1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Statistics and Its Application
  4. Volume 2, 2015
  5. Article

Annual Review of Statistics and Its Application

Volume 2, 2015

A Framework for Statistical Inference in Astrophysics

Abstract

The rapid growth of astronomical data sets, coupled with the complexity of the questions scientists seek to answer with these data, creates an increasing need for the utilization of advanced statistical inference methods in astrophysics. Here, focus is placed on situations in which the underlying objective is the estimation of cosmological parameters, the key physical constants that characterize the Universe. Owing to the complex relationship between these parameters and the observable data, this broad inference goal is best divided into three stages. The primary objective of this article is to describe these stages and thus place into a coherent framework the class of inference problems commonly encountered by those working in this field. Examples of such inference challenges are presented.

Keyword(s): astrophysics, complex data, complex models, measurement error, parameter estimation
Loading

Article metrics loading...

/content/journals/10.1146/annurev-statistics-022513-115538
2015-04-10
2024-04-25
Loading full text...

Full text loading...

/deliver/fulltext/statistics/2/1/annurev-statistics-022513-115538.html?itemId=/content/journals/10.1146/annurev-statistics-022513-115538&mimeType=html&fmt=ahah

Literature Cited

  1. Ade PAR, Aghanim N, Alves MIR, Armitage-Caplan C, Arnaud M. 2014a. Planck 2013 results. I. Overview of products and scientific results. Astron. Astrophys. 571:A1 [Google Scholar]
  2. Ade PAR, Aghanim N, Armitage-Caplan C, Arnaud M, Ashdown M. et al. (Planck Collab.) 2014b. Planck 2013 results. XV. CMB power spectra and likelihood. Astron. Astrophys 571:A15 [Google Scholar]
  3. Ade PAR, Aghanim N, Armitage-Caplan C, Arnaud M, Ashdown M. et al. (Planck Collab.) 2014c. Planck 2013 results. XVI. Cosmological parameters. Astron. Astrophys 571:A16 [Google Scholar]
  4. Ade PAR, Aikin RW, Barkats D, Benton SJ, Bischoff CA. et al. (BICEP2 Collab.) 2014. BICEP2 I: detection of B-mode polarization at degree angular scales. Phys. Rev. Lett. 112:241101 [Google Scholar]
  5. Ahn CP, Alexandroff R, Allende Prieto C, Anders F. et al. 2013. The tenth data release of the Sloan Digital Sky Survey: first spectroscopic data from the SDSS-III Apache Point Observatory Galactic Evolution Experiment. arXiv:1307.7735 [astro-ph.IM]
  6. Akritas MG, Bershady MA. 1996. Linear regression for astronomical data with measurement errors and intrinsic scatter. Astrophys. J. 470:706 [Google Scholar]
  7. Angulo RE, Springel V, White SDM, Jenkins A, Baugh CM, Frenk CS. 2012. Scaling relations for galaxy clusters in the Millennium-XXL simulation. Mon. Not. R. Astron. Soc. 426:2046–62 [Google Scholar]
  8. Avni Y. 1976. Energy spectra of X-ray clusters of galaxies. Astrophys. J 210:642–46 [Google Scholar]
  9. Ball NM, Brunner RJ, Myers AD, Strand NE, Alberts SL, Tcheng D. 2008. Robust machine learning applied to astronomical data sets. III. Probabilistic photometric redshifts for galaxies and quasars in the SDSS and GALEX. Astrophys. J 683:12–21 [Google Scholar]
  10. Barro G, Pérez-González PG, Gallego J, Ashby MLN, Kajisawa M. et al. 2011. UV-to-FIR analysis of Spitzer/IRAC sources in the extended Groth strip. II. Photometric redshifts, stellar masses, and star formation rates. Astrophys. J. Suppl. 193:30 [Google Scholar]
  11. Benítez N. 2000. Bayesian photometric redshift estimation. Astrophys. J. 536:571–83 [Google Scholar]
  12. Bennett CL, Banday AJ, Gorski KM, Hinshaw G, Jackson P. et al. 1996. Four-Year COBE DMR cosmic microwave background observations: maps and basic results. Astrophys. J. Lett. 464:L1 [Google Scholar]
  13. Bennett CL, Larson D, Weiland JL, Jarosik N, Hinshaw G. et al. 2013. Nine-year Wilkinson Microwave Anisotropy Probe (WMAP) observations: final maps and results. Astrophys. J. Suppl. 208:20 [Google Scholar]
  14. Bentley JL. 1975. Multidimensional binary search trees used for associative searching. Commun. ACM 18:509–17 [Google Scholar]
  15. Bevington P. 1969. Data Reduction and Error Analysis for the Physical Sciences New York: McGraw-Hill
  16. Binney J, Merrifield M. 1998. Galactic Astronomy Princeton, NJ: Princeton University Press
  17. Blinnikov SI, Röpke FK, Sorokina EI, Gieseler M, Reinecke M. et al. 2006. Theoretical light curves for deflagration models of type Ia supernova. Astron. Astrophys. 453:229–40 [Google Scholar]
  18. Blocker A, Protopapas P. 2013. Semi-parametric robust event detection for massive time-domain databases. Statistical Challenges in Modern Astronomy V ED Feigelson, GJ Babu 177–87 New York: Springer [Google Scholar]
  19. Bloom JS, Richards JW, Nugent PE, Quimby RM, Kasliwal MM. et al. 2012. Automating discovery and classification of transients and variable stars in the synoptic survey era. Publ. Astron. Soc. Pac. 124:1175–96 [Google Scholar]
  20. Bolton AS, Schlegel DJ, Aubourg É, Bailey S, Bhardwaj V. et al. 2012. Spectral classification and redshift measurement for the SDSS-III Baryon Oscillation Spectroscopic Survey. Astron. J. 144:144 [Google Scholar]
  21. Boylan-Kolchin M, Springel V, White SDM, Jenkins A, Lemson G. 2009. Resolving cosmic structure formation with the Millennium-II Simulation. Mon. Not. R. Astron. Soc. 398:1150–64 [Google Scholar]
  22. Boyle BJ, Shanks T, Croom SM, Smith RJ, Miller L. et al. 2000. The 2df QSO Redshift Survey—I. The optical luminosity function of quasi-stellar objects. Mon. Not. R. Astron. Soc. 317:1014–22 [Google Scholar]
  23. Boyle BJ, Shanks T, Peterson BA. 1988. The evolution of optically selected QSOs. II. Mon. Not. R. Astron. Soc. 235:935–48 [Google Scholar]
  24. Budavári T. 2009. A unified framework for photometric redshifts. Astrophys. J. 695:747–54 [Google Scholar]
  25. Butchins SA. 1981. Predicted redshifts of galaxies by broadband photometry. Astron. Astrophys. 97:407–9 [Google Scholar]
  26. Cabella P, Marinucci D. 2009. Statistical challenges in the analysis of cosmic microwave background radiation. Ann. Appl. Stat. 3:61–95 [Google Scholar]
  27. Cappé O, Douc R, Guillin A, Marin JM, Robert CP. 2008. Adaptive importance sampling in general mixture classes. Stat. Comput. 18:447–59 [Google Scholar]
  28. Carliles S, Budavári T, Heinis S, Priebe C, Szalay AS. 2010. Random forests for photometric redshifts. Astrophys. J. 712:511–15 [Google Scholar]
  29. Carrasco Kind M, Brunner RJ. 2013. TPZ: photometric redshift PDFs and ancillary information by using prediction trees and random forests. Mon. Not. R. Astron. Soc. 432:1483–501 [Google Scholar]
  30. Chatzopoulos E, Wheeler JC, Vinko J. 2012. Generalized semi-analytical models of supernova light curves. Astrophys. J. 746:121 [Google Scholar]
  31. Christensen N, Meyer R, Knox L, Luey B. 2001. Bayesian methods for cosmological parameter estimation from cosmic microwave background measurements. Class. Quantum Gravity 18:2677–88 [Google Scholar]
  32. Collister AA, Lahav O. 2004. ANNz: estimating photometric redshifts using artificial neural networks. Publ. Astron. Soc. Pac. 116:345–51 [Google Scholar]
  33. Connolly AJ, Csabai I, Szalay AS, Koo DC, Kron RG, Munn JA. 1995. Slicing through multicolor space: galaxy redshifts from broadband photometry. Astron. J. 110:2655 [Google Scholar]
  34. Csabai I, Dobos L, Trencséni M, Herczegh G, Józsa P. et al. 2007. Multidimensional indexing tools for the virtual observatory. Astron. Nachr. 328:852 [Google Scholar]
  35. Cunha CE, Lima M, Oyaizu H, Frieman J, Lin H. 2009. Estimating the redshift distribution of photometric galaxy samples II. Applications and tests of a new method. Mon. Not. R. Astron. Soc. 396:2379–98 [Google Scholar]
  36. Dahlen T, Mobasher B, Faber SM, Ferguson HC, Barro G. et al. 2013. A critical assessment of photometric redshift methods: a CANDELS investigation. Astrophys. J. 775:93 [Google Scholar]
  37. Efron B, Petrosian V. 1999. Nonparametric methods for doubly truncated data. J. Am. Stat. Assoc. 94:824–34 [Google Scholar]
  38. Efstathiou G. 2004. Myths and truths concerning estimation of power spectra: the case for a hybrid estimator. Mon. Not. R. Astron. Soc. 349:603–26 [Google Scholar]
  39. Eisenstein DJ, Weinberg DH, Agol E, Aihara H, Allende Prieto C. et al. 2011. SDSS-III: massive spectroscopic surveys of the distant Universe, the Milky Way, and extra-solar planetary systems. Astron. J. 142:72 [Google Scholar]
  40. Firth AE, Lahav O, Somerville RS. 2003. Estimating photometric redshifts with artificial neural networks. Mon. Not. R. Astron. Soc. 339:1195–202 [Google Scholar]
  41. Freeman PE, Newman JA, Lee AB, Richards JW, Schafer CM. 2009. Photometric redshift estimation using spectral connectivity analysis. Mon. Not. R. Astron. Soc. 398:2012–21 [Google Scholar]
  42. Genovese CR, Freeman P, Wasserman L, Nichol RC, Miller C. 2009. Inference for the dark energy equation of state using type Ia supernova data. Ann. Appl. Stat. 3:144–78 [Google Scholar]
  43. Genovese CR, Miller CJ, Nichol RC, Arjunwadkar M, Wasserman L. 2004. Nonparametric inference for the cosmic microwave background. Stat. Sci. 19:308–21 [Google Scholar]
  44. Gerdes DW, Sypniewski AJ, McKay TA, Hao J, Weis MR. et al. 2010. ArborZ: photometric redshifts using boosted decision trees. Astrophys. J. 715:823–32 [Google Scholar]
  45. Gilbank DG, Baldry IK, Balogh ML, Glazebrook K, Bower RG. 2010. The local star formation rate density: assessing calibrations using [OII], Hα and UV luminosities. Mon. Not. R. Astron. Soc. 405:2594–614 [Google Scholar]
  46. Górski KM, Hivon E, Banday AJ, Wandelt BD, Hansen FK. et al. 2005. HEALPix: a framework for high-resolution discretization and fast analysis of data distributed on the sphere. Astrophys. J. 622:759–71 [Google Scholar]
  47. Grogin NA, Kocevski DD, Faber SM, Ferguson HC, Koekemoer AM. et al. 2011. CANDELS: The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey. Astrophys. J. Suppl. 197:35 [Google Scholar]
  48. Gunn JE, Carr M, Rockosi C, Sekiguchi M, Berry K. et al. 1998. The Sloan Digital Sky Survey photometric camera. Astron. J. 116:3040–81 [Google Scholar]
  49. Guo Q, White S, Boylan-Kolchin M, De Lucia G, Kauffmann G. et al. 2011. From dwarf spheroidals to cD galaxies: simulating the galaxy population in a ΛCDM cosmology. Mon. Not. R. Astron. Soc. 413:101–31 [Google Scholar]
  50. Guth AH. 1981. Inflationary universe: a possible solution to the horizon and flatness problems. Phys. Rev. D 23:347–56 [Google Scholar]
  51. Hivon E, Górski KM, Netterfield CB, Crill BP, Prunet S, Hansen F. 2002. MASTER of the cosmic microwave background anisotropy power spectrum: a fast method for statistical analysis of large and complex cosmic microwave background data sets. Astrophys. J. 567:2–17 [Google Scholar]
  52. Hubble E. 1929. A relation between distance and radial velocity among extra-galactic nebulae. PNAS 15:168–73 [Google Scholar]
  53. Ivezic Z, Tyson JA, Acosta E, Allsman R, Anderson SF. et al. 2014. LSST: from science drivers to reference design and anticipated data products. arXiv:0805.2366 [astro-ph]
  54. Jha S, Kirshner RP, Challis P, Garnavich PM, Matheson T. et al. 2006. UBVRI light curves of 44 type Ia supernovae. Astron. J. 131:527–54 [Google Scholar]
  55. Kelly BC, Fan X, Vestergaard M. 2008. A flexible method of estimating luminosity functions. Astrophys. J. 682:874–95 [Google Scholar]
  56. Kessler R, Bassett B, Belov P, Bhatnagar V, Campbell H. et al. 2010a. Results from the Supernova Photometric Classification Challenge. Publ. Astron. Soc. Pac. 122:1415–31 [Google Scholar]
  57. Kessler R, Bernstein JP, Cinabro D, Dilday B, Frieman JA. et al. 2009. SNANA: a public software package for supernova analysis. Publ. Astron. Soc. Pac. 121:1028–35 [Google Scholar]
  58. Kessler R, Conley A, Jha S, Kuhlmann S. 2010b. Supernova Photometric Classification Challenge. arXiv:1001.5210 [astro-ph.IM]
  59. Kilbinger M, Benabed K, Cappé O, Cardoso J-F, Coupon J. et al. 2011. CosmoPMC: cosmology population Monte Carlo. arXiv:1101.0950 [astro-ph.CO]
  60. Knox L, Christensen N, Skordis C. 2001. The age of the Universe and the cosmological constant determined from cosmic microwave background anisotropy measurements. Astrophys. J. Lett. 563:L95–98 [Google Scholar]
  61. Koekemoer AM, Faber SM, Ferguson HC, Grogin NA, Kocevski DD. et al. 2011. CANDELS: the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey—the Hubble Space Telescope observations, imaging data products, and mosaics. Astrophys. J. Suppl. 197:36 [Google Scholar]
  62. Koo DC. 1985. Optical multicolors: a poor person's Z machine for galaxies. Astron. J. 90:418–40 [Google Scholar]
  63. Kosowsky A, Milosavljevic M, Jimenez R. 2002. Efficient cosmological parameter estimation from microwave background anisotropies. Phys. Rev. D 66:063007 [Google Scholar]
  64. Large Synoptic Survey Telescope (LSST) Sci. Collab., LSST Proj 2009. LSST Science Book, Version 2.0 arXiv:0912.0201 [astro-ph.IM]. http://www.lsst.org/lsst/scibook
  65. Laurino O, D'Abrusco R, Longo G, Riccio G. 2011. Astroinformatics of galaxies and quasars: a new general method for photometric redshifts estimation. Mon. Not. R. Astron. Soc. 418:2165–95 [Google Scholar]
  66. Lee H, Kashyap VL, van Dyk DA, Connors A, Drake JJ. et al. 2011. Accounting for calibration uncertainties in X-ray analysis: effective areas in spectral fitting. Astrophys. J. 731:126 [Google Scholar]
  67. Lewis A. 2013. Efficient sampling of fast and slow cosmological parameters. Phys. Rev. D 87:103529 [Google Scholar]
  68. Lewis A, Bridle S. 2002. Cosmological parameters from CMB and other data: a Monte Carlo approach. Phys. Rev. D 66:103511 [Google Scholar]
  69. Lima M, Cunha CE, Oyaizu H, Frieman J, Lin H, Sheldon ES. 2008. Estimating the redshift distribution of photometric galaxy samples. Mon. Not. R. Astron. Soc. 390:118–30 [Google Scholar]
  70. Lomb NR. 1976. Least-squares frequency analysis of unequally spaced data. Astrophys. Space Sci. 39:447–62 [Google Scholar]
  71. Loredo T. 2013. Bayesian astrostatistics: a backward look to the future. Astrostatistical Challenges for the New Astronomy JM Hilbe 15–40 New York: Springer [Google Scholar]
  72. Lynden-Bell D. 1971. A method of allowing for known observational selection in small samples applied to 3CR quasars. Mon. Not. R. Astron. Soc. 155:95–118 [Google Scholar]
  73. Mahabal AA, Djorgovski SG, Drake AJ, Donalek C, Graham MJ. et al. 2011. Discovery, classification, and scientific exploration of transient events from the Catalina Real-time Transient Survey. Bull. Astron. Soc. India 39:387–408 [Google Scholar]
  74. Marinucci D. 2004. Testing for non-Gaussianity on cosmic microwave background radiation: a review. Stat. Sci. 19:294–307 [Google Scholar]
  75. Marshall HL, Huchra JP, Tananbaum H, Avni Y, Braccesi A. et al. 1984. A complete sample of quasars at B=19.80. Astrophys. J. 283:50–58 [Google Scholar]
  76. Matthews TA, Sandage AR. 1963. Optical identification of 3C 48, 3C 196, and 3C 286 with stellar objects. Astrophys. J. 138:30–56 [Google Scholar]
  77. Montero-Dorta AD, Prada F. 2009. The SDSS DR6 luminosity functions of galaxies. Mon. Not. R. Astron. Soc 399:1106–18 [Google Scholar]
  78. Pâris I, Petitjean P, Aubourg É, Ross NP, Myers AD. et al. 2014. The Sloan Digital Sky Survey quasar catalog: tenth data release. Astron. Astrophys. 563:A54 [Google Scholar]
  79. Park T, Kashyap VL, Siemiginowska A, van Dyk DA, Zezas A. et al. 2006. Bayesian estimation of hardness ratios: modeling and computations. Astrophys. J. 652:610–28 [Google Scholar]
  80. Pei YC. 1995. The luminosity function of quasars. Astrophys. J. 438:623–31 [Google Scholar]
  81. Press WH, Schechter P. 1974. Formation of galaxies and clusters of galaxies by self-similar gravitational condensation. Astrophys. J. 187:425–38 [Google Scholar]
  82. Richards GT, Strauss MA, Fan X, Hall PB, Jester S. et al. 2006. The Sloan Digital Sky Survey quasar survey: quasar luminosity function from Data Release 3. Astron. J. 131:2766–87 [Google Scholar]
  83. Richards JW, Starr DL, Brink H, Miller AA, Bloom JS. et al. 2012. Active learning to overcome sample selection bias: application to photometric variable star classification. Astrophys. J. 744:192 [Google Scholar]
  84. Sarjeant S. 2010. Observational Cosmology Cambridge, UK: Cambridge Univ. Press
  85. Scargle JD. 1982. Studies in astronomical time series analysis. II. Statistical aspects of spectral analysis of unevenly spaced data. Astrophys. J. 263:835–53 [Google Scholar]
  86. Schafer CM. 2007. A statistical method for estimating luminosity functions using truncated data. Astrophys. J. 661:703–13 [Google Scholar]
  87. Schechter P. 1976. An analytic expression for the luminosity function for galaxies. Astrophys. J. 203:297–306 [Google Scholar]
  88. Schmidt M. 1968. Space distribution and luminosity functions of quasi-stellar radio sources. Astrophys. J. 151:393–409 [Google Scholar]
  89. Schneider DP, Richards GT, Fan X, Hall PB, Strauss MA. et al. 2002. The Sloan Digital Sky Survey quasar catalog. I. Early data release. Astron. J 123:567–77 [Google Scholar]
  90. Schneider MD, Knox L, Habib S, Heitmann K, Higdon D, Nakhleh C. 2008. Simulations and cosmological inference: a statistical model for power spectra means and covariances. Phys. Rev. D 78:063529 [Google Scholar]
  91. Seljak U, Zaldarriaga M. 1996. A line-of-sight integration approach to cosmic microwave background anisotropies. Astrophys. J. 469:437–44 [Google Scholar]
  92. Seljak U, Zaldarriaga M. 1999. CMBFAST: a microwave anisotropy code. Astrophys. Source Code Libr. http://ascl.net/9909.004
  93. Sheldon ES, Cunha CE, Mandelbaum R, Brinkmann J, Weaver BA. 2012. Photometric redshift probability distributions for galaxies in the SDSS DR8. Astrophys. J. Suppl. 201:32 [Google Scholar]
  94. Sparke LS, Gallagher JS. 2007. Galaxies in the Universe: An Introduction Cambridge, UK: Cambridge Univ. Press, 2nd ed..
  95. Springel V. 2005. The cosmological simulation code GADGET-2. Mon. Not. R. Astron. Soc. 364:1105–34 [Google Scholar]
  96. Springel V, White SDM, Jenkins A, Frenk CS, Yoshida N. et al. 2005. Simulating the joint evolution of quasars, galaxies and their large-scale distribution. Nature 435:629–36 [Google Scholar]
  97. Tully RB, Fisher JR. 1977. A new method of determining distances to galaxies. Astron. Astrophys. 54:661–73 [Google Scholar]
  98. Verde L, Peiris HV, Spergel DN, Nolta MR, Bennett CL. et al. 2003. First-year Wilkinson Microwave Anisotropy Probe (WMAP) observations: parameter estimation methodology. Astrophys. J. Suppl. 148:195–211 [Google Scholar]
  99. Weyant A, Schafer C, Wood-Vasey WM. 2013. Likelihood-free cosmological inference with type Ia supernovae: approximate Bayesian computation for a complete treatment of uncertainty. Astrophys. J. 764:116 [Google Scholar]
  100. Woodroofe M. 1985. Estimating a distribution function with truncated data. Ann. Stat. 13:163–77 [Google Scholar]
  101. Woosley SE, Kasen D, Blinnikov S, Sorokina E. 2007. Type Ia supernova light curves. Astrophys. J. 662:487–503 [Google Scholar]
  102. Wraith D, Kilbinger M, Benabed K, Cappé O, Cardoso JF. et al. 2009. Estimation of cosmological parameters using adaptive importance sampling. Phys. Rev. D 80:023507 [Google Scholar]
  103. Xia L, Cohen S, Malhotra S, Rhoads J, Grogin N. et al. 2009. Improved photometric redshifts with surface luminosity priors. Astron. J. 138:95–101 [Google Scholar]
  104. Zhang Y, Ma H, Peng N, Zhao Y, Wu X-B. 2013. Estimating photometric redshifts of quasars via the k–nearest neighbor approach based on large survey databases. Astron. J. 146:22 [Google Scholar]
/content/journals/10.1146/annurev-statistics-022513-115538
Loading
A Framework for Statistical Inference in Astrophysics
Annual Review of Statistics and Its Application 2, 141 (2015); https://doi.org/10.1146/annurev-statistics-022513-115538
/content/journals/10.1146/annurev-statistics-022513-115538
/content/journals/10.1146/annurev-statistics-022513-115538
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error