The XMM Cluster Survey Project summary and Cosmology

The XMM Cluster Survey: Project summary and Cosmology Forecasts Kathy Romer University of Sussex

XCS Collaboration • Institutes: Sussex, Porto, Edinburgh, Liverpool John Moore’s, Portsmouth, etc. • Students: Mark Hosmer, Nicola Mehrtens, Martin Sahlen, Ben Hoyle • Post. Doc’s: Ed Lloyd-Davies, John Stott, Matt Hilton (Durban) • Faculty: Collins, Kay (Manchester), Liddle, Mann, Miller (CTIO), Nichol, Stanford (UC Davis), Romer, Viana, West (ESO) • Funding: Institutes; STFC (UK); Chandra and XMM Guest Observing programmes (USA)

Talk Overview And related publications 1. Project Summary • • Romer et al. 2001 (9910217) Stanford et al. 2006 (0606075) Hilton et al. 2007 (0708. 3258) Collins et al. 2008 (submitted to Nature) 2. Cosmology Forecasts • Sahlen et al. (in press; 0802. 4462)

1. 1 Project Summary XCS is an X-ray cluster survey based on all XMM data in the public archive Goals – Cosmological parameters – Scaling Relations Distinguishing Features – – Area Selection function X-ray spectroscopy Added value science

1. 2 Justification for Goals • Parameters: – Clusters probe a different part of the parameter space to CMB and SNe • Scaling relations: – we need to know these relations to do cosmology – they tell us about structure formation – (see next talk)

1. 3 Features (Area) • Distinguishing Features – Area • 170 square degrees already • [conservative] prediction of 500 sq. deg by the end of XMM • These values account for overlaps and exclude regions unsuitable for cluster finding; in Galactic plane, near low-z clusters etc. Public (or soon to be public) XMM observations – Selection function – X-ray spectroscopy – Added value science

1. 4 Features (Selection Function) • Distinguishing Features – Area – Selection function • XCS is run using pipelines • we add fake clusters to test to the XCS sensitivity and build up selection functions • For Sahlen et al. 2008, we used simple analytical models, but recently we switched over to hydro-simulations Just one of the many XCS pipelines that convert data into the archive into catalogues of point sources and cluster candidates – X-ray spectroscopy – Added value science

1. 5 Features (Selection Function) • Distinguishing Features – Area – Selection function • XCS is run using pipelines • we add fake clusters to test to the XCS sensitivity and build up selection functions • For Sahlen et al. 2008, we used simple analytical models, but recently we switched over to hydro-simulations An XCS image before the addition of a fake cluster – X-ray spectroscopy – Added value science

1. 6 Features (Selection Function) • Distinguishing Features – Area – Selection function • XCS is run using pipelines • we add fake clusters to test to the XCS sensitivity and build up selection functions • For Sahlen et al. 2008, we used simple analytical models, but recently we switched over to hydro-simulations An XCS image after the addition (and detection) of a fake cluster – X-ray spectroscopy – Added value science

1. 7 Features (X-ray Spectroscopy) • Distinguishing Features – Area – Selection function – X-ray spectroscopy • ~300 XCS candidates were detected with 500 or more counts • 124 XCS 500 clusters have redshifts already • (see next talk) – Added value science The XCS L-T relation (see next talk)

1. 8 Features (Added value science) • Distinguishing Features – – Area Selection function X-ray spectroscopy Added value science • Rare object discovery: – Fossil Groups – High redshift clusters • Mass calibration for future cluster surveys: An XCS discovery of a Fossil Group – Dark Energy Survey – Planck • Galaxy Evolution • Quasar properties

1. 9 Features (Added value science) • Distinguishing Features – – Area Selection function X-ray spectroscopy Added value science • Rare object discovery: – Fossil Groups – High redshift clusters • Mass calibration for future cluster surveys: Combined J & K MOIRCS image of XMM XCSJ 2215 (z=1. 45) – Dark Energy Survey – Planck • Galaxy Evolution • Quasar properties

1. 10 Features (Added value science) • Distinguishing Features – – Area Selection function X-ray spectroscopy Added value science • Rare object discovery: – Fossil Groups – High redshift clusters • Mass calibration for future cluster surveys: Comparison of optical and X-ray properties for XCS (and 400 sq. deg) clusters in the SDSS region – Dark Energy Survey – Planck • Galaxy Evolution • Quasar properties

1. 11 Features (Added value science) • Distinguishing Features – – Area Selection function X-ray spectroscopy Added value science • Rare object discovery: – Fossil Groups – High redshift clusters • Mass calibration for future cluster surveys: An XCS Quasar: we have 100 with both optical and X-ray spectroscopy – Dark Energy Survey – Planck • Galaxy Evolution • Quasar properties

2. 1 Cosmology Forecasts XCS will deliver Omega-M to 10% and Sigma-8 to 6% These predictions: – Are based on a XCS 500 sample drawn from a 500 sq. deg survey – Assume a flat Universe – Use beta-model clusters for the selection function – Allow for errors in X-ray temperatures and photo-z’s – Include self-calibration of the luminosity-temperature relation – See 0802. 4462 for full details

2. 2 Cosmology Forecasts Understanding scaling relations is essential • We have made fake XCS catalogues using a variety of assumptions about scaling relations and find the catalogue properties vary significantly • Making the wrong assumptions when fitting for parameters distorts the results

2. 3 Cosmology Forecasts Understanding scaling relations is essential • We have made fake XCS catalogues using a variety of assumptions about scaling relations and find the catalogue properties vary significantly • Making the wrong assumptions when Assume the wrong scaling relation and fitting for parameters the correct parameter values can lie distorts the results outside the 90% confidence region!

2. 4 Cosmology Forecasts We are extending and improving the forecasts • Selection functions were based on simple beta models and flat geometry – Now we use hydro “clusters” – And non-flat cosmologies • We are forecasting for other surveys: contiguous XMM surveys; XEUS follow-up of XCS etc. • We need independent mass The inner black contours represent the estimates (e. g. from lensing) improvement in parameters if all XCS – XSC parameter constraints clusters have measured temperatures require an external M-Tx calibration (e. g. from XMM follow-up or XEUS)

Summary XCS is producing object catalogues for a variety of science applications