LeR
ler is a statistics-based Python package for simulating compact-binary
gravitational-wave (GW) populations and calculating detectable event rates. It
supports both unlensed and strongly lensed events, with workflows for source
populations, lens populations, image properties, detector selection effects,
and rate estimation.
ler is intended for gravitational-wave population studies, lensing studies,
and forecasting for current and future detector networks.
Installation
ler supports Python 3.10+; Python 3.11 is recommended in the
documentation.
Recommended installation with uv:
uv add ler
You can also install with pip:
pip install ler
For development, clone the repository and install in editable mode:
git clone https://github.com/hemantaph/ler.git
cd ler
pip install -e ".[dev]"
Quick start
from ler import LeR
ler = LeR(npool=4) # npool sets the number of parallel processes for sampling and integration. It should be set according to your system's capabilities.
# Generate simulated populations and save parameters in json files
unlensed_param = ler.unlensed_cbc_statistics(size=100000)
lensed_param = ler.lensed_cbc_statistics(size=100000)
# Calculate detectable rates, saving detected parameters in json files
unlensed_rate, unlensed_param_detected = ler.unlensed_rate()
lensed_rate, lensed_param_detected = ler.lensed_rate()
# Compare lensed and unlensed rates
ratio = ler.rate_ratio()
What ler does
- samples compact-binary source populations, including BBH, BNS, and NSBH systems
- calculates detectable unlensed gravitational-wave event rates
- samples strongly lensed source and lens populations
- computes lensing image properties such as magnifications and time delays
- calculates detectable strongly lensed event rates using image-level detection criteria
- supports configurable source, lens, and detection models
- uses
gwsnrfor efficient signal-to-noise ratio and detection-probability calculations - uses
lenstronomyand in-house EPL+Shear routines for lensing calculations - uses Monte Carlo integration, multiprocessing, and
numba-compiled routines for large simulations
Method overview
Unlensed rates are estimated by drawing source parameters from astrophysical priors, evaluating the probability of detection, and averaging over the population:
$$ \frac{\Delta N^{\mathrm{obs}}{\mathrm{U}}}{\Delta t} = \mathcal{N}{\mathrm{U}} \bigg\langle P(\mathrm{obs} \mid \vec{\theta}) \bigg\rangle_{\vec{\theta} \sim P(\vec{\theta})} $$
Strongly lensed rates extend the same idea by sampling source redshift, lens redshift, lens parameters, and source position under the strong-lensing condition:
$$ \frac{\Delta N^{\mathrm{obs}}{\mathrm{L}}}{\Delta t} = \mathcal{N}{\mathrm{L}} \bigg\langle P(\mathrm{obs}\mid \vec{\theta}{\mathrm{U}}, \vec{\theta}{\mathrm{L}}, \vec{\beta}, \mathrm{SL}) \bigg\rangle_{\substack{ \vec{\theta}{\mathrm{U}},\vec{\theta}{\mathrm{L}} \sim P(\vec{\theta}{\mathrm{U}},\vec{\theta}{\mathrm{L}} \mid z_L, z_s, \mathrm{SL}) \ \vec{\beta} \sim P(\vec{\beta} \mid z_s, \vec{\theta}_{\mathrm{L}}, \mathrm{SL}) }} $$
The lensed workflow uses the optical depth, the multi-image caustic cross-section, lens-equation solutions, and a requirement that at least two lensed images satisfy the chosen detection criterion.
Documentation
The documentation is available at:
Useful sections include:
Community guidelines
Guidelines for contributing, reporting issues, and seeking support are available in CONTRIBUTING.md.
Issues can be reported at:
https://github.com/hemantaph/ler/issues
Citation
If ler supports your research, please cite the project as described in the
documentation and repository metadata.
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file ler-1.0.1.tar.gz.
File metadata
- Download URL: ler-1.0.1.tar.gz
- Upload date:
- Size: 12.2 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/6.1.0 CPython/3.10.18
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
24a3aa4fe66ef20b7e8e7a2c9512602448248ed735a39767b789167f2c8583a5
|
|
| MD5 |
f6518c644e19a967230a21e01c5571d0
|
|
| BLAKE2b-256 |
499f06fa39564b3d521c7f15793c693538b9593f19146a8173c5273a35d5fd6c
|
File details
Details for the file ler-1.0.1-py3-none-any.whl.
File metadata
- Download URL: ler-1.0.1-py3-none-any.whl
- Upload date:
- Size: 12.2 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/6.1.0 CPython/3.10.18
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
8fc9ef82a44fa336127d89146e372c1438309e1864ad12f4b983ce38b1fbe8d1
|
|
| MD5 |
d763c305b085f96e751cb56b5a7cefe1
|
|
| BLAKE2b-256 |
9c7cdf33516110d109bf1b042595a038578f9fc8f03e03a6c36ad1eaf4c71ea3
|
