1 / 13

Cluster Catalogs from 161 deg 2 of CFHTLS-Wide gri imaging Karun Thanjavur

Cluster Catalogs from 161 deg 2 of CFHTLS-Wide gri imaging Karun Thanjavur Supervisors: Dr. David Crampton, HIA/ UVic Dr. Jon Willis, UVic SL2S Meeting, Marseilles, 11 May 2009. Cluster Catalogs from 161 deg 2 of CFHTLS-Wide gri imaging.

eydie
Télécharger la présentation

Cluster Catalogs from 161 deg 2 of CFHTLS-Wide gri imaging Karun Thanjavur

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Cluster Catalogs from 161 deg2 of CFHTLS-Wide gri imaging Karun Thanjavur Supervisors: Dr. David Crampton, HIA/ UVic Dr. Jon Willis, UVic SL2S Meeting, Marseilles, 11 May 2009

  2. Cluster Catalogs from 161 deg2 of CFHTLS-Wide gri imaging Objective:Build acatalog of confirmed lensed, emission line galaxiessuitable for IFU observations (  redshift interval, z ≤ 1.5, surface brightness, i ≤ 22.5 mag/arc.sec2) 3-step approach: 1. Detect galaxy groups and clusters with an automated search 2. Identify lensed arc features around each detected group or cluster 3. Confirm with longslit or multi-object spectroscopy. Methodology: Automated method designed to work with available g, r and i filters imaging in the CFHTLS-Wide survey(using Terapix T05 photometric catalogs)

  3. K2, an automated galaxy group and cluster detector (Method targets the population of early type galaxies in the cluster core using density enhancements in position and colors) Clustering in position Clustering in color

  4. Tests bright galaxies in Terapix T05 catalogs for cluster membership • Uses available Terapix T05 g, r and i photometric catalogs for each square degree of CFHTLS-Wide fields. • Tests each bright galaxy, (BG, 16 ≤ i’ ≤ 20mag) for cluster membership (~2000 BG and 175,000 field objects in each square degree)

  5.  For each BG, obtains positions and colors of all galaxies within a pre-defined aperture(diameter 1h-1 Mpc at z=0.5) and within a pre-defined color cut (±0.15mag of BG color) • Using the relative differences, , between the selected galaxies and BG inposition and in (g-r) and (r-i) colors, compute the metric of overdensity (referred as the cluster weight, Wc Cluster weight = metric of position + color overdensity

  6. Wc and Wf = cluster and field metrics of overdensity; f = statistical spread in field metric Compute field weight and detection significance  Place the BG at 100 random positions in the field, and compute the metric of field overdensity, measured by Wf and f (mean and standard deviation of the 100 trials)  Compute the detection significance, S,for each BG by comparing the cluster vis-à-vis field metric values in each color ( >3  candidate cluster member)

  7. Linking and classification of cluster candidates  Classifycandidate cluster members into Gold( >5 in both colors),Silver( >5 in one color, >3 in the other), andBronze( >3 in both colors) categories  Link all identified members, which lie within an aperture radius of each other and within the color cuts, as members of a candidate cluster  Determine properties for candidate clusters, (number of members, Abell richness and photometric redshift, where five filter imaging is available)  write to cluster catalogs for each field

  8. Calibration of K2 using Monte-Carlo simulations for contamination and completeness • Test the recovery of a synthetic cluster from a random position within a field galaxy population. • Properties of clusters and field galaxies generated using published observational results • False detection rate 1-2%, for a selection significance threshold of 3 • Complete to 80% for Coma-like clusters up to z=0.8, to z=0.6 for Fornax-like and z=0.3 for a poor (WBL) cluster

  9. Characterization of K2 cluster candidates by comparison with published cluster catalogs XMM-LSS and the Matched Filter cluster catalogs for the CFHTLS-D1 • 12 of the 17 XMM-LSS clusters in D1 also detected by our method. • All XMM-LSS clusters at z < 0.8 are detected, matching the Monte-Carlo results. • All MF Class-A clusters at z < 0.8 detected by our method • Non-detections lie at higher redshifts An example of a XMM-LSS cluster also detected by K2 ; the red square marks the center of X-ray emission, while the detected BCG is marked by the blue cross.

  10. K2 candidates from 161 deg2 of CFHTLS-W imaging with significances above detection threshold in each color Histograms showing detection significances of candidate cluster members in the (g-r) and (r-i) colors Gold = 5 detection in (g-r) and (r-i); Silver = 5 in one, 3 in the other; Bronze = 3 in both colors

  11. K2 candidates from 161 deg2 of CFHTLS-W imaging Breakdown by Gold, Silver and Bronze classes Histograms showing gold, silver and bronze candidates in each field.

  12. K2 candidates from 161 deg2 of CFHTLS-W imaging Breakdown by Abell Richness classes Histograms showing Abell richness classes of candidates in each field W = Richness -1 (group-scale) ; F = Richness 0 (Fornax-like) ; C = Richness 1 or higher (Coma-like)

  13. SL2S cluster lens detected as a Gold candidate by K2 (Detected BCG and cluster members indicated in red)

More Related