Useful links

  • Welcome
  • Why use BXA?
  • Setup
  • Download spectra
  • Session 0 - X-ray spectral fitting
  • 0.1 - binning data
  • 0.2 - loading data
  • 0.3 - plotting
  • 0.4 - fitting
  • 0.5 - simulating data
  • Session 1 - BXA
  • 1.1 - prior predictive checks
  • 1.2 - fitting a model
  • 1.3 - fit some more models
  • 1.4 - error propagation
  • 1.5 - fit multiple datasets
  • 1.6 - background fitting
  • Session 2 - model checking
  • 2.1 - visualisation
  • 2.2 - quantile-quantile checks
  • 2.3 - posterior predictive checks
  • Session 3 - model comparison
  • 3.1 - Bayes factors
  • 3.2 - type I errors
  • 3.3 - type II errors
  • 3.4 - extension
  • Session 4 - parent distributions
  • 4.1 - generating data
  • 4.2 - Gaussian model
  • 4.3 - histogram model
  • Session 5 - advanced usage
  • 5.1 - information gain
  • 5.2 - custom priors
  • 5.3 - speeding up BXA
  • Tips & tricks
  • Useful links
  • Contact
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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

Lectures & videos

  • BiD4BEST Bayesian X-ray Spectral Analysis lectures by Johannes Buchner:
    1. https://youtu.be/_r4ecpje7nE
    2. https://youtu.be/EIzhDG-0Ji8
  • Graduate-level course: Monte Carlo inference methods by Johannes Buchner:
    1. Bayesian inference workflow in astronomy and physics:
      https://youtu.be/D2P6xBR_2bQ
    2. Bayesian model comparison, curse of dimensionality, Importance Sampling:
      https://youtu.be/TfaGfFnsmW8
    3. Markov Chain Monte Carlo (MCMC) and diagnostics:
      https://youtu.be/YA6Ezh8CsrY
    4. Hamiltonian Monte Carlo (HMC) by Dr. Francesca Capel:
      https://youtu.be/nmHGyXsiaeI
    5. Nested Sampling from scratch - Practical Inference for Researchers in the Physical Sciences:
      https://youtu.be/baLFl_4ZwXw
    6. 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:
    • https://youtu.be/c1pMDhKFAWA
  • 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
  • Do you know of any others? Please get in touch!
  • Previous: Tips & tricks

    Next: Welcome

    Contact

    Raising issues


    • BXA: github.com/JohannesBuchner/BXA/issues
    • UltraNest: github.com/JohannesBuchner/UltraNest/issues

    Email


    • Peter Boorman: boorman[at]caltech[dot]edu
    • Johannes Buchner: jbuchner[at]mpe[dot]mpg[dot]de