astrophysicscatalogsclassificationcorrelationsdata analysisdensityev rangefunctionsgalaxy clustersRecent measurements of large-scale peculiar velocities from the cumulative kinematic Sunyaev-Zeldovich (KSZ) effect identified a bulk flow of galaxy clusters at $\\sim 600-1,000$ km s$^{-1}$ on scales of ...
The2019 Kavli Summer Program in Astrophysicsat UC Santa Cruz will focus on “Machine Learning in the Era of Large Astronomical Surveys,” bringing together scientists and students from a broad range of backgrounds to learn about machine learning techniques and their applications in astronomy. The Kav...
Object classification, Virgo Cluster membership, photometric redshifts, catalog cross-matching, and spatial clustering can potentially be achieved with greatly improved efficiency.doi:10.1007/978-1-4614-3520-4_44Nicholas M. BallNational Research Council Herzberg Institute of AstrophysicsSpringer New York...
“An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. ...
Astrophysics - Solar and Stellar AstrophysicsWe develop a mixed long short‐term memory (LSTM) regression model to predict the maximum solar flare intensity within a 24﹉r time window 0–24, 6–30, 12–36, and 24–48 hr ahead of time using 6, 12, 24, and 48 hr of data (predictors)...
Astrophysics, and solar physics in particular, is an observational science in which we cannot change the experimental conditions, we simply observe. Therefore, the only way of learning is by confronting observations with state-of-the-art theoretical modeling. The models are then tuned until the obs...
Paper tables with annotated results for Applications and Techniques for Fast Machine Learning in Science
Comments: 11 pages, 5 figures, published in A&A Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG) Cite as: arXiv:2504.07235 [astro-ph.EP] (or arXiv:2504.07235v1 [astro-ph.EP] for this version...
题目:Cosmology in the machine learning era 时间:2025年3月19日 9:30 线上ZOOM会议:821 9739 9872 密码: 128029 主持人:王云 嘉宾简介 Introduction of SpeakerFrancisco (Paco) Villaescusa-Navarro,美国纽约 Flatiron Institute 的研究员。...
Diverse many-body systems, from soap bubbles to suspensions to polymers, learn and remember patterns in the drives that push them far from equilibrium. This learning may be leveraged for computation, memory, and engineering. Until now, many-body learning has been detected with thermodynamic properti...