archeo 1.6.12

Bayesian framework for inferring natal kick, ancestral masses and spins of black holes.

ARCHEO

Archeo is a Python package designed to infer the natal kick, ancestral masses, and spins of black holes in the Pair-instability Supernova (PISN) gap, with a particular focus on hierarchical black hole formation.

Basic Usage with Command Line Interface (CLI)

We provide a command line interface (CLI) for archeo, which allows users to generate preset priors and visualize prior distributions easily.

> python -m archeo --help

Usage: python -m archeo [OPTIONS] COMMAND [ARGS]...

  Command line interface for archeo

Options:
  --help  Show this message and exit.

Commands:
  generate-preset-prior  Generate a preset prior.
  visualize-prior        Visualize the prior distribution.

In the following, we will introduce the available commands in the CLI.

Generate a Preset Prior with CLI

We provide a command to generate preset priors, which can be used for further analysis.

> python -m archeo generate-preset-prior --help
Usage: python -m archeo generate-preset-prior [OPTIONS]

  Generate a preset prior.

Options:
  -n, --name TEXT        Preset prior name, available values are default,
                         agnostic_precessing_spin, agnostic_aligned_spin,
                         precessing_spin, aligned_spin,
                         positively_aligned_spin.
  -o, --output-dir TEXT  Directory to save the generated prior configuration.
  --help                 Show this message and exit.

Here is an example of how to generate a preset prior using the CLI:

python -m archeo generate-preset-prior

This command will generate the default prior configuration and save it in the current directory. Note that the default prior is an aligned spin prior with only 1000 samples. So we expect it to be fast to generate (within 1 minute). To generate other priors, you can specify the --name option with one of the available values. For example,

python -m archeo generate-preset-prior --name agnostic_precessing_spin

Visualize the Prior Distribution with CLI

We provide a command to visualize the generated ancestral prior distribution.

> python -m archeo visualize-prior --help
Usage: python -m archeo visualize-prior [OPTIONS]

  Visualize the prior distribution.

Options:
  -f, --filepath TEXT    Path to the prior data.  [required]
  -o, --output-dir TEXT  Directory to save the visualization output.
  --help                 Show this message and exit.

Here is an example of how to visualize the prior distribution using the CLI:

python -m archeo visualize-prior --filepath ./prior.parquet

This command will read the prior data from prior.parquet and save the visualization output in the current directory. Note that the visualizations include:

• Animation of how distributions (various parameters) change over kick magnitude constraint.
• 2D histogram of the mass-spin distribution.
• Kick distribution for each spin-bin (binwidth=0.1).

Ancestral Parameter Estimation

The following example demonstrates how to use the package to visualize the prior and posterior distributions of a single event.

import archeo

# Load the mass/spin samples from a file (usually PE results from LVK)
# They are expected to be a list of floats
mass_posterior = [68.0, 71.4, ..., 91.4]
spin_posterior = [0.31, 0.54, ..., 0.64]

# Create a prior
prior = archeo.Prior.from_config("precessing_spin")
# Create a posterior from the samples and the prior
posterior = prior.to_posterior(mass_posterior, spin_posterior)

# Visualize the prior and the posterior
archeo.visualize_prior_distribution(prior, output_dir="./")
archeo.visualize_posterior_estimation({"GW190521": posterior}, output_dir="./")

Available Preset Priors

This table provides an overview of the different prior configurations available in archeo.

Name	Samples	Fits Model	Spin Aligned	Only Up-Aligned Spin	$\chi_1$	$\chi_2$	$\phi_1$ [rad]	$\phi_2$ [rad]	$\theta_1$ [rad]	$\theta_2$ [rad]	$m_1 [M_\odot]$	$m_2 [M_\odot]$	$q$	Uniform in $q$
agnostic_precessing_spin (default)	2,000,000	NRSur7dq4Remnant	❌	❌	0 - 1	0 - 1	0 - $2\pi$	0 - $2\pi$	0 - $\pi$	0 - $\pi$	5 - 200	5 - 200	1 - 6	❌
agnostic_aligned_spin	2,000,000	NRSur3dq8Remnant	✅	❌	0 - 1	0 - 1	0 - $2\pi$	0 - $2\pi$	0 - $\pi$	0 - $\pi$	5 - 200	5 - 200	1 - 6	❌
precessing_spin	2,000,000	NRSur7dq4Remnant	❌	❌	0 - 1	0 - 1	0 - $2\pi$	0 - $2\pi$	0 - $\pi$	0 - $\pi$	5 - 65	5 - 65	1 - 6	❌
aligned_spin	2,000,000	NRSur3dq8Remnant	✅	❌	0 - 1	0 - 1	0 - $2\pi$	0 - $2\pi$	0 - $\pi$	0 - $\pi$	5 - 65	5 - 65	1 - 6	❌
positively_aligned_spin	2,000,000	NRSur3dq8Remnant	✅	✅	0 - 1	0 - 1	0 - $2\pi$	0 - $2\pi$	0 - $\pi$	0 - $\pi$	5 - 65	5 - 65	1 - 6	❌

Configure your own prior

Check out the preset priors in quick.py. From that, one should be able to create their own prior by following the same structure.

Try our UI

Archeo also provides a simple web-based user interface to visualize the distributions of remnant properties. To run the UI locally, simply run the following command:

pip3 install archeo[ui]
python3 -m archeo.ui

Then the UI will be available at localhost:8501.

You may also try our demo version online, which is hosted on Streamlit Community Cloud.

Getting Help

The code is maintained by Henry Wong under Juan Calderon Bustillo's supervision. You can find the list of contributors here. Please report bugs by raising an issue on our GitHub repository.

License

Archeo has a MIT License - see the LICENSE.