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NASA NTRS2021

Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Automatic Differentiation

Remote sensing measurements from multi-angle polarimeters (MAPs) contain rich aerosol microphysical property information, and these sensors have been used to perform retrievals in optically complex atmosphere and ocean systems. Previous studies have concluded that, generally, five moderately separated viewing angles in each spectral band provide sufficient accuracy for aerosol property retrievals, with performance gradually saturating as angles are added above that threshold. The Hyper-Angular Rainbow Polarimeter (HARP) instruments provide high angular sampling with a total of 90-120 unique angles across four bands, a capability developed mainly for liquid cloud retrievals. In practice, not all view angles are optimal for aerosol retrievals due to impacts of clouds, sun glint, and other impediments. The many viewing angles of HARP can provide resilience to these effects, if the impacted views are screened from the dataset, as the remaining views may be sufficient for successful analysis. In this study, we discuss how the number of available viewing angles impacts aerosol and ocean color retrieval uncertainties, as applied to two versions of the HARP instrument. AirHARP is an airborne prototype that was deployed in the ACEPOL field campaign, while HARP2 is an instrument in development for the upcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Based on synthetic data, we find that a total of 20-30 angles across all bands (i.e. five to eight viewing angles per band) are sufficient to achieve good retrieval performance. Following from this result, we develop an adaptive multi-angle polarimetric data screening (MAPDS) approach to evaluate data quality by comparing measurements with their best-fitted forward model. The FastMAPOL retrieval algorithm is used to retrieve scene geophysical values, by matching an efficient, deep learning-based, radiative transfer emulator to observations. The data screening method effectively identifies and removes viewing angles affected by thin cirrus clouds and other anomalies, improving retrieval performance. This was tested with AirHARP data, and we found agreement with the High Spectral Resolution Lidar-2 (HSRL-2) aerosol data. The data screening approach can be applied to modern satellite remote sensing missions, such as PACE, where a large amount of multi-angle, hyperspectral, polarimetric measurements will be collected.

multi-angle polarimeter↗

NASA NTRS2022

Intrinsic Dimensionality as a Metric for the Impact of Mission Design Parameters

High-resolution space-based spectral imaging of the Earth's surface delivers critical information for monitoring changes in the Earth system as well as resource management and utilization. Orbiting spectrometers are built according to multiple design parameters, including ground sampling distance (GSD), spectral resolution, temporal resolution, and signal-to-noise ratio. Different applications drive divergent instrument designs, so optimization for wide-reaching missions is complex. The Surface Biology and Geology component of NASA's Earth System Observatory addresses science questions and meets applications needs across diverse fields, including terrestrial and aquatic ecosystems, natural disasters, and the cryosphere. The algorithms required to generate the geophysical variables from the observed spectral imagery each have their own inherent dependencies and sensitivities, and weighting these objectively is challenging. Here, we introduce intrinsic dimensionality (ID), a measure of information content, as an applications-agnostic, data-driven metric to quantify performance sensitivity to various design parameters. ID is computed through the analysis of the eigenvalues of the image covariance matrix, and can be thought of as the number of significant principal components. This metric is extremely powerful for quantifying the information content in high-dimensional data, such as spectrally resolved radiances and their changes over space and time. We find that the ID decreases for coarser GSD, decreased spectral resolution and range, less frequent acquisitions, and lower signal-to-noise levels. This decrease in information content has implications for all derived products. ID is simple to compute, providing a single quantitative standard to evaluate combinations of design parameters, irrespective of higher-level algorithms, products, applications, or disciplines.

Intrinsic dimensionality↗

DOE OSTI2018

Morphological instability of aqueous dissolution of silicate glasses and minerals [Nonlinear dynamics of aqueous dissolution of silicate glasses and minerals: Morphological instability]

Understanding of aqueous dissolution of silicate glasses and minerals is of great importance to both Earth science and materials science. Silicate dissolution exhibits complex temporal evolution and spatial pattern formations. Recently, we showed how observed complexity could emerge from a simple self-organizational mechanism: dissolution of the silica framework in a material could be catalyzed by the cations released from the reaction itself. This mechanism enables us to systematically predict many key features of a silicate dissolution process including the occurrence of a sharp corrosion front (vs. a leached surface layer), oscillatory dissolution and multiple stages of the alteration process (e.g., an alteration rate resumption at a late stage of glass dissolution). Here, through a linear stability analysis, we show that this same mechanism can also lead to morphological instability of an alteration front, which, in combination with oscillatory dissolution, can potentially lead to a whole suite of patterning phenomena, as observed on archaeological glass samples, including wavy dissolution fronts, growth rings, incoherent bandings of alteration products, and corrosion pitting. Here, the result thus further demonstrates the importance of the proposed self-accelerating mechanism in silicate material degradation.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Multivariate Pointwise Information-Driven Data Sampling and Visualization

With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Ptychographic reconstructions performed in real time and offline have equivalent quality

Abstract Ptychography is a burgeoning imaging technique that enables high-resolution, lensless reconstruction of complex samples by analysing overlapping diffraction patterns, making it invaluable in fields like materials science, biology, and nanotechnology. Real-time ptychographic reconstructions are gaining interest in the scientific community as they provide immediate feedback. Yet their potential to replace offline reconstructions remains uncertain, in part due to questions about the quality of the resulting images. This study quantitatively compares real-time and offline reconstructions at different overlap conditions. Offline reconstructions, using all diffraction patterns at once, and real-time reconstructions, where new frames are added to the reconstructions in small chunks as the diffraction patterns are recorded, were indistinguishable and identical in reconstruction quality. These results hold consistently across all tested overlap ratios. This study represents the first quantitative analysis of real-time ptychographic reconstruction using a growing dataset, demonstrating the potential for real-time reconstructions to replace or at least complement offline reconstructions.

Science & Technology - Other Topics↗

DOE OSTI2023

Deciphering phase evolution in complex metal oxide thin films via high-throughput materials synthesis and characterization

Discovery of structure-property relationships in thin film alloys of complex metal oxides enabled by high-throughput materials synthesis and characterization facilities is demonstrated here with a case-study. In this study, thin films of binary transition metal oxides (Ti–Zn) are prepared by pulsed laser deposition with continuously varying Ti:Zn ratio, creating combinatorial samples for exploration of the properties of this material family. The atomic structure and electronic properties are probed by spatially resolved techniques including x-ray absorption near edge structures (XANES) and x-ray fluorescence (XRF) at the Ti and Zn K-edge, x-ray diffraction, and spectroscopic ellipsometry. The observed properties as a function of Ti:Zn ratio are resolved into mixtures of five distinguishable phases by deploying multivariate curve resolution analysis on the XANES spectral series, under constraints set by results from the other characterization techniques. First-principles computations based on density function theory connect the observed properties of each distinct phase with structural and spectral characteristics of crystalline polymorphs of Ti–Zn oxide. Continuous tuning of the optical absorption edge as a function of Ti:Zn ratio, including the unusual observation of negative optical bowing, exemplifies a functional property of the film correlated to the phase evolution.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Commercial, industrial, and institutional discount rate estimation for efficiency standards analysis: Sector-level data 1998–2021

Underlying each of the Department of Energy’s (DOE’s) federal appliance and equipment energy conservation standards are a set of complex analyses of the projected costs and benefits of regulation. Any new or amended standard must be designed to achieve significant additional energy conservation, provided that it is technologically feasible and economically justified (42 U.S.C. 6295(o)(2)(A)). DOE determines economic justification based on whether the benefits exceed the burdens, considering a variety of factors, including the economic impact of the standard on consumers of the product and the savings in lifetime operating cost compared to any increase in price or maintenance expenses (42 U.S.C. 6295(o)(2)(B)). As part of this determination, DOE conducts a Life-Cycle Cost (LCC) analysis, which models the combined impact of appliance first cost and operating cost changes on a representative commercial building sample in order to identify the fraction of customers achieving LCC savings or incurring net cost at the considered efficiency levels. Thus, the commercial discount rate value(s) used to calculate the present value of energy cost savings within the LCC model implicitly plays a role in estimating the economic impact of potential standard levels. This report provides an in-depth discussion of the commercial discount rate estimation process. It is an update to previous reports on estimating commercial discount rates from firm-level financial data (Fujita, 2016). Major topics covered in this report include: Discount rate estimation methods and rationale; -Data sources used and data limitations; -Discount rate distributions for use in standards analysis; -Discount rate estimation methods and distributions specific to the small business subgroup analysis. Going forward, this report will be updated as data allow and analyses necessitate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Commercial, industrial, and institutional discount rate estimation for efficiency standards analysis: Sector-level data 1998–2022

Underlying each of the U.S. Department of Energy’s (DOE’s) federal appliance and equipment energy conservation standards are a set of complex analyses of the projected costs and benefits of regulation. Any new or amended standard must be designed to achieve significant additional energy conservation, provided that it is technologically feasible and economically justified (42 U.S.C. 6295(o)(2)(A)). DOE determines economic justification based on whether the benefits exceed the burdens, considering a variety of factors, including the economic impact of the standard on consumers of the product and the savings in lifetime operating cost compared to any increase in price or maintenance expenses (42 U.S.C. 6295(o)(2)(B)). As part of this determination, DOE conducts a life-cycle cost (LCC) analysis, which models the combined impact of appliance first cost and operating cost changes on a representative commercial building sample to identify the fraction of customers achieving LCC savings or incurring net cost at the considered efficiency levels. Thus, the commercial discount rate value(s) used to calculate the present value of energy cost savings within the LCC model implicitly plays a role in estimating the economic impact of potential standard levels. This report provides an in-depth discussion of the commercial discount rate estimation process. It is an update to previous reports on estimating commercial discount rates from firm-level and sector-level financial data (e.g., Fujita, 2021, 2016). Major topics covered in this report include the following: -Discount rate estimation methods and rationale -Data sources used and data limitations -Discount rate distributions for use in standards analysis -Discount rate estimation methods and distributions specific to the small business subgroup analysis A version of this analysis was most recently released in 2022. Going forward, this report will be updated as data allow and analyses necessitate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Commercial, industrial, and institutional discount rate estimation for efficiency standards analysis: Sector-level data 1998–2023

Underlying each of the U.S. Department of Energy’s (DOE’s) federal appliance and equipment energy conservation standards are a set of complex analyses of the projected costs and benefits of regulation. Any new or amended standard must be designed to achieve significant additional energy conservation, provided that it is technologically feasible and economically justified (42 U.S.C. 6295(o)(2)(A)). DOE determines economic justification based on whether the benefits exceed the burdens, considering a variety of factors, including the economic impact of the standard on consumers of the product and the savings in lifetime operating cost compared to any increase in price or maintenance expenses (42 U.S.C. 6295(o)(2)(B)). As part of this determination, DOE conducts a life-cycle cost (LCC) analysis, which models the combined impact of appliance first cost and operating cost changes on a representative commercial building sample to identify the fraction of customers achieving LCC savings or incurring net cost at the considered efficiency levels. Thus, the commercial discount rate value(s) used to calculate the present value of energy cost savings within the LCC model implicitly plays a role in estimating the economic impact of potential standard levels. This report provides an in-depth discussion of the commercial discount rate estimation process relying on the Capital Asset Pricing Model (CAPM) to estimate a business’ cost of equity, and by adding a risk adjustment factor to the risk-free rate associated with long-term U.S. Treasury bonds to estimate their cost of debt. It is an update to previous reports on estimating commercial discount rates from firm-level and sector-level financial data (e.g., Fujita, 2021, 2016). Major topics covered in this report include the following: • Discount rate estimation methods and rationale • Data sources used and data limitations • Discount rate distributions for use in standards analysis • Discount rate estimation methods and distributions specific to the small business subgroup analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1992

Digital tracking loops for a programmable digital modem

In this paper, an analysis and hardware emulation of the tracking loops for a very flexible programmable digital modem (PDM) will be presented. The modem is capable of being programmed for 2, 4, 8, 16-PSK, 16-QAM, MSK, and Offset-QPSK modulation schemes over a range of data rates from 2.34 to 300 Mbps with programmable spectral occupancy from 1.2 to 1.8 times the symbol rate; these operational parameters are executable in burst or continuous mode. All of the critical processing in both the modulator and demodulator is done at baseband with very high-speed digital hardware and memory. Quadrature analog front-ends are used for translation between baseband and the IF center frequency. The modulator is based on a table lookup approach, where precomputed samples are stored in memory and clocked out according to the incoming data pattern. The sample values are predistorted to counteract the effects of the other filtering functions in the link as well as any transmission impairments. The demodulator architecture was adapted from a joint estimator-detector (JED) mathematical analysis. Its structure is applicable to most signalling formats that can be represented in a two-dimensional space. The JED realization uses interdependent, mutually aiding tracking loops with post-detection data feedback. To expedite and provide for more reliable synchronization, initial estimates for these loops are computed in a parallel acquisition processor. The cornerstone of the demodulator realization is the pre-averager received data filter which allows operation over a broad range of data rates without any hardware changes and greatly simplifies the implementation complexity. The emulation results confirmed tracking loop operation over the entire range of operational parameters listed above, as well as the capability of achieving and maintaining synchronization at BER's in excess of 10(exp -1). The emulation results also showed very close agreement with the tracking loop analysis, and validated the resolution apportionment of the various hardware elements in the tracking loops.

Poklemba, John J.↗

NASA NTRS2011

Sample Handling and Processing on Mars for Future Astrobiology Missions

In most analytical investigations, there is a need to process complex field samples for the unique detection of analytes especially when detecting low concentration organic molecules that may identify extraterrestrial life. Sample processing for analytical instruments is time, resource and manpower consuming in terrestrial laboratories. Every step in this laborious process will have to be automated for in situ life detection. We have developed, and are currently demonstrating, an automated wet chemistry preparation system that can operate autonomously on Earth and is designed to operate under Martian ambient conditions. This will enable a complete wet chemistry laboratory as part of future missions. Our system, namely the Automated Sample Processing System (ASPS) receives fines, extracts organics through solvent extraction, processes the extract by removing non-organic soluble species and delivers sample to multiple instruments for analysis (including for non-organic soluble species).

Beegle, Luther↗

DOE OSTI2023

Using synthetic infrared spectra derived from n/k optical constants for standoff detection of chemical deposits

We report results from a recent field experiment to test the validity of using physics-based synthetic infrared spectra to serve as endmembers in a spectral database targeted at chemical deposits. Specifically, the optical constants n and k, (the real and imaginary part of the refractive index) were used to first model infrared reflectance spectra for different thicknesses of chemical layers (e.g. acetaminophen, methylphosphonic acid – MPA, etc.) on various conducting and insulating substrates such as aluminum, wood, and glass. In the experimental portion of the research, thin films of the solid and liquid analytes were deposited onto such substrates to form micron-thick layers of the analytes at different thicknesses: Standoff data from an imaging instrument were then recorded and analyzed to not only identify the different analytes, but also quantify the layer/deposit thickness. To gauge success, the detection results using the synthetic data were compared to the results from hemispherical reflectance (HRF) spectra that were collected for the same sample planchets measured in the field via standoff methods. Preliminary results indicate good agreement between the synthetic reference data as compared to the lab-measured HRF data in terms of their ability to quantitatively reduce longwave infrared data. Specifically, modeled IR spectra for acetaminophen on an aluminum planchet at various thicknesses (1, 2, 5, 10, 15, and 20 µm) were synthesized and compared with standoff field reflectance data as well as HRF laboratory reflectance spectra for two samples: a 5.2 µm- and 12.8 µm-thick layer of acetaminophen on aluminum. Using a first-order approximation, analysis of the field data estimates the thicknesses of the samples to be 2 and 10 µm for the two samples, respectively, while the HRF laboratory data yields thickness estimates of between 5-10 µm and 10 µm, respectively. Both yield reasonable estimates, with the uncertainty most likely due to factors yet to be accounted for in the synthetic spectra such as light scattering.

complex index of refraction, optical constants, hy↗

DOE OSTI2019

Structural Study of Analogues of Titan’s Haze by Trapped Ion Mobility Coupled with a Fourier Transform Ion Cyclotron Mass Spectrometer

The aerosols present in the atmosphere of the Saturn’s moon Titan are of particular planetary science interest and several spacecraft missions are already allowed to gather spectroscopic data. Titan haze’s analogs, so-called tholins, were produced on earth to push forward the comprehension of their formation and properties. In this study, this highly complex mixture was analyzed here for the first time by trapped ion mobility spectrometry coupled to ultra-high resolution mass spectrometry (FTICR MS). Electrospray ionization revealed the characteristic CHN{sub x}-class components, with CHN{sub 5–6} and DBE 6–7 most abundant. Deploying specialized visualization, enabled by accurate mass measurements and elemental composition assignments, the adapted Kendrick mass defect analysis highlights the C{sub 2}H{sub 3}N homolog series, whereas the nitrogen-modified van Krevelen diagram exhibits a clear trend towards H/C 1.5 and N/C 0.5. More interestingly, the representation of m/z versus collision cross section (CCS) allowed hypothesizing a ramified N-PAH structural motif. State-of-the-art IMS is currently not able to resolve the isomeric continuum of ultra-complex mixtures; thus, peak parameters other than the CCS value are explored. As such, analyzing the mobility peak width versus m/z shows a linear increase in isomeric diversity between m/z 170 and 350 and a near plateau in diversity at higher m/z for the N-PAH-like structure. Due to the high complexity of the sample, these structural insights are only to be revealed by TIMS-FTICR MS. .

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Assessment of two benzylation strategies for the analysis of nerve-agent derived ethyl- and pinacolyl methyl phosphonic acids in sandy loam soil by GC–MS

Despite their prohibition by the Chemical Weapons Convention, nerve agents (NAs) remain in use against military and civilian targets. Due to their high reactivity, NAs readily degrade to phosphonic acids, making them important markers in the inspection of areas of presumed NA use. In this work, we assess the use of benzylation to modify ethyl- and pinacolyl methylphosphonic acids, degradation products of VX and Soman respectively, for their efficient detection in a soil matrix at ~10 and ~1 μg/g using GC–MS. The soil matrix, Sandy Loam (SL), was chosen for its ubiquitous nature, complex composition with silica particles embedded in clay, and low organic content. In this study, we demonstrate that benzylation via benzyl bromide yields a LOD = 25.6 ng/mL for benzylated-EMPA and LOD = 30.1 ng/mL for benzylated-PMPA. This is superior to the use of p-methoxybenzyl trichloroacetimidate in providing stable phosphonic acid ester derivatives for analysis. A base-modified procedure for p-methoxybenzylation was explored in this study yielding a LOD = 29.1 ng/mL for p-methoxybenzylated-EMPA and LOD = 39.8 ng/mL for p-methoxybenzylated-PMPA. Both benzylation pathways (benzyl bromide and p-methoxybenzyl trichloroacetimidate) can be used to yield phosphonic acid derivatives that provide further confirmation of these Soman and VX degradation products in soil samples in investigative scenarios. The work herein represents the first application of benzylation methods for the analysis of these NA markers in the acidic, silicon-based SL soil.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning

Computational models that formalize complex human behaviors enable study and understanding of such behaviors. However, collecting behavior data required to estimate the parameters of such models is often tedious and resource intensive. Thus, estimating dataset size as part of data collection planning (also known as Sample Size Determination) is important to reduce the time and effort of behavior data collection while maintaining an accurate estimate of model parameters. In this paper, we present a sample size determination method based on Uncertainty Quantification (UQ) for a specific Inverse Reinforcement Learning (IRL) model of human behavior, in two cases: 1) pre-hoc experiment design—conducted in the planning stage before any data is collected, to guide the estimation of how many samples to collect; and 2) post-hoc dataset analysis—performed after data is collected, to decide if the existing dataset has sufficient samples and whether more data is needed. Here, we validate our approach in experiments with a realistic model of behaviors of people with Multiple Sclerosis (MS) and illustrate how to pick a reasonable sample size target. Our work enables model designers to perform a deeper, principled investigation of effects of dataset size on IRL.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2017

One Step Closer to Mars with Aquaponics: Cultivating Citizen Science in K12 Schools

The Microbial Ecology and Biogeochemistry Research Laboratory at NASA Ames Research Center focuses primarily on the nutrient cycling and diversity of complex microbial communities. NASA is interested in the composition and functioning of microbial mat communities as these processes fundamentally shape the form and function of these analogs for the earliest forms of life on Earth (3.6 billion years ago), and likely will on other planets as well. Aquaponics systems are supported by microbial communities who perform many complex ecosystem services, including cycling nitrogen. Microbes are integral to the stability and productivity of aquaponics systems, which are analogous to microbial communities in food production systems that are essential for building efficient life support systems for long-distance space travel. Students at Meadow Park Middle School created 10 parallel aquaponics systems and took temporal microbial samples to characterize whether any macro-ecology variables impacted or changed the microbial diversity of these systems. Students additionally created a website so that other classrooms can pursue similar projects in their own schools (https://go.nasa.gov/2uJhxmF). Our lab at NASA Ames has sequenced water samples from each of the 10 tanks at 3 timepoints using a MinION sequencer. MPMS students will be involved in the analysis of the bioinformatics data generated through this collaboration. Our ongoing collaboration aims to collect and analyze data in the classroom setting that has utility for research scientists, while involving students as collaborators in the research process.

Kolattukudy, Maria↗

DOE OSTI2022

Enabling machine learning-ready HPC ensembles with Merlin

With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computing (HPC) environment. Here, we present Merlin, a workflow framework to enable large ML-friendly ensembles of scientific HPC simulations. By augmenting traditional HPC with distributed compute technologies, Merlin aims to lower the barrier for scientific subject matter experts to incorporate ML into their analysis. As a producer–consumer workflow model, Merlin enables multi-machine, cross-batch job, dynamically allocated yet persistent workflows capable of utilizing surge-compute resources. Key features of Merlin are a flexible HPC-centric interface, low per-task overhead, multi-tiered fault recovery, and a hierarchical sampling algorithm that allows for $\mathscr{O}$(N) task execution and $\mathscr{O}$(N ln N) task queuing to ensembles of millions of tasks. In addition to Merlin’s design, we test the algorithm’s performance in an HPC center and demonstrate the ability to enqueue 40 million simulations in 100 s, with a 30 millisecond per-task overhead that is independent of ensemble size. Finally, we describe some example applications that Merlin has enabled on leadership-class HPC resources, such as the ML-augmented optimization of nuclear fusion experiments and the calibration of infectious disease models to study the progression of and possible mitigation strategies for COVID-19.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Global Sensitivity Analyses for Test Planning with Black-Box Models for Mars Sample Return

This work describes sensitivity analyses performed on complex black-box models used to support experimental test planning under limited resources in the context of the Mars Sample Return program, which aims at bringing to Earth rock and atmospheric samples from Mars. We develop a systematic workflow that allows the analysts to simultaneously obtain quantitative insights on key drivers of uncertainty, on the direction of impact, and the presence of interactions. We apply novel optimal transport-based global sensitivity measures to tackle the multivariate nature of the output. On the modeling side, we apply multi-fidelity techniques that leverage low-fidelity models to speed up the calculations and make up for the limited amount of high-fidelity samples, while keeping these in the loop for accuracy guarantees. The sensitivity analysis reveals insights useful for the analysts to understand the model's behavior and identify the factors to focus on during testing in order to maximize the value of information extracted from them to ensure mission success when limited resources are available.

Giuseppe Cataldo↗