astro automata focuses on automating astrophysics research with AI. It is both a personal research blog and collection of related content.
Research (full list)
Discovering Symbolic Models from Deep Learning
We describe a technique for converting a deep learning model into an analytic equation, focusing on graph networks. We validate it on injected force laws and Hamiltonians, and then discover a new equation to accurately predict the overdensity of dark matter from its environment.
Lagrangian Neural Nets
We show how one can learn a Lagrangian from data with a Neural Network. Such an architecture conserves energy in a learned simulator without requiring canonical coordinates.
Normalizing Flows for Stellar Isochrones
A normalizing flow is a highly flexible probability distribution, perfect for the nonlinear surface of an HR diagram. Here, we show how to learn a flow for Gaia’s HR diagram.
Posts
An introduction to likelihood-free inference
I give an introduction to likelihood-free (simulation-based) inference for scientists.
The use and abuse of machine learning in astronomy
I argue that machine learning is both underused and overused in astrophysics.
Symmetries in Neural Networks
Summary of a Twitter discussion about symmetries in Neural Networks
Related articles
How Self-Driving Telescopes Could Transform Astronomy
By Ryan F. Mandelbaum. What if an autonomously operating telescope, free from human biases and complications, could find the solutions we’ve been missing?
AI Will Help Scientists Ask More Powerful Questions
By Pushmeet Kohli. Self-learning systems can discover hidden patterns in immense data sets, transcending what humans could ever find on their own