This is a set of useful python notebooks related to photometric surveys and redshift estimation from noisy flux measurements.
Content:
- Photoz galaxy survey mock and N(z) inference.ipynb: notebook to generate a photometric survey mock, with realistic fluxes, redshifts and underlying galaxy types. Also recovers the underlying distributions via the hierarchical model/sampling of Leistedt, Mortlock and Peiris (2016).
- bayeshist.py: MPI implementation of the hierarchical model and Gibbs sampler of Leistedt, Mortlock and Peiris (2016) for inferring histograms of underlying distributions with binned likelihoods.
- filters and seds contain copies of the CWW galaxy SED templates and the SDSS photometric filters.
Contributors:
- Boris Leistedt (NYU)
- Daniel Mortlock (Imperial College)
- Hiranya Peiris (UCL)
- add your name here
Related papers:
- Hierarchical Bayesian inference of galaxy redshift distributions from photometric surveys by Leistedt, Mortlock and Peiris. arxiv:1602.05960.
This code is released under MIT License. Please cite the relevant papers if you use this code (ask the contributors if you're not sure). Feel free to contribute via pull requests!