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Target selection for photo- z/spec-z surveys and their cross-correlation

Target selection for photo- z/spec-z surveys and their cross-correlation. Stephanie Jouvel , Fil Abdalla , Donnacha Kirk, Sarah Bridle Ofer Lahav & DESpec target selection team. Cross-Correlation conference, UCL jun 2014. Outline: . Introduction

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Target selection for photo- z/spec-z surveys and their cross-correlation

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  1. Target selection for photo-z/spec-z surveys and their cross-correlation Stephanie Jouvel, FilAbdalla,Donnacha Kirk, Sarah Bridle OferLahav & DESpectarget selection team. Cross-Correlation conference, UCL jun 2014

  2. Outline: • Introduction • Scientific motivation (has an impact on target selection) • Summary of target selections considered • Mock galaxy catalogues • Galaxy target selection results

  3. Questions of cosmology • What is the nature of Dark Matter ? • What is the geometry of the Universe ? • What causes the acceleration of the expansion ? • What is the nature of Dark Energy ? • What is the mass content (3D mass map) ? • Is the GR a good description of the laws of gravity ? • What is the contribution of neutrinos in the Universe evolution ?

  4. Imaging survey: galaxy position: 2D correlation function galaxy shape: Weak Lensing redshift (Weak lensing & BAO) => multi-band photometry (transients detection: SuperNovae) Spectroscopic survey: galaxy position: 3D correlation function: BAO & Redshift distortions (redshift of SuperNovaeIa) Future dark energy mission :Optical/Near-Infrared Observables

  5. www.darkenergysurvey.org The Dark Energy Survey Blanco 4-meter at CTIO • Stage III DE project using 4 complementary* techniques: • I. Cluster Counts • II. Weak Lensing • III. Large-scale Structure • IV. Supernovae • Two multiband surveys: • 5000 deg2 grizYto 24th mag • 30 deg2 repeat (SNe) • • Build new 3 deg2 FOV camera • and Data management system • Survey 2012-2017 (525 nights) *in systematics & in cosmological parameter degeneracies *geometric+structure growth: test Dark Energy vs. Gravity

  6. Landscape of (Potential) Collaborating Projects • South Pole Telescope (SPT) • 2500 sq deg completed. MOU to facilitate collaboration approved • Vista Hemisphere Survey (VHS) • Deeper JHK imaging over DES footprint underway. MOU approved. • Vista VIDEO Survey • Near-coincident NIR imaging of SN fields planned. Informal agreement exists • eBOSS: 500deg2 overlap with DES, discussions underway • DESI (BigBOSS/DESpec):Joint Working Group just reported. Revised footprint overlaps ~600 sq deg w/ BigBOSS/eBOSS. Larger overlap would degrade image quality, given 525-night constraint. Solution: e(xtended)-DES, in collaboration w/ other projects, optimally scheduled with DES. • Euclid:letter of support for collaboration • Skymapper, HSC, PanSTARRS, LSST: cross-calibration • Community users of DECam • DECam Community Use Workshop demonstrated community interest in mini-survey-scale projects and in collaboration with DES. Discussion of north equatorial survey.

  7. Outline: • Introduction • Scientific motivation (has an impact on targets selection) • Summary of target selections considered • Mock galaxy catalogues • Galaxy target selection results

  8. FOM for dark energy studies. BAO + P(k) + RSD’s... • 1- Constant density 0.2<z<1.7, 10^7galaxies • 2- Constant density 0.2<z<0.5, plus I<22.5 for 0.5<z<1.7 @65% eff’y. Total 10^7galaxies. Note redshiftcut-off • 3- Constant density 0.2<z<0.7, plus emission line galaxies for 0.7<z<1.7. Total 10^7galaxies. DES(WL) + DESpec(LSS) Kirk, Lahav, Bridle, Jouvel, Abdalla submitted Jouvel, Abdalla, Kirk, Lahav, MNRAS Improved FOM for DE and modified gravity. Towards a realistic n(z) and Nbr targets.

  9. Outline: • Introduction • Scientific motivation (has an impact on how to select targets...) • Summary of target selections considered • Mock galaxy catalogues • Galaxy target selection results

  10. Classic ELGs target selection from optical colors : 0.7<z<1.2 1.2<z<1.6 1.6<z<2 • Colours are different depending on redshift ranges => color box • Strategies of DEEP2, VIPERS • R, g and i bands.(~23 mag) • Panstarrs + PTF surveys • Compared n(z) with other selection. • Target selection aspect -> depends on the photometry only • Spec success rate depends on the instrument.

  11. Classic LRG optical-IR selection: • WISE: • 3.4μm AB ~ 19 • 4.6μm AB ~ 19 • 12 μm AB ~ 17 • 22μm AB ~ 14 • Selecting LRG’s • Higher z LRG’s have the break moving out of the I band, hence z bands or IR bands are needed. • One way of getting higher redsfhit LRG’s-> use far IR space data. Use the H minus 1.6 micron bump. (see figure from Sawicki 02) • BigBoss will probably be able to select these very well. • Obtaining a flat n(z)=cst will require photo-zs with near-IR data.

  12. Multidimensional method based on NN selection Find LRG/ELG using DES photometry in NN ? Colour Colour • Blue galaxies have successful redshifts from the sensitivities. • Black galaxies do not! • If we change the exposure time the picture changes.

  13. Multidimensional method based on NN selection Find LRG/ELG using DES photometry in NN ? Colour Colour • Red galaxies are LRGs on the mock. • Blue galaxies are not.

  14. Outline: • Introduction • Scientific motivation (has an impact on how to select targets...) • Summary of target selections considered • Mock galaxy catalogues • Galaxy target selection results

  15. the COSMOS survey 2deg2 (representative)‏ 30 photometric bands from UV to IR with HST, Galex, Spitzer, Subaru, VLA, NOAO HST/ACS I band observation: galaxy sizes & shapes zCosmos spectroscopic survey Galaxy Properties from Deep Surveys • the VVDS “Deep” survey • VIMOS/VLT deep spectroscopic survey on ~0.5 sq.deg • ~9000 spectra from 0<z<5 down to IAB ~24 (magnitude selected)‏ Koekemoer et al. 2007 Le fèvre et al. 2005

  16. Building Mock Catalogues Use this catalogue COSMOS Mock catalogue GOODS LuminosityFunction catalogue COSMOS catalogue (position, size, photometry)‏ 1 million galaxies Simulated Catalogue based on GOODS LF(z,type)‏ GOODS LF from Dahlen et al. 2005 Jouvel et al. 2009 Photoz, SED, emissionlines COSMOS size distribution, emissionlines - Realistic redshift distribution, - Realistic size distribution, - Emission line fluxes catalogue COSMOS photoz from Ilbert et al. 2009 Photometry in DE mission filter sets + Photometricerrors Jouvel et al 2010 DE Forecast

  17. COSMOS Mock Catalog (CMC)‏ : Spectro-validation Validation of the redshift distribution and emission line fluxes using the VVDS-DEEP I~24 AB (Lamareille et al 2008)‏ Emission line prediction : Kennicutt et al. 1998 : UV-OII relation Jouvel et al. 2009

  18. Outline: • Introduction • Scientific motivation (has an impact on how to select targets...) • Summary of target selections considered • Mock galaxy catalogues • Galaxy target selection results

  19. Strawman LRG target selection vs WISE vs full ANN selection PanSTARRS+PTF +WISE shallow color selection DES+VHS deep color selection DES+VHS ANNz

  20. Use of NN to select LRGs : Color-color vs NN selection template

  21. Final target selection: High number density for bias cross-corr at z~0.7 Number density to be shot noise limited is around 80 per sq deg in a 0.1 z bin

  22. FoM forecast assumptions • Photometric WGL survey with photo-zs, DES-like:5000deg2, 300M galaxies, 10gal/arcmin2, 5 tomographic bins, gaussianphotoz errors s~0.07(1+z) • Spectroscopic survey with spec-z, DESI-like: 5000deg2, 100M galaxies, 20 tomographic bins, • (Smail-like redshift distribution) • We marginalise over the standard 7 cosmological parameters + a galaxy bias model: b(k, z)=AbQ(k, z) • FOMDE=1/[4det(F-1)DE]

  23. Target selection optimisation using FoM Using realistic redshift distribution of LRG/ELG from our mocks and different population fraction : Same & different sky patches 20% FoM improvement in going to the same patch of the sky for DESxDESpec Kirk et al. 2014

  24. LRG/ELG fraction Looking at different exposure time with a 1 sweep survey strategy => Best ratio at 30/70 LRG/ELG Jouvel et al. 2013

  25. Survey strategy and exposure time 1 sweep 5000deg2 LRG/ELG 30/70

  26. Survey strategy and exposure time Multiple sweeps 5000deg2 LRG/ELG 30/70

  27. Conclusion Quick run through the science motivation -> how it might affect the target selection, i.e. RSD’s BAO’s P(k). Quick outline of the survey strategy looking into future spectroscopic surveys. Outline of the work done for DESpec/DESI with some comparisons with the other target selection strategies. Mocks & instrument & strategy & etc… Work needed on real data to test new target selection methods Work needed to make sure the selection function is not too complex

  28. Thanks

  29. Slide From Don. Kirk

  30. Sensitivities from 0.6-1um (H. Lin) How do we select ELG: z>1 target selection based on OII emitters - 30 mins exposure time on 2 arcsecø fibers (70% light fraction) - Resolution R=2500 at 0.9um. - Calculation include DES throughput + sky noise and atmospheric lines from Hanuschik (2003) Using photoz & NN target selection we will shape the redshift distribution

  31. Redshift distribution / em lines gal Redshift will be measured with OII mainly for a 0.6 to 1um spectrograph

  32. Target selection We will select galaxies based on their photoz and ANNz target selection

  33. Mock surveys from Donnacha’s talk: • Assume ~300 nights, 4000 fibres over 3deg2 • Scenario 1: 5000 deg2, 33% LRG 67%ELG • Scenario 2: 7500 deg2, 25% LRG 75%ELG • Scenario 3: 7500 deg2, 50% LRG 50%ELG • Scenario 4: 15000 deg2, 67% LRG 33%ELG • Scenario 5: 15000 deg2, 33% LRG 67%ELG • 300 nights at ~7 hours a night inc eff. ~30 mins exposure => ~4200 pointings *4000 fibers = ~16.8M targetted galaxies. Times eff to get final no of gals. • Scenario 1 requires target density of ~3000 per sqdeg. • Scenarios 2 & 3 ~ 2000 per sq deg • Scenarios 4 & 5 ~ 1000 per sq deg. -> • We can do all, now optimise this…!

  34. Target selection pipeline: • Production of a mock catalogue: • Colours and spectra • Production of sensitivity curves • Production of redshift success rate from the spectrum for each simulated galaxy. • Production of an algorythm for selection • Investigation of impact of this algorythm • Allocation efficiency from fiber positioner • Allocation efficiency from a real pointing strategy • Link with FOM for the given n(z)

  35. Colours and ANNz

  36. Zoubian, Jouvel, Kneib et al 2012 in prep

  37. COSMOS Mock Catalog (CMC)‏ : Validation Zoubian, Jouvel, Kneib et al 2012 in prep

  38. Spectroscopic Success Rate (SSR) :Validation using VVDS SSR : galaxy rate for whichwewillbe able to measure a redshift Validation : reproducing the VVDS SSR Jouvel et al. 2009

  39. LRG target selection To get the Balmer break at 1<z<2 To get the Balmer break at 0.5<z<1

  40. Collister & Lahav 2004 http://www.star.ucl.ac.uk/~lahav/annz.html Photo-z’s, target selection and Neural networks: • Has an architecture: defined by a number of inputs/ outputs and nodes in hidden layers Internally values range from 0 to 1 roughly

  41. Use of NN to select em line galaxies We assign a value of : • 1 for targetted ELG • 0 for non targetted galaxies We then use ANNz and train a NN on a sample of 10000 galaxies from the CMC (DES+VISTA) Results for i(DES) < 23… Using Huan Lin sensitivities cut between 0.6-1um

  42. Which ANNz selection criterion ? Fig : Cumulative nbgalaxies/deg2 fctANNz target probability 1.5<photoz<3 ANNzsel>0.8 allows to get most of the high-redshift galaxies at i<23.5 0.9 Using sensitivities cut between 0.6-1um

  43. Which ANNz selection criterion ? Solid : percentage of galaxies for which we will measure a redshift/total nbr gal & Dashed : SSR fctANNz target probability At ANNzsel>0.8 you will target about 66% of galaxies at i<23 in the photoz range for which you’ll measure a redshift for 95.8% of the targets Using sensitivities cut between 0.6-1um

  44. Selection of ELG (WFMOS/KAOS)Original old strategy… From K. Glazebrook old KAOS talk

  45. Collister & Lahav 2004 http://www.star.ucl.ac.uk/~lahav/annz.html Multidimensional method based on NN selection Find LRG/ELG using DES photometry in NN ?

  46. Collister & Lahav 2004 http://www.star.ucl.ac.uk/~lahav/annz.html Multidimensional method based on NN selection Find LRG/ELG using DES photometry in NN ? Colour

  47. Target selection:success rate! • Selection of the correct galaxies (enough bands) • Selection of galaxies with enough signal to noise => Realistic photometric scatter • Need proper simulation => Realistic noise realisation of the spectrum • Line in the correct spectral range for the spectrograph. => Mostly Ha and OII for a 0.6-1.1um spectrograph

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