AI Archives

Example Tails interface for the scanning of comet candidates. Adapted from Fig. 5 of (Duev et al., 2021) and reproduced with the permission of the authors and AAS Journals. -- astro-ph.EP

AI - Data - Apps - Cybernetics, Astrochemistry, Comets and Asteroids, Imaging & Spectroscopy, Status Report

Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning

by Keith CowingSeptember 24, 2024September 24, 2024

In this chapter, we will discuss the use of Machine Learning methods for the identification and localization of cometary activity for Solar System objects in ground and in space-based wide-field […]

Memory Crystal With Human Genome -- University of Southampton

AI - Data - Apps - Cybernetics, Biosignatures & Paleobiology, Genomics, Proteomics, Bioinformatics, Imaging & Spectroscopy, Intelligence & Language, Interstellar, Nanotechnology & SynBio, Panspermia, Press Release, Science Fiction, SETI & Technosignatures

The Human Genome Has Been Stored On An Everlasting Memory Crystal

by Keith CowingSeptember 20, 2024September 20, 2024

University of Southampton scientists have stored the full human genome on a 5D memory crystal – a revolutionary data storage format that can survive for billions of years. The team […]

Example solution for the α(x, y) shown in Fig. 1 for the direction of the light in positive x-direction (φ = 0). Top: The complete solution (left), the solution if scattering would be treated as absorption (middle) and the difference between the two: the scattered light component (right), all in units of u0 := u(x = −1, φ = 0) = 1. Bottom: The logarithm (log10) of the respective residuals of the differential equations. The middle plot shows the residual of ua with respect to the RTE without scattering and the right one the residual of (ua + us) with respect to the complete RTE. The left plot is calculated from the two residuals by taking their root mean square. -- Monthly Notices of the Royal Astronomical Society

AI - Data - Apps - Cybernetics, Astronomy & Telescopes, Atmospheres, Climate, Weather, Exoplanets, -moons, -comets, Press Release

Astrophysics: A.I. Shines A New Light On Exoplanets

by Keith CowingSeptember 20, 2024

Researchers from LMU, the ORIGINS Excellence Cluster, the Max Planck Institute for Extraterrestrial Physics (MPE), and the ORIGINS Data Science Lab (ODSL) have made an important breakthrough in the analysis […]

AI - Data - Apps - Cybernetics, Atmospheres, Climate, Weather, Biochemistry & Organic Chemistry, Biophysics, Biosignatures & Paleobiology, Extremeophiles and Extreme Environments, Genomics, Proteomics, Bioinformatics, Habitable Zones, Microbiology & Virology, Press Release, Water/Hycean Worlds & Oceanography

AI And An Extreme Microbe Reveal How Earth Life’s Building Blocks Adapt To High Pressure

by Keith CowingSeptember 10, 2024September 10, 2024

An assist from a Google Artificial Intelligence tool has helped scientists discover how the proteins of a heat-loving microbe respond to the crushing conditions of the planet’s deepest ocean trenches, […]

The Lomb-Scargle periodograms computed from the photometric light curves of the exoplanet host stars in our dataset. The periodograms identify several statistically significant periodic signals present in the data, with the strongest peak (gray shaded line) in each plot corresponding to the estimated stellar rotation period. To quantify the uncertainty in these period estimates, we have fitted a Gaussian function to the primary peak and determined the half-width at half-maximum (HWHM) of the Gaussian (red-dashed line). This HWHM value provides an estimate of the period uncertainty that accounts for the sampling characteristics of the periodogram. -- astro-ph.SR

AI - Data - Apps - Cybernetics, Astronomy & Telescopes, Atmospheres, Climate, Weather, Exoplanets, -moons, -comets, Imaging & Spectroscopy, Status Report

Advancing Machine Learning for Stellar Activity and Exoplanet Period Rotation

by Keith CowingSeptember 10, 2024September 10, 2024

This study applied machine learning models to estimate stellar rotation periods from corrected light curve data obtained by the NASA Kepler mission. Traditional methods often struggle to estimate rotation periods […]

All-sky distribution of the 124 new SMDET discoveries in equatorial coordinates with the Aitoff projection. Sources are color-coded based on spectral type estimates from §4, while those with a black interior are either candidate subdwarfs discussed in §6 or lack a photometric spectral type estimate. -- astro-ph.SR

AI - Data - Apps - Cybernetics, Astronomy & Telescopes, Imaging & Spectroscopy, Status Report

Discovery Of 118 New Ultracool Dwarf Candidates Using Machine Learning Techniques

by Keith CowingAugust 28, 2024August 27, 2024

We present the discovery of 118 new ultracool dwarf candidates, discovered using a new machine learning tool, named SMDET, applied to time series images from the Wide-field Infrared Survey Explorer.

A Phylogeny of long type argonaute proteins from archaea, bacteria, and eukaryotes with cyclases as outgroup (grey). B Structure-based homology of argonautes. -- Nature Communications

Genomics, Proteomics, Bioinformatics, Microbiology & Virology, Press Release

Modern Immunity And Ancient Microbes

by Keith CowingAugust 22, 2024August 22, 2024

When you get infected with a virus, some of the first weapons your body deploys to fight it were passed down to us from our microbial ancestors billions of years […]

Dung beetles use the Milky Way at night to navigate. Engineers have used the same technique to develop an AI sensor for navigation in low light. Credit University of South Australia

AI - Data - Apps - Cybernetics, Astronomy & Telescopes, Imaging & Spectroscopy, Intelligence & Language, Press Release, Stellar Cartography

Insect Astronomy: How Dung Beetles and the Milky Way Are Helping To Improve Navigation Systems

by Keith CowingAugust 22, 2024August 22, 2024

An insect species that evolved 130 million years ago is the inspiration for a new research study to improve navigation systems in drones, robots, and orbiting satellites.

Comparison between the performance of the orbital outcome regressor (top row) and a non-ML baseline model (bottom row) on the validation set of 103,404 three-planet systems. Predicted orbital elements (i.e., semi-major axis, eccentricity, and inclination) for the new, merged planets are plotted against their true orbital elements. The ML model predicts orbital elements with somewhat less scatter and bias about the true values than the baseline model, approaching the accuracy limits imposed by chaos (see Table 1). -- astro-ph.EP

AI - Data - Apps - Cybernetics, Astrogeology, Atmospheres, Climate, Weather, Biogeochemical Cycles & Geobiology, Comets and Asteroids, Extinction events, Habitable Zones, Impact events, Status Report

Accelerating Giant Impact Simulations With Machine Learning

by Keith CowingAugust 20, 2024August 20, 2024

Constraining planet formation models based on the observed exoplanet population requires generating large samples of synthetic planetary systems, which can be computationally prohibitive.

An overview of the inputs for each step in our processing pipeline. a) A region in the HiRISE image of ESP 018707 2205 is shown at the native resolution (0.3m/px) with a blue box highlighting the window size for our classifier network. b) A 256 x 256 pixel window is used as input for our spatial classifier algorithm however the window is at 1/16 the original resolution (1.2m/px). A grid of 8x8 squares is displayed showing how that image gets tiled and preprocessed using parts of a JPEG encoder which involves the discrete cosine transform (d). c) A single 8x8 tile flattened into a 1d array which is used for the DCT transform (d). e) A block of Fourier coefficients is rearranged into a data cube and used as input for our Fourier classifier. Reducing the image size and ultimately the channel size after the first conv. layer significantly shortens the network’s processing time compared to the spatial image input. -- astro-ph.EP

AI - 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

by Keith CowingAugust 13, 2024August 13, 2024

One of the main objectives of the Mars Exploration Program is to search for evidence of past or current life on the planet.

AI

Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning

The Human Genome Has Been Stored On An Everlasting Memory Crystal

Astrophysics: A.I. Shines A New Light On Exoplanets

AI And An Extreme Microbe Reveal How Earth Life’s Building Blocks Adapt To High Pressure

Advancing Machine Learning for Stellar Activity and Exoplanet Period Rotation

Discovery Of 118 New Ultracool Dwarf Candidates Using Machine Learning Techniques

Modern Immunity And Ancient Microbes

Insect Astronomy: How Dung Beetles and the Milky Way Are Helping To Improve Navigation Systems

Accelerating Giant Impact Simulations With Machine Learning

Mapping “Brain Terrain” Regions on Mars Using Deep Learning

Sulfate Deposits On Iani Chaos On Mars

Correlations Between The Carbon-to-Oxygen Ratio and the Orbital and Physical Properties of Exoplanets

Pathways Of Early Evolution From The Perspectives Of A Riboreplisome — The Ultimate RNA Machine Of Life

On The Compositions Of Interstellar Objects

Planets Around Solar Twins/Analogs (PASTA) III: Chemical Clock Relations in Planet-Hosting Solar Analogs