16 papers
Reducing Model Error Using Optimised Galaxy Selection: Weak Lensing Cluster Mass Estimation
Markus Michael Rau, Florian Kéruzoré, Nesar Ramachandra +1
Galaxy clusters are one of the most powerful probes to study extensions of General Relativity and the Standard Cosmological Model. Upcoming surveys like the Vera Rubin Observatory'…
Predicting New Concept-Object Associations in Astronomy by Mining the Literature
Jinchu Li, Yuan-Sen Ting, Alberto Accomazzi +2
We construct a concept-object knowledge graph from the full astro-ph corpus through July 2025. Using an automated pipeline, we extract named astrophysical objects from OCR-processe…
Uncovering Physical Drivers of Dark Matter Halo Structures with Auxiliary-Variable-Guided Generative Models
Arkaprabha Ganguli, Anirban Samaddar, Florian Kéruzoré +4
Deep generative models (DGMs) compress high-dimensional data but often entangle distinct physical factors in their latent spaces. We present an auxiliary-variable-guided framework…
AstroMLab 4: Benchmark-Topping Performance in Astronomy Q&A with a 70B-Parameter Domain-Specialized Reasoning Model
Tijmen de Haan, Yuan-Sen Ting, Tirthankar Ghosal +7
General-purpose large language models (LLMs), despite their broad capabilities, often struggle with specialized domain knowledge. This gap hinders their deployment as reliable rese…
Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…
Emulator-Based Inference of Cosmological Subgrid Models
Nesar Ramachandra, Nicholas Frontiere, Michael Buehlmann +4
The formation of structure in the Universe at large scales is dominated by gravity, with baryonic physics becoming significant at scales. To capture the impact of b…