The integrated cluster finder - a part of the ARCHES project Alexey - - PowerPoint PPT Presentation

The integrated cluster finder - a part of the ARCHES project

Alexey Mints, Axel Schwope and ARCHES consortium

{Leibniz-Institut f¨ ur Astrophysik Potsdam (AIP)}

November 30, 2015

ARCHES Integrated cluster finder

Goal

Search for galaxy clusters and estimate their parameters (redshift, sizes) in multi-wavelength photometric and spectroscopic data, using X-ray information on the expected cluster positions.

Catalog table

catalogue Frequency range Covered Area (deg.2) Number

• f objects

3XMMe clusters

• verlap

Catalogs used AllWISE MIR 41000 747,634,026 1543 UKIDSS JHK 4000 82,655,526 298 SDSS (DR9) ugriz 14555 932,891,133 959 CFTHLS-Wide photo-z 157 35,651,677 149 CFTHLS-Deep photo-z 5.25 2,293,851 38 ALHAMBRA photo-z 4 441,303 18 Spectroscopic catalogs used SDSS-BOSS spec

• 859,322
• VIPERS

spec

• 57,204

Cluster finder basics

◮ Use optical AND infrared colors ⇒ we need a

cross-match tool (ARCHES Xmatch)

Cluster finder basics

◮ Use optical AND infrared colors ⇒ we need a

cross-match tool (ARCHES Xmatch)

◮ Utilize color-redshift relation to estimate redshift, a.k.a.

redMaPPer (Rykoff et al., 2014)

Cluster finder basics

◮ Use optical AND infrared colors ⇒ we need a

cross-match tool (ARCHES Xmatch)

◮ Utilize color-redshift relation to estimate redshift, a.k.a.

redMaPPer (Rykoff et al., 2014)

◮ Use spectral observations to calibrate color-redshift

relation

Cluster finder basics

◮ Use optical AND infrared colors ⇒ we need a

cross-match tool (ARCHES Xmatch)

◮ Utilize color-redshift relation to estimate redshift, a.k.a.

redMaPPer (Rykoff et al., 2014)

◮ Use spectral observations to calibrate color-redshift

relation

◮ Estimate background and spurious detection probability

Cluster finder basics

◮ Use optical AND infrared colors ⇒ we need a

cross-match tool (ARCHES Xmatch)

◮ Utilize color-redshift relation to estimate redshift, a.k.a.

redMaPPer (Rykoff et al., 2014)

◮ Use spectral observations to calibrate color-redshift

relation

◮ Estimate background and spurious detection probability ◮ Inputs: position (X-ray source coordinates)

Cluster finder basics

◮ Use optical AND infrared colors ⇒ we need a

cross-match tool (ARCHES Xmatch)

◮ Utilize color-redshift relation to estimate redshift, a.k.a.

redMaPPer (Rykoff et al., 2014)

◮ Use spectral observations to calibrate color-redshift

relation

◮ Estimate background and spurious detection probability ◮ Inputs: position (X-ray source coordinates) ◮ Assumptions: luminosity function, density profile,

color-redshift relation...

Color-redshift relation

Limitations of SDSS, UKIDSS and WISE

Cluster membership probability

λ(z) =

• r<R

P(z, r, m, χ2(z, C)) λ – multiplicity; z – redshift, m – magnitude, C – colors, r – distance from the X-ray source; χ2 – the probability of the galaxy with colors C to have redshift z (incomplete set of colors can be used);

Cluster membership probability

λ(z) =

• r<R

P(x = (z, r, m, χ2(z, C))) =

• λ(z)u(x)

λ(z)u(x) + b(x) u(x) – density profile of the cluster (NFW ⊗ LF); Background is tabulated as b(z, m, χ2); Solved iteratively for λ for each redshift on a pre-defined grid (from 0.02 to 0.8 with a step of 0.01).

Example of λ(z).

Spurious detection probability

Based on approximation of the distribution of spurious detections in λ and rNFW . Confidence = 1 - pspurious

Validation

Tests against other cluster catalogs.

Cluster catalogue Number of objects Subset used for testing Reference z range Used objects Recovered Wen and Han 1757 0.16-0.8 524 313 (60%) Wen et al. (2011) Takey et al. 530 0.03-0.7 515 491 (95%) Takey et al. (2013)

3XMMe cluster sample

◮ 1543 extended X-ray sources (from 3XMMe), 850 with

SDSS photometry;

◮ Run ICF on these sources: 729 detections; ◮ 509 detections after duplicate removal (361 with

spectroscopic redshift);

◮ Select X-ray spectra from XMM archive; ◮ Fit temperature and luminosity for spectra;

Redshift distribution

Products

Integrated Cluster Finder server

Command-line client for ICFs (Python and bash); ICF web interface (http://serendib.unistra.fr/icf), Hands-on session tomorrow;

Integrated Cluster catalog

List of cluster candidates; List of possible cluster members; Associations with other cluster catalogs; Images (SDSS colour + XMM contours);

Thank you for the attention!

Colours

Peak detection

photo-z

pν(z) = 1 Σphotoz

• 2πσ2

photoz

exp −(z − zphot)2 2σ2

photoz

(1) σ2

photoz

= δz2

phot + 4∆z2

(2) Σphotoz = erf

• ∆z
• 2

σ2

photoz

• (3)

Weighted radius

The inverse cumulative NFW function F −1(t) : F −1(F(r)) = r. rNFW = F −1 n

i=1 F(ri)

n

• (4)

rNFW ≈ 0.5 if members are distributed perfectly at random.

Extra numbers

729 detections in 516 fields 509 detections in 440 fields after duplicate removal (361 (329) with spectroscopic redshift);

3XMMe cluster cuts

• 1. Observations with high background, hotspots and

corrupted mosaic mode data were removed;

• 2. Low exposure (< 5ks) observations were removed;
• 3. 0 < EP EXTENT < 80 arcseconds. This only considers

detections with real extent that is below the upper limit

• f 80 arcsecs imposed in the source detection step within

the standard XMM-Newton pipeline processing.

• 4. EP EXTENT ERR < 10. Excludes poorly constrained

extent values.

• 5. The galactic latitude must satisfy the constraint

|bII| > 20.3 degrees

• 6. EP 9 DET ML > 10. Demands a minimum detection

likelihood value of 10 in band 9 (XID band = 0.5-4.5 keV)

• 7. SUM FLAG < 2. Excludes manually flagged detections

and also detections with sum flag = 2 – generally detections that are extended and close to other sources or within the envelopes of other extended sources.