Computational Methods

Computational methods, astrostatistics, machine learning, visualization, and general coding.

Courses

• Machine Learning for Physics and Astronomy through the Flatiron CCA

Textbooks + Longer Articles

• Python for Astronomers by Imad Pasha and Chris Agostino
• Python Data Science Handbook by Jake VanderPlas
• Scientific Visualization: Python + Matplotlib by Nicolas P. Rougier
• Hydrodynamic methods and sub-resolution models for cosmological simulations (Valentini & Dolag) chapter from the Numerical Simulations in Cosmology in the Encyclopedia of Cosmology
• Dive into Deep Learning (Zhang, Lipton, Li, & Smola)
• Neural Networks and Deep Learning by Michael Nielsen
• How to set up your first machine learning project in astronomy (Buchner & Fotopoulou)
• Introduction to Machine Learning (Younes)
• Astrocomputing: From Raw Data to Science Discovery by Luciano Silva
• Statistical Machine Learning for Astronomy – A Textbook by Yuan-Sen Ting
• Fisher Matrix for Beginners by David Wittman

Tutorials + Notebooks

• Resources from the LSSTC Data Science Fellowship Program sessions
• Resources from Code/Astro workshops
• Introductory tutorials from Yale’s ASTR 255/PHYS 295: Research Methods in Astrophysics for undergraduate majors by Ava Polzin
• Notebooks from Daniel Green’s Physics 105B: Computational Physics at University of California, San Deigo
• Notebooks from Daniel Green’s Physics 39: Scientific Computing with Python at UCSD
• Tutorials from Philip Mocz on Medium – note that these are “member only”, which limits how many can be accessed per month without an account
• Python tutorials from Jackie Champagne
• Lectures and tutorials from the 10-week KITP program Building a Physical Understanding of Galaxy Evolution with Data-Driven Astronomy led by Tjitske Starkenburg, Francisco Villaescusa-Navarro, Peter Behroozi, and John Wu (overall program here)
• Scientific Computing Toolbox by Shane Burns (see also Linear Fitting)
• MacOSX Unix Tutorials by Shane Burns
• REACHpy Python and data science tutorials from Northwestern University’s Research Experiences in Astronomy at CIERA for High School Students program

Lecture Materials + Course Notes

• Yale’s senior undergraduate ASTR 330: Scientific Computing in Astrophysics by Imad Pasha and Marla Geha
• Stony Brook’s graduate PHY 504: Computational Methods in Physics & Astrophysics I by Michael Zingale
• Stony Brook’s undergraduate AST 390: Computational Astrophysics by Michael Zingale
• Lecture notes from Daniel Green’s Physics 105B: Computational Physics at the UCSD
• Notes from UCSD’s Physics 39: Scientific Computing with Python by Daniel Green
• Notebooks and materials from Viviana Acquaviva’s Machine Learning for Physics and Astronomy
• Model Interpretability from the 2023 IAIFI Summer School by Alex Gagliano and Josh Speagle
• Practical Guidance for Bayesian Inference in Astronomy – Eadie et al. (2023)
• Statistics and Machine Learning for Astronomy lecture videos and course website for Australia National University’s ASTR 4004/8008: Astronomical computing by Yuan-Sen Ting
• Direct download of Romain Teyssier’s course notes in Scientific Computing
• Direct download of Romain Teyssier’s course notes for Computational Astrophysics for PhD students
• Yale University’s Astro Research Preparation Series
• Slides from University of Wisconsin-Madison’s undergraduate Machine Learning in Physics by Moritz Münchmeyer
• Notes from Stanford KIPAC’s graduate Physics 366: Statistical Methods in Astrophysics (also on GitHub)
• Notes from Stanford KIPAC’s Physics 89L: Introduction to Data Analysis, with Python and Jupyter
• Materials from the Astrostatistics in Crete summer schools

Other Resources

• Matplotlib cheatsheets
• A Review of Unsupervised Learning in Astronomy by Sotiria Fotopoulou
• ULULA: An ultra-lightweight 2D hydrodynamics code for teaching and experimentation by Benedikt Diemer
• TeXnique game to test LaTeX math mode capability (especially good coupled with Detexify and/or the Matthias Heinkenschloss’s LaTeX math cheatsheet)