Davide Gerosa

Research data

Here you can find my public numerical codes and some online material supporting my publications.

Public repositories are available on github (or here for a nicer list). Some of the pages below contain the same information of the repositories’ README, some others instead have more results.

From here:

• precession: Dynamics of spinning black-hole binaries with python. 
Public python module to perform post-Newtornian evolution of precessing exploiting multi-timescale methods. The code is described carefully arXiv:1605.01067 and has by now been used in many papers by us and others.

• spinprecession: Black-hole binary inspiral: a precession-averaged approach. 
Some animations and data on black-hole binary spin precession, supporting arXiv:1411.0674, 1506.03492, 1506.09116, 1711.10038, 1811.05979, 2003.02281, and 1302.4442.

• corecollapse: Numerical simulations of stellar collapse in scalar-tensor theories of gravity
Did you know supernova can be used to test gravity? Animations and data release on core-collapse simulations in scalar-tensor theories of gravity, supporting arXiv:1602.06952.

• surrkick: Black-hole kicks from numerical-relativity surrogate models
Public python module to extract black hole recoils from waveform approximants by directly integrating the linear momentum flux in gravitational waves. The approach is described in arXiv:1802.04276, here we also provide some animations from that paper.

• gwdet: Detectability of gravitational-wave signals from compact binary coalescences
Tiny python module to compute the probability that a gravitational-signal will be detected averaging over sky location, detector antenna pattern, etc.

• spops: “S”pinning black-hole binary “Pop”ulation “S”ynthesis
Database containing population synthesis simulations from arXiv:1808.02491, together with a simple python code to query it.

• pdetclassifier: Gravitational-wave selection effects using neural-network classifiers
Training samples and pre-trained neural networks to estimate LIGO/Virgo detectability. Supporting arXiv:2007.06585.

From there:

• welovespins: Asymmetries and selection biases in effective-spin measurements
Estimate your own effective-spin posterior with the recipe presented in arXiv:1805.03046.

• gw_catalog_mining: Mining gravitational-wave catalogs
What are we going to do with thousands of gravitational wave observations? Maybe Gaussain process emulators and hierarchical analyses. Webpage and public code supporting arXiv:1806.08365.

• GWpriors: Impact of bayesian priors on the characterization of binary black hole coalescences
Full posterior samples of the three LIGO 01 events, obtained under a variety of astrophysically motivated prior assumptions. Data release supporting arXiv:1707.04637.

• surfinBH: SURrogate FINal Black Hole properties for mergers of binary black holes 
Public python module to estimate post-merger masses, spins, and kicks for generic systems (yes, this includes spin precession). Supporting arXiv:1809.09125.

• binaryBHexp: The binary black hole explorer
On-the-fly visualization of precessing binary black holes. Use ours, or do your own with our code. Supporting arXiv:1811.06552.

• Milky Way Satellites Shining Bright in Gravitational Waves
LISA white dwarf posteriors. Supporting arXiv:2002.10465.