All Events 

CCA-KITP Joint Workshop

Workshop

Computational Astrophysics

When

  • – ET

  • All Day

  • All Day

  • – ET

Where

Ingrid Daubechies Auditorium (IDA)

160 Fifth Avenue

New York, NY 10010

Galaxy formation and evolution is a field that combines many complex and interdependent physical processes across different spatial and time scales. Moreover, observational data exists across a large wavelength range, and with heterogeneous spatial resolutions and observing depths, each sensitive to different physical tracers. Astrostatistics and machine learning can help us mine the huge existing and upcoming multiwavelength datasets, effectively down to the pixel and spaxel level, in order to determine the fundamental relationships between galaxy properties and evolutionary processes. Additionally, novel techniques are required to facilitate comparisons between galaxy formation theories and all observational data — including outlier galaxy populations, and to integrate galaxy formation physics in the inference of cosmological parameters. However, connecting statistics and machine learning results to an improved understanding of key astrophysical processes remains a major challenge. This program will initiate a discussion between the empirical exploration of the extremely large and diverse data sets from both observations and simulations and the deduction of the underlying physics, aiming to answer the question: How do we optimally combine the empirical intuition gained using data science tools with theoretical intuition and knowledge, with the deductive goal of understanding the physical processes that govern the formation and evolution of galaxies?

For more information go here.

January 30, 2023

Review Talk – Marc Huertas-Company: Has deep learning solved classification in astronomy?

January 30, 2023

Short Talks – Luis Suelves, Aimee Schechter and Sotiria Fotopoulou

January 31, 2023

Review Talk – Shirley Ho: Deep Learning as a last resort in galaxy formation?

January 31, 2023

Short Talks – Amandine Le Brun, Shivam Pandey, Yongseok Jo and Naomi Gluck

February 1, 2023

Review Talk – Benjamin Wandelt: Implicit inference based on simulations

February 1, 2023

Short Talks – Robert Feldmann, Megan Tillman, Festa Bucinca and Yu-Heng Lin

February 2, 2023

Review Talk – Henry Ferguson: Applications of Machine Learning for Studying Galaxy Evolution with JWST and Roman

February 2, 2023

Short Talks – Andreea Petric, Francisco Javier Castander Serentill and Toby Brown

February 3, 2023

Review Talk – Dovi Poznanski: Unsupervised Learning: How to sift through astronomical haystacks without getting lost in metaphors, hyperboles or in a random forest

February 3, 2023

Review Talk – Viviana Acquaviva: From ML x Astrophysics to ML x Climate: A journey across disciplines

February 3, 2023

Short Talks – Mariel Pettee: Interdisciplinary ML for Fundamental Physics

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