Machine Learning for Astrophysics

Francois Lanusse · Marc Huertas-Company · Vanessa Boehm · Brice Menard · Xavier Prochaska · Uros Seljak · Francisco Villaescusa-Navarro · Ashley Villar

Room 337 - 338

As modern astrophysical surveys deliver an unprecedented amount of data, from the imaging of hundreds of millions of distant galaxies to the mapping of cosmic radiation fields at ultra-high resolution, conventional data analysis methods are reaching their limits in both computational complexity and optimality. Deep Learning has rapidly been adopted by the astronomical community as a promising way of exploiting these forthcoming big-data datasets and of extracting the physical principles that underlie these complex observations. This has led to an unprecedented exponential growth of publications with in the last year alone about 500 astrophysics papers mentioning deep learning or neural networks in their abstract. Yet, many of these works remain at an exploratory level and have not been translated into real scientific breakthroughs.The goal of this workshop is to bring together Machine Learning researchers and domain experts in the field of Astrophysics to discuss the key open issues which hamper the use of Deep Learning for scientific discovery. Rather than focusing on the benefits of deep learning for astronomy, the proposed workshop aims at overcoming its limitations.Topics that we aim to cover include, but are not limited to, high-dimensional Bayesian inference, simulation-based inference, uncertainty quantification and robustness to covariate shifts, anomaly and outlier detection, symmetries and equivariance. In addition, we plan on hosting meta-research panel discussions on successfully bringing ML to Astrophysics.

Chat is not available.
Timezone: America/Los_Angeles »