[University of Washington] It’s a dizzyingly busy time for scientists searching for life on distant planets. To date, astronomers have cataloged more than 6,000 planets beyond our solar system and […]
AI – Data – Apps – Cybernetics
Posted inExoplanets, -moons, -comets
ExoVetter and a TESS Exoplanet Transit Search of Kepler Stars as Ground Truth
[astro-ph.EP] We introduce a Python package called exoVetter that produces vetting metrics and visualizations to evaluate transiting exoplanet signals found in high-cadence time-series photometry.
Posted inAI - Data - Apps - Cybernetics
The Great Chat In The Sky: Aligning Exotic Astronomical Data To Language With AstroBridge
[astro-ph.IM] Astronomical observations include images, spectra, and light curves, while the scientific literature describes these objects in natural language. We present AstroBridge, a framework that connects these observations to an […]
![Left. Two-dimensional UMAP representation of the TMC-1 inventory using the original ChemBERTa input vectors. Right. Same representation but using the last hidden layer of the neural netwok model. The red rectangle highlights the region of the UMAP representation where highly abundant molecules are located. The different dot sizes represent the observed column density of each molecule. -- [astro-ph.GA]](https://i0.wp.com/astrobiology.com/wp-content/uploads/2026/10/A-neural.png?resize=1200%2C725&ssl=1)
Posted inAstrochemistry
A Neural-network Model To Find Correlations In Astrochemical Space
[astro-ph.GA] The detection of new interstellar molecules is central to astrochemistry, and arguably the most transformative development in the field in past decade has been the increase in both number […]
![A comparison of the dominant heating and cooling mechanism for different times and at the time of the maximum temperature. Each model is started at 𝑇 = 10 K. -- [astro-ph.GA]](https://i0.wp.com/astrobiology.com/wp-content/uploads/2026/10/UCLCHEM.png?resize=1200%2C716&ssl=1)
Posted inAstrochemistry
UCLCHEM 4.0: An Open Source Gas-grain Astrochemistry Simulation Framework
[astro-ph.GA] Astrochemical modeling is a key tool for the understanding of the formation and destruction of molecules in the dense gas of the interstellar medium, as observed by modern day […]
![Overview of the ML-DH framework. Top: Training stage, using DFT energies, forces, and dipoles to train the NNFF and EPNN. Bottom: Prediction stage, yielding IR spectra via NNFF optimization, normal mode analysis, and EPNN dipole derivatives.-- [astro-ph.GA]](https://i0.wp.com/astrobiology.com/wp-content/uploads/2026/10/A-machine.png?resize=1200%2C719&ssl=1)
Posted inAstrochemistry
A Machine-learning-driven Dataset Of 144,111 Polycyclic Aromatic Hydrocarbon Infrared Spectra For Interpreting Aromatic Infrared Bands
[astro-ph.GA] The interpretation of interstellar aromatic infrared bands (AIBs) relies on reference spectral libraries, but the chemical space covered by existing databases remains incomplete relative to the vast structural diversity […]
Posted inNanotechnology & SynBio
Designing Protein Structures From Natural Language
[cs.LG] A central challenge in de novo protein design is generating proteins with desired structural and functional properties.
![Set Transformer actor–critic architecture used by the RL policy. The input candidate set 𝑋 ∈ R 𝑁×𝐹 is first projected into a latent 𝑑-dimensional representation and processed by 𝐿 Induced Set Attention Blocks (ISABs). Learned inducing tokens provide the queries in the first attention operation of each ISAB, after which information is redistributed to the candidate tokens. The actor combines each contextualised target representation with the global mission state to produce one logit per available observation. Each logit is then converted into a probability to select a corresponding observation. The critic uses Pooling by Multihead Attention (PMA) to obtain a permutation-invariant summary of the candidate set, which is combined with the same global state to estimate 𝑉 (𝑠). -- [astro-ph.EP]](https://i0.wp.com/astrobiology.com/wp-content/uploads/2026/10/aRieL-Reinforcement.png?resize=1200%2C715&ssl=1)
Posted inAriel Mission
aRieL – Reinforcement Learning For The Ariel Space Telescope Scheduling
[astro-ph.EP] The Ariel mission will conduct a population level survey of hundreds of exoplanet atmospheres, requiring observations to be scheduled across a large and diverse catalogue of potential targets.
Posted inAI - Data - Apps - Cybernetics
Giving NASA’s SciX A Quick Astrobiology Roadtest
Keith’s note: Goodbye ADS – Hello SciX. According to an editorial “Farewell NASA ADS” in Nature magazine, “After more than 33 years, NASA’s Astrophysics Data System will close its virtual […]
![Architecture and workflow of AstroGenesis. Researchers interact with the system through a chatbot interface and select an appropriate DSRM corresponding to the astrophysical source class under investigation. Within the selected DSRM, a Supervisor Agent coordinates task execution through a Planner/Replanner and a set of specialized agents responsible for literature analysis, observational data retrieval, theoretical modeling, and research ideation. These agents operate using external tools which include, astrophysical tools, data-analysis modules, and pre-trained models to perform modeling of the data, etc. -- [astro-ph.IM]](https://i0.wp.com/astrobiology.com/wp-content/uploads/2026/10/AstroGenesis.png?resize=1200%2C722&ssl=1)
Posted inAI - Data - Apps - Cybernetics
AstroGenesis: A Domain-Specific Multi-Agent AI for Astrophysical Research
[astro-ph.IM] Modern astrophysical research requires the integration of rapidly expanding scientific literature, heterogeneous observational data, and increasingly complex physical models.