Metadata-Version: 2.4
Name: GBconfusion
Version: 0.1.0
Summary: Tools for galactic confusion estimation with LISA
Author-email: Alice Cravioglio <acravioglio@gmail.com>
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: astropy
Requires-Dist: matplotlib
Requires-Dist: fastgb
Requires-Dist: pycbc
Requires-Dist: h5py
Requires-Dist: argparse
Requires-Dist: lisaorbits
Requires-Dist: pandas
Requires-Dist: pathlib
Requires-Dist: tqdm

## Description

This package provides the tools to estimate the confusion noise PSD generated by galactic binaries. Starting from a source catalog of the binaries, it generates waveforms using the `FastGB` code. Then, the confusion is estimated through an iterative subtraction process of the loudest sources. The outputs are: a set of resolved binaries, the resiual confusion noise PSD, and the parameters of the run 

The following settings can be modified if needed:
- TDI generation 1.5 or 2.0 (default: 2.0)
- Time of observation (default: 4 years)
- LISA sampling time (default: 5 seconds)
- SNR threshold (default: 7)
- Median filter size for PSD smoothing (default: 2000)
- Instrumental noise (default: TDI A/E channel)

During the pre-processing of the catalog (waveform generation step), there is the possibility to apply a pre-exclusion of weak sources, based on an approximate SNR calculation. This is done through the argument `snr_preselection` (default: 0.01). It is recommended to use a pre-selection SNR not higher than 0.01, to avoid excluding possibly resolvable sources. Pre-excluded sources will be skipped during the waveform generation, and their contribution to the noise automatically added to the PSD.

