNIST and ImageNet datasets. We connect this lack of persistence with decision boundary geometry by measuring angles of interpolants with respect to decision boundaries. Finally, we connect this approach with robustness by developing a manifold alignment gradient metric and demonstrating the increase in robustness that can be achieved when training with the addition of this metric.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Effect of likelihood misspecification in Gaussian process-driven autonomous experimentation

In recent years, several groups have designed Autonomous Experiment (AE) models with the aim of using them as an alternative method for neutron scattering scanning. In an AE, Gaussian processes (GPs) are most frequently used due to their interpretability, their non-parametric nature, their universal approximation, and their closed-form predictive distribution. GPs have two key components, namely, the model for the likelihood of a neutron count knowing the underlying dynamic structure factor and the acquisition function. In this paper, we investigate the impact, on the quality of an AE, of the likelihood and acquisition function choices, in energy scans and (Q, ω) ones, with respect to the signal-over-noise ratio. While we hypothesized that the quality of GP predictions would decrease when the normal to Poisson likelihood approximation breaks down at low count rates, we found that the use of the correct Poisson likelihood does not improve the quality of the data collected, as well as yields very poor results in (Q, ω) scans at low count rates. In fact, the best results are obtained with a combination of normal likelihood, including the observation noise, and the change in variance acquisition function. In addition, we find that the performance, or quality of the predictive distribution, is a misleading measure of efficiency, that is, of the quality of the data collected.

Perryman, David Elliott [Inst. Laue-Langevin (ILL)↗

DOE OSTI2024

SUNBIRD : a simulation-based model for full-shape density-split clustering

Combining galaxy clustering information from regions of different environmental densities can help break cosmological parameter degeneracies and access non-Gaussian information from the density field that is not readily captured by the standard two-point correlation function (2PCF) analyses. However, modelling these density-dependent statistics down to the non-linear regime has so far remained challenging. We present a simulation-based model that is able to capture the cosmological dependence of the full shape of the density-split clustering (DSC) statistics down to intra-halo scales. Our models are based on neural-network emulators that are trained on high-fidelity mock galaxy catalogues within an extended-ΛCDM framework, incorporating the effects of redshift-space, Alcock–Paczynski distortions, and models of the halo–galaxy connection. Our models reach sub-percent level accuracy down to $1 \, h^{-1}\text{Mpc}$ and are robust against different choices of galaxy–halo connection modelling. When combined with the galaxy 2PCF, DSC can tighten the constraints on ω cdm , σ 8 , and n s by factors of 2.9, 1.9, and 2.1, respectively, compared to a 2PCF-only analysis. DSC additionally puts strong constraints on environment-based assembly bias parameters.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1996

DCTune Perceptual Optimization of Compressed Dental X-Rays

In current dental practice, x-rays of completed dental work are often sent to the insurer for verification. It is faster and cheaper to transmit instead digital scans of the x-rays. Further economies result if the images are sent in compressed form. DCTune is a technology for optimizing DCT (digital communication technology) quantization matrices to yield maximum perceptual quality for a given bit-rate, or minimum bit-rate for a given perceptual quality. Perceptual optimization of DCT color quantization matrices. In addition, the technology provides a means of setting the perceptual quality of compressed imagery in a systematic way. The purpose of this research was, with respect to dental x-rays, 1) to verify the advantage of DCTune over standard JPEG (Joint Photographic Experts Group), 2) to verify the quality control feature of DCTune, and 3) to discover regularities in the optimized matrices of a set of images. We optimized matrices for a total of 20 images at two resolutions (150 and 300 dpi) and four bit-rates (0.25, 0.5, 0.75, 1.0 bits/pixel), and examined structural regularities in the resulting matrices. We also conducted psychophysical studies (1) to discover the DCTune quality level at which the images became 'visually lossless,' and (2) to rate the relative quality of DCTune and standard JPEG images at various bitrates. Results include: (1) At both resolutions, DCTune quality is a linear function of bit-rate. (2) DCTune quantization matrices for all images at all bitrates and resolutions are modeled well by an inverse Gaussian, with parameters of amplitude and width. (3) As bit-rate is varied, optimal values of both amplitude and width covary in an approximately linear fashion. (4) Both amplitude and width vary in systematic and orderly fashion with either bit-rate or DCTune quality; simple mathematical functions serve to describe these relationships. (5) In going from 150 to 300 dpi, amplitude parameters are substantially lower and widths larger at corresponding bit-rates or qualities. (6) Visually lossless compression occurs at a DCTune quality value of about 1. (7) At 0.25 bits/pixel, comparative ratings give DCTune a substantial advantage over standard JPEG. As visually lossless bit-rates are approached, this advantage of necessity diminishes. We have concluded that DCTune optimized quantization matrices provide better visual quality than standard JPEG. Meaningful quality levels may be specified by means of the DCTune metric. Optimized matrices are very similar across the class of dental x-rays, suggesting the possibility of a 'class-optimal' matrix. DCTune technology appears to provide some value in the context of compressed dental x-rays.

Watson, Andrew B.↗

NASA NTRS2011

Constraining the Physical Properties of Meteor Stream Particles by Light Curve Shapes Using the Virtual Meteor Observatory

Different authors have produced models for the physical properties of meteoroids based on the shape of a meteor's light curve, typically from short observing campaigns. We here analyze the height profiles and light curves of approx.200 double-station meteors from the Leonids and Perseids using data from the Virtual Meteor Observatory, to demonstrate that with this web-based meteor database it is possible to analyze very large datasets from different authors in a consistent way. We compute the average heights for begin point, maximum luminosity, and end heights for Perseids and Leonids. We also compute the skew of the light curve, usually called the F-parameter. The results compare well with other author's data. We display the average light curve in a novel way to assess the light curve shape in addition to using the F-parameter. While the Perseids show a peaked light curve, the average Leonid light curve has a more flat peak. This indicates that the particle distribution of Leonid meteors can be described by a Gaussian distribution; the Perseids can be described with a power law. The skew for Leonids is smaller than for Perseids, indicating that the Leonids are more fragile than the Perseids.

Koschny, D.↗

NASA NTRSDate not supplied

Algorithmic Detection of Elemental Biosignatures

Machine learning models that classify a sample as indicative or non-indicative of life could play an important role in life-detection missions. Their predictions result from agnostic algorithms and thereby add redundancy to judgements resulting from human expertise. Additionally, their important features can reveal the most informative measurements within the operational constraints of a life-detection mission. The Ladder of Life Detection (Neveu 2018) identifies the need for an understanding of how combinations of multiple biosignatures affect overall confidence. The present work provides a starting point to answer this need, and future work will expand the data types to obtain even more predictive combinations of features. Elemental abundance was chosen as a starting set of features due to its availability in diverse sample types, which are needed to train a generalizable model. A standardized dataset was collected, including 35 non-indicative, e.g., lunar rock, basalt; 19 indicative mixed, e.g., seawater, agricultural soil; 46 indicative non-alive, e.g., coal, chalk; and 10 indicative alive, e.g., biofilm, bacteria. This dataset could be valuable for complementary biosignature research. The samples were standardized to the same limit of detection of a simulated mission scenario. Four classification models were used: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), and Gaussian naïve Bayes (GNB). To obtain feature importances, KNN was run on three principal components of the training data and LR and SVM were run with L1 and L2 regularization. The performances and feature importances of the six model variants on 40:60 train to validation ratios were assessed with Monte Carlo simulations. ROC AUC and mean accuracy scores ranged between 82% - 94%, with sensitivity greater than specificity. For indicative of life predictors, all models had C and Ca as strong and Cl as medium; a majority of models had N, K, and P as medium. For non-indicative of life predictors, all models had Si as strong, and a majority of models had Mg, Al, and Ti as medium. Varied elements were Fe (slightly non-indicative), H (slightly indicative), O (widely varied), Na, Mn, and S. These results serve as a proof of concept and suggest important elemental signals beyond merely the CHNOPS of Earth-based life.

Algorithmic↗

DOE OSTI2025

From chiral effective field theory to perturbative QCD: A Bayesian model mixing approach to symmetric nuclear matter

Constraining the equation of state (EOS) of strongly interacting, dense matter is the focus of intense experimental, observational, and theoretical effort. Chiral effective field theory (𝜒⁢EFT ) can describe the EOS between the typical densities of nuclei and those in the outer cores of neutron stars, while perturbative QCD (pQCD) can be applied to properties of deconfined quark matter, both with quantified theoretical uncertainties. However, describing the full range of densities in between with a single EOS that has well-quantified uncertainties is a challenging problem. Bayesian multimodel inference from 𝜒⁢EFT and pQCD can help bridge the gap between the two theories. In this work, we introduce a correlated Bayesian model mixing framework that uses a Gaussian process (GP) to assimilate different information into a single QCD EOS for symmetric nuclear matter. The present implementation uses a stationary GP to infer this mixed EOS solely from the EOSs of 𝜒⁢EFT and pQCD while accounting for the truncation errors of each theory. The GP is trained on the pressure as a function of number density in the low- and high-density regions where 𝜒⁢EFT and pQCD are, respectively, valid. We impose priors on the GP kernel hyperparameters to suppress unphysical correlations between these regimes. This, together with the assumption of stationarity, results in smooth 𝜒⁢EFT-to-pQCD curves for both the pressure and the speed of sound. We show that using uncorrelated mixing requires uncontrolled extrapolation of at least one of 𝜒⁢EFT or pQCD into regions where the perturbative series breaks down and leads to an acausal EOS. Here, we also discuss extensions of this framework to nonstationary and less differentiable GP kernels, its future application to neutron-star matter, and the incorporation of additional constraints from nuclear theory, experiment, and multimessenger astronomy.

Bayesian methods↗

NASA NTRS2021

Population Properties of Compact Objects from the Second LIGO-Virgo Gravitational-Wave Transient Catalog

We report on the population of 47 compact binary mergers detected with a false-alarm rate of <1yr^(−1) in the second LIGO–Virgo Gravitational-Wave Transient Catalog. We observe several characteristics of the merging binary black hole (BBH) population not discernible until now. First, the primary mass spectrum contains structure beyond a power law with a sharp high-mass cutoff; it is more consistent with a broken power law with a break at 39.7^(+20.3)(-9.1)M or a power law with a Gaussian feature peaking at 33.1^(+4.0)(-5.6)M (90% credible interval). While the primary mass distribution must extend to ~65M or beyond, only 2.9^(+3.5)(-1.7)M% of systems have primary masses greater than 45M. Second, we find that a fraction of BBH systems have component spins misaligned with the orbital angular momentum, giving rise to precession of the orbital plane. Moreover, 12% to 44% of BBH systems have spins tilted by more than 90°, giving rise to a negative effective inspiral spin parameter, χeff. Under the assumption that such systems can only be formed by dynamical interactions, we infer that between 25% and 93% of BBHs with nonvanishing |χeff| > 0.01 are dynamically assembled. Third, we estimate merger rates, finding RBBH = 23.9^(+14.3)(-8.6) Gpc^(-3) yr^(-1) for BBHs and RBNS = 320^(+490)(-240) Gpc^(-3) yr^(-1) for binary neutron stars. We find that the BBH rate likely increases with redshift (85% credibility) but not faster than the star formation rate (86% credibility). Additionally, we examine recent exceptional events in the context of our population models, finding that the asymmetric masses of GW190412 and the high component masses of GW190521 are consistent with our models, but the low secondary mass of GW190814 makes it an outlier.

R Abbott↗

DOE OSTI2024

PYSIMFRAC: A Python library for synthetic fracture generation and analysis

In this paper, we introduce PYSIMFRAC, an open-source python library for generating 3-D synthetic fracture realizations, integrating with fluid simulators, and performing analysis. PYSIMFRAC allows the user to specify one of three fracture generation techniques (Box, Gaussian, or Spectral) and perform statistical analysis including the autocorrelation, moments, and probability density functions of the fracture surfaces and aperture. This analysis and accessibility of a python library allows the user to create realistic fracture realizations and vary properties of interest. In addition, PYSIMFRAC includes integration examples to two different pore-scale simulators and the discrete fracture network simulator, dfnWorks. The capabilities developed in this work provides opportunity for quick and smooth adoption and implementation by the wider scientific community for accurate characterization of fluid transport in geologic media. We present PYSIMFRAC along with integration examples and discuss the ability to extend PYSIMFRAC from a single complex fracture to complex fracture networks.

58 GEOSCIENCES↗