This paper proposes a methodology for discovering meaningful properties in data by exploring the latent space of unsupervised deep generative models. We combine manipulation of individual latent variables to extreme […]
Machine learning
Posted inAI - Data - Apps - Cybernetics, Europa, Mapping, Geodesy, Cartography, Bathymetry, Status Report
Feature Extraction And Classification From Planetary Science Datasets Enabled By Machine Learning
In this paper we present two examples of recent investigations that we have undertaken, applying Machine Learning (ML) neural networks (NN) to image datasets from outer planet missions to achieve […]
Posted inAI - Data - Apps - Cybernetics, Imaging & Spectroscopy, Status Report
Constructing Impactful Machine Learning Research For Astronomy: Best Practices For Researchers And Reviewers
Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients […]
Posted inAI - Data - Apps - Cybernetics, Biosignatures & Paleobiology, Genomics, Proteomics, Bioinformatics, Status Report, Tricorders & Sensors, Water/Hycean Worlds & Oceanography
Tricorder Tech: Translation As A Biosignature
Life on Earth relies on mechanisms to store heritable information and translate this information into cellular machinery required for biological activity. In all known life, storage, regulation, and translation are […]
Posted inAI - Data - Apps - Cybernetics, Astrochemistry, Biochemistry & Organic Chemistry, Status Report
Machine-learning Identified Molecular Fragments Responsible For Infrared Emission Features Of Polycyclic Aromatic Hydrocarbons
Machine learning feature importance calculations are used to determine the molecular substructures that are responsible for mid and far-infrared (IR) emission features of neutral polycyclic aromatic hydrocarbons (PAHs).
Posted inAI - Data - Apps - Cybernetics, Astrochemistry, Status Report
A Statistical And Machine Learning Approach To The Study Of Astrochemistry
In order to obtain a good understanding of astrochemistry, it is crucial to better understand the key parameters that govern grain-surface chemistry. For many chemical networks, these crucial parameters are […]
Posted inAI - Data - Apps - Cybernetics, Astronomy & Telescopes, Exoplanets, -moons, -comets, Press Release
Discovery of 69 New Exoplanets Using Machine Learning
In a groundbreaking achievement, a team of machine learning scientists and astronomers from Universities Space Research Association (USRA), the SETI Institute, and NASA discovered 69 new exoplanets using advanced machine […]
Posted inAstronomy & Telescopes, Atmospheres, Climate, Weather, Exoplanets, -moons, -comets, Imaging & Spectroscopy, Press Release
A Catalogue Of Exoplanet Atmospheric Retrieval Codes
Exoplanet atmospheric retrieval is a computational technique widely used to infer properties of planetary atmospheres from remote spectroscopic observations.
Posted inAI - Data - Apps - Cybernetics, Astronomy & Telescopes, Exoplanets, -moons, -comets, Imaging & Spectroscopy, Status Report
ExoplANNET: A Deep Learning Algorithm To Detect And Identify Planetary Signals In Radial Velocity Data
The detection of exoplanets with the radial velocity method consists in detecting variations of the stellar velocity caused by an unseen sub-stellar companion.
Posted inAI - Data - Apps - Cybernetics, Astronomy & Telescopes, SETI & Technosignatures, Status Report
A Deep-learning Search For Technosignatures Of 820 Nearby Stars
The goal of the Search for Extraterrestrial Intelligence (SETI) is to quantify the prevalence of technological life beyond Earth via their “technosignatures”. One theorized technosignature is narrowband Doppler drifting radio […]