Building a Photometric Hubble Diagram from DES-SN Data

Transcription

1 Building a Photometric Hubble Diagram from DES-SN Data R. Kessler University of Chicago SN ClassificaCon Workshop April 28 26

2 The Dark Energy Survey Survey project using 4 complementary techniques: I. Cluster Counts II. Weak Lensing III. Large-scale Structure IV. Supernovae Two multiband imaging surveys: 5 deg 2 grizy to 24th mag 3 deg 2 repeat griz (SNe) DECam on the Blanco New 3 deg 2 FOV camera on the Blanco 4m telescope Survey (525 nights) Facility instrument for astronomy community (DES 3% time) Largest optical etendue (FoV x mirror-area) 2

3 DES-SN Overview Visit each field once per week in griz 8 shallow fields (24 deg 2 ), depth deep fields (6 deg 2 ), depth 24.5 Finished three `5-month seasons 3

4 Source of Photometry (Y+Y2+Y3) Forced Photometry (PSF-fi_ed fluxes) from Difference-Imaging Pipeline used to discover transients Subtracted images from A.Becker s hotpants CalibraCon tweaked few percent based on overlap of PS supercal (Scolnic et al 25) More refined photometry (Scene-Modeling) in progress, but not ready yet. 4

5 Source of Redshies Host Galaxy Spectroscopic redshies, mostly from OzDES using AAT (Yuan 25) Matching algorithm (DLR) from Gupta et al, 26 (arxiv:64.638) 5

6 SimulaCons ClassificaCon and HD Fikng make extensive use of SNANA simulacons. Simulate SNIa with SALT2 model (α SIM =.4, β SIM =3.2; c,x populacon from SK6) and intrinsic sca_er models that include chromacc variacons. Simulate CC based on 42 templates (from photometric challenge, 2). CC Rate, Ibc/II raco, and LF from Li 2. 6

7 Pre-Analysis PreparaCon: Analyze Fake SNIa Overlaid on Images Are photometry errors correct? à Yes, aeer correccng for surface brightness (see Figs 9- in arxiv:57.537) Does fast SNANA sim reproduce fake SNe? à yes, aeer scaling simulated error vs. PSF (see Sim-validaCon slides to follow) 7

8 Sim-ValidaCon: Overview SN Ia Fakes DiffImg Pipeline (weeks) SNANA SimulaCon (minutes) Analysis & fikng Compare DiffImg Fakes to SNANA sim 8

9 Empirical Flux-Error Adjustment for SNANA sim.5 Shallow Fields, Y+Y2.5 err(forcephoto) / err(snana Sim) PSF-g PSF-i g i PSF-r PSF-z r z Scale simulated flux-errors by black curves at lee. Purple-dash curve is prediccon based on floacng background in PSF fit. forcephoto-error excess at small-psf is not understood. PSF units: sigma, pixels [ =.6 PSF(fwhm,arcsec) ] 9

10 Empirical Flux-Error Adjustment for SNANA sim.5 Shallow Fields, Y+Y2.5.5 Deep Fields, Y+Y err(forcephoto) / err(snana Sim) PSF-g PSF-i g i PSF-r PSF-z r z PSF-g PSF-i g i PSF-r PSF-z r z PSF units: sigma, pixels [ =.6 PSF(fwhm,arcsec) ]

11 Fake/Sim Overlays of Fi_ed Params 6 Shallow Fields, Y SALT2c err(c) Fake SN à 2DiffImg 3 err(pkmjd) SNANA sim PKMJD PKMJD true SALT2x 2 25 SALT2mB err(x)..2.3 err(mb) z phot err(z phot ) ZPHOT ZPHOT true mean c vs z phot mean x vs z phot

12 Done with pre-analysis of fake SN Now start real analysis on data 2

13 Photometric SelecCon Box cuts: * 3 bands with obs having SNR>5 * at least one epoch with Trest < -2 * at least one epoch with Trest > + * x < 4 * FitProb >.5 (from SALT2 LC fit) Nearest Neighbor (Sako 24) P Ia >.5 with >σ significance P fit = Fit Prob 3

14 Nearest Neighbor (NN): 3D Space of Redshie, Stretch, Color DATA Fit color. Fit stretch 4

15 Nearest Neighbor (NN): 3D Space of Redshie, Stretch, Color DATA Sim(Ia+CC) Fit color. Fit color P Ia =8/2 True SNIa True SN CC Fit stretch Fit stretch NN Training finds opcmal radius of 3D sphere by maximizing Eff x Purity. 5

16 NN Performance on SimulaCon: Aeer box cuts: SNIa Purity 9% Aeer P NN,Ia > ½: SNIa Purity à 98% with 2% SNIa loss 6

17 Data/Sim Comparisons Box cuts + P NN,Ia > ½ Sim zhost tuned to match DES data (Eff zhost not known yet) No other sim tuning (except flux-errors to match fakes) µ(α=.4, β=3.2) DATA, NN(z,s,c) zspec zspec µ(α=.4, β=3.2) SIM, NN(z,s,c) zspec 7

18 Data Sim Data/Sim Comparisons tuned zhd SNRMAX err c err x SALT2-c SALT2-x err mb 2 3 err PKMJD SALT2-mB NN-P Ia SNRMAX P fit 8

19 c B Data Sim err c err x Data/Sim 2 Comparisons err mb 75 5 err x ( mag) tuned zhd SNRMAX err err c PKMJD SNRMAX3 Shallow Fields (23.5 mag) P fit err PKMJD DEEP Fields err x SALT2-c SALT2-x err mb 2 3 err PKMJD AX P fit SALT2-mB NN-P Ia SNRMAX P fit 9

20 Data/Sim Comparisons NN(z,s,c) 2 DATA SIM (Ia+CC) SIM (CC-only) µ SN µ fit (SALT2mu fit) Sim CC/Tot =.6%. Data contaminacon is clearly few Cmes more. 2

21 When all else Fails, Tune the CC Sim 2 DATA SIM (Ia+CC) SIM (CC-only) Untuned: CC/Tot=.6% CC sim - 2 DATA SIM (Ia+CC) SIM (CC-only) CC.4 mag Brighter: CC/Tot=2.3% µ SN µ fit (SALT2mu fit) DATA SIM (Ia+CC) SIM (CC-only) µ SN µ fit (SALT2mu fit) Double CC: CC/Tot=2.6% µ SN µ fit (SALT2mu fit) Can tune CC to match μ-resid tail, but cannot fix shoulder problem. See Jones talk for tests with 9bg, Iax.... 2

22 Fikng the Hubble Diagram: CorrecCng for Biases and CC ContaminaCon DATA, NN(z,s,c) z µ(α=.4, β=3.2) SIM, NN(z,s,c) Average Bias Corrected zspec Distances zspec 22

23 Fikng the Hubble Diagram: Framework From Hlozek 2 L = L(Ia) + L(CC) μ μ model with NN redshift 23

24 Fikng the Hubble Diagram: Framework From Hlozek 2 L = L(Ia) + L(CC) Fix cosmology params (w,ω M ) and fit for μ-offset in redshie bins (along with α,β): SALTmu program in SNANA, Marriner 2. Can then fit mu-offset vs. redshie with any cosmo program 24

25 Fikng the Hubble Diagram: CorrecQng for Biases and CC ContaminaCon Avg bias correccons from simulacon using color & stretch populacons from SK6: CorrecCons binned in 3D map of { z, x, c } 25

26 Fikng the Hubble Diagram: CorrecQng for Biases and CC ContaminaCon.6.5 c <.5 and x < c <..6.5 x <.4 δ mb = m B m B,sim αδ x = α(x x,sim ) < x < 2 βδ c = β(c c sim ) c >.2 c < < x < c < redshift redshift redshift 26

27 Fikng the Hubble Diagram: CorrecCng for Biases : Sim SNIa only fit µ offset fit µ offset a) chi2 only 2.9σ slope redshift c) chi2 + 2ln(σ) 7.σ slope redshift SNIa only (no CC) fit µ offset fit µ offset b) chi2 + biascor 5.9σ slope redshift d) chi2 + 2ln(σ) + biascor.σ slope redshift Unbiased results require both ln(σ) term + biascor (leaving out either is bad): α fit /α SIM =.977(6) β fit /β SIM =.(8) 27

28 Fikng the Hubble Diagram: CorrecQng for Biases and CC ContaminaQon fit µ offset Fit Ia + double-cc Sim c) ln(pa+pcc) 7.4σ slope redshift redshift fit µ offset redshift d) ln(pa+pcc) + biascor.σ slope redshift Unbiased results Require biascor: α fit /α SIM =.986(2) β fit /β SIM =.999() S CC =.82() 28

29 Fikng the Hubble Diagram: CorrecQng for Biases and CC ContaminaQon fit µ offset d) ln(pa+pcc) + biascor.5σ slope redshift DES Data: Blind Analysis For data, SALT2mu program adds crazy μ-offset in each redshie bin unless explicitly asked to unblind. Fi_ed α, β, σ int are shown to compare with spec samples. 29

30 Final Caveat: Bias CorrecCon is Similar to NN Cut NN cut removes events where sim CC are majority in local {z,x,c} region Bias CorrecQon removes events where there are no sim-ia in local {z,x,c} bin (since correccon cannot be made) Requirement ater box cuts True CC/Tot raqo for sim with double CC None. Bias CorrecCon (no NN).39 NN (no bias correccon).26 NN + Bias correccon.24 3

31 Conclusions Overall data/sim agreement is good. Fake SN à photometry pipeline is important for tuning & validacng SNANA simulacon. Simulated Hubble Residuals underescmate high-side (faint) shoulder: Wrong LF? Wrong rate? Missing templates? New HD fikng method to simultaneously account for biases and CC contaminacon. 3