17 papers
Energy-Aware Compression-Computation Co-Adaptation for Latency Minimization in Multi-User Semantic Communication
Loc X. Nguyen, Yumin Park, Avi Deb Raha +4
Deep joint source-channel coding-enabled (DeepJSCC) semantic communication (SemCom) has excelled at delivering high perceptual quality at low channel-bandwidth ratios, which positi…
A Parameter-Masked Mock Data Challenge for Beyond-Two-Point Galaxy Clustering Statistics
2pt Collaboration, Elisabeth Krause, Yosuke Kobayashi +23
The paper presents the first results of the "Beyond-2pt" community data challenge, where participants used various beyond‑two‑point galaxy clustering methods on high‑precision mock…
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…
Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics
Pablo Mercader-Perez, Carolina Cuesta-Lazaro, Daniel Muthukrishna +5
Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a signal from measurement-depende…
DiscoverPhysics: Benchmarking LLMs for Out-of-the-Box Scientific Thinking
Matt L. Wiemann, Lindsay M. Smith, Peter Melchior +4
Frontier LLMs now perform strongly across a wide range of physics evaluations, but it is hard to disentangle genuine reasoning from recall of established science. We introduce Disc…
Augmenting representations with scientific papers
Nicolò Oreste Pinciroli Vago, Rocco Di Tella, Carolina Cuesta-Lázaro +3
Astronomers have acquired vast repositories of multimodal data, including images, spectra, and time series, complemented by decades of literature that analyzes astrophysical source…