5 citations · 7 across the 5 of their papers we have counts for
5 papers
LMC-induced Perturbations in the Milky Way Halo II: Bridging Field-level Inference and Summary-level Simulation-Based Inference
Yanjun Sheng, Yuan-Sen Ting, Tomasz Różański +3
The gravitational interaction between the Milky Way (MW) and the Large Magellanic Cloud (LMC) drives the outer halo into dynamical disequilibrium, imprinting the masses and structu…
AGNFormer I: Reconstruction of AGN spectra using a probabilistic transformer model
Benedict L. Rouse, Franz E. Bauer, Tomasz Różański +8
We explore how an uncertainty-aware transformer-based architecture can leverage information embedded across the entire observed optical spectra of AGN, focusing on the algorithm's…
Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group
Yuan-Sen Ting, Digvijay Wadekar, Phill Cargile +21
Recent community assessments identify education as a principal barrier to adopting modern machine learning in astronomy. We present Deep Learning for Astrophysics, a freely availab…
AstroLLaMA: Towards Specialized Foundation Models in Astronomy
Tuan Dung Nguyen, Yuan-Sen Ting, Ioana Ciucă +21
Large language models excel in many human-language tasks but often falter in highly specialized domains like scholarly astronomy. To bridge this gap, we introduce AstroLLaMA, a 7-b…
Toward a Spectral Foundation Model: An Attention-Based Approach with Domain-Inspired Fine-Tuning and Wavelength Parameterization
Tomasz Różański, Yuan-Sen Ting, Maja Jabłońska
Astrophysical explorations are underpinned by large-scale stellar spectroscopy surveys, necessitating a paradigm shift in spectral fitting techniques. Our study proposes three enha…