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HOW TO FIND MERGING CLUSTERS AND THE FRONTS INSIDE THEM

This study investigates the dynamical state and cool core state of Planck-selected clusters and compares them to X-ray-selected samples. It also explores the role of the cool core bias and the presence of X-ray underluminous clusters in the Planck catalog. Additionally, the study examines the connection between shock fronts and particle acceleration in merging clusters.

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HOW TO FIND MERGING CLUSTERS AND THE FRONTS INSIDE THEM

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  1. HOW TO FIND MERGING CLUSTERS AND THE FRONTS INSIDE THEM FABIO GASTALDELLO INAF-IASF MILAN G. CAPURRI, A. BOTTEON, M. ROSSETTI, G. PANTIRI, M. DELLA TORRE, G. FERIOLI, G. BRUNETTI, R. KALE, D. DALLA CASA,D. ECKERT, S. MOLENDI, S. GHIZZARDI, S. DE GRANDI, S. ETTORI

  2. DYNAMICAL STATE AND COOL CORE STATE OF A PLANCK SELECTED SAMPLE AND LESSONS LEARNED ABOUT SZ vs X-RAY SELECTION (M. ROSSETTI, G. FERIOLI, G.PANTIRI, M. DELLA TORRE) • SHOCK FRONTS AND PARTICLE ACCELERATION IN RELICS IN A115 AND EL GORDO (A. BOTTEON) • APPLICATION OF EDGE DETECTION TECHNIQUES TO WELL KNWON MERGING CLUSTERS TO SEARCH FOR SURFACE BRIGHTNESS DISCONTINUITIES, i.e. COLD FRONTS, SHOCK FRONTS (G. CAPURRI) OUTLINE

  3. “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 expected? 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

  4. 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

  5. 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

  6. 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

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

  8. 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

  9. Relaxed clusters usually feature a centrally peaked density profile, causing a prominent surface brightness peak COOL CORE BIAS Flux limit or 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

  10. 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

  11. 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)

  12. 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)

  13. 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

  14. 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

  15. 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

  16. Relics are diffuse and polarized radio sources on Mpc scales found in the outskirts of GC in merging state RELIC-SHOCK CONNECTION

  17. ABELL 115 A. BOTTEON, FG, et al (2016) MNRAS 460, L84

  18. THE SHOCK IN ABELL 115 A. BOTTEON, FG, et al (2016) MNRAS 460, L84

  19. Consistent with an off-axis merger with a mass ratio 1:3, scenario consistent with the optical analysis of Barrena+07 THE SHOCK POSITION

  20. ELECTRON RE-ACCELERATION IN A115 DSA requires too large acceleration efficiency for acceleration of thermal electrons, so re-acceleration is invoked (e.g. Bonafede+14, Shimwell+15, van Weeren+17). A115 also requires very high acceleration efficiency so a population of seed electrons (provided by radio galaxies) alleviates the energetic requirements

  21. EL GORDO A. BOTTEON, FG, et al (2016) MNRAS 463, 1534

  22. THE SHOCK IN EL GORDO A. BOTTEON, FG, et al (2016) MNRAS 463, 1534

  23. THE SHOCK IN EL GORDO One of the few M ≥ 3 shocks known in clusters (Bullet; Markevitch+02, A665; Dasadia+16). For such relatively strong shocks DSA for thermal electrons can not be ruled out

  24. EDGE = rapid change in the brightness of the image • Maximum of the modulus of the gradient • ⟶ Gradient Based Methods • Zeros of the Laplacian • ⟶ Zero Crossing Methods EDGE DETECTION One clear problem is Poissonian noise: you need to smooth the image and reach a compromise between reduction of the noise and conservation of the features G. CAPURRI, FG, et al in prep.

  25. 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.

  26. 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.

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

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

  29. 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.

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

  31. 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 • RELIC-SHOCK CONNECTION STRENGTHNED BY OTHER TWO DISCOVERED CASES, ONE WITH A RELIC IN A VERY UNUSUAL POSITION (A115) AND ONE OF THE FEW STRONG SHOCK CASES IN CLUSTERS (EL GORDO) • EDGE DETECTION TECHNIQUES APPLIED TO MERGING CLUSTERS CAN BE APPLIED TO REVEAL INTERESTING FEATURES SUMMARY

  32. THANKS !

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