Seminar:- Transcending the Limits of Astrostatistics with Machine Learning Methods
Recent advancements in astronomical instrumentation have led to an unprecedented influx of data, revolutionizing the field of astronomy. However, the inherent complexity and multi-dimensionality of astronomical observations, ranging from intricate imaging of weak lensing, reionization, and protoplanetary disks to the comprehensive analysis of galaxy mergers across cosmic history, pose significant challenges to traditional astrostatistical methods. In this colloquium, the speaker will discuss two distinct machine learning approaches aimed at tackling these complex astronomical systems. First, he will explore the Mathematics of Information, focusing on how machine learning can optimize information compression and extract higher-order moments in stochastic processes. Second, he will introduce a Generative Paradigm, demonstrating how generative models, such as normalizing flows and diffusion models, enable precise modeling of astronomical datasets, facilitating accurate inferences on intricate astronomical systems. By leveraging these cutting-edge machine learning techniques, we can transcend the limitations of conventional astrostatistics, furthering making inferences on complex astronomical systems.
時間：2023.04.30(二) 15:00
地點：香港城市大學 楊建文學術樓* Y5-305
講者：Prof. TING Yuan-Sen 丁源森 教授 (Associate Professor, Australian National University, Australia)
語言：英語
【此屬轉載信息，以主事單位發布為準】
*Yeung Kin Man Academic Building
此活動由香港城巿大學物理系主辦。
