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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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NASA NTRS1979

Molecular phylogenetic trees - On the validity of the Goodman-Moore augmentation algorithm

A response is made to the reply of Nei and Tateno (1979) to the letter of Holmquist (1978) supporting the validity of the augmentation algorithm of Moore (1977) in reconstructions of nucleotide substitutions by means of the maximum parsimony principle. It is argued that the overestimation of the augmented numbers of nucleotide substitutions (augmented distances) found by Tateno and Nei (1978) is due to an unrepresentative data sample and that it is only necessary that evolution be stochastically uniform in different regions of the phylogenetic network for the augmentation method to be useful. The importance of the average value of the true distance over all links is explained, and the relative variances of the true and augmented distances are calculated to be almost identical. The effects of topological changes in the phylogenetic tree on the augmented distance and the question of the correctness of ancestral sequences inferred by the method of parsimony are also clarified.

Holmquist, R.↗
DOE OSTI2024

VALIDATION, VERIFICATION, AND CALIBRATION THROUGH A CAUSAL LENS

This paper presents an alternative method based on causal inference to perform validation, verification, and calibration of simulation models. While classical validation and verification approaches focus on the identification of the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on the identification of causal relationships between data elements. Statistical and machine learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between datasets without explicitly considering the hypotheses behind them. This can lead to the erroneous conclusion that, if two data sets are close enough, then the models that generated them are similar. In addition, when experimental and simulated data differ to an extent that fails to meet the acceptance criteria, calibration techniques are used to tweak simulation model parameters to reduce the gap between the two types of data. This produces the false expectation that a simulation model will match reality. The methods presented in this paper move away from these strictly data-driven methods for validation and calibration toward more robust, model-driven methods based on causal inference. Causal inference aims to identify the possible mechanisms that might have generated data. Thus, this analysis targets the prediction of the effects when one (or more) of the identified mechanisms are altered. There are many approaches to identify, quantify and illustrate causal relationships. For the scope of this paper, directed graphs are employed as causal models. If the directed graph lacks cycles it is known as a directed acyclic graph (DAG). A node in such a graph represents an observed data element while a directed edge connecting two nodes represents a causal relationship between two variables. The developed causal methods are designed to extract causal models from simulation models and from experimental data. Causal models capture the causal relationships between data elements (e.g., simulated and experimental data). In this context, validation and verification are performed by comparing causal models. The proposed approach does not only inform system analysts on how a simulation model matches real-world data, but also identifies elements of the simulation model that should be revised when discrepancies between simulation and experimental data are observed. Through these causal methods, analysts have a means to identify the portion of the model equation(s) that are behind an edge connecting two variables. Hence, once the structural differences between causal models have been determined, model calibration can occur by changing only those model parameters that impact the identified causal relationships.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Disentangling the Black Hole Mass Spectrum with Photometric Microlensing Surveys

Abstract From the formation mechanisms of stars and compact objects to nuclear physics, modern astronomy frequently leverages surveys to understand populations of objects to answer fundamental questions. The population of dark and isolated compact objects in the Galaxy contains critical information related to many of these topics, but is only practically accessible via gravitational microlensing. However, photometric microlensing observables are degenerate for different types of lenses, and one can seldom classify an event as involving either a compact object or stellar lens on its own. To address this difficulty, we apply a Bayesian framework that treats lens type probabilistically and jointly with a lens population model. This method allows lens population characteristics to be inferred despite intrinsic uncertainty in the lens class of any single event. We investigate this method’s effectiveness on a simulated ground-based photometric survey in the context of characterizing a hypothetical population of primordial black holes (PBHs) with an average mass of 30 M ⊙ . On simulated data, our method outperforms current black hole (BH) lens identification pipelines and characterizes different subpopulations of lenses while jointly constraining the PBH contribution to dark matter to ≈25%. Key to robust inference, our method can marginalize over population model uncertainty. We find the lower mass cutoff for stellar origin BHs, a key observable in understanding the BH mass gap, particularly difficult to infer in our simulations. This work lays the foundation for cutting-edge PBH abundance constraints to be extracted from current photometric microlensing surveys.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Velocity reconstruction in the era of DESI and Rubin/LSST. I. Exploring spectroscopic, photometric, and hybrid samples

Peculiar velocities of galaxies and halos can be reconstructed from their spatial distribution alone. This technique is analogous to the baryon acoustic oscillations reconstruction, using the continuity equation to connect density and velocity fields. The resulting reconstructed velocities can be used to measure imprints of galaxy velocities on the cosmic microwave background like the kinematic Sunyaev-Zel’dovich effect or the moving lens effect. As the precision of these measurements increases, characterizing the performance of the velocity reconstruction becomes crucial to allow unbiased and statistically optimal inference. In this paper, we quantify the relevant performance metrics: the variance of the reconstructed velocities and their correlation coefficient with the true velocities. We show that the relevant velocities to reconstruct for kSZ and moving lens are actually the halo—rather than galaxy—velocities. We quantify the impact of redshift-space distortions, photometric redshift errors, satellite galaxy fraction, incorrect cosmological parameter assumptions and smoothing scale on the reconstruction performance. Here, we also investigate hybrid reconstruction methods, where velocities inferred from spectroscopic samples are evaluated at the positions of denser photometric samples. We find that using exclusively the photometric sample is better than performing a hybrid analysis. The 2 Gpc/ℎ length simulations from abacussummit with realistic galaxy samples for DESI and Rubin LSST allow us to perform this analysis in a controlled setting. In the companion paper [B. Hadzhiyska, S. Ferraro, B. Ried Guachalla, and E. Schaan, companion paper, Phys. Rev. D 109, 103534 (2024).], we further include the effects of evolution along the light cone and give realistic performance estimates for DESI luminous red galaxies, emission line galaxies, and Rubin LSST-like samples.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2016

Impact of Scattering Model on Disdrometer Derived Attenuation Scaling

NASA Glenn Research Center (GRC), the Air Force Research Laboratory (AFRL), and the Politecnico di Milano (POLIMI) are currently entering the third year of a joint propagation study in Milan, Italy utilizing the 20 and 40 GHz beacons of the Alphasat TDP5 Aldo Paraboni scientific payload. The Ka- and Q-band beacon receivers were installed at the POLIMI campus in June of 2014 and provide direct measurements of signal attenuation at each frequency. Collocated weather instrumentation provides concurrent measurement of atmospheric conditions at the receiver; included among these weather instruments is a Thies Clima Laser Precipitation Monitor (optical disdrometer) which records droplet size distributions (DSD) and droplet velocity distributions (DVD) during precipitation events. This information can be used to derive the specific attenuation at frequencies of interest and thereby scale measured attenuation data from one frequency to another. Given the ability to both predict the 40 GHz attenuation from the disdrometer and the 20 GHz timeseries as well as to directly measure the 40 GHz attenuation with the beacon receiver, the Milan terminal is uniquely able to assess these scaling techniques and refine the methods used to infer attenuation from disdrometer data.In order to derive specific attenuation from the DSD, the forward scattering coefficient must be computed. In previous work, this has been done using the Mie scattering model, however, this assumes a spherical droplet shape. The primary goal of this analysis is to assess the impact of the scattering model and droplet shape on disdrometer derived attenuation predictions by comparing the use of the Mie scattering model to the use of the T-matrix method, which does not assume a spherical droplet. In particular, this paper will investigate the impact of these two scattering approaches on the error of the resulting predictions as well as on the relationship between prediction error and rain rate.

statistics↗
DOE OSTI2026

PISCES two-detector covariance matrix fit for the NOvA Experiment

NOvA is a long-baseline neutrino oscillation experiment with two functionally identical detectors: a Near Detector (ND) at Fermilab, placed 1 km from the neutrino source, and a Far Detector (FD) located 810 km away from the ND in Minnesota. NOvA's primary physics goals are the precise measurements of neutrino oscillation parameters $\theta_{23}$ and $\Delta m^2_{32}$ , determine the neutrino mass ordering, and constrain the value of $\delta_{CP}$, via the study of muon neutrino to electron neutrino oscillation. In the standard NOvA three-flavor analysis, oscillation parameters are extracted using an extrapolation technique in which the ND data constrain the FD prediction through a ratio method. While this allows for systematic uncertainties sharing the same effects in both detectors to cancel, it remains an FD-only fit and does not fully leverage the constraining power of the high-statistics ND. This analysis proposes a simultaneous ND+FD fit using the PISCES method. PISCES (Parameter Inference with Systematic Covariance and Exact Statistics) is a framework designed to support complex configurations such as a joint ND+FD fit. This allows PISCES to take full advantage of the ND data to directly constrain systematic uncertainties across all samples. In PISCES, systematic uncertainties are encoded in a fractional covariance matrix, and statistical uncertainties are handled with a Poisson likelihood, making the approach well suited for low-statistics samples. For interpretability, we further use a Newton–Raphson + PCA method to recover per-systematic pulls from the covariance formulation. This poster presents the full PISCES joint ND+FD fit for the NOvA three-flavor analysis, describes its implementation and evaluates its performance through extensive robustness tests and fake data studies. It also provides a comparison between the PISCES joint ND+FD results and the standard NOvA extrapolation method.

Rajaoalisoa, Miriama [Cincinnati U.] (ORCID:000000↗
NASA NTRS2016

Impact of Scattering Model on Disdrometer Derived Attenuation Scaling

NASA Glenn Research Center (GRC), the Air Force Research Laboratory (AFRL), and the Politecnico di Milano (POLIMI) are currently entering the third year of a joint propagation study in Milan, Italy utilizing the 20 and 40 GHz beacons of the Alphasat TDP#5 Aldo Paraboni scientific payload. The Ka- and Q-band beacon receivers were installed at the POLIMI campus in June of 2014 and provide direct measurements of signal attenuation at each frequency. Collocated weather instrumentation provides concurrent measurement of atmospheric conditions at the receiver; included among these weather instruments is a Thies Clima Laser Precipitation Monitor (optical disdrometer) which records droplet size distributions (DSD) and droplet velocity distributions (DVD) during precipitation events. This information can be used to derive the specific attenuation at frequencies of interest and thereby scale measured attenuation data from one frequency to another. Given the ability to both predict the 40 gigahertz attenuation from the disdrometer and the 20 gigahertz time-series as well as to directly measure the 40 gigahertz attenuation with the beacon receiver, the Milan terminal is uniquely able to assess these scaling techniques and refine the methods used to infer attenuation from disdrometer data. In order to derive specific attenuation from the DSD, the forward scattering coefficient must be computed. In previous work, this has been done using the Mie scattering model, however, this assumes a spherical droplet shape. The primary goal of this analysis is to assess the impact of the scattering model and droplet shape on disdrometer-derived attenuation predictions by comparing the use of the Mie scattering model to the use of the T-matrix method, which does not assume a spherical droplet. In particular, this paper will investigate the impact of these two scattering approaches on the error of the resulting predictions as well as on the relationship between prediction error and rain rate.

Radio Transmission↗
DOE OSTI2022

RF-Net 2: fast inference of virus reassortment and hybridization networks

Abstract Motivation A phylogenetic network is a powerful model to represent entangled evolutionary histories with both divergent (speciation) and convergent (e.g. hybridization, reassortment, recombination) evolution. The standard approach to inference of hybridization networks is to (i) reconstruct rooted gene trees and (ii) leverage gene tree discordance for network inference. Recently, we introduced a method called RF-Net for accurate inference of virus reassortment and hybridization networks from input gene trees in the presence of errors commonly found in phylogenetic trees. While RF-Net demonstrated the ability to accurately infer networks with up to four reticulations from erroneous input gene trees, its application was limited by the number of reticulations it could handle in a reasonable amount of time. This limitation is particularly restrictive in the inference of the evolutionary history of segmented RNA viruses such as influenza A virus (IAV), where reassortment is one of the major mechanisms shaping the evolution of these pathogens. Results Here, we expand the functionality of RF-Net that makes it significantly more applicable in practice. Crucially, we introduce a fast extension to RF-Net, called Fast-RF-Net, that can handle large numbers of reticulations without sacrificing accuracy. In addition, we develop automatic stopping criteria to select the appropriate number of reticulations heuristically and implement a feature for RF-Net to output error-corrected input gene trees. We then conduct a comprehensive study of the original method and its novel extensions and confirm their efficacy in practice using extensive simulation and empirical IAV evolutionary analyses. Availability and implementation RF-Net 2 is available at https://github.com/flu-crew/rf-net-2. Supplementary information Supplementary data are available at Bioinformatics online.

Markin, Alexey (ORCID:0000000332809050)↗
DOE OSTI2018

Void lensing as a test of gravity

We propose a consistency test of gravity based on the weak lensing signal of cosmic voids. For a given void profile, as traced by galaxies, the lensing signal can vary in different gravity theories. Thus the comparison of the lensing shear profile of such voids with the general relativistic prediction can test for deviations from general relativity (GR). For concreteness, we calculate the expected lensing signal in two gravity theories involving scalar fields with derivative couplings. We find that the scalar field has the potential to boost the tangential shear both within and outside the void radius. Reversing the method, one can infer the void central density parameter from the lensing signal, and compare to the value estimated independently using the galaxy tracer profiles of voids. Hence, one can check for consistency between the behavior of light and matter under the assumption of GR. We use voids traced by luminous red galaxies in SDSS to demonstrate our methodology, finding that the void central density parameter can shift from its GR value by up to 20% in some Galileon gravity models. Although Galileon gravity is now disfavored as a source of cosmic acceleration by other data sets, the methods we demonstrate here can be used to test for more general fifth force effects with upcoming void lensing data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Large-Scale Inference of Multivariate Regression for Heavy-Tailed and Asymmetric Data

Large-scale multivariate regression is a fundamental statistical tool with a wide range of applications. Here, this study considers the problem of simultaneously testing a large number of general linear hypotheses, encompassing covariate-effect analysis, analysis of variance, and model comparisons. The challenge that accompanies a large number of tests is the ubiquitous presence of heavy-tailed and/or highly skewed measurement noise, which is the main reason for the failure of conventional least squares-based methods. For large-scale multivariate regression, we develop a set of robust inference methods to explore data features such as heavy tailedness and skewness, which are not visible to least squares methods. The new testing procedure is based on the data-adaptive Huber regression and a new covariance estimator of regression estimates. Under mild conditions, we show that our methods produce consistent estimates of the false discovery proportion. Extensive numerical experiments and an empirical study on quantitative linguistics demonstrate the advantage of the proposed method over many state-of-the-art methods when the data are generated from heavy-tailed and/or skewed distributions.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

Comparing Spatial Distributions of Solar Prominence Mass Derived from Coronal Absorption

In the present work we extend the use of this mass-inference technique to a sample of prominences observed in at least two coronal lines. This approach, in theory, allows a direct calculation of prominence mass and helium abundance and how these properties vary spatially and temporally. Our motivation is two-fold: to obtain a He(exp 0)/H(exp 0) abundance ratio, and to determine how the relative spatial distribution of the two species varies in prominences. The first of these relies on the theoretical expectation that the amount of absorption at each EUV wavelength is well-characterized. However, in this work we show that due to a saturation of the continuum absorption in the 625 A and 368 A lines (which have much higher opacity compared to 195 A-) the uncertainties in obtaining the relative abundances are too high to give meaningful estimates. This is an important finding because of its impact on future studies in this area. The comparison of the spatial distribution of helium and hydrogen presented here augments previous observational work indicating that cross-field diffusion of neutrals is an important mechanism for mass loss. Significantly different loss timescales for neutral He and H (helium drains much more rapidly than hydrogen) can impact prominence structure, and both the present and past studies suggest this mechanism is playing a role in structure and possibly dynamics. Section 2 of this paper contains a description of the observations and Section 3 summarizes the method used to infer mass along with the criteria imposed in choosing prominences appropriate for this study. Section 3 also contains a discussion of the problems due to limitations of the available data and the implications for determining relative abundances. We present our results in Section 4, including plots of radial-like scans of prominence mass in different lines to show the spatial distribution of the different species. The last section contains a discussion summarizing the importance of the qualitative results found in this work. The Appendix provides a detailed derivation of how to obtain prominence mass and helium abundance (A 1) and includes the data for all prominences studied (A2).

Gilbert, Holly↗
DOE OSTI2023

Neglecting Model Parametric Uncertainty Can Drastically Underestimate Flood Risks

Abstract Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi‐sector dynamics and the design of risk‐management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example, the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (a) stepwise line search, (b) precalibration or screening, and (c) the Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle‐based approach that takes advantage of the massive parallelization afforded by modern high‐performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Selecting Appropriate Model Complexity: An Example of Tracer Inversion for Thermal Prediction in Enhanced Geothermal Systems

Abstract A major challenge in the inversion of subsurface parameters is the ill‐posedness issue caused by the inherent subsurface complexities and the generally spatially sparse data. Appropriate simplifications of inversion models are thus necessary to make the inversion process tractable and meanwhile preserve the predictive ability of the inversion results. In this study, we investigate the effect of model complexity on fracture aperture inversion and thermal performance prediction in a field‐scale EGS model. Principal component analysis was used to map the aperture field to a low‐dimensional latent space. The complexity of the inversion model was quantitatively represented by the percentage of total variance in the original aperture fields preserved by the latent space. Tracer, pressure and flow rate data were used to invert for fracture aperture through an ensemble‐based inversion method, and the inferred aperture field was used to predict thermal performance. With an over‐simplified aperture model, ensemble collapse occurred. The inverted aperture models failed to resolve necessary flow and transport features, leading to a biased thermal performance prediction. A complex aperture model involved excessive features and was prone to overinterpreting the inversion data. Both the tracer/pressure/flow rate data reproduction and thermal prediction showed significant uncertainties, making it difficult to properly estimate long‐term thermal performance. Fortunately, our results indicate that there exists an appropriate model complexity which can simultaneously match inversion data and predict thermal performance with an acceptable uncertainty. The quality of the fit of tracer data appears to be a useful indicator of such an appropriate model complexity.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

The possibility of hydrogen-water demixing in Uranus, Neptune, K2-18 b and TOI-270 d

Context. The internal structures of Uranus and Neptune remain unknown. In addition, sub-Neptunes are now thought to be the most common type of exoplanets. Improving our understanding of the physical processes that govern the interiors of such planets is therefore essential. Phase separation between planetary constituents may occur, in particular, hydrogen-water immiscibility in cold, water-rich intermediate-mass planets. Aims. We assess whether hydrogen-water demixing could occur in Uranus, Neptune, K2-18 b and TOI-270 d, and investigate its effect on the planetary evolution and inferred internal structure. Methods. We couple planetary evolution models with recent ab initio calculations of the hydrogen-water phase diagram, allowing for temperature shifts to account for uncertainties in miscibility gaps. Results. We find that demixing may occur and could lead to a complete depletion of water in the outermost regions of Uranus and Neptune. Temperature offsets of up to 1100 K lead to a depleted region comprising as much as 16% of the planet’s mass, and an increase in planetary radius by nearly 20%. For K2-18 b, our models suggest that hydrogen-water demixing is ongoing and may explain the absence of water features in its JWST spectrum. A temperature offset of 500 K is required to get a complete depletion of water in the atmosphere of K2-18b. TOI-270d may also have experienced hydrogen-water demixing. When applying a similar temperature offset on the phase diagram as for K2-18 b, we find a partial depletion of water in the atmosphere of TOI-270 d, consistent with JWST’s detection of water. Conclusions. Hydrogen-water immiscibility may play a key role in shaping the structure and evolution of both Solar System giant planets like Uranus and Neptune, and cold/temperate exoplanets such as K2-18 b and TOI-270 d. Accounting for such internal processes is crucial to accurately interpret atmospheric observations from current (e.g., JWST) and upcoming (e.g., ARIEL) missions.

planets and satellites: composition↗
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 OSTI2026

Frequentist cosmological constraints from full-shape clustering measurements in DESI DR1

We present a frequentist analysis of clustering measurements from Data Release 1 of the Dark Energy Spectroscopic Instrument (DESI) using the standard profile likelihood method. While Bayesian inferences for effective field theory models of galaxy clustering can be highly sensitive to prior choices for extended cosmological models, frequentist inferences are not susceptible to such effects. We compare frequentist and Bayesian constraints for the parameter set {σ 8 , H 0 , Ω m , w 0 , w a } using the full-shape power spectrum multipoles, post-reconstruction baryon acoustic oscillation (BAO) measurements, and external datasets from the CMB and type Ia supernovae measurements. The frequentist confidence intervals are significantly shifted relative to the Bayesian credible intervals for the w 0 w a CDM model, unless supernovae data are included. When DESI full-shape and BAO data are fit jointly, we obtain the following 1σ frequentist confidence intervals for ΛCDM (w 0 w a CDM): σ 8 = 0.863 +0.048 -0.040 , H 0 = 68.96 +0.81 -0.80 km s -1 Mpc -1 , Ω m = 0.3034 ± 0.0110 (σ 8 = 0.782 +0.060 -0.036 , H 0 = 63.7 +4.2 -2.0 km s -1 Mpc -1 , Ω m = 0.378 +0.024 -0.047 , w 0 = -0.16 +0.10 -0.50 , w a = -3.0 +1.7 ), corresponding to 0.8σ, 0.3σ, 0.7σ (2.1σ, 4.1σ, 6.5σ, 6.3σ, 6.6σ) shifts between the maximum likelihood estimate and the Bayesian posterior mean for ΛCDM (w 0 w a CDM) respectively.

Bayesian reasoning↗
DOE OSTI2019

Integrating the evidence from evidence factors in observational studies

Summary A sensitivity analysis for an observational study assesses how much bias, due to nonrandom assignment of treatment, would be necessary to change the conclusions of an analysis that assumes treatment assignment was effectively random. The evidence for a treatment effect can be strengthened if two different analyses, which could be affected by different types of biases, are both somewhat insensitive to bias. The finding from the observational study is then said to be replicated. Evidence factors allow for two independent analyses to be constructed from the same dataset. When combining the evidence factors, the Type I error rate must be controlled to obtain valid inference. A powerful method is developed for controlling the familywise error rate for sensitivity analyses with evidence factors. It is shown that the Bahadur efficiency of sensitivity analysis for the combined evidence is greater than for either evidence factor alone. The proposed methods are illustrated through a study of the effect of radiation exposure on the risk of cancer. An R package, evidenceFactors, is available from CRAN to implement the methods of the paper.

Karmakar, B.↗
DOE OSTI2019

Multiwavelength cluster mass estimates and machine learning

ABSTRACT One emerging application of machine learning methods is the inference of galaxy cluster masses. In this note, machine learning is used to directly combine five simulated multiwavelength measurements in order to find cluster masses. This is in contrast to finding mass estimates for each observable, normally by using a scaling relation, and then combining these scaling law based mass estimates using a likelihood. We also illustrate how the contributions of each observable to the accuracy of the resulting mass measurement can be compared via model-agnostic Importance Permutation values. Thirdly, as machine learning relies upon the accuracy of the training set in capturing observables, their correlations, and the observational selection function, and as the machine learning training set originates from simulations, two tests of whether a simulation’s correlations are consistent with observations are suggested and explored as well.

Cohn, J. D.↗