/
Finding and Characterizing the Giant Arcs Finding and Characterizing the Giant Arcs

Finding and Characterizing the Giant Arcs - PowerPoint Presentation

natalia-silvester
natalia-silvester . @natalia-silvester
Follow
385 views
Uploaded On 2015-12-03

Finding and Characterizing the Giant Arcs - PPT Presentation

Bingxiao Xu Johns Hopkins University Outlines Science motivation Automate arcfinder Test the arcfinder by simulations Priliminary results Future prospects Why Giant Arcs The abundance of the giant arcs is sensitive to the inner structure of the clusters and cosmology ID: 213489

arcs arc giant intensity arc arcs intensity giant orientation test pixels pixel rigid 2004 detection arcfinder discrepancy gradient distribution

Share:

Link:

Embed:

Download Presentation from below link

Download Presentation The PPT/PDF document "Finding and Characterizing the Giant Arc..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.


Presentation Transcript

Slide1

Finding and Characterizing the Giant Arcs

Bingxiao Xu

Johns Hopkins UniversitySlide2

Outlines

Science motivation

Automate arcfinder

Test the arcfinder by simulations

Priliminary results

Future prospectsSlide3

Why Giant Arcs?

The abundance of the giant arcs is sensitive to the inner structure of the clusters and cosmology

The enhanced signal-to-noise ratio allows us to resolve the substructures within the highly magnified objectsSlide4

Advantages of CLASH samples

Less biased selection

Depth: ~20 orbits per cluster

Higher limiting mag for arc detection

Higher resolution for substructure

Extensive multi-bands imaging

Redshift distribution of giant arcs

Stellar population within the highly

magnified galaxiesSlide5

Arc detection algorithm

Set intensity threshold as positive median value of the difference of Gaussian (DoG) images, to obtain the image segmentation

Use eccentricity to filter out the less elongated featuresSlide6

Gradient based algorithm

Calculate the intensity gradient of the each pixel to obtain an orientation map

quantize the orientation into 4 directions and assign a digit to each pixel (1,2,3,4)Slide7

Maximum supression

The intensity of the pixels on the arc's rigid line should be larger than that of the adjacent pixel along its gradient direction and opposite direction

Slide8

Shift the pixels to local maximaSlide9

Intensity-INDEPENDENT pixel selection

The orientation of the pixels close to the arc's rigid lines should not change much

The intensity value of the pixels close to the arc's rigid lines experience in the DoG and original image during the shifting should be positiveSlide10
Slide11

Orientation Criteria

The tangential arc’s orientation should be perpendicular to the line connecting the arc and the center of cluster

Turn off the criteria at the very center (r < 100 pix ) to preserve the radial arcs Slide12

Removal of the star spikesSlide13

Test the arcfinder

(Furlanetto et al. 2013)Slide14

Detection rate testSlide15

Contamination rate testSlide16

Preliminary Results Slide17

Preliminary Results ( 176 arcs )Slide18

Future Prospects

Arc statistics

Distribution of the Einstein radius

De-lensing the giant arcs to study galaxy formation and evolution at high redshift Slide19

Arc Statistics

Order of magnitude discrepancy (Bartelmann M., et al 1998) recent works! No longer discrepancy, but tension still exists...

Possible solutions

Central BCGs and substructure (Hennawi et al. 2007; Meneghetti et al 2010)

Triaxiality of clusters (Oguri et al. 2003)

Major merger (Torri et al. 2004; Fedeli et al. 2006)

Distribution of the background sources (Wambsganss et al. 2004; Dalal et al. 2004)

…… there is still a factor of 2 discrepancy out there