archeo 2.0.0.dev6

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

ARCHEO (V2)

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 black hole binaries and visualize black hole population properties 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:
  - simulate-agnostic-black-hole-population
  - simulate-second-generation-black-hole-population
  - visualize-black-hole-population

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

Simulate a black hole remnant population with agnostic assumptions/second generation mergers

We provide a command to generate black hole binaries with preset distribution assumptions, which can be used for further analysis.

> python -m archeo simulate-agnostic-black-hole-population --help

  Simulate a population of agnostic black hole binaries. The function
  simulates both aligned and precession spin configurations based on the
  user's choice.

  Command example: >> python -m archeo simulate-agnostic-black-hole-population
  --aligned-spin

Options:
  -n, --size INTEGER        Number of black holes to simulate.
  -np, --n-workers INTEGER  Number of cores to use for simulation.
  -o, --output-dir TEXT     Directory to save the generated data.
  -as, --aligned-spin       Toggle to simulate aligned spin binaries.
  --help                    Show this message and exit.

> python -m archeo simulate-second-generation-black-hole-population --help

Usage: python -m archeo simulate-second-generation-black-hole-population
           [OPTIONS]

  Simulate a population of second generation black hole binaries. The function
  simulates both aligned and precession spin configurations based on the
  user's choice.

  Command example: >> python -m archeo simulate-second-generation-black-hole-
  population --aligned-spin

Options:
  -n, --size INTEGER        Number of black holes to simulate.
  -np, --n-workers INTEGER  Number of cores to use for simulation.
  -o, --output-dir TEXT     Directory to save the generated data.
  -as, --aligned-spin       Toggle to simulate aligned spin binaries.
  --help                    Show this message and exit.

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

python -m archeo simulate-agnostic-black-hole-population --aligned-spin

After the commend is executed, we should see two files generated in the current directory:

• simulated_binaries.parquet: a parquet file containing the simulated binary properties (masses, spins, kick velocities, etc.).
• binary_generator_config.json: a json file containing the configuration used for the simulation.

Visualize the Prior Distribution with CLI

We provide a command to visualize the generated black hole binary population properties, which can be used to understand the prior distribution of black hole properties.

> python -m archeo visualize-black-hole-population --help

Usage: python -m archeo visualize-black-hole-population [OPTIONS]

  Generate some visualizations for a black hole population.

  Command example: >> python -m archeo visualize-black-hole-population
  --filepath ./simulated_binaries.parquet

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

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

python -m archeo visualize-black-hole-population --filepath ./simulated_binaries.parquet

This command will read the binary data from simulated_binaries.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
pipeline = archeo.get_binary_generation_pipeline("2g_aligned_spin")
df_prior, binary_generator = pipeline(size=10000, n_workers=-1)
# Infer the posterior distribution
df_posterior = infer_ancestral_posterior_distribution(
    df_binaries=df_prior,
    mass_posterior_samples=mass_posterior,
    spin_posterior_samples=spin_posterior,
    n_workers=-1,
)

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

Documentation for Developers

To import archeo in your Python code, please refer to the documentation page at https://wyhwong.github.io/archeo/.

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.