Black holes and gravitational waves are among the most fascinating phenomena in modern astrophysics. Formed from the collapse of massive stars, black holes are made of pure gravity and challenge our understanding of physics under extreme conditions. When two black holes collide, they emit gravitational waves—perturbations of spacetime itself. The first direct detection of gravitational waves by LIGO in 2015 opened a new way to look at the Universe: besides observing it with telescopes, we are now “hearing” it with gravitational waves. These discoveries are revolutionizing our understanding of astrophysics, relativity, and fundamental physics.

The analysis of gravitational-wave data presents unique challenges. Extracting meaningful physical insights requires advanced techniques, traditionally in the context of Bayesian parameter estimation but now with prominent machine learning applications, to determine the properties of black holes, such as their masses, spins, and orbital dynamics. These methods involve computationally intensive algorithms and rely on sophisticated modeling of waveforms and noise. This makes gravitational waves not only an exciting new territory for physics but also a frontier in computational science.

The vast majority of our software, from data preprocessing to signal modeling and statistical inference, is written in Python. This talk will introduce the physics of black holes and gravitational waves, focusing on the dynamics of binary systems and the signals they produce. It will also highlight Python’s pivotal role in enabling this cutting-edge science, discussing tools and frameworks that have been crucial for discovery.

I am a Professor of Astrophysics at the University of Milano-Bicocca, with 10+ years of experience in computational physics. My research focus is the dynamics of spinning black hole binaries. I am the lead developer of the Python module PRECESSION, tailored to this goal, which is now mainstream in our research field and has been used in 80+ scientific publications. I regularly teach courses on scientific computing and machine learning.