Despite widespread adoption of deep learning models to address a variety of computer vision tasks, planetary science has yet to see extensive utilization of such tools to address its unique […]
Deep Learning
Posted inAI - Data - Apps - Cybernetics, Astrogeology, Away Teams & Field Reports, Caves, Craters, Mountains, Subsurface, Lava Tubes, Exploration Gear & Tech, Fossils & Paleontology, Imaging & Spectroscopy, Mapping, Geodesy, Cartography, Bathymetry, Mars, Missions & Hardware, Robotics/Rovers/UAVs, Sample Return, Status Report, Tricorders & Sensors
Mapping “Brain Terrain” Regions on Mars Using Deep Learning
One of the main objectives of the Mars Exploration Program is to search for evidence of past or current life on the planet.
Posted inAI - Data - Apps - Cybernetics, Exploration Gear & Tech, Missions & Hardware, Robotics/Rovers/UAVs, ROVs/Submersibles/Diving, Status Report, Tricorders & Sensors
AI in Space for Scientific Missions: Strategies for Minimizing Neural-Network Model Upload
Artificial Intelligence (AI) has the potential to revolutionize space exploration by delegating several spacecraft decisions to an onboard AI instead of relying on ground control and predefined procedures.
Posted inAlpha Centauri, Astronomy & Telescopes, Biosignatures & Paleobiology, Exoplanets, -moons, -comets, Habitable Zones, Imaging & Spectroscopy, Status Report, tau Ceti
Improving Earth-like Planet Detection in Radial Velocity Using Deep Learning
Many novel methods have been proposed to mitigate stellar activity for exoplanet detection as the presence of stellar activity in radial velocity (RV) measurements is the current major limitation.
![Analysis of the stellar parameters derived with our methodology. The dots are colour-coded according to the estimated metallicity. The size of the dots is proportional to the estimated projected rotational velocity. The top left panel shows a Kiel diagram, with the red, black, and blue dashed lines corresponding to 5 Gyr PARSEC isochrones with [M/H] = −0.4, 0.0 and 0.1 dex, respectively. Empty squares represent the stars reported to have a behaviour akin to subdwarfs both in Mar21 and Schw19 (same for bottom left panel). Top right: black and grey dashed lines correspond to solar metallicity PARSEC isochrones for 5 and 0.1 Gyr, respectively. Black and grey dotted lines correspond to solar metallicity Baraffe et al. (2015) isochrones for 5 and 0.1 Gyr, respectively. Bottom left: triangles represent stars identified as Hα active in Schöfer et al. (2019). Empty stars depict members of the thick disc Galactic population (Cortés-Contreras et al., in prep.). Bottom right: plus symbols correspond to stars identified as members of the young disc Galactic population by Cortés-Contreras et al. (in prep.). Empty circles represent stars with a possible membership in a young stellar associaton, as explained in Section 4.1. -- astro-ph.SR](https://i0.wp.com/astrobiology.com/wp-content/uploads/2024/05/Using-Autoencoders-And-Deep.png?resize=1200%2C717&ssl=1)
Posted inAI - Data - Apps - Cybernetics, Astronomy & Telescopes, Imaging & Spectroscopy, Status Report, Stellar Cartography
Using Autoencoders And Deep Transfer Learning To Determine The Stellar Parameters Of 286 CARMENES M Dwarfs
Deep learning (DL) techniques are a promising approach among the set of methods used in the ever-challenging determination of stellar parameters in M dwarfs.
Posted inAI - Data - Apps - Cybernetics, Astronomy & Telescopes, Exoplanets, -moons, -comets, Imaging & Spectroscopy, Status Report
Discovery of Small Ultra-short-period Planets Orbiting KG Dwarfs in Kepler Survey Using GPU Phase Folding and Deep Learning Detection System
Since the discovery of the first hot Jupiter orbiting a solar-type star, 51 Peg, in 1995, more than 4000 exoplanets have been identified using various observational techniques.
Posted inAI - Data - Apps - Cybernetics, Astrogeology, Fossils & Paleontology, Imaging & Spectroscopy, Mapping, Geodesy, Cartography, Bathymetry, Mars, Status Report
Mapping “Brain Coral” Regions On Mars Using Deep Learning
One of the main objectives of the Mars Exploration Program is to search for evidence of past or current life on the planet. To achieve this, Mars exploration has been […]
Posted inAI - Data - Apps - Cybernetics, Atmospheres, Climate, Weather, Exoplanets, -moons, -comets, Status Report
Reconstructing Atmospheric Parameters of Exoplanets Using Deep Learning
Exploring exoplanets has transformed our understanding of the universe by revealing many planetary systems that defy our current understanding.
Posted inAI - Data - Apps - Cybernetics, Astronomy & Telescopes, Exoplanets, -moons, -comets, Imaging & Spectroscopy, Status Report
Combining Multi-spectral Data With Statistical And Deep-learning Models For Improved Exoplanet Detection In Direct Imaging At High Contrast
Exoplanet detection by direct imaging is a difficult task: the faint signals from the objects of interest are buried under a spatially structured nuisance component induced by the host star. […]
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.