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

OUTLINE 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)

PROPERTIES OF PLANCK CLUSTERS “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 )M.ROSSETTI, FG, G. FERIOLI et al (2016) MNRAS 457, 4515

DYNAMICAL STATE OF PLANCK CLUSTERS Abell 754 Abell 496 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 DISTURBED Large offset RELAXED Small offset )M.ROSSETTI, FG, G. FERIOLI et al (2016) MNRAS 457, 4515

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) DYNAMICAL STATE OF PLANCK CLUSTERS )M.ROSSETTI, FG, G. FERIOLI et al (2016) MNRAS 457, 4515

PLANCK SZ vs. X-RAY SAMPLES 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 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

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

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

COOL CORE BIAS Relaxed clusters usually feature a centrally peaked density profile, causing a prominent surface brightness peak 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

THE CC STATE OF PLANCK CLUSTERS 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) C=0.02 C=0.30 Abell 2204: CC Abell 2069: NCC NEW: M. ROSSETTI, FG, M. DELLA TORRE, G. PANTIRI, D. ECKERT et al (2017) , astro-ph:1702.06961

THE CC STATE OF PLANCK CLUSTERS Santos+ 08 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)

THE CC STATE OF PLANCK CLUSTERS Santos+ 08 CC fraction Planck: 29±4% ME-MACS: 59 ±5% 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)

Secondary CC peak in simulated distribution THE ROLE OF THE CC BIAS Simulations updated from Eckert et al (2011) to reproduce CC-bias in a ME-MACS-like survey starting from a Planck-like sample Input CC frac: 29% Output CC frac: 48% Secondary CC peak in simulated distribution

BEYOND THE CC BIAS Other effects can contribute to the difference: anti-CC bias in Planck due to masked radio-galaxies (1-2%) bias towards merging clusters in SZ due to shocked regions bias towards multiple systems in Planck Possible difference in pressure profiles between CC and NCC at large scales (>R500) X-ray underluminous clusters

X-RAY UNDERLUMINOUS CLUSTERS 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!

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

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

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

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

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

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

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

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

EDGE DETECTION 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 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.

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

CANNY EDGE DETECTOR 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) G. CAPURRI, FG, et al in prep.

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

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

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

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

SUMMARY 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

THANKS !