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Bayesian Template-Based Approach to Classifying SDSS-II Supernovae from 3-Year Survey

Bayesian Template-Based Approach to Classifying SDSS-II Supernovae from 3-Year Survey. Brian Connolly Photometric Supernova ID Workshop 3/16/12. Outline. The Goal PSNID SDSS-II 3-year Supernova Survey Analysis Results Comments Conclusions. Goal.

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Bayesian Template-Based Approach to Classifying SDSS-II Supernovae from 3-Year Survey

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  1. Bayesian Template-Based Approach to Classifying SDSS-II Supernovae from 3-Year Survey Brian Connolly Photometric Supernova ID Workshop 3/16/12

  2. Outline • The Goal • PSNID • SDSS-II 3-year Supernova Survey • Analysis • Results • Comments • Conclusions

  3. Goal “Use the 3 year SDSS-II survey as a test bed to identify photometric SN Ia candidates [using a Bayesian methodology] with realistic estimates of purity” (obtained by using spectroscopically confirmed Ia’s)

  4. Plan • Estimate purity and efficiency (FoM) with spectroscopic sample • Type photometric sample assuming these estimates • Results (e.g., see if we can do cosmology)

  5. Motivation for Bayesian Approach • c2(Ia) simply deviation from Ia hypothesis • Estimate p-values, tail probabilities, severely underestimate Type I error rate • No alternative included (not true with other classical tests) • No alternative exists in classical approach • Gives you want you want (derived from logically consistent framework) • Includes information about lesser fits vs Thomas Bayes Ronald Fischer

  6. PSNID • Simplest Template-Based Bayesian Classifier • Directly sum over all templates for all parameters and all types to find

  7. Evidences Likelihoods ** c2 includes uncertainties in the model (which give good c2/dof for high S/N)

  8. Priors • Host-z: or flat • Type • Milky Way RV=3.1, SN RV=2.2 • AV, Tmax, m Flat

  9. Idea Kuznetsova and Connolly (2007) advocated using knowledge of P(Ia) in addition to best fit. Plot P(Ia) vs. cr, find region in spec confirmed sample that maximizes purity and efficiency in photometric sample

  10. SDSS-II Three Year Supernova Survey • Sept-Nov 2005-2007 • 300 deg2 region along celestial equator using 2.5m telescope • ugriz • 0.1<z<0.4 • Cadence 4 days (average) • >10,000 new variable and transients in differenced images • Small number ID-ed as Ia’s PSNID and spec confirmed • Largest uniform sample of SN candidates to date to study classification (3221 photometric candidates pass quality cuts, 2776 no spec observations) SPLIT SAMPLE INTO SPECTROSCOPICALLY CONFIRMEDANDUNCONFIRMED

  11. Templates • Ia’s: Sako et al. (2008) • CC: start with Nugent, Nugent et al. (2002), near SDSS light curves-II, D’Andrea et al. (2010) and choose those that maximize Ia purity (and efficiency) 24→8 CC templates

  12. Marginalized AV and m distributions factor of 2 larger than w/ spec. redshift prior Spec. Confirmed Ia 2006jz, z=0.2

  13. * Parameter estimation done with MCMC Spec. Confirmed Ia 2005it, z=0.3

  14. Spectroscopically Confirmed Sample 367 SNIa 45 CC 83 AGN ----------- 495 PSNID Analysis w/out Host Z 508 SNIa 80 CC 202 AGN PSNID Analysis w Host Z 551 Candidates • Small number of CC’s, account for this by comparing how galaxies targeted (have well defined selection criteria) -5<t<5 one epoch >15 days S/N>5 in two gri bands One search season

  15. Cuts on Confirmed Sample (candidates near SDSS galaxy spectrum)

  16. FoM True Ia’s ID-ed as Ia’s (1) True Ia’s after cuts Different for spec and photo sample (2) (Contamination) (3)

  17. Results

  18. Spectroscopically Unconfirmed Sample • 3221 candidates • 2776 no host-z • 445 with host-z

  19. Unconfirmed Sample Total Unconfirmed Sample 860 candidates 94% Purity 92% Efficiency c2 optimized for P(Ia)>0.9

  20. Toward a Hubble Diagram

  21. Hubble Diagram

  22. Comments

  23. SN Challenge

  24. pSNid II • Template fitting • Classification schemes • “Classical” • Color • Rising light curves • Sequential analysis • Input: ascii, FITS, database • Output: ascii

  25. Software Package:pSNid II Does It All JPAS Study www.sas.upenn.edu/~brianco/psnid

  26. Comments • Reduced c2 taken with grain of salt • Start cutting in parameter space giving up purely Bayesian framework (Kunz et al., loss functions, etc.) • Can set purity and efficiency independently – SEQUENTIAL ANALYSIS METHODS Milton Friedman

  27. Conclusions • Described method of photometrically classifying a large SN sample with the help of a small spectroscopic subsample • 1070 photometric SN Ia candidates from the SDSS-II SN Survey data • 94% purity and 6% contamination • Hubble diagram • Eliminating AV>1 eliminates problems M. Sako et al.m, The Astrophysical Journal, Volume 738, Issue 2, article id. 162 (2011).

  28. backup

  29. Tuning W to get Purity/Efficiency Correct in Photometric Sample • Ia/CC ratio incorrect as Ia’s mag limited • Choose a mag-limited sample of those SNe with galaxy spectra • Take ratio of W=[P(Ia)>0.9]/[P(Ia)<0.1]

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