![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](https://i0.wp.com/astrobiology.com/wp-content/uploads/2025/03/Utilizing-Machine-Learning-to-Predict.png?resize=1200%2C718&ssl=1)
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.
AI
Posted inAI - Data - Apps - Cybernetics, Biochemistry & Organic Chemistry, Biophysics, Biosignatures & Paleobiology, Citizen Science / Open Science, Commercial & Private Sector, Genomics, Proteomics, Bioinformatics, Nanotechnology & SynBio, Status Report
Genome Modeling And Design Across All Domains Of Life With Evo 2
All of life encodes information with DNA. While tools for sequencing, synthesis, and editing of genomic code have transformed biological research, intelligently composing new biological systems would also require a […]
Posted inAI - Data - Apps - Cybernetics, Genomics, Proteomics, Bioinformatics, Origin & Evolution of Life, Status Report
Simulating 500 Million Years Of Evolution With A Language Model
More than three billion years of evolution have produced an image of biology encoded into the space of natural proteins.
Posted inAstrochemistry, 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
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) […]
Posted inAI - Data - Apps - Cybernetics, Atmospheres, Climate, Weather, Imaging & Spectroscopy, Status Report, Titan
Rapid Automated Mapping of Clouds on Titan With Instance Segmentation
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 […]
Posted inAI - Data - Apps - Cybernetics, Intelligence & Language, Nanotechnology & SynBio, Press Release
Non-Living Intelligence: Cracking The Code For Materials That Can Learn
It’s easy to think that machine learning is a completely digital phenomenon, made possible by computers and algorithms that can mimic brain-like behaviors.
Posted inAI - Data - Apps - Cybernetics, Biosignatures & Paleobiology, Exploration Gear & Tech, Fossils & Paleontology, Imaging & Spectroscopy, Nomenclature, Systematics, Natural History, Origin & Evolution of Life, Status Report, Tricorders & Sensors
Machine Learning Used To Classify Fossils Of Extinct Pollen – Offworld Astrobiology Applications?
Editor’s note: when we begin to study inhabited worlds we are going to have to take a stab at something that has taken centuries on Earth: systematic classification of life […]
Posted inAI - Data - Apps - Cybernetics, Atmospheres, Climate, Weather, Biogeochemical Cycles & Geobiology, Exoplanets, -moons, -comets, Habitable Zones, Icy Worlds, Press Release, Terraforming & Geoengineering, Water/Hycean Worlds & Oceanography
Planetary Weather Modeling Advance: GenCast Predicts Earth’s Weather With State-of-the-art Accuracy
Editor’s note: We should all stop every once in a while and remember that Earth – our home world – is a planet too. We have the benefit of living […]
Posted inAI - Data - Apps - Cybernetics, Apps - Games, Astronomy & Telescopes, Status Report
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research.
![[L] The x-axis is the Sagan’s parameter of the Kardashev civilization Type K. The y-axis is the mass of PBH in solar mass for a fraction fPBH = 0.01. The colour-bar of the contours shows the quantity Kachieved/K − 1, which Kachieved is the amount of civilization parameter achieved by PBHs. [R] The x-axis is the Sagan’s parameter of the Kardashev civilization type. The y-axis is the fraction of PBH for a mass of MPBH = 1M⊙. And the colour-bar of the contours shows the quantity Kachieved/K − 1, which Kachieved is the amount of civilization parameter achieved from PBHs. -- astro-ph.GA](https://i0.wp.com/astrobiology.com/wp-content/uploads/2024/12/In-Search-of-Extraterrestrial-Artificial-.png?resize=1200%2C719&ssl=1)
Posted inImaging & Spectroscopy, Intelligence & Language, Interstellar, Science Fiction, SETI & Technosignatures, Status Report
In Search of Extraterrestrial Artificial Intelligence through the Dyson Sphere-like structures around the Primordial Black Holes
Are we alone? It is a compelling question that human beings have confronted for centuries. The search for extraterrestrial life is a broad range of quests for finding the simple […]