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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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Fine-scale contemporary recombination variation and its fitness consequences in adaptively diverging stickleback fish

Despite deep evolutionary conservation, recombination rates vary greatly across the genome and among individuals, sexes and populations. Yet the impact of this variation on adaptively diverging populations is not well understood. Here we characterized fine-scale recombination landscapes in an adaptively divergent pair of marine and freshwater populations of threespine stickleback from River Tyne, Scotland. Through whole-genome sequencing of large nuclear families, we identified the genomic locations of almost 50,000 crossovers and built recombination maps for marine, freshwater and hybrid individuals at a resolution of 3.8 kb. We used these maps to quantify the factors driving variation in recombination rates. We found strong heterochiasmy between sexes but also differences in recombination rates among ecotypes. Hybrids showed evidence of significant recombination suppression in overall map length and in individual loci. Recombination rates were lower not only within individual marine–freshwater-adaptive loci, but also between loci on the same chromosome, suggesting selection on linked gene ‘cassettes’. Through temporal sampling along a natural hybrid zone, we found that recombinants showed traits associated with reduced fitness. Our results support predictions that divergence in cis-acting recombination modifiers, whose functions are disrupted in hybrids, may play an important role in maintaining differences among adaptively diverging populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Arbuscular mycorrhizal fungi equalize differences in plant fitness and facilitate plant species coexistence through niche differentiation

Mycorrhizal fungi are essential to the establishment of the vast majority of plant species but are often conceptualized with contradictory roles in plant community assembly. On the one hand, host-specific mycorrhizal fungi may allow a plant to be competitively dominant by enhancing growth. On the other hand, host-specific mycorrhizal fungi with different functional capabilities may increase nutrient niche partitioning, allowing plant species to coexist. Here, to resolve the balance of these two contradictory forces, we used a controlled greenhouse study to manipulate the presence of two main types of mycorrhizal fungus, ectomycorrhizal fungi and arbuscular mycorrhizal fungi, and used a range of conspecific and heterospecific competitor densities to investigate the role of mycorrhizal fungi in plant competition and coexistence. We find that the presence of arbuscular mycorrhizal fungi equalizes fitness differences between plants and stabilizes competition to create conditions for host species coexistence. Furthermore, our results show how below-ground mutualisms can shift outcomes of plant competition and that a holistic view of plant communities that incorporates their mycorrhizal partners is important in predicting plant community dynamics.

09 BIOMASS FUELS↗
DOE OSTI2022

Learning protein fitness models from evolutionary and assay-labeled data

Machine learning-based models of protein fitness typically learn from either unlabeled, evolutionarily related sequences or variant sequences with experimentally measured labels. For regimes where only limited experimental data are available, recent work has suggested methods for combining both sources of information. Toward that goal, we propose a simple combination approach that is competitive with, and on average outperforms more sophisticated methods. Our approach uses ridge regression on site-specific amino acid features combined with one probability density feature from modeling the evolutionary data. Within this approach, we find that a variational autoencoder-based probability density model showed the best overall performance, although any evolutionary density model can be used. Moreover, our analysis highlights the importance of systematic evaluations and sufficient baselines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

VarIabiLity seLection of AstrophysIcal sources iN PTF (VILLAIN): I. Structure function fits to 71 million objects

Light-curve variability is well-suited to characterizing objects in surveys with high cadence and a long baseline. This is especially relevant in view of the large datasets to be produced by the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). We aim to determine variability parameters for objects in the Palomar Transient Factory (PTF) and explore differences between quasars (QSOs), stars, and galaxies. We relate variability and colour information in preparation for future surveys. We fit joint likelihoods to structure functions (SFs) of 71 million PTF light curves with a Markov chain Monte Carlo method. For each object, we assume a power-law SF and extract two parameters: the amplitude on timescales of one year, A, and a power-law index, γ. With these parameters and colours in the optical (Pan-STARRS1) and mid-infrared (WISE), we identify regions of parameter space dominated by different types of spectroscopically confirmed objects from SDSS. Candidate QSOs, stars, and galaxies are selected to show their parameter distributions. QSOs show high-amplitude variations in the R band, and the highest γ values. Galaxies have a broader range of amplitudes and their variability shows relatively little dependency on timescale. With variability and colours, we achieve a photometric selection purity of 99.3% for QSOs. Even though hard cuts in monochromatic variability alone are not as effective as seven-band magnitude cuts, variability is useful in characterizing object subclasses. Through variability, we also find QSOs that were erroneously classified as stars in the SDSS. We discuss perspectives and computational solutions in view of the upcoming LSST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Particle transport constraints via Bayesian spectral fitting of multiple atomic lines

Optimized operation of fusion devices demands detailed understanding of plasma transport, a problem that must be addressed with advances in both measurement and data analysis techniques. In this work, we adopt Bayesian inference methods to determine experimental particle transport, leveraging opportunities from high-resolution He-like ion spectra in a tokamak plasma. The Bayesian spectral fitting code is used to analyze resonance (w), forbidden (z), intercombination (x, y), and satellite (k, j) lines of He-like Ca following laser blow-off injections on Alcator C-Mod. This offers powerful transport constraints since these lines depend differently on electron temperature and density, but also differ in their relation to Li-like, He-like, and H-like ion densities, often the dominant Ca charge states over most of the C-Mod plasma radius. Using synthetic diagnostics based on the AURORA package, we demonstrate improved effectiveness of impurity transport inferences when spectroscopic data from a progressively larger number of lines are included.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Extension of the flux fit method for estimating power deposition profiles

The flux fit method is used to self-consistently estimate the power deposition profile and heat transport profiles from temperature measurements originating from perturbative experiments with a modulated source. Furthermore, this letter improves on this method by addressing the limitations and assumptions. The most crucial improvement is the additional freedom in the source deposition profile. Allowing for a variable central deposition location and height and including a skewness parameter produces deposition profiles more consistent with the measurement data, but still wider than equilibrium ray tracing in two different DIII-D discharges. Moreover, we show that the quality of the estimated deposition profile is key to the accuracy of diffusivity and connectivity estimates, but inversely, the estimated transport parameters hardly affect the quality of the power deposition estimate. Using this method, we show that the power deposition profile estimate is broadened with respect to ray-tracing by about 1.7-1.8 times in two DIII-D discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Implementing Faraday effect measurement constraints into the Grad–Shafranov equilibrium fitting code EFIT

A new tool for the exploration and diagnosis of the internal magnetic field of plasmas in the DIII-D tokamak in the form of a constraint on the EFIT (Equilibrium Fitting) Grad–Shafranov code based on the Faraday-effect Radial Interferometer-Polarimeter (RIP) diagnostic is presented, including description, verification, and sample application. The physics underlying the diagnostic and its implementation into EFIT are discussed, and the results showing the verification of the model are given, and the model’s limitations are discussed. The influence of the diagnostic’s input on the resulting equilibrium parameters is characterized. Furthermore, the effect of electron density profile refinement is evaluated and found to be negligible. A sample application of the diagnostic is shown, indicating that the RIP constraint has similar effects on the equilibrium as motional Stark effect constraints do.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Avoiding biases in binned fits

Abstract Binned maximum likelihood fits are an attractive option when analysing large datasets, but require care when computing likelihoods of continuous PDFs in bins.For many years the widely used statistical modelling package evaluated probabilities at the bin centre, leading to significant biases for strongly curved probability density functions.We demonstrate the biases with real-world examples, and introduce a PDF class to that removes these biases.The physics and computation performance of this new class are discussed.

Instruments & Instrumentation↗
DOE OSTI2022

Comparative chemical genomic profiling across plant-based hydrolysate toxins reveals widespread antagonism in fitness contributions

Abstract The budding yeast Saccharomyces cerevisiae has been used extensively in fermentative industrial processes, including biofuel production from sustainable plant-based hydrolysates. Myriad toxins and stressors found in hydrolysates inhibit microbial metabolism and product formation. Overcoming these stresses requires mitigation strategies that include strain engineering. To identify shared and divergent mechanisms of toxicity and to implicate gene targets for genetic engineering, we used a chemical genomic approach to study fitness effects across a library of S. cerevisiae deletion mutants cultured anaerobically in dozens of individual compounds found in different types of hydrolysates. Relationships in chemical genomic profiles identified classes of toxins that provoked similar cellular responses, spanning inhibitor relationships that were not expected from chemical classification. Our results also revealed widespread antagonistic effects across inhibitors, such that the same gene deletions were beneficial for surviving some toxins but detrimental for others. This work presents a rich dataset relating gene function to chemical compounds, which both expands our understanding of plant-based hydrolysates and provides a useful resource to identify engineering targets.

09 BIOMASS FUELS↗
DOE OSTI2024

Fitness factors impacting survival of a subsurface bacterium in contaminated groundwater

Many factors contribute to the ability of a microbial species to persist when encountering complexly contaminated environments, including time of exposure, the nature and concentration of contaminants, availability of nutritional resources, and possession of a combination of appropriate molecular mechanisms needed for survival. Herein we sought to identify genes that are most important for survival of Gram-negative Enterobacteriaceae in contaminated groundwater environments containing high concentrations of nitrate and metals using the metal-tolerant Oak Ridge Reservation isolate, Pantoea sp. MT58 (MT58). Survival fitness experiments in which a randomly barcoded transposon insertion (RB-TnSeq) library of MT58 was exposed directly to contaminated Oak Ridge Reservation groundwater samples from across a nitrate and mixed metal contamination plume were used to identify genes important for survival with increasing exposure times and concentrations of contaminants, and availability of a carbon source. Genes involved in controlling and using carbon, encoding transcriptional regulators, and related to Gram-negative outer membrane processes were among those found to be important for survival in contaminated Oak Ridge Reservation groundwater. A comparative genomics analysis of 75 Pantoea genus strains allowed us to further separate the survival determinants into core and non-core genes in the Pantoea pangenome, revealing insights into the survival of subsurface microorganisms during contaminant plume intrusion.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Dark Energy Survey Year 1 Results: Wide-field mass maps via forward fitting in harmonic space

Herein we present new wide-field weak lensing mass maps for the Year 1 Dark Energy Survey (DES) data, generated via a forward fitting approach. This method of producing maps does not impose any prior constraints on the mass distribution to be reconstructed. The technique is found to improve the map reconstruction on the edges of the field compared to the conventional Kaiser–Squires method, which applies a direct inversion on the data; our approach is in good agreement with the previous direct approach in the central regions of the footprint. The mapping technique is assessed and verified with tests on simulations; together with the Kaiser–Squires method, the technique is then applied to data from the DES Year 1 data and the differences between the two methods are compared. We also produce the first DES measurements of the convergence Minkowski functionals and compare them to those measured in simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A revised SALT2 surface for fitting Type Ia supernova light curves

ABSTRACT We present a revised Spectral Adaptive Light Curve Template (SALT2) surface (SALT2-2021) for fitting the light curves of Type Ia supernovae (SNe Ia), which incorporates new measurements of zero-point calibration offsets and Milky Way extinction. The most notable change in the new surface occurs in the UV region. This new surface alters the distance measurements of SNe Ia, which can be used to investigate the nature of dark energy by probing the expansion history of the Universe. Using the revised SALT2 surface on public data from the first 3 yr of the Dark Energy Survey Supernova Program (combined with an external low-z SNe Ia sample) and combining with cosmic microwave background constraints, we find a change in the dark energy equation-of-state parameter, Δw = 0.015 ± 0.004. This result highlights the continued importance of controlling and reducing systematic uncertainties, particularly with the next generation of supernova analyses aiming to improve constraints on dark energy properties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Unveiling the purely young star formation history of the SMC’s northeastern shell from colour–magnitude diagram fitting

ABSTRACT We obtain a quantitative star formation history (SFH) of a shell-like structure (‘shell’) located in the northeastern part of the Small Magellanic Cloud (SMC). We use the Survey of the MAgellanic Stellar History to derive colour–magnitude diagrams (CMDs), reaching below the oldest main-sequence turnoff, from which we compute the SFHs with CMD-fitting techniques. We present, for the first time, a novel technique that uses red clump (RC) stars from the CMDs to assess and account for the SMC’s line-of-sight depth effect present during the SFH derivation. We find that accounting for this effect recovers a more accurate SFH. We quantify an $\sim$7 kpc line-of-sight depth present in the CMDs, in good agreement with depth estimates from RC stars in the northeastern SMC. By isolating the stellar content of the northeastern shell and incorporating the line-of-sight depth into our calculations, we obtain an unprecedentedly detailed SFH. We find that the northeastern shell is primarily composed of stars younger than $\sim$500 Myr, with significant star formation enhancements around $\sim$250 and $\sim$450 Myr. These young stars are the main contributors to the shell’s structure. We show synchronicity between the northeastern shell’s SFH with the Large Magellanic Cloud’s (LMC) northern arm, which we attribute to the interaction history of the SMC with the LMC and the Milky Way (MW) over the past $\sim$500 Myr. Our results highlight the complex interplay of ram pressure stripping and the influence of the MW’s circumgalactic medium in shaping the SMC’s northeastern shell.

Sakowska, Joanna D. (ORCID:0000000215941466)↗
DOE OSTI2020

New method for fitting coefficients in standard model effective theory

We present an alternative method for carrying out a principal-component analysis of Wilson coefficients in standard model effective field theory (SMEFT). The method is based on singular-value decomposition (SVD). The SVD method provides information about the sensitivity of experimental observables to physics beyond the standard model that is not accessible in the Fisher-information method. In principle, the SVD method can also have computational advantages over diagonalization of the Fisher information matrix. We demonstrate the SVD method by applying it to the dimension-6 coefficients for the process of top-quark decay to a b quark and a W boson and use this example to illustrate some pitfalls in widely used fitting procedures. We also outline an iterative procedure for applying the SVD method to dimension-8 SMEFT coefficients.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Characterizing the expected behavior of non-Poissonian template fitting

We have performed a systematic study of the statistical behavior of non-Poissonian template fitting (NPTF), a method designed to analyze and characterize unresolved point sources in general counts datasets. Here, in this paper, we focus on the properties and characteristics of the Fermi-LAT gamma-ray data set. In particular, we have simulated and analyzed gamma-ray sky maps under varying conditions of exposure, angular resolution, pixel size, energy window, event selection, and source brightness. We describe how these conditions affect the sensitivity of NPTF to the presence of point sources, for inner-galaxy studies of point sources within the Galactic Center excess, and for the simplified case of isotropic emission. We do not find opportunities for major gains in sensitivity from varying these choices, within the range available with current Fermi-LAT data. We provide an analytic estimate of the NPTF sensitivity to point sources for the case of isotropic emission and perfect angular resolution, and find good agreement with our numerical results for that case.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

PyCrystalField : software for calculation, analysis and fitting of crystal electric field Hamiltonians

PyCrystalField is a Python software package for calculating single-ion crystal electric field (CEF) Hamiltonians. This software can calculate a CEF Hamiltonian ab initio from a point charge model for any transition or rare earth ion in either the J basis or the LS basis, perform symmetry analysis to identify nonzero CEF parameters, calculate the energy spectrum and observables such as neutron spectrum and magnetization, and fit CEF Hamiltonians to any experimental data. Here, the theory, implementation and examples of its use are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Simulating and benchmarking neutron total scattering instrumentation from inception of events to reduced and fitted data

In the design and realization of modern neutron scattering instrumentation, particularly when designing beamline concepts from the ground up, it is desirable to fully benchmark against realistically simulated data. This is especially true for total scattering beamlines, where the future deliverable data is to be analysed in both reciprocal- and real-space representations, and needs must be carefully balanced to ensure sufficient range, resolution and flux of the instrument. An approach to optimize the design of neutron scattering instrumentation via a workflow including ray-tracing simulations, event-based data reduction, heuristic analysis and fitting against realistically simulated spectra is demonstrated here. The case of the DISCOVER beamline concept at the Spallation Neutron Source is used as an example. The results of the calculations are benchmarked through simulation of existing instrumentation and subsequent direct comparison with measured data. Finally, on the basis of the validated models, the ability to explore design characteristics for future beamline concepts or future instrument improvements is demonstrated through the examples of detector tube size and detector layout.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Sheaves as a Framework for Understanding and Interpreting Model Fit

As data grows in size and complexity, finding frameworks which aid in interpretation and analysis has become critical. This is particularly true when data comes from complex systems where extensive structure is available, but must be drawn from peripheral sources. In this paper we argue that in such situations, sheaves can provide a natural framework to analyze how well a statistical model fits at the local level (that is, on subsets of related datapoints) vs the global level (on all the data). The sheaf-based approach that we propose is suitably general enough to be useful in a range of applications, from analyzing sensor networks to understanding the feature space of a deep learning model.

Kvinge, Henry J.↗