Image credit: Jee et al. 2014, NASA, ESA

Transcription

1 SnowCluster 2015 The return of the merging galaxy subclusters of ACT-CL J , El Gordo? (arxiv: ) Karen Y. Ng Will Dawson, David Wittman, James Jee, Jack Hughes, Felipe Menanteau, Cristóbal Sifón 03/20/2015 Image credit: Jee et al. 2014, NASA, ESA 1

2 Sub-clusters have collided, moved past max. sep, and (arxiv: ) E N Image credit: Jee et al. 2014, NASA, ESA 2

3 Ready for a second collision? E N Image credit: Jee et al. 2014, NASA, ESA 3

4 Overview (Very brief) Background of El Gordo Method: Monte Carlo simulation Inferring the merger scenario with radio relic Proposed trajectory of different cluster components in the merger of El Gordo Pending questions for simulators 4

5 Unusual facts about merging galaxy cluster El Gordo High redshift z = 0.87 Total mass ~ Need a volume of ~ (Menanteau et al. 2013) (Jee et al. 2014) to find El Gordo informative observables at multiple wavelengths 5

6 Image credit: Jee et al. 2014, NASA, ESA E N Dark matter (DM) contours Jee et al. 2014

7 Intracluster medium (ICM) Dense cool core Menanteau et al E N Image credit: Jee et al. 2014, NASA, ESA 7

8 Radio emission tracing merger shock fronts Lindner et al Menanteau et al E N Image credit: Jee et al. 2014, NASA, ESA 8

9 Monte Carlo simulation (dawson 2013): Simplified model to infer unknowns 9

10 Two most important unknowns to infer 1)Projection angle 10

11 Two most important unknowns to infer only have one snapshot 2) time-dependence, e.g. time-since-pericenter (TSP) 11

12 (Dawson 2013) Data driven Monte Carlo simulation draw 2 million sets of initial conditions from: weak lensing studies (Jee 2014) spectroscopic redshifts of member galaxies (Sifón 2014 and Menanteau 2013) distance of dark matter density peaks 12

13 (Dawson 2013) Monte Carlo simulation 13

14 (Dawson 2013) Monte Carlo simulation Time-since-pericenter 14

15 (Dawson 2013) Use Monte Carlo weights to incorporate known info Time-since-pericenter 15

16 (Dawson 2013) Use Monte Carlo weights to incorporate known info weight on time-since-pericenter (TSP) Time-since-pericenter 16

17 Design Monte Carlo weights based on radio relic properties Observed relic of cluster merger CIZA J polarization merger direction van Weeren et al

18 Predicted projection as a function of integrated polarization fraction Ensslin et al

19 Predicted projection as a function of integrated polarization fraction Ensslin et al.1998 Simulation Edge-on Face-on Polarization fraction Skillman S. W. et al.,

20 Predicted projection as a function of integrated polarization fraction polarization weights Ensslin et al.1998 Simulation Edge-on Face-on Polarization fraction Skillman et al.,

21 Compare TSP to physical time scales sound-crossing time for gas for ICM wake to disappear radio relics observable time-scale Vazza et al. 21

22 Comparison to physical time scales sound-crossing time for gas for comet-tails to disappear radio relics observable time-scale Vazza et al. Outgoing 22

23 Comparison to physical time scales sound-crossing time for gas for comet-tails to disappear radio relics observable time-scale Vazza et al. Outgoing scenario Returning scenario 23

24 Use Monte Carlo results to predict radio relic location for each merger scenario { { estimates from Monte Carlo 24

25 Use Monte Carlo results to predict radio relic location for each merger scenario { { estimates from Monte Carlo put an educated guess for the time-averaged shock propagation velocity 25

26 Finding time-averaged merger shock propagation speed in center of mass (CM) frame Cannot directly use Mach number of shock dependent on temperature profile in the reference frame of local gas medium gas medium speed for Bullet cluster ~1100 km /s (Springel & Farrar 2007)

27 Finding merger shock propagation speed in center of mass frame in each Monte Carlo realization j : { time-averaged shock velocity { pericenter velocity in CM frame 27

28 Finding merger shock propagation speed in center of mass frame in each Monte Carlo realization j : { time-averaged shock velocity { collisional velocity in CM frame most likely range (Springel & Farrar 2007 Paul et al etc.) range that we examined 28

29 NW relic PDF outgoing returning Integrate the pdf with respect to separation marginalize over projected sep. from C.M. SE relic probability ratio favors returning scenario PDF outgoing ~2.1 (NW) and ~460 (SE) returning projected sep. from C.M. 29

30 Comparison of El Gordo to the Bullet Cluster Similarities: relative outgoing phase similar projection angle of ~20 degrees (Dawson 2013) both major bimodal merger 30

31 Comparison of El Gordo to the Bullet Cluster Bullet Cluster Clowe, D et al

32 Bullet Cluster is a classic example of the outgoing scenario Bullet Cluster Clowe, D et al Red and blue curves based on simulations by Matthis et al and Vazza et al. 2012, physical motivation described in Markevitch & Vikhlinin

33 A returning scenario can explain positions of components for El Gordo El Gordo (arxiv: ) Image credit: Jee et al. 2014, NASA, ESA Red and blue curves based on simulations by Matthis et al. and Vazza et al. 2012, physical motivation described in Markevitch & Vikhlinin 2007

34 Pending questions for simulators better understanding of shock propagation in the CM frame more quantitative bound on the observable time-scale of radio relic 34

35 Insights from El Gordo about using cluster mergers as SIDM probes (arxiv: ) unexpected direction of travel of components from returning scenario need better understanding of how components travel offset uncertainties (Ng et al. in prep) 35

36 Summary about this study of El Gordo (arxiv: ) showed first use of polarization info of radio relic to reduce uncertainties for estimates showed that the returning scenario is more favored Image credit: Jee et al. 2014, NASA, ESA 36 73

37 The Merging Cluster Collaboration Image credit: Jee et al. 2014, NASA, ESA 37 83

38 Thank you for your attention! Questions? Image credit: Jee et al. 2014, NASA, ESA

39 Image credit: Jee et al

40 Sensitivity analyses perturbed alpha = 21 by +/- 5 degrees, the most extreme change: 40

41 Bootstrapped member galaxy redshifts Menanteau

42 Galaxy memberships Menanteau

43 43

44 How to marginalize 44

45 SE relic NW relic 45

46 Merger brightness enhancement from cosmological simulation Skillman et al