Occupy Dark Matter: Accessing the 99% of dusty galaxies that lie beneath the confusion noise floor. Marco Viero - Caltech

Transcription

1 Occupy Dark Matter: Accessing the 99% of dusty galaxies that lie beneath the confusion noise floor Marco Viero - Caltech

2 hermes.sussex.ac.uk

3 The Team Marco Viero, Plus engineers, instrument builders, software developers etc.

4 data XMM-LSS LOCKMAN-SWIRE CDFS-SWIRE ELAIS-S1 SMAP Team Alex Conley Louis Levenson Gaelen Marsden Bernhard Schulz Marco Viero Mike Zemcov BOOTES Viero & Wang et al. (2012b) arxiv: SPIRE maps at 250, 350, and 500 μm Maps made with HerMES SMAP pipeline 5 fields totaling ~ 70 deg 2 Modes >~ 0.5 deg filtered Sources > 50, 100, 200, 300 mjy masked

5 350 μm HerMES power spectra 250 μm 350 μm 500 μm 500 μm 250 μm Viero & Wang et al. (2012b) arxiv:

6 350 μm HerMES power spectra 250 μm 350 μm 500 μm 500 μm 250 μm Viero & Wang et al. (2012b) arxiv:

7 HerMES power spectra l = 216 l = 21,600 Combined 5 fields over 70 deg 2 Viero & Wang et al. (2012b) arxiv:

8 HerMES power spectra l = 216 l = 21,600 Combined 5 fields over 70 deg 2 Poisson Viero & Wang et al. (2012b) arxiv:

9 HerMES power spectra l = 216 l = 21,600 Combined 5 fields over 70 deg 2 2-halo 2-halo Poisson Viero & Wang et al. (2012b) arxiv:

10 HerMES power spectra l = 216 l = 21,600 Combined 5 fields over 70 deg 2 2-halo 2-halo 1-halo Poisson 1-halo Viero & Wang et al. (2012b) arxiv:

11 HerMES power spectra l = 216 l = 21,600 2-halo Combined 5 fields over 70 deg 2 Poisson 1-halo Viero & Wang et al. (2012b) arxiv:

12 HerMES power spectra Poisson vs. Masking Level l = 216 l = 21,600 P(kθ) (Jy 2 sr -1 ) 2-halo Combined 5 fields over 70 deg 2 1-halo Poisson } masking level (mjy) Viero & Wang et al. (2012b) arxiv:

13 HerMES power spectra l = 216 l = 21,600 1-Halo (non-linear) Term Combined 5 fields over 70 deg 2 2-halo Poisson } 1-halo Viero & Wang et al. (2012b) arxiv:

14 HerMES power spectra l = 216 l = 21,600 1-Halo (non-linear) Term Combined 5 fields over 70 deg 2 2-halo Poisson } 1-halo Viero & Wang et al. (2012b) arxiv:

15 HerMES power spectra l = 216 l = 21,600 1-Halo (non-linear) Term Combined 5 fields over 70 deg 2 2-halo Poisson } 1-halo Viero & Wang et al. (2012b) arxiv:

16 350 μm HerMES power spectra 250 μm 350 μm 500 μm 500 μm 250 μm Combined 5 fields over 70 deg 2 Viero & Wang et al. (2012b) arxiv:

17 HerMES power spectra l = , , ,600 Viero & Wang et al. (2012b) arxiv:

18 HerMES power spectra Viero & Wang et al. (2012b) arxiv:

19 HerMES power spectra Viero & Wang et al. (2012b) arxiv:

20 HerMES power spectra Viero & Wang et al. (2012b) arxiv:

21 PI: Viero Colormap: IRAS 100μm HerMES Large-Mode Survey (HeLMS)

22 15.5 PI: Viero Colormap: IRAS 100μm HerMES Large-Mode Survey (HeLMS)

23 15.5 PI: Viero Colormap: IRAS 100μm HerMES Large-Mode Survey (HeLMS)

24 halo model Requirements Fit all spectra simultaneously Fits counts (and Poisson level) as well Few parameters as possible Ingredients (extension of Shang et al. 2012) Luminosity-Mass (L-M) relationship single SED template 7 parameters (5 free): X X

25 Viero & Wang et al. (2012b) arxiv: halo model fits Power spectra Number counts

26 Viero & Wang et al. (2012b) arxiv: halo model fits Power spectra Number counts log(m peak /M ) = 13.9 ± 0.6

27 Viero & Wang et al. (2012b) arxiv: halo model fits Power spectra Number counts log(m peak /M ) = 13.9 ± ± ± 0.1

28

29

30 measured template SEDs redshift distribution of flux dream halo model: wish-list

31 k-band selected sources at z 1.2 on SPIRE 250μm map

32 thumbnail stacking

33 thumbnail stacking Phil Korngut (Caltech)

34 10,000 iterations no bias uncorrelated source simulation

35 clustering induced bias

36 clustering induced bias

37 clustering induced bias

38 clustering induced bias

39 simultaneous stacking make hits map from catalog Formalism developed w/ Lorenzo Moncelsi (Caltech); also see Kurczynski & Gawiser (2010), Roseboom et al. (2010)

40 simultaneous stacking make hits map from catalog convolve with map p.s.f. Formalism developed w/ Lorenzo Moncelsi (Caltech); also see Kurczynski & Gawiser (2010), Roseboom et al. (2010)

41 simultaneous stacking make hits map from catalog convolve with map p.s.f. regress to find stacked flux Formalism developed w/ Lorenzo Moncelsi (Caltech); also see Kurczynski & Gawiser (2010), Roseboom et al. (2010)

42 non-target induced bias

43 non-target induced bias

44 non-target induced bias

45 non-target induced bias

46 + + simultaneous stacking catalog 1 C1 + catalog 2 C2 catalog N CN Formalism developed w/ Lorenzo Moncelsi (Caltech) sky map

47 simultaneous stacking sim unbiased Viero, Moncelsi, Quadri et al. (in prep.)

48 simultaneous stacking sim unbiased Viero, Moncelsi, Quadri et al. (in prep.)

49 determine contribution to the CIB from galaxies identified in the optical/nir, as a function of galaxy: goals redshift stellar mass luminosity??

50 data catalogs (Williams & Quadri, in prep.) maps (HerMES; Oliver et al. 2012) UKIDSS/UDS [2/3 deg 2 ] ubvrizjhk + IRAC ch1234 K-band magnitude cut 24 AB 54,000 sources in ~0.63 deg 2 redshifts - EAZY (Brammer 2008) Spitzer/MIPS 24, 70, 160um Herschel/SPIRE 250, 350, 500um ASTE/AzTEC 1100um masses - FAST (Kriek 2009)

51 mjy stacked flux density p r e l i m i n a r y Viero, Moncelsi, Quadri et al. (in prep.)

52 stacked flux density 24μm p r e l i m i n a r y mjy Viero, Moncelsi, Quadri et al. (in prep.)

53 stacked flux density 250μm p r e l i m i n a r y Viero, Moncelsi, Quadri et al. (in prep.)

54 stacked flux density 1100μm p r e l i m i n a r y Viero, Moncelsi, Quadri et al. (in prep.)

55 log(νfν) [L ] mjy stacked flux density p r e l i m i n a r y Viero, Moncelsi, Quadri et al. (in prep.) wavelength [μm]

56 SEDs mass slices redshift slices p r e l i m i n a r y

57 best-fit SEDs p r e l i m i n a r y

58 Herschel Redshift Survey (HeRS)

59 measured template SEDs redshift distribution of flux halo model wish-list

60 redshift distribution of CIB

61 ds/dz: redshift distribution of background light Models do not agree on ds/dz for z > 1 redshift distribution of CIB

62 ds/dz: redshift distribution of background light Models do not agree on ds/dz for z > 1 Aim to constrain ds/dz from 2 > z > 3 to 5σ redshift distribution of CIB

63 cross-correlation with tracer

64 Want DSFGs at z > 1 cross-correlation with tracer

65 Want DSFGs at z > 1 high-z DSFGs dominated by foreground noise cross-correlation with tracer

66 Want DSFGs at z > 1 high-z DSFGs dominated by foreground noise cross-correlation with tracer

67 Want DSFGs at z > 1 high-z DSFGs dominated by foreground noise cross-correlate DSFGs with tracers of the same dark matter cross-correlation with tracer

68 foreground z>1.8 LAEs HETDEX LAEs from 1.8 < z < 3.5 Biased tracer of the dark matter, bias known to 1-2% Herschel Redshift Survey (HeRS)

69 HeRS is here! 22 Collaborations welcome! More info at:

70 HeRS is here! 22 3 ACT Strip ACT SHELA HETDEX Collaborations welcome! More info at:

71 HeRS is here! 22 Collaborations welcome! More info at:

72 HeRS is here! 22 Clusters (Geach et al. 2012) Collaborations welcome! More info at:

73 HeRS is here! 22 Clusters (Geach et al. 2012) BOSS quasars Wigglez Collaborations welcome! More info at:

74 [0,0] 70deg 2 280deg 2 SPIRE Surveys: P.I. Viero Colormap: IRAS 100μm HeLMS/HeRS

75 [0,0] SPIRE Surveys: P.I. Viero Colormap: IRAS 100μm HeLMS/HeRS

76 HeRS projects underway: z-distribution of brightest resolved sources characterizing scattered starlight in the ISM dust content in optically identified clusters cross-correlations with dark matter mass tracers including: quasars clusters (optical, x-ray, SZ) lyman-alpha forest maxbcgs lensed CMB kappa maps (w/ ACT) SED evolution of quasars ssfr of IRAC galaxies as function of stellar mass cluster mass diagnostic: comparing optical, SZ and x-ray mass estimates stacking satellites in mass and radius bins to measure star-formation dependence on environment and consequences for SZ decrement developing clump finding technique to identify Galactic cirrus clouds modeling (halo model and abundance matching) the CIB based on stacked SED templates follow-up of lensed SMGs determining SFRs of SN hosts as a function of SN type more ideas/collaborators (including students!) welcome

77 what s next? P(k) on largest scales from HeLMS, SPT-SPIRE & Planck Cross-frequency spectra with CMB maps (see Amir s talk!) and crosscorrelations with many known tracers Building models constrained by measurements to recover the SFhistory at all z...and much more