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Dynamical State of Planck-Selected vs X-ray-Selected Clusters: A Comparative Study

This study compares the dynamical properties of Planck-selected and X-ray-selected clusters, examining the evidence for significant morphological disturbances. The analysis includes the offset between the X-ray peak and the Brightest Cluster Galaxy position, as well as the concentration parameter and cool core fraction. Results are compared with other relevant cluster samples.

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Dynamical State of Planck-Selected vs X-ray-Selected Clusters: A Comparative Study

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  1. SZ vs X-ray CLUSTER SELECTION FABIO GASTALDELLO INAF-IASF MILAN M. ROSSETTI, G. PANTIRI, M. DELLA TORRE, G. FERIOLI, D. ECKERT, S. MOLENDI

  2. “The majority of newly discovered Planck clusters show evidence for significant morphological disturbances” (Planck Collaboration 2011, Planck Early results IX) Are Planck-selected objects more dynamically disturbed than X-ray selected objects ? Compare a well-defined Planck subsample with X-ray selected samples PROPERTIES OF PLANCK CLUSTERS )M.ROSSETTI, FG, G. FERIOLI et al (2016) MNRAS 457, 4515

  3. Offset between X-ray peak and BCG* position as a dynamical indicator (Hudson et al 2010, Sanderson et al 2009, Mann & Ebeling 12 ) *BCG= Brightest Cluster Galaxy Abell 496 Abell 754 DYNAMICAL STATE OF PLANCK CLUSTERS DISTURBED Large offset RELAXED Small offset )M.ROSSETTI, FG, G. FERIOLI et al (2016) MNRAS 457, 4515

  4. DYNAMICAL STATE OF PLANCK CLUSTERS Measured on almost complete sample (128/132) of Planck high S/N clusters with public X-ray (Chandra or XMM) observations and BCG identification (literature + archival analysis) )M.ROSSETTI, FG, G. FERIOLI et al (2016) MNRAS 457, 4515

  5. Literature information on the BCG – Xray peak offset available for many samples, often with heterogeneous selection. We compared only with purely X-ray selected samples PLANCK SZ vs. X-RAY SAMPLES ME-MACS(Mann & Ebeling 2012): 108, most massive high-z (>0.15) objects in RASS data HIFLUGCS (Zhang+, 2011): 62, Brightest X-ray clusters, local, low mass objects REXCESS(Haarsma+2010): 30, intermediate mass and z )M.ROSSETTI, FG, G. FERIOLI et al (2016) MNRAS 457, 4515

  6. Relaxed Disturbed PLANCK SZ vs. X-RAY SAMPLES )M.ROSSETTI, FG, G. FERIOLI et al (2016) MNRAS 457, 4515

  7. Relaxed Disturbed Kolmogorov-Smirnov test KS Statistic=0.228 Nullhypothesisprobability= 0.4% Relaxed fraction Planck: 52±4% ME-MACS: 73±4% (HIFLUGCS: 74±5%, REXCESS 77±7%) PLANCK SZ vs. X-RAY SAMPLES Fewer relaxed objects in Planck than in (all) X-ray samples

  8. Relaxed clusters usually feature a centrally peaked density profile, causing a prominent surface brightness peak COOL CORE BIAS Flux limit SB limit It affects X-rays surveys (Ix ≈ne2 ,Pesce et al 1990,Eckert et al 2011) and is predicted to be small in SZ-surveys (ISZ ≈ne, Lin et al 2015, Pipino & Pierpaoli 2010), especially with Planck

  9. DX-BCG is not a direct indicator of the presence of a prominent density peak Redo the analysis with the concentration parameter (Santos et al 2008) THE CC STATE OF PLANCK CLUSTERS C=0.30 C=0.02 Abell 2204: CC Abell 2069: NCC NEW: M. ROSSETTI, FG, M. DELLA TORRE, G. PANTIRI, D. ECKERT et al (2017) , astro-ph:1702.06961

  10. C=0.075 Santos+ 08 THE CC STATE OF PLANCK CLUSTERS Measured on a almost complete sample (169/189) of Planck high S/N clusters based on PSZ1 cosmo catalogue. Comparison with 104 ME-MACS clusters (overlap in M and z) ROSSETTI et al (2017)

  11. C=0.075 Santos+ 08 CC fraction Planck: 29±4% ME-MACS: 59 ±5% THE CC STATE OF PLANCK CLUSTERS KS test: KS statistic D=0.35, Null hyp. Prob. p0=1.7 10-5 Even more significant difference between Planck and ME-MACS ROSSETTI et al (2017)

  12. Simulations updated from Eckert et al (2011) to reproduce CC-bias in a ME-MACS-like survey starting from a Planck-like sample THE ROLE OF THE CC BIAS Input CC frac: 29% Output CC frac: 48% Secondary CC peak in simulated distribution Probability of measuring CC frac 29% in Planck-like and 59% in ME-MACS-like: 0.2% Rossetti, FG et al (2017) , astro-ph:1702.06961

  13. Cool core fraction Planck(0.3-0.6) (25 +/- 8)% SPT(0.3-0.6) (29 +/-7)% COMPARISON WITH SPT SPT data from McDonald et al (2014)

  14. OTHER SAMPLES

  15. CONSISTENT RESULTS Andrade-Santos et al (2017) Based on ESZ (Early catalogue in the first months of data, S/N > 6 z < 0.15) and brightest clusters in RASS with fx>7.5 10-12 ergs cm-2 s-1 (extended from Voevodkin & Vikhlinin, 04,all at z<0.2) ESZ (36 ± 5 %) vs X-ray (60 ± 7 %) )

  16. NOT SO CONSISTENT RESULTS ? Nurgaliev et al (2017)

  17. NOT SO CONSISTENT RESULTS ? Eckert et al (2011) 400 SD HAS A HIGH FLUX CUT OF THE SAMPLE WITH RESPECT TO THE FLUX LIMIT. ITS AIM WAS TO HAVE A REPRESENTATIVE CLUSTER POPULATION AT Z=0.3-0.8 (BURENIN+07)

  18. “THERE PERSISTS A MISCONCEPTION THAT SZ SELECTED CLUSTERS ARE, ON AVERAGE, MORE OFTEN MERGERS THAN X-RAY SELECTED CLUSTERS”IN TENSION WITH MANY RESULTS “WE CONCLUDE THAT THE COMMON MISCONCEPTION THAT SZ SELECTED SAMPLES CONTAIN A NON-REPRESENTATIVE FRACTION OF MERGERS STEMS FROM SOME COMBINATION OF THESE TWO POINTS” (PLANCK PSF AND ATTENTION PAID TO INITIAL DETECTION OF MAJOR MERGERS) ACTUALLY WE ARE ASSUMING THE CONTRARY: THAT SZ (PLANCK AND SPT) CONTAIN A REPRESENTATIVE MIX OF THE SUBCLASSES OF CLUSTERS NOT SO CONSISTENT RESULTS ? Nurgaliev et al (2017)

  19. Othereffects can contribute to the difference: • anti-CC bias in Planck due to masked radio-galaxies(1-2%) • biastowardsmerging clusters in SZ due to shockedregions • biastowardsmultiple systemsin Planck • Possibledifference in pressure profilesbetween CC and NCC atlarge scales(>R500) • X-rayunderluminous clusters BEYOND THE CC BIAS

  20. Clusters in Planck but NOT in ME-MACS Presence of X-ray underluminous objects in Planck catalogue (Planck Coll. 2016) In our sample they are all NCC! X-RAY UNDERLUMINOUS CLUSTERS

  21. SZ SAMPLES ARE DIFFERENT IN CC FRACTION THAN X-RAY ONES CLEARLY POINTING TO DIFFERENT SELECTION PROCESSES (ROLE OF CC BIAS IN X-RAY SAMPLES) • SZ SAMPLES CONTAIN A REPRESENTATIVE MIX OF THE RELAXED AND DISTURBED SYSTEMS • CC BIAS PLAYS A MAJOR ROLE IN EXPLAINING THE DISCREPANCY. THERE IS POSSIBLY ROOM FOR A POPULATION OF X-RAY UNDER-LUMINOUS OBJECTS SUMMARY

  22. APPLICATION OF EDGE DETECTION TECHNIQUES TO WELL KNWON MERGING CLUSTERS TO SEARCH FOR SURFACE BRIGHTNESS DISCONTINUITIES, i.e. COLD FRONTS, SHOCK FRONTS (G. CAPURRI) BONUS FEATURE: EDGE DETECTION

  23. Image smoothing ⟶ kernel (e.g. Gaussian) • Calculation of the gradient ⟶ finite difference technique • The two processes in one go ⟶ convolution with a single kernel GRADIENT BASED METHODS SOBEL FILTER G. CAPURRI, FG, et al in prep.

  24. Developed by Canny (1986) as an optimal filter • Sobel filter + direction of the gradient • Edge definition by choice of two thresholds through an hysteresis process (THINNING) CANNY EDGE DETECTOR G. CAPURRI, FG, et al in prep.

  25. Gaussiansmoothing • Calculation of Laplacian UNSHARP MASKING Numerical approximation → Difference of two Gaussians G. CAPURRI, FG, et al in prep.

  26. SOBEL FILTER smoothing σ = 5 pixel UNSHARP MASKING σ₁= 2 pixel σ₂= 15 pixel BULLET CLUSTER G. CAPURRI, FG, et al in prep.

  27. CANNY EDGE DETECTOR Smoothing with σ = 3 pixel High = 0.9999 Low = 0.1 CANNY EDGE DETECTOR Smoothing with σ = 3 pixel High = 0.95 Low = 0.1 BULLET CLUSTER G. CAPURRI, FG, et al in prep.

  28. Output of Canny and radio contours by Shimwell+15 BULLET CLUSTER G. CAPURRI, FG, et al in prep.

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