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Using Galaxy Clusters for Cosmology and the XCS Ben Hoyle, Bob Nichol, David Bacon, Ed Lloyd-Davies, Kathy Romer & the XCS Cape Town April ‘08.

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Presentation on theme: "Using Galaxy Clusters for Cosmology and the XCS Ben Hoyle, Bob Nichol, David Bacon, Ed Lloyd-Davies, Kathy Romer & the XCS Cape Town April ‘08."— Presentation transcript:

1 Using Galaxy Clusters for Cosmology and the XCS Ben Hoyle, Bob Nichol, David Bacon, Ed Lloyd-Davies, Kathy Romer & the XCS Cape Town April ‘08

2 Overview  Clusters  Clusters & Cosmology  Cluster collapse; cosmology  Cluster surveys  X-ray cluster catalogues  The XMM Cluster Survey [XCS]  Detecting clusters  Cluster classification  Redshift estimates  High redshift cluster  Mass estimate 1; Scaling relations  Mass estimate 2; Weak lensing  The magnification bias  The future  Clusters  Clusters & Cosmology  Cluster collapse; cosmology  Cluster surveys  X-ray cluster catalogues  The XMM Cluster Survey [XCS]  Detecting clusters  Cluster classification  Redshift estimates  High redshift cluster  Mass estimate 1; Scaling relations  Mass estimate 2; Weak lensing  The magnification bias  The future

3 Clusters Properties of clusters of galaxies:  Size ~ few Mpc  Mass Emit broad range of EM  Galaxies -> optical  ICM -> X-rays Properties of clusters of galaxies:  Size ~ few Mpc  Mass Emit broad range of EM  Galaxies -> optical  ICM -> X-rays

4 Clusters & cosmology The number density and mass of clusters, the mass function, n(M,z), are related to by: Sheth & Tormen, ellipsoidal collapse Press & Schechter, spherical collapse ParameterDescritptionFunction of Barrier value Varience of density field Normalisation Of den. field Growth of strtucture

5 Cluster collapse; cosmology Ellipsodial collapseSpherical collapse Vary matter content Vary w=const Modify Gravity Schaefer & Koyama

6 Cluster surveys Cluster cosmology checklist:  Lots of clusters  Broad redshift range  Mass estimates Cluster cosmology checklist:  Lots of clusters  Broad redshift range  Mass estimates Optical cluster catalogues Large numbers Redshifts No masses X-ray cluster catalogues Mass estimates: Cavaliere & Fusco-Femiano ’ 78, Dai et al ’ 06 Small numbers Redshifts difficult

7 X-ray cluster catalogues X-ray identified cluster catalogues are mainly taken from the literature. CatalogueClustersRedshifts?P.I. XCS~1800<10%Romer ‘01 BCS206YesEbling ‘98 eBCS107YesEbling ‘02 R400D242YesBurenin ‘02

8 Overview  Clusters  Clusters & Cosmology  Cluster collapse; cosmology  Cluster surveys  X-ray cluster catalogues  The XMM cluster survey [XCS]  Detecting clusters  Cluster classification  Redshift estimates  High redshift cluster  Mass estimate 1; Scaling relations  Mass estimate 2; Weak lensing  The magnification bias  The future  Clusters  Clusters & Cosmology  Cluster collapse; cosmology  Cluster surveys  X-ray cluster catalogues  The XMM cluster survey [XCS]  Detecting clusters  Cluster classification  Redshift estimates  High redshift cluster  Mass estimate 1; Scaling relations  Mass estimate 2; Weak lensing  The magnification bias  The future

9 XMM Cluster Survey [XCS] The XCS:  Archival pointings  Serendipitous detections  170 sq. deg. Present  500 sq. deg. Total  0.1

10 Detecting clusters Color key  Extended sources, Green ellipses  Point sources, red circles  Unsure, Pink circles Color key  Extended sources, Green ellipses  Point sources, red circles  Unsure, Pink circles From simulations we can recover our selection function. Extended sources Simulated clusters

11 Cluster classification Cluster zoo  SDSS optical images  Centered on X-ray ra,dec  Optical & X-ray overlays  X-ray photon density contours  610 XCS extended sources  7 classification types  9+ classifications Cluster zoo  SDSS optical images  Centered on X-ray ra,dec  Optical & X-ray overlays  X-ray photon density contours  610 XCS extended sources  7 classification types  9+ classifications

12 Cluster classification Results:  Gold sample  High Z  False detections  Cuts improve sample Results:  Gold sample  High Z  False detections  Cuts improve sample Soft counts% Gold Clusters % All Clusters >01855 > >

13 Redshift estimates Empirically, see LRGs inhabit the central regions of clusters. SDSS LRGs Spec and Photo redshifts Good Agreement Test on ROSAT 400 sq. deg Spectroscopic redshifts Look along line of sight of the cluster, encounter an clump of LRGs. Assign LRG redshift to cluster

14 Redshift estimates Apply the LRG redshift estimate technique to the XCS 193 free cluster redshifts

15 Redshift estimates Dedicated XCS photometric follow up NOAO + XCS -> NXS More than 300 redshifts 136 for

16 High redshift cluster The most distant spectroscopically confirmed cluster of galaxies found to date. XMM-XCS J or J pointings of a z=2.215 quasar Cluster redshift 1.45 Temp > 6KeV Standford et al astro-ph/ Hilton et al astro-ph/

17 High redshift cluster The most distant spectroscopically confirmed cluster of galaxies found to date. XMM-XCS J or J pointings of a z=2.215 quasar Cluster redshift 1.45 Temp > 6KeV Standford et al astro-ph/ Hilton et al astro-ph/

18 Overview  Clusters  Clusters & Cosmology  Cluster collapse; cosmology  Cluster surveys  X-ray cluster catalogues  The XMM Cluster Survey [XCS]  Detecting clusters  Cluster classification  Redshift estimates  High redshift cluster  Mass estimates 1; Scaling relations  Mass estimate 2; Weak lensing  The magnification bias  The future  Clusters  Clusters & Cosmology  Cluster collapse; cosmology  Cluster surveys  X-ray cluster catalogues  The XMM Cluster Survey [XCS]  Detecting clusters  Cluster classification  Redshift estimates  High redshift cluster  Mass estimates 1; Scaling relations  Mass estimate 2; Weak lensing  The magnification bias  The future

19 Mass estimate 1: Scaling relations Combining optical [large numbers, no masses] and X-ray [small numbers, masses] cluster catalogues to obtain a mass proxy applicable to optically selected clusters. X-ray clusters eBCs,BCS,R400d,XCS Reprocessed equally [Dai et al ‘06] Optical Clusters Mass estimates Constrain cosmology Empirical Relationship Examine properties of X-ray clusters with a counter part in SDSS

20 Mass estimates 2: Weak lensing The Sloan has enabled the creation of two massive optical catalogues: We can follow Scranton et al ’05, and use a weak lensing mass measurement, called magnification bias. MaxBCG clusters [Koester et al ’ 07]  ~13.10^3  0.1

21 The magnification bias The QSO unlensed source density:

22 The magnification bias Lensing changes the measured source density, by stretching the solid angle

23 The magnification bias Lensing magnifies the source flux

24 The magnification bias The sign of the correlation The signal strength The sign of the correlation The signal strength The cross correlation, is a function of bias, b, the weak lensing departure from unity,,and the line of sight integrated density contrast, Where alpha is the gradient of the QSO distribution, multiplied by 2.5 There is a change of sign of Schnieder & Bartelmann astro-ph/

25 The magnification bias, results We find [preliminary results]: Expect a strong correlation Expect a weak anti-correlation Stack similar clusters, expect the signal strength to increase with cluster mass.

26 The future Weak Lensing  SDSS DR6 QSO  1 million QSO’s  Redo with full MaxBCG  Check systematics Weak Lensing  SDSS DR6 QSO  1 million QSO’s  Redo with full MaxBCG  Check systematics Mass estimates  Compare X-ray & weak lensing mass measurements  Assign MaxBCG clusters masses Cosmology  Assume LCDM constrain cosmological parameters  Constrain modified gravity models [Schaefer & Koyama ‘07]


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