Useful links
Documentation
- Xspec & PyXspec documentation:
https://heasarc.gsfc.nasa.gov/xanadu/xspec/ - Sherpa reference for commands:
https://cxc.cfa.harvard.edu/sherpa/ahelp/sherpa4.html - UltraNest documentation:
https://johannesbuchner.github.io/UltraNest/readme.html - BXA documentation: https://johannesbuchner.github.io/BXA/index.html
Places to ask questions
- Xspec Facebook group:
https://www.facebook.com/groups/320119452570/?ref=share - Astrostatistics Facebook group:
https://www.facebook.com/groups/astro.r/?ref=share - Email!
Useful papers & documents
- Buchner and Boorman (2023):
https://ui.adsabs.harvard.edu/abs/2023arXiv230905705B/abstract - X-ray Data Primer by CXC:
https://cxc.cfa.harvard.edu/cdo/xray_primer.pdf - Information on C-statistics (& modified C-statistics, aka W-stat) used in Xspec & Sherpa by Giacomo Vianello:
https://giacomov.github.io/Bias-in-profile-poisson-likelihood/ - Nested sampling review - Buchner (2021)
https://arxiv.org/abs/2101.09675 - PCA background - Simmonds et al., (2018, A&A)
https://ui.adsabs.harvard.edu/abs/2018A%26A...618A..66S/abstract - Simulation-based calibration of false-positive & false-negative rates - Baronchelli et al., (2018, MNRAS)
https://ui.adsabs.harvard.edu/abs/2018MNRAS.480.2377B/abstract - Bayesian Hierarchical Modelling with BXA:
- Baronchelli et al., (2020, MNRAS), Section A1
https://ui.adsabs.harvard.edu/abs/2020MNRAS.498.5284B/abstract - Kuraszkiewicz et al., (2021, ApJ), Section A1
https://ui.adsabs.harvard.edu/abs/2021ApJ...913..134K/abstract - Liu et al., (2021, arXiv), Section 3.7
https://ui.adsabs.harvard.edu/abs/2021arXiv210614522L/abstract
- Baronchelli et al., (2020, MNRAS), Section A1
Lectures & videos
- BiD4BEST Bayesian X-ray Spectral Analysis lectures by Johannes Buchner:
- Graduate-level course: Monte Carlo inference methods by Johannes Buchner:
- Bayesian inference workflow in astronomy and physics: https://youtu.be/D2P6xBR_2bQ
- Bayesian model comparison, curse of dimensionality, Importance Sampling: https://youtu.be/TfaGfFnsmW8
- Markov Chain Monte Carlo (MCMC) and diagnostics: https://youtu.be/YA6Ezh8CsrY
- Hamiltonian Monte Carlo (HMC) by Dr. Francesca Capel: https://youtu.be/nmHGyXsiaeI
- Nested Sampling from scratch - Practical Inference for Researchers in the Physical Sciences: https://youtu.be/baLFl_4ZwXw
- State-of-the art in nested sampling and MCMC: literature review: https://youtu.be/HFaqcB_H6MA
- BXA Tutorial at Chandra Data Science 2021 by Peter Boorman & Johannes Buchner:
- Interactive visualisation of the nested sampling algorithm in UltraNest:
https://johannesbuchner.github.io/UltraNest/method.html?highlight=demo#visualisation
Scripts
- Sherpa script for fitting obscured AGN with BXA:
https://github.com/JohannesBuchner/BXA/blob/master/examples/sherpa/xagnfitter.py - PosteriorStacker (for deriving sample distributions from individual source posterior distributions)
https://github.com/JohannesBuchner/PosteriorStacker - example_advanced_priors.py (shows how to include custom priors in BXA)
https://github.com/JohannesBuchner/BXA/blob/master/examples/xspec/example_advanced_priors.py
Citations
- Xspec (& PyXspec) citation - Arnaud et al., (1996; ASPC):
https://ui.adsabs.harvard.edu/abs/1996ASPC..101...17A/abstract - Sherpa citation - Freeman et al., (2001; SPIE):
https://ui.adsabs.harvard.edu/abs/2001SPIE.4477...76F/abstract In CIAO 4.13, you can use this command to get information on the latest release:sherpa> sherpa.citation('latest') - BXA citation - Buchner et al., (2014; A&A)
https://ui.adsabs.harvard.edu/abs/2014A%26A...564A.125B/abstract - UltraNest citation - Buchner (2021, JOSS)
https://arxiv.org/abs/2101.09604