Search NASA⌕ Search

SEARCH · Search NASA

Results for “deep space ranging”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Leveraging operator learning to accelerate convergence of the preconditioned conjugate gradient method

We propose a new deflation strategy to accelerate the convergence of the preconditioned conjugate gradient (PCG) method for solving parametric large-scale linear systems of equations. Unlike traditional deflation techniques that rely on eigenvector approximations or recycled Krylov subspaces, we generate the deflation subspaces using operator learning, specifically the Deep Operator Network (DeepONet). To this aim, we introduce two complementary approaches for assembling the deflation operators. The first approach approximates near-null space vectors of the discrete PDE operator using the basis functions learned by the DeepONet. The second approach directly leverages solutions predicted by the DeepONet. To further enhance convergence, we also propose several strategies for prescribing the sparsity pattern of the deflation operator. Here, a comprehensive set of numerical experiments encompassing steady-state, time-dependent, scalar, and vector-valued problems posed on both structured and unstructured geometries is presented and demonstrates the effectiveness of the proposed DeepONet-based deflated PCG method, as well as its generalization across a wide range of model parameters and problem resolutions.

Deflation↗
DOE OSTI2020

Spectroscopic confirmation of a mature galaxy cluster at a redshift of 2

Galaxy clusters are the most massive virialized structures in the Universe and are formed through the gravitational accretion of matter over cosmic time. The discovery of an evolved galaxy cluster at redshift z = 2, corresponding to a look-back time of 10.4 billion years, provides an opportunity to study its properties. The galaxy cluster XLSSC 122 was originally detected as a faint, extended X-ray source in the XMM Large Scale Structure survey and was revealed to be coincident with a compact over-density of galaxies with photometric redshifts of 1.9 ± 0.2. Subsequent observations at millimetre wavelengths detected a Sunyaev–Zel’dovich decrement along the line of sight to XLSSC 122, thus confirming the existence of hot intracluster gas, while deep imaging spectroscopy from the European Space Agency’s X-ray Multi-Mirror Mission (XMM-Newton) revealed an extended, X-ray-bright gaseous atmosphere with a virial temperature of 60 million Kelvin, enriched with metals to the same extent as are local clusters. Here we report optical spectroscopic observations of XLSSC 122 and identify 37 member galaxies at a mean redshift of 1.98, corresponding to a look-back time of 10.4 billion years. We use photometry to determine a mean, dust-free stellar age of 2.98 billion years, indicating that star formation commenced in these galaxies at a mean redshift of 12, when the Universe was only 370 million years old. The full range of inferred formation redshifts, including the effects of dust, covers the interval from 7 to 13. These observations confirm that XLSSC 122 is a remarkably mature galaxy cluster with both evolved stellar populations in the member galaxies and a hot, metal-rich gas composing the intracluster medium.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Improving Photometric Redshift Estimates with Training Sample Augmentation

Abstract Large imaging surveys will rely on photometric redshifts (photo- z 's), which are typically estimated through machine-learning methods. Currently planned spectroscopic surveys will not be deep enough to produce a representative training sample for Legacy Survey of Space and Time (LSST), so we seek methods to improve the photo- z estimates that arise from nonrepresentative training samples. Spectroscopic training samples for photo- z 's are biased toward redder, brighter galaxies, which also tend to be at lower redshift than the typical galaxy observed by LSST, leading to poor photo- z estimates with outlier fractions nearly 4 times larger than for a representative training sample. In this Letter, we apply the concept of training sample augmentation, where we augment simulated nonrepresentative training samples with simulated galaxies possessing otherwise unrepresented features. When we select simulated galaxies with ( g - z ) color, i -band magnitude, and redshift outside the range of the original training sample, we are able to reduce the outlier fraction of the photo- z estimates for simulated LSST data by nearly 50% and the normalized median absolute deviation (NMAD) by 56%. When compared to a fully representative training sample, augmentation can recover nearly 70% of the degradation in the outlier fraction and 80% of the degradation in NMAD. Training sample augmentation is a simple and effective way to improve training samples for photo- z 's without requiring additional spectroscopic samples.

Moskowitz, Irene (ORCID:0000000222068589)↗
DOE OSTI2023

NETL Plastic Pipes Project (Final Report)

Plastic or composite pipelines have been the bane of the utility locating industry because they are neither conductive nor magnetic which are the properties traditionally used to locate buried utilities. Ground penetrating radar (GPR) is an effective geophysical tool for locating plastic/composite pipelines where resistive cover allows for adequate penetration of radar energy. However, GPR has limited applicability in areas where the soil cover is conductive due to significant clay and/or salt content. This study examines complementary near-surface geophysical methods that are potentially useful for locating buried plastic/composite pipelines, either singly or in combination. Specifically, this modeling study used computational numerical methods to forward model the response of GPR, resistivity, seismic, gravity gradiometry, and photoacoustic/thermoacoustic imaging methods to plastic/composite pipelines for various scenarios including: (1) pipe diameters ranging between 2 in. to 12 in.; (2) burial depths ranging between 3 ft. to 4 ft.; (3) various degrees in contrast in physical properties (i.e., electrical permittivity, elasticity, resistivity, density); and (4) various experimental acquisition choices (e.g., GPR radar and seismic source frequencies, electrode spacing). Numerical modeling performed herein reconfirmed that GPR is the preferred method for detecting/locating plastic pipelines. A caveat for GPR detection is that the material covering the plastic pipe (trench fill material and adjacent soil) must be sufficiently resistive to allow the two-way propagation to the required depth of investigation and back to the surface. GPR was the only method modeled in this study that can be used to directly detect plastic pipelines of 2-in.-diameter and larger when buried 3-ft-deep. GPR data processing and imaging also can determine pipe depth, pipe diameter, trench dimensions, and moisture conditions. Seismic modeling results suggest that direct detection of a 12-in.-diameter plastic pipe at 3-ft.-depth may be possible under favorable conditions; however, the associated signature would be weak (e.g., surface- to S-wave, backscattered surface-waves, and/or forward scattered surface-waves to S-wave). Direct pipe detection under field conditions with noise and strong lateral geologic heterogeneity is doubtful. Numerical modeling also suggests that plastic pipelines can be indirectly located by detecting the trench in which they are buried. GPR, direct current (DC) resistivity, and seismic methods have the potential to locate the pipeline trench if there is sufficient contrast between the trench-wall and trench-fill materials for the physical property being measured by each method (i.e., electrical permittivity for GPR; resistivity for DC resistivity; or density, compressional velocity, or shear velocity for seismic). Modeling also indicated that currently available (commercial) gravity gradiometers would be unable to directly detect/locate plastic pipelines ≤ 8-in.-diameter when buried 3-ft.-deep given the typical instrument noise floor for field surveying as well as the expected density variations due to geologic heterogeneity. The numerical modeling performed in this project did not identify a universal geophysical technology that can locate buried plastic pipelines in all parts of the United States (although GPR is suggested for all areas with resistive cover). However, the project results suggest that a towed land streamer simultaneously acquiring multiple geophysical data types including multi-offset GPR, multi-channel DC resistivity, seismic geophone- and/or distributed acoustic sensing (DAS), and potentially photoacoustic/thermoacoustic data would be an appropriate platform for locating buried plastic pipeline. Moreover, the complementary multiphysics data acquired by a towed land streamer would permit the use of joint and/or cooperative inversion frameworks for a more rigorous and consistent data interpretation.

42 ENGINEERING↗
DOE OSTI2020

A Hybrid Deep Learning Approach to Cosmological Constraints from Galaxy Redshift Surveys

We present a deep machine learning (ML)–based technique for accurately determining σ g and Ω m from mock 3D galaxy surveys. The mock surveys are built from the AbacusCosmos suite of N -body simulations, which comprises 40 cosmological volume simulations spanning a range of cosmological parameter values, and we account for uncertainties in galaxy formation scenarios through the use of generalized halo occupation distributions (HODs). We explore a trio of ML models: a 3D convolutional neural network (CNN), a power spectrum–based fully connected network, and a hybrid approach that merges the two to combine physically motivated summary statistics with flexible CNNs. We describe best practices for training a deep model on a suite of matched-phase simulations, and we test our model on a completely independent sample that uses previously unseen initial conditions, cosmological parameters, and HOD parameters. Despite the fact that the mock observations are quite small (~0.07 h -3 Gpc 3 ) and the training data span a large parameter space (six cosmological and six HOD parameters), the CNN and hybrid CNN can constrain estimates of σ g and Ω m to ~3% and ~4%, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Anomalous temperature dependence of the experimental x-ray structure factor of supercooled water

The structural changes of water upon deep supercooling were studied through wide-angle x-ray scattering at SwissFEL. The experimental setup had a momentum transfer range of 4.5 Å-1, which covered the principal doublet of the x-ray structure factor of water. The oxygen–oxygen structure factor was obtained for temperatures down to 228.5 ± 0.6 K. Similar to previous studies, the second diffraction peak increased strongly in amplitude as the structural change accelerated toward a local tetrahedral structure upon deep supercooling. We also observed an anomalous trend for the second peak position of the oxygen–oxygen structure factor (q 2 ). We found that q 2 exhibits an unprecedented positive partial derivative with respect to temperature for temperatures below 236 K. Based on Fourier inversion of our experimental data combined with reference data, we propose that the anomalous q 2 shift originates from that a repeat spacing in the tetrahedral network, associated with all peaks in the oxygen–oxygen pair-correlation function, gives rise to a less dense local ordering that resembles that of low-density amorphous ice. The findings are consistent with that liquid water consists of a pentamer-based hydrogen-bonded network with low density upon deep supercooling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Observational window effects on multi-object reverberation mapping

ABSTRACT Contemporary reverberation mapping campaigns are employing wide-area photometric data and high-multiplex spectroscopy to efficiently monitor hundreds of active galactic nuclei (AGNs). However, the interaction of the window function(s) imposed by the observation cadence with the reverberation lag and AGN variability time-scales (intrinsic to each source over a range of luminosities) impact our ability to recover these fundamental physical properties. Time dilation effects due to the sample source redshift distribution introduce added complexity. We present comprehensive analysis of the implications of observational cadence, seasonal gaps, and campaign baseline duration (i.e. the survey window function) for reverberation lag recovery. We find that the presence of a significant seasonal gap dominates the efficacy of any given campaign strategy for lag recovery across the parameter space, particularly for those sources with observed-frame lags above 100 d. Using the Australian Dark Energy Survey as a baseline, we consider the implications of this analysis for the 4MOST/Time-Domain Extragalactic Survey campaign providing concurrent follow-up of the Legacy Survey of Space and Time deep-drilling fields, as well as upcoming programmes. We conclude that the success of such surveys will be critically limited by the seasonal visibility of some potential field choices, but show significant improvement from extending the baseline. Optimizing the sample selection to fit the window function will improve survey efficacy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Field study of group effects on the pullout capacity of “deep” helical piles in sand

This paper presents the results of a field load test program used to investigate group effects on the pullout capacity of single-helix “deep” helical piles/anchors in sand. The high tensile capacity and silent installation of helical piles has given them serious consideration as an alternative to conventional deep foundations and anchors for offshore renewable energy structures. New offshore applications may consider the use of groups of helical piles to resist structural loads. Group interaction effects are known to occur in helical piles, but there is a scarcity of field data on groups in sands under tensile loading. This study involved the installation and load testing of single-helix 152mm diameter round shaft piles and pile groups embedded in sand to depths of 12 and 18 helix diameters below the ground surface. The study was designed to explore the effects of close pile spacing, group configuration (i.e., number of piles), and soil strength (i.e., friction angle) as interpreted from cone penetration test (CPT) resistance. The results showed group efficiencies ranging from about 0.6 to 1.0 at a horizontal spacing of 2 to 3 times the helix diameter in sands with friction angles of about 39° to 44°. The data from this study may also be useful for validation of numerical models for analysis of helical pile group interactions.

Engineering↗
DOE OSTI2023

Deep Reinforcement Learning Based Smart Water Heater Control for Reducing Electricity Consumption and Carbon Emission

Water heating is the third largest electricity consumer in U.S. households, after space heating and cooling. Thus, water heaters represent a significant potential for reducing electricity consumption and associated CO2 emissions of residential buildings. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity consumption and the CO2 emissions of a heat pump water heater without affecting user comfort. In this approach, a set of RL agents focusing on either electricity saving or emission reduction, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on different hot water usage and Marginal Operating Emissions Rate (MOER) profiles. The testing results showed that the RL agents that focus on electricity saving can save electricity in the range of 12–22% by operating the water heater with maximum heat pump efficiency and minimum electric element utilization. On the other hand, the RL agents that focus on emission reduction reduced emissions in the range of 18–37% by making use of the variable MOER values. These RL agents used the heat pump and/or an element when the MOER values are low due to the availability of renewable energy sources (e.g., solar and wind) and mostly avoided the periods of carbon-intensive periods. Overall, these results showed that the proposed RL approach can help minimize the electricity consumption and the CO2 emissions of a heat pump water heater without having any prior knowledge about the device.

Amasyali, Kadir↗
DOE OSTI2025

Selection of high-redshift Lyman-Break Galaxies from broadband and wide photometric surveys

Here, in this paper, we investigate the possibility of selecting high-redshift Lyman-Break Galaxies (LBG) using current and future broadband wide photometric surveys, such as the Ultraviolet Near Infrared Optical Northern Survey (UNIONS) or the Vera C. Rubin Legacy Survey of Space and Time (LSST), using a Random Forest algorithm. This work is conducted in the context of future large-scale structure spectroscopic surveys like DESI-II, the next phase of the Dark Energy Spectroscopic Instrument (DESI), which will start around 2029. We use deep imaging data from the Hyper Suprime Camera (HSC) and the Canada-France-Hawaii Telescope Large Area U-band Deep Survey (CLAUDS) on the COSMOS and XMM-LSS fields. To predict the selection performance of LBGs with image quality similar to UNIONS, we degrade the u,g,r,i and z bands to UNIONS depth. The Random Forest algorithm is trained with the u,g,r,i and z bands to classify LBGs in the 2.5 < z < 3.5 range. We find that fixing a target density budget of 1,100 deg -2 , the Random Forest approach gives a density of z > 2 targets of 873 deg -2 , and a density of 493 deg -2 of confirmed LBGs after spectroscopic confirmation with DESI. This UNIONS-like selection was tested in a dedicated spectroscopic observation campaign of 1,000 targets with DESI on the COSMOS field, providing a safe spectroscopic sample with a mean redshift of 3. This sample is used to derive forecasts for DESI-II, assuming a sky coverage of 5,000 deg 2 . We predict uncertainties on Alcock-Paczynski parameters α ⊥ and α ∥ to be 0.7% and 1% for 2.6 < z < 3.2, resulting in a potential 2% measurement of the dark energy fraction at high redshift. Additionally, we estimate the uncertainty in local non-Gaussianity and predict σ fNL ≈ 7, which would be comparable to the current best precision achieved by Planck. The latter forecast suggests that achieving the precision required to place stringent constraints on inflationary models (σ fNL ≈ 1) using spectroscopic galaxy surveys necessitates the development of a next-generation (Stage V) spectroscopic survey.

cosmological parameters from LSS↗
DOE OSTI2020

Estimating Atmospheric Parameters of DA White Dwarf Stars with Deep Learning

The determination of atmospheric parameters of white dwarf stars (WDs) is crucial for researches on them. Traditional methodology is to fit the model spectra to observed absorption lines and report the parameters with the lowest χ 2 error, which strongly relies on theoretical models that are not always publicly accessible. In this work, we construct a deep learning network to model-independently estimate T eff and log g of DA stars (DAs), corresponding to WDs with hydrogen-dominated atmospheres. The network is directly trained and tested on the normalized flux pixels of full optical wavelength range of DAs spectroscopically confirmed in the Sloan Digital Sky Survey. Experiments in test yield that the rms error for T eff and log g approaches 900 K and 0.1 dex, respectively. This technique is applicable for those DAs with T eff from 5000 to 40,000 K and log g from 7.0 to 9.0 dex. Furthermore, the applicability of this method is verified for the spectra with degraded resolution of ~200. So it is also practical for the analysis of DAs that will be detected by the Chinese Space Station Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks

Abstract This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one‐to‐many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root‐mean‐squared‐error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser‐matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Comprehensive Neural Posterior Estimation for Galaxy-Galaxy Strong Lensing

We present a deep learning model based on neural posterior estimation (NPE) for comprehensive extraction of astrophysical parameters from galaxy-scale strong gravitational lenses. The unprecedentedly large amount of galaxy-scale strong lenses expected in future cosmological surveys (${\cal O}(10^5)$) promises to enable valuable statistical constraints in various studies ranging from galaxy formation to the nature of dark matter, but it also poses a significant challenge for traditional modelling pipelines. To this end, our automated model includes several new, state-of-the-art features and approaches leveraging the framework of simulation-based inference (SBI). We infer a total of 20 parameters describing the mass and light profiles of both lens and source galaxies, using simulated raw multi-band data modelled under noise and observing conditions expected by the Legacy Survey of Space and Time (LSST), with its summary statistics generated by a residual network. We examine the efficacy of multi-band data in extracting nearly 20 model parameters simultaneous from strong lensing images including lens light. Finally, We perform a comprehensive set of diagnostics for SBI models, evaluating the model's prediction accuracy, stability, and uncertainty quantification.

Zhao, Roy J. [Chicago U., KICP]↗
DOE OSTI2025

CMB-S4: Foreground-cleaning Pipeline Comparison for Measuring Primordial Gravitational Waves

We compare multiple foreground-cleaning pipelines for estimating the tensor-to-scalar ratio, r, using simulated maps of the planned CMB-S4 experiment within the context of the South Pole Deep Patch. To evaluate robustness, we analyze bias and uncertainty on r across various foreground suites using map-based simulations. The foreground-cleaning methods include: a parametric maximum likelihood approach applied to auto- and cross-power spectra between frequency maps; a map-based parametric maximum-likelihood method; and a harmonic-space internal linear combination using frequency maps. We summarize the conceptual basis of each method to highlight their similarities and differences. To better probe the impact of foreground residuals, we implement an iterative internal delensing step, leveraging a map-based pipeline to generate a lensing B-mode template from the large aperture telescope frequency maps. Our results show that the performance of the three approaches is comparable for simple and intermediate-complexity foregrounds, with σ(r) ranging from 3–5 ×10 −4 . However, biases at the 1σ–2σ level appear when analyzing more complex forms of foreground emission. By extending the baseline pipelines to marginalize over foreground residuals, we demonstrate that contamination can be reduced to within statistical uncertainties, albeit with a pipeline-dependent impact on σ(r), which translates to a detection significance between 2σ and 4σ for an input value of r = 0.003. These findings suggest varying levels of maturity among the tested pipelines, with the auto- and cross-spectra-based approach demonstrating the best stability and overall performance. Moreover, given the extremely low noise levels, mutual validation of independent foreground-cleaning pipelines is essential to ensure the robustness of any potential detection.

astronomy data analysis↗
DOE OSTI2024

MedNAS: Multiscale Training-Free Neural Architecture Search for Medical Image Analysis

Deep neural networks have demonstrated impressive results in medical image analysis, but designing suitable architectures for each specific task is expertise dependent and time consuming. Neural architecture search (NAS) offers an effective means of discovering architectures. It has been highly successful in numerous applications, particularly in natural image classification. Yet, medical images possess unique characteristics, such as small regions and a wide variety of lesion sizes, that differentiate them from natural images. Furthermore, most current NAS methods struggle with high computational costs, especially when dealing with high-resolution image datasets. In this article, we present a novel evolutionary NAS method called multiscale training-free neural architecture search (MSTF-NAS) to address these challenges. Specifically, to accommodate the broad range of lesion region sizes in disease diagnosis, we develop a new reduction cell search space that enables the search algorithm to explicitly identify the optimal scale combination for multiscale feature extraction. Further, to overcome the issue of high computational costs, we utilize training-free indicators as performance measures for candidate architectures, which allows us to search for the optimal architecture more efficiently. More specifically, by considering the capability and simplicity of various networks, we formulate a multiobjective optimization problem that involves two training-free indicators and model complexity for candidate architectures. Extensive experiments on a large medical image benchmark and a publicly available breast cancer detection dataset are conducted. The empirical results demonstrate that our MSTF-NAS outperforms both human-designed architectures and current state-of-the-art NAS algorithms on both datasets, indicating the effectiveness of our proposed method.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Microwave-Assisted Solution Synthesis of Metastable Intergrowth of AgInS2 Polymorphs

The intergrowth of stable and metastable AgInS2 polymorphs was synthesized using a microwave-assisted synthesis. The samples were synthesized in water and in a deep eutectic solvent (DES) consisting of choline chloride and thiourea. An increase in the metal precursor concentration improved the crystallinity of the synthesized samples and affected the particle size. AgInS2 cannot be synthesized from crystalline binary Ag2S or In2S3 via this route. The solution synthesis reported here results in the intergrowth of the thermodynamically stable polymorph (space group I4¯2d, chalcopyrite structure) and the high-temperature polymorph (space group Pna21, wurtzite-like structure) that is metastable at room temperature. A scanning transmission microscopy (STEM) study revealed the intergrowth of tetragonal and orthorhombic polymorphs in a single particle and unambiguously established that the long-thought hexagonal wurtzite polymorph has pseudo-hexagonal symmetry and is best described with the orthorhombic unit cell. The solution-synthesized AgInS2 polymorphs intergrowth has slightly lower bandgap values in the range of 1.73 eV–1.91 eV compared to the previously reported values for tetragonal I4¯2d (1.86 eV) and orthorhombic Pna21 (1.98 eV) polymorphs.

Adeyemi, Adedoyin N. (ORCID:0000000340096150)↗
DOE OSTI2025

Alpha Decay Chains as Thermal Power Sources: Analysis and Applications for RTGs

Radioactive sources can provide power in remote and environmentally harsh locations such as the arctic or space. The generators powered by such sources are rugged and can withstand extreme temperatures, lack of sunlight, and require no human intervention for multiple years. Radioisotopes are used in thermoelectric generators to provide power at remote sites and deep in space. Isotopes like Pu-238, Cm-244, and Am-241 are used in these generators by NASA for power in space probes and spacecrafts. These power sources deliver a steady supply of energy over extended periods of time. Alpha particles created during decay do not travel far in a material. Their kinetic energy is transferred to heat that we can then convert into energy. Unlike beta and gamma decay, the slower-moving alpha particles stop in the material, making their energy available for use. Energy from these natural decay processes provides a reliable source of power. Spontaneous fission is rare and unreliable, and unlike induced fission processes, alpha decay occurs naturally and does not require external management or ignition. The ideal properties of an isotope for use as a power source depend upon the intended use. For use in an Arctic research base over a period of several years, but less than a decade, an isotope that provides high power output over a shorter lifespan may be the most suitable option. Whereas, for deep space missions where a consistent power source for decades or perhaps more than 100 years is needed that would require a very different isotope. One with a much longer half-life that would provide consistent power throughout that time and survive in that state in for these extended periods of time. These examples represent two extreme sides in terms of time frames. By analyzing the power produced by different radioactive decay processes over time, we can evaluate the suitability of various isotope decay chains for specific uses. Some unstable isotopes undergo a series of radioactive decays, transforming into different isotopes at each step and resulting in a stable isotope. The lists of isotopes in these decay processes are known as decay chains. Some of these chains, illustrated in the figures below, are currently being investigated for use in radioisotope thermoelectric generators (RTGs) designed for a range of operational durations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Geothermal Fault Zone and Fluid Imaging through Joint Airborne ZTEM and Ground MT Data Inversion Analysis

This project has aimed to achieve detailed electrical resistivity resolution at geothermal reservoir scales by combining airborne natural electromagnetic (EM) field surveying (ZTEM) with ground magnetotelluric (MT) measurements to approximate an airborne MT geophysical method. MT alone is relatively expensive and may have permitting challenges in sensitive areas. Airborne ZTEM field data contains only the magnetic field, requires a background assumption, and has been limited to relatively high frequencies, thus suffering uniqueness problems. Based on proto-type 2D simulations, ZTEM ambiguities may be reduced through formal incorporation with possibly sparse ground MT soundings, which we pursued in full 3D for this project. The methodology was tested at the high-temperature Roosevelt Hot Springs geothermal system, Utah, which was considered advantageous given the near total exposure of crystalline reservoir rocks across the project area. ZTEM and ground MT survey data were acquired in 2017, subcontracted to outside parties with which we have worked in the past. These included 80 remote-referenced tensor MT soundings over the Mineral Mountains and adjacent Roosevelt Hot Spring producing geothermal system. These MT stations abut later coverage of a similar number of MT stations taken for the Utah FORGE project providing excellent total data aperture to re-solve structure beneath both project areas better than either set alone. The airborne ZTEM survey covered 704 line kilometers in E-W flight lines with a 250 m line spacing. Although this survey was timed during a maintenance-related shutdown of power production at the Roosevelt Hot Springs, other noise sources difficult to identify but including two high-voltage state-scale transmission lines compromised the ZTEM survey badly leading to unusable responses. Thus, with DOE management concurrence, the project proceeded to emphasize inversion and interpretation of the joint SubTER-FORGE MT data sets with regard to the Roosevelt Hot Springs reservoir recharge and to deep heat sources for both it and the Utah FORGE EGS project area. We also investigated the joint ZTEM-MT sampling concept with data sets from the Eleven Mile Canyon prospect area donated by the U.S. Navy (A. Sabin, PoC). Inversion of the SubTER-FORGE MT data using the HexMT 3D finite element algorithm reveals a large, low-resistivity anomaly extending sub-vertically through the depth range of the crust beneath the western Mineral Mountains. The steep conductive zone connects in the lower crust to a more tabular conductor characteristic of much of the Great Basin that generally is ascribed to current mafic magmatic underplating, hybridization and fluid release. The location of the resolved anomaly relative to the recent (0.5-0.8 Ma) eruptive centers of the Mineral Mountains implicates it as remnants of the magma body which fed these centers. This structure appears to be currently feeding heat and fluids upward into the Roosevelt Hot Springs hydrothermal system, as well as heat laterally to the FORGE project area. Separate and joint inversion models were carried out for the donated Eleven Mile Canyon MT-ZTEM data set to demonstrate concept. ZTEM only inversion showed two main alteration zones in the western portion of the project area known from geological mapping. Joint inversion including an E-W profile of MT soundings sharpened these features considerably. It also resolved in much greater detail the graben related normal faulting structure of the central project area which lies at depths exceeding the sensitivity of ZTEM alone. The sparse number of MT da-ta relative to the ZTEM required upweighting the former by a factor of several, but an exact procedure awaits future research. Our final impression is that sparse MT data can improve resolution of the subsurface over that of ZTEM alone. However, well sampled MT data are to be preferred and offer the simplicity of interpreting just one data type, and possess the superior resolution capability coming with the electric field everywhere, and from their high bandwidth.

15 GEOTHERMAL ENERGY↗