Skip to main content

Bayesian Parameter Inference for SNe Ia Data

  • Chapter
  • First Online:
Advanced Statistical Methods for Astrophysical Probes of Cosmology

Part of the book series: Springer Theses ((Springer Theses))

  • 705 Accesses

Abstract

Whilst carrying out the work for Bayesian Doubt described in chap. 6, two problems became apparent with the supernovae type Ia data:

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Notice that we neglect correlations between different SNIa, which is reflected in the fact that \(\Sigma _C\) takes a block-diagonal form. It would be however very easy to add arbitrary cross-correlations to our formalism (e.g., coming from correlated systematic within survey, for example zero point calibration) by adding such non-block diagonal correlations to Eq. (7.101).

References

  1. Astier, P. and Guy, J.: 2006, A & A 447, 31.

    Google Scholar 

  2. Kowalski, M., Rubin, D., Aldering, G., Agostinho, R. J., & Amadon, A. (2008). ApJ, 686, 749.

    Google Scholar 

  3. Kessler, R., Becker, A. C., and Cinabro: 2009a, ApJS 185, 32.

    Google Scholar 

  4. Tripp, R.: 1998, A & A 331, 815.

    Google Scholar 

  5. D’Agostini, G.: 2005, arXiv:0511182.

    Google Scholar 

  6. Gull, S.: 1989, Maximum Entropy and Bayesian, Methods pp 511–518.

    Google Scholar 

  7. March, M. C., Trotta, R., Berkes, P., Starkman, G. D., & Vaudrevange, P. M. (2011c). MNRAS, 418, 2308.

    Google Scholar 

  8. Sivia, D., & Skilling, J. (2006). Data Analysis. A Bayesian Tutorial: Oxford University Press.

    Google Scholar 

  9. D’Agostini, G.: 1995, arXiv:hep-ph/9512295.

    Google Scholar 

  10. Miknaitis, G., & Pignata, G. (2007). ApJ, 666, 674.

    Google Scholar 

  11. Wood-Vasey, W. M., Miknaitis, G., & Stubbs, C. W. (2007). ApJ, 666, 694.

    Google Scholar 

  12. Jha, S., Riess, A. G., & Kirshner, R. P. (2007). ApJ, 659, 122.

    Google Scholar 

  13. Garnavich, P. M., Kirshner, R. P., and Challis, P. a.: 1998, ApJL 493, L53+.

    Google Scholar 

  14. Knop, R. A., Aldering, G., Amanullah, R., & Astier, P. (2003). ApJ, 598, 102.

    Google Scholar 

  15. Riess, A. G., & Strolger, L. (2004). ApJ, 607, 665.

    Google Scholar 

  16. Riess, A. G., & Strolger, L. (2007). ApJ, 659, 98.

    Google Scholar 

  17. Kessler, R., Bernstein, J. P., Cinabro, D., Dilday, B., Frieman, J. A., Jha, S., et al. (2009b). PASP, 121, 1028.

    Google Scholar 

  18. Freedman, W. L., Madore, B. F., Gibson, B. K., Ferrarese, L., Kelson, D. D., Sakai, S., et al. (2001). ApJ, 553, 47.

    Google Scholar 

  19. Skilling, J. (ed.): 2004, Nested Sampling, Vol. 735 of American Institute of Physics Conference Series.

    Google Scholar 

  20. Skilling, J. (2006). Bayesian Analysis, 1, 833.

    Google Scholar 

  21. Feroz, F., & Hobson, M. P. (2008b). MNRAS, 384, 449.

    Google Scholar 

  22. Feroz, F., Hobson, M. P., & Bridges, M. (2009b). MNRAS, 398, 1601.

    Google Scholar 

  23. Feroz, F., Cranmer, K., Hobson, M., de Austri, R. R., and Trotta, R.: 2011, arXiv:1101.3296.

    Google Scholar 

  24. Komatsu, E., Dunkley, J., Nolta, M. R., Bennett, C. L., Gold, B., Hinshaw, G., et al. (2009). ApJS, 180, 330.

    Google Scholar 

  25. Eisenstein, D. J., Zehavi, I., Hogg, D. W., Scoccimarro, R., Blanton, M. R., Nichol, R. C., et al. (2005). ApJ, 633, 560.

    Google Scholar 

  26. Wiseman, T. and Withers, B.: 2010, ArXiv e-prints.

    Google Scholar 

  27. Schwarz, D. J. and Weinhorst, B.: 2007, A & A 474, 717.

    Google Scholar 

  28. Antoniou, I., & Perivolaropoulos, L. (2010). JCAP, 12, 12.

    Google Scholar 

  29. Cooke, R., & Lynden-Bell, D. (2010). MNRAS, 401, 1409.

    Google Scholar 

  30. Webb, J. K., King, J. A., Murphy, M. T., Flambaum, V. V., Carswell, R. F., and Bainbridge, M. B.: 2010, ArXiv e-prints.

    Google Scholar 

  31. Sullivan, M., et al. (2006). Astrophys. J., 648, 868.

    Google Scholar 

  32. Mandel, K. S., Narayan, G., and Kirshner, R. P.: 2010, arXiv:1011.5910.

    Google Scholar 

  33. Sullivan, M., et al. (2011). Astrophys. J., 737, 102.

    Google Scholar 

  34. Diaferio, A., Ostorero, L., and Cardone, V. F.: 2011, arXive:astro-ph/1103.5501.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marisa Cristina March .

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

March, M.C. (2013). Bayesian Parameter Inference for SNe Ia Data. In: Advanced Statistical Methods for Astrophysical Probes of Cosmology. Springer Theses. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35060-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-35060-3_7

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35059-7

  • Online ISBN: 978-3-642-35060-3

  • eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)

Publish with us

Policies and ethics