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NASA NTRS2017

Binary Source Microlensing Event OGLE-2016-BLG-0733: Interpretation of a Long-Term Asymmetric Perturbation

In the process of analyzing an observed light curve, one often confronts various scenarios that can mimic the planetary signals causing difficulties in the accurate interpretation of the lens system. In this paper, we present the analysis of the microlensing event OGLE-2016-BLG-0733. The light curve of the event shows a long-term asymmetric perturbation that would appear to be due to a planet. From the detailed modeling of the lensing light curve, however, we find that the perturbation originates from the binarity of the source rather than the lens. This result demonstrates that binary sources with roughly equal-luminosity components can mimic long-term perturbations induced by planets with projected separations near the Einstein ring. The result also represents the importance of the consideration of various interpretations in planet-like perturbations and of high-cadence observations for ensuring the unambiguous detection of the planet.

microlensing event OGLE-2016-BLG-0733↗

NASA NTRS2017

A Physical Interpretation of the Titius-Bode Rule and Its Connection to the Closed Orbits of Bertrand's Theorem

We consider the geometric Titius-Bode rule for the semimajor axes of planetary orbits. We derive an equivalent rule for the midpoints of the segments between consecutive orbits along the radial direction and we interpret it physically in terms of the work done in the gravitational field of the Sun by particles whose orbits are perturbed around each planetary orbit. On such energetic grounds, it is not surprising that some exoplanets in multiple-planet extrasolar systems obey the same relation. However, it is surprising that this simple interpretation of the Titius-Bode rule also reveals new properties of the bound closed orbits predicted by Bertrand's theorem, which has been known since 1873.

Titus-Bode rule↗

NASA NTRS2019

Spectral Interpretation of Magmatic Evolution, Oxidation, and Crystallinity in a Volcanic Planetary Analog System

Volcanic surfaces are common and varied throughout the terrestrial planets. Remote spectroscopy is often the only method for determining surface chemistry and mineralogy of such provinces, and is thus critical for understanding petrologic processes and constraining planetary interior evolution and chemistry. Natural volcanic systems exhibit variability in magmatic chemical evolution, crystallinity, oxidation, and eruption-related alteration (e.g. hydrothermal). The extent to which spectroscopy can identify these characteristics alongside each other is thus a key question for interpreting volcanic processes from orbit. While the effects of each of these on visible/near infrared (VNIR) and thermal infrared (TIR) spectra of igneous rocks has been studied separately to varying degrees, their combined spectral effects (and interpretability of such spectra) are understudied.

Scudder, N. A.↗

NASA NTRSDate not supplied

Simulation-Aided Design and Interpretation of Results of Microgravity-Based Solidification Experiments

The preparation and interpretation of microgravity experiments conducted on the International Space Station (ISS) can be challenging because of the remote location of the equipment. Although a “ground unit” is typically available on Earth, it is not located in the laboratory of the principal investigator. Furthermore, test experiments in the ground unit cannot fully duplicate heat transfer conditions of the weightless environment, and thus be a reliable reference for experiment design. Under such conditions, numerical simulations are a valuable tool both for preparing the experiments and interpreting the results obtained under terrestrial and micro-gravity conditions. In this work, we developed a detailed 3D model of the “SUBSA” furnace located at the ISS, and demonstrate its use in our present investigation which includes melt growth and vapor growth of InI crystals on the ISS. In 2002, the SUBSA furnace was designed and built for the growth of InSb crystals in the Microgravity Science Glovebox (MSG) at the ISS. The SUBSA furnace features a transparent section without heating elements, where the axial temperature gradient is high. In our present investigation, we have used the same furnace to grow four InI crystals from the melt, and two InI crystals from the vapor phase (2018-2021). InI crystals have ~20 times lower thermal conductivity than InSb crystals. As a result, during melt growth, the InI crystals were exposed to extremely high temperature gradients. To reduce the axial temperature gradient, we added external jackets made from high thermal conductivity material to the growth ampoules. The SUBSA furnace could not be modeled as asymmetric because of its rectangular shape and right-angled transparent section, which contains a convoluted geometry. The rectangular shape of the transparent section further complicates modelling of radiative heat transfer, which dominates in the zone. At present, numerical simulations are being conducted to resolve an apparent discrepancy between the readings of the 5 thermocouples located in the SUBSA furnace, and the observed position of the solid and molten material. The 3D simulation will be presented, along with the calibration and growth experiment results conducted in microgravity and in the SUBSA ground unit. The developed model should be useful for upcoming microgravity investigations planned to be conducted in the SUBSA facility.

V Riabov↗

NASA NTRS2023

Another Geometric Interpretation of Cramer’s Rule

We develop a geometric interpretation of Cramer’s rule as a generalization of projection onto orthogonal basis vectors using the rows of the adjugate. This interpretation makes connections between elementary linear algebra concepts like the solution to linear equations, inner products, and projections. Such connections are useful for introducing broader concepts related to Hilbert spaces and geometric algebras like Grassman algebra. Such connections were essential for the author’s mathematical education as an engineer.

linear algebra↗

NASA NTRSDate not supplied

Interpretable Tree-Based and Graph Neural Network Approaches for Novel Solid State Electrolyte Design

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes simultaneously possessing high ionic conductivity and good chemical and electrochemical stabilities has proven to be a challenge. I will present our informatics approach to explore the Li compound space for promising solid electrolytes using high-throughput multi-property screening and interpretable machine learning. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds. We use tree-based ensemble learning methods and graph neural network approaches to accurately learn relationships between crystal structures and corresponding thermodynamic and kinetic properties, with interpretability being a major focus. Our models give us the ability to enable rapid discovery and design of novel solid-state battery chemistries.

Materials discovery↗

NASA NTRS2021

KMT-2021-BLG-0322: Severe Degeneracy Between Triple-lens and Higher-order Binary-lens Interpretations

We investigate the microlensing event KMT-2021-BLG-0322, for which the light curve exhibits three distinctive sets of caustic-crossing features. It is found that the overall features of the light curve are approximately described by a binary-lens (2L1S) model, but the model leaves substantial residuals. We test various interpretations with the aim of explaining the residuals. Methods. We find that the residuals can be explained either by considering a nonrectilinear lens-source motion caused by the microlens-parallax and lens-orbital effects or by adding a low-mass companion to the binary lens (3L1S model). The degeneracy between the higher-order 2L1S model and the 3L1S model is very severe, making it difficult to single out a correct solution based on the photometric data. This degeneracy was known before for two previous events (MACHO-97-BLG-41 and OGLE-2013-BLG-0723),which led to the false detections of planets in binary systems, and thus the identification of the degeneracy for KMT-2021-BLG-0322illustrates that the degeneracy can be not only common but also very severe, emphasizing the need to check both interpretations of deviations from 2L1S models. Results. From the Bayesian analysis conducted with the measured lensing observables of the event timescale, angular Einstein radius, and microlens parallax, it was estimated that the binary lens components have masses(M1,M2)=(0.62+0.25−0.26M,0.07+0.03−0.03M), for both2L1S and 3L1S solutions, and the mass of the tertiary lens component according to the 3L1S solution isM3=6.40+2.64−2.78MJ.

Gravitational lensing: micro↗

NASA NTRSDate not supplied

Interpretable ML Approaches for Novel Solid State Electrolyte Design

Shreyas J Honrao↗

NASA NTRS2023

Interpretation of Probabilistic Surface Ozone Forecasts: A Case Study for Philadelphia

The use of probabilistic forecasting has been growing in a variety of disciplines because of its potential to emphasize the degree of uncertainty inherent in a prediction. Interpretation of probabilistic forecasts, however, is oftentimes difficult, deterring users who may benefit from such forecasts. To encourage broader use of probabilistic forecasts in the field of air quality, a process for interpreting forecasts from a statistical probabilistic air quality surface ozone model [the Regression in Self Organizing Map (REGiS)] is demonstrated. Four procedures to convert probabilistic to deterministic forecasts are explored for the Philadelphia, Pennsylvania, metropolitan area. These procedures calibrate the predicted probability of daily maximum 8-h-average ozone exceeding a standard value by 1) estimating climatological relative frequency, 2) establishing a probability of an exceedance threshold as 50%, 3) maximizing the threat score, and 4) determining the unit bias ratio. REGiS is trained using 2000–11 ozone-season (1 May–30 September) data, calibrated using 2012–14 data, and evaluated using 2015–18 data. Assessment of the calibration data with the Pierce skill score suggests an exceedance threshold based on climatological relative frequency for the conversion from probabilistic to deterministic forecasts. Calibrated REGiS generally compares well to predictions from the U.S. national air quality model and operational “expert” forecasts over the evaluation period. For other probabilistic models and situations, different procedures of converting probabilistic to deterministic forecasts may be more beneficial. The methods presented in this paper represent an approach for operational air quality forecasters seeking to use probabilistic model output to support forecasts designed to protect public health.

Nikolay Balashov↗

NASA NTRS2024

Interpretable Machine Learning for Molecular Biosignatures: a Novel Single-Sample Feature Importance Method That Is Sensitive To Statistical Interactions

Isotope ratio mass spectrometry (IRMS) of volatiles (e.g., CO 2 ) promises to be a powerful tool for potential biosignature detection for future missions to ocean worlds (OW) such as Europa and Enceladus. Machine learning (ML) methods for IRMS data could enable science autonomy by onboard prediction of seawater chemistry and biosignature presence. However, ML models are likely to be complex and involve statistical interactions between features (variables), which can make predictions seem opaque and enigmatic. For ML predictions as significant as extraterrestrial biosignatures, we must place extraordinary confidence in models. It is therefore essential that these models make interpretable predictions (i.e., human-understandable) and include false-prediction diagnostics. We achieve high accuracy and interpretability in ML biosignature and seawater chemistry models for OW through a nearest-neighbors feature selection tool that detects statistical interactions between predictors, constructs interaction networks for visualization of selected features working together to make a prediction, and reports single-sample feature importance scores for false-detection diagnostics. Here we develop a novel single-sample nearest-neighbors projected distance regression(ssNPDR) feature selection method that improves upon existing single-sample algorithms through the inclusion of statistical interactions while providing false-prediction diagnostics for ML models.

geochemistry↗

NASA NTRS1963