![View of the Hindsight application: drive panel synchronization (A) ensures consistent arrangement of data sources. The playhead (B) is accessible across the drive panel, tying all visualizations to the same timestamp. The fault in question is highlighted across data in red via the fault overlay (C). -- [cs.HC]](https://i0.wp.com/astrobiology.com/wp-content/uploads/2026/08/Hindsight.png?resize=1200%2C723&ssl=1)
[cs.HC] While Mars rover operators plan drives across hazardous Martian terrain and diagnose unexpected faults, the necessary information is distributed across separate systems and often reconstructed through manual correlation and […]
AI
![Ciara Sypherd, Ph.D. candidate in bioengineering (Pil Rosted Christensen) [Harvard]](https://i0.wp.com/astrobiology.com/wp-content/uploads/2026/08/Decoding-Animal.png?resize=1200%2C721&ssl=1)
Posted inSETI & Technosignatures
Decoding Animal Communication With AI
[Harvard] Fresh off degrees in astrobiology and aerospace engineering at Arizona State University, Ciara Sypherd took on a one-year research position at the University of Copenhagen to study how neural […]
Posted inBiosignatures & Paleobiology
Mineral Biosignature Identification From Raman Spectroscopy Using Machine Learning
[PNAS] Biosignature detection remains a key challenge in astrobiology, yet robust mineral biosignatures remain limited. Raman spectroscopy is increasingly applied in planetary exploration, but its high-dimensional spectral information has not […]
![Modes of interstellar expansion across two dimensions. The horizontal axis (expansion agent) runs from biological (crewed colonization, settlers, generation ships) to machine (autonomous probes, robotic seed systems). The vertical axis (visibility) runs from quiet (low-mass, low duty cycle, weak technosignatures) to loud (high-power, high waste heat, galaxy-scale signatures). The four quadrants summarize alternative expansion modes considered in the literature; the quiet expansion filter proposed in this paper occupies the lower-right quadrant (highlighted). -- [astro-ph.IM]](https://i0.wp.com/astrobiology.com/wp-content/uploads/2026/07/Autonomous-AI-Cosmoindustry.png?resize=1200%2C717&ssl=1)
Posted inSETI & Technosignatures
Autonomous AI-Cosmoindustry and the Quiet Expansion Filter: A Threshold-Based Resolution of the Fermi Paradox
[astro-ph.IM] The Fermi paradox is sharpened, not weakened, by plausible extrapolations of artificial intelligence, autonomous robotics, in-situ resource utilization, orbital manufacturing, space-based computing, and uncrewed interstellar probes.
Posted inGenomics, Proteomics, Bioinformatics
BioBloom, A Method For Barcoded Saturation Mutagenesis Of An Entire Bacterial Genome
Editor’s note: As we expand outward from Earth to other worlds we are almost certainly going to encounter things we did not expect to find – things that are unlikely […]
Posted inExoplanets, -moons, -comets
CARMApy: An Open-Source Python Framework for Simulating Microphysical Clouds in Planetary Atmospheres
[astro-ph.EP] CARMApy is a new open-source python code that performs bin-scheme microphysical modeling of clouds in exoplanet atmospheres.
Posted inAI - Data - Apps - Cybernetics
The Role Of Artificial Intelligence In The SKA Era
[astro-ph.IM] The Square Kilometre Array Observatory (SKAO) will usher in an era of unprecedented data complexity and scientific opportunity in radio astronomy, producing petabyte-scale datasets and terabit-per-second streams that challenge […]
Posted inCourses
Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group
[astro-ph.IM] The astronomical community’s ability to use modern machine learning shapes the science return of upcoming facilities.
Posted inGenomics, Proteomics, Bioinformatics
Responsible Use of Large Language Models in Microbial Genomics and Bioinformatics: A Life-Science Framework for Reliability, Reproducibility, and Risk-Aware Interpretation
Editor’s note: As we expand outward from Earth to other worlds we are almost certainly going to encounter things we did not expect to find – things that are unlikely […]
Posted inUncategorized
Machine Learning And Deep Learning For Exoplanet Detection And Atmospheric Characterization With JWST And The Upcoming Ariel Mission
[astro-ph.IM] The detection and atmospheric characterization of exoplanets have entered a new data-intensive era driven by the James Webb Space Telescope and the upcoming Ariel mission.