Correlate BORG reconstructions with CMB data
- Python 100%
| cmborg | ||
| notes | ||
| scripts | ||
| tests | ||
| .gitattributes | ||
| .gitignore | ||
| MANIFEST.in | ||
| pyproject.toml | ||
| README.md | ||
| requirements-dev.txt | ||
| requirements.txt | ||
CMBORG
Cross-correlate BORG density field reconstructions with CMB data (thermal Sunyaev-Zel'dovich effect).
Installation
Clone the repository and install in development mode:
git clone https://git.aquila-consortium.org/Aquila-Consortium/CMBORG
cd CMBORG
pip install -e .
For development (testing, linting):
pip install -e ".[dev]"
Quick start
Run a toy mock inference with scripts/main.py.
This generates a synthetic tSZ map from a halo catalog, adds noise, and
runs NUTS to recover the profile parameters (P0, xc0, n):
cd scripts
python main.py
Outputs: tsz_map_healpix.png, trace_plot.png, corner_plot.png.
Tests
pytest tests/
Tests cover the interpolation module (1D/2D Catmull-Rom splines, correction factor accuracy vs scipy), tSZ profile models (physical sanity, interpolated vs analytic agreement, JAX differentiability), and cosmological distances.
## To Do
- Decide whether point source mask is necessary
- Find what halo mass cut should be used for 10' beam
- Check inference ok for nside=2048
- Check factors of h in e.g. scaling
License
MIT