Machine learning Archives

Overview of the proposed methodology. The workflow consists of a local data-centric pipeline including data pre-processing, feature engineering, and uncertainty-aware modeling. The trained model is then deployed on the Kaggle platform to perform inference and evaluation on the public and private challenge datasets. Insights from the public leader board and model evaluation are fed back into the pipeline (yellow arrow) to improve data processing and enhance model generalization in a data-centric approach. -- cs.LG

NeurIPS 2024 Ariel Data Challenge: Characterisation of Exoplanetary Atmospheres Using a Data-Centric Approach

by Keith CowingMay 21, 2025

The characterization of exoplanetary atmospheres through spectral analysis is a complex challenge. The NeurIPS 2024 Ariel Data Challenge, in collaboration with the European Space Agency’s (ESA) Ariel mission, provided an […]

Northern Hemisphere Ice - Credit Niccolò Maffezzoli - https://nmaffe.github.io/iceboost_webapp/

AI - Data - Apps - Cybernetics, Analog Studies, Arctic / Antarctic / Alpine, Astrogeology, Cryobiology, Icy Worlds, Imaging & Spectroscopy, Mapping, Geodesy, Cartography, Bathymetry, Press Release

Ice Planet Assay: Machine Learning Model Calculates The Volume Of All Earth’s Glaciers

by Keith CowingMay 16, 2025May 16, 2025

A team of researchers led by Niccolò Maffezzoli, “Marie Curie” fellow at Ca’ Foscari University of Venice and the University of California, Irvine, and an associate member of the Institute […]

Bee swarm plot of the seven features considered. The x-axis represents the SHAP value of the feature for each instance, and the y-axis represents the seven features considered ranked from the most important (top) to the least (bottom). The colour of the dots represents the value of the feature itself, red being high values and blue being low values. -- astro-ph.EP

AI - Data - Apps - Cybernetics, Atmospheres, Climate, Weather, Biogeochemical Cycles & Geobiology, Biosignatures & Paleobiology, Exoplanets, -moons, -comets, Gaia - Planetary Perspectives, Habitable Zones, Status Report, Water/Hycean Worlds & Oceanography

Earth-like Planet Predictor: A Machine Learning Approach

by Keith CowingApril 11, 2025April 11, 2025

Searching for planets analogous to Earth in terms of mass and equilibrium temperature is currently the first step in the quest for habitable conditions outside our Solar System and, ultimately, […]

An integrated approach to date bacterial evolution and reconstruct the history of oxygen adaptation. We inferred a bacterial timetree by integrating genomic, fossil, and geochemical data and linking oxygen tolerance and aerobic metabolism to the GOE. Colors denote anaerobic (blue) and aerobic (red) states, whereas shades of purple show the fraction of aerobic lineages within extant bacterial phyla. Mitochondria and plastids were included to leverage the more extensive eukaryotic fossils. Land plants and animals are indicated for temporal comparison. -- Science

AI - Data - Apps - Cybernetics, Biophysics, Biosignatures & Paleobiology, Fossils & Paleontology, Genomics, Proteomics, Bioinformatics, Microbiology & Virology, Origin & Evolution of Life, Press Release

Machine Learning Helps Construct An Evolutionary Timeline Of Bacteria

by Keith CowingApril 4, 2025April 4, 2025

University of Queensland scientists have helped to construct a detailed timeline for bacterial evolution, suggesting some bacteria used oxygen long before evolving the ability to produce it through photosynthesis.

AI - Data - Apps - Cybernetics, Exploration Gear & Tech, Icy Worlds, Imaging & Spectroscopy, Missions & Hardware, NASA Astrobiology Program, Press Release, Reports, Roadmaps, & Plans, Robotics/Rovers/UAVs, ROVs/Submersibles/Diving, Sample Return, Tricorders & Sensors

Science Autonomy Using Machine Learning For Astrobiology – A White Paper For 2025 NASA DARES

by Keith CowingApril 3, 2025May 25, 2025

In recent decades, artificial intelligence (AI) including machine learning (ML) have become vital for space missions enabling rapid data processing, advanced pattern recognition, and enhanced insight extraction.

(a) Visualization of statistical effects of the highly predictive biosignature features using a Regression-based Association-Interaction Network (RAIN) with node numbers in order of NPDR-LURF importance. RAIN encodes feature main effects and statistical interactions in a network. Nodes 2, 3, 4, and 5 have the largest main effects. For nodes 2 and 4, the main effect increases the probability of an abiotic class prediction. The TS feature diff2_acff1 is also a hub in the interaction network, participating in three large interactions with nodes 2, 3, and 5 that influence the probability for both biotic and abiotic predictions. (b) The Random Forest classifier trained on NPDR-LURF features for biosignatures yields a training accuracy of 87.9%, trained on a sample size m = 140. Dark gray and green diagonals represent the number of correct predictions: 81/89 abiotic samples are correctly classified, and 42/51 biotic samples are correctly classified. The precision for biotic samples in the training data is 0.824 and the recall is 0.84. (c) The same classifier yields a high test accuracy of 88.2%: 20/22 abiotic samples are correctly classified, and 10/12 biotic samples are correctly classified. The biotic precision for the test data is 0.833 and the recall is also 0.833. -- Earth and Space Science

Biochemistry & Organic Chemistry, Biogeochemical Cycles & Geobiology, Biosignatures & Paleobiology, Habitable Zones, Icy Worlds, Status Report, Water/Hycean Worlds & Oceanography

Interpretable Machine Learning Biosignature Detection From Ocean Worlds Analogue CO2 Isotopologue Data

by Keith CowingMarch 25, 2025March 25, 2025

Future missions to icy ocean worlds (OW) such as Europa and Enceladus will evaluate the habitability and potential for biosignatures on these worlds.

The computational expense of solving non-equilibrium chemistry equations in astrophysical simulations poses a significant challenge, particularly in high-resolution, large-scale cosmological models. -- astro-ph.IM)

AI - Data - Apps - Cybernetics, Biochemistry & Organic Chemistry, Biophysics, Biosignatures & Paleobiology, Genomics, Proteomics, Bioinformatics, Origin & Evolution of Life, Status Report

Bridging Machine Learning And Cosmological Simulations: Using Neural Operators To Emulate Chemical Evolution

by Keith CowingMarch 17, 2025March 17, 2025

In this work, we explore the potential of machine learning, specifically Neural Operators, to emulate the Grackle chemistry solver, which is widely used in cosmological hydrodynamical simulations.

The [Na/Fe] vs. [Fe/H] abundance plot (Ensemble 4) showing the stars that are most likely to host a planet (green), the known planet hosts (blue), and the stars less likely to host a planet (orange). Histograms are also included that show the [Na/Fe] relative distribution to the right of the scatterplot and [Fe/H] relative distribution at the top. We note the clear sub-solar Na abundances for each experiment’s known and predicted planet hosts (see Section 5). - Small planets -- astro-ph.EP

AI - Data - Apps - Cybernetics, Astronomy & Telescopes, Exoplanets, -moons, -comets, Habitable Zones, Imaging & Spectroscopy, Status Report, Stellar Cartography

Utilizing Machine Learning to Predict Host Stars and the Key Elemental Abundances of Small Planets

by Keith CowingMarch 5, 2025

Stars and their associated planets originate from the same cloud of gas and dust, making a star’s elemental composition a valuable indicator for indirectly studying planetary compositions.

Ratio of the mass-loss rates predicted for the test planets by interpol2021 (panels a and c) and interpol2024 (panels b and d) to the true values given by the hydrodynamic model, against Λ and Λ × K, respectively. The points are colour-coded according to the Jtrans value. In panels (c) and (d), the red points have Jbord = 1, while the red points marked by a yellow plus are those with Jtrans = 2. For reference, the two horizontal black lines in each panel lie at 0.5 and 2. -- astro-ph.EP

AI - Data - Apps - Cybernetics, Atmospheres, Climate, Weather, Exoplanets, -moons, -comets, Status Report

Grid-based Exoplanet Atmospheric Mass Loss Predictions Through Neural Network

by Keith CowingFebruary 12, 2025February 12, 2025

The fast and accurate estimation of planetary mass-loss rates is critical for planet population and evolution modelling.

Library cleaning and accuracy determination workflow. In this study, (A) two GCMS spectral libraries (NIST17 and MoNA) were filtered to include only experimental spectra from monosubstituted α-amino acids. A third library (IOCB) was composed of experimental GCMS spectra from seventeen amino acids, also not found in the NIST dataset. (B) The accuracy of predicted GCMS spectra is measured through three metrics: root mean square error, spectral contrast angle, and weighted cosine similarity. -- chemrxiv.org

Astrochemistry, Biochemistry & Organic Chemistry, Biophysics, Biosignatures & Paleobiology, Genomics, Proteomics, Bioinformatics, Imaging & Spectroscopy, Nanotechnology & SynBio, Status Report

Mapping the Edges of Mass Spectral Prediction: Evaluation of Machine Learning EIMS Prediction for Xeno Amino Acids

by Keith CowingJanuary 15, 2025January 15, 2025

Mass spectrometry is one of the most effective analytical methods for unknown compound identification. By comparing observed m/z spectra with a database of experimentally determined spectra, this process identifies compound(s) […]

Machine learning

NeurIPS 2024 Ariel Data Challenge: Characterisation of Exoplanetary Atmospheres Using a Data-Centric Approach

Ice Planet Assay: Machine Learning Model Calculates The Volume Of All Earth’s Glaciers

Earth-like Planet Predictor: A Machine Learning Approach

Machine Learning Helps Construct An Evolutionary Timeline Of Bacteria

Science Autonomy Using Machine Learning For Astrobiology – A White Paper For 2025 NASA DARES

Interpretable Machine Learning Biosignature Detection From Ocean Worlds Analogue CO2 Isotopologue Data

Bridging Machine Learning And Cosmological Simulations: Using Neural Operators To Emulate Chemical Evolution

Utilizing Machine Learning to Predict Host Stars and the Key Elemental Abundances of Small Planets

Grid-based Exoplanet Atmospheric Mass Loss Predictions Through Neural Network

Mapping the Edges of Mass Spectral Prediction: Evaluation of Machine Learning EIMS Prediction for Xeno Amino Acids

NASA Spaceline Current Awareness List #1,208 17 July 2026 (Space Life Science Research Results)

Tidal Evolution Of Packed Moon Systems Around An Earth-mass Planet

Deep-sea Creatures’ Epic Migrations Between Hydrothermal Vents

Efficient Interstellar Grain Growth From High Sticking Coefficients On Amorphous Carbon Dust

A Pressure-Supported Pneumatic Architecture for Robust Deep Drilling on Mars: Performance Modeling, Sizing, and Mission Integration.