Coherent Satellite Planes around Cosmological Milky Way Hydrodynamic Simulations
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1 Coherent Satellite Planes around Cosmological Milky Way Hydrodynamic Simulations Sheehan H Ahmed & Dr. Alyson Brooks Hammer-Aitoff Projections Cyan = Subhalo Position Magenta = Subhalo Angular Momentum Vector DM only DM + baryons Planes exist Similar in DM and DM + baryons Some with coherent rotation
2 Examining(the(Evolving( Proper2es(of(Dwarf(Galaxies( Kenza&S.&Arraki( 10 4 (M ( <(M star (<(10 5 (M ( 10 5 (M ( <(M star (<(10 6 (M ( 10 6 (M ( <(M star (<(10 7 (M ( M star (>(10 7 (M (
3 Spa$al&segrega$on&in&star&cluster&merger& remnants&
4 Deason, van der Marel, Guhathakurta et al. 2013
5 Signatures of the M31-M32 Galactic Collision Marion Dierickx, Avi Loeb (Harvard CfA) and Laura Blecha (U. Maryland) 2014 ApJL 788:L36 The Astrophysical Journal Letters, 788:L38 (6pp), 2014 June 20 Andromeda s nested ring-like structure, and M32 s unusual compact elliptical morphology Dierickx, Blecha, & Loeb (a) (b) (c) (d) WISE"view"of"Andromeda" ~"10"kpc" Figure 1. Simulated collision between Andromeda and M32 shown at the time of best match to current observations. Panels (a) and (b): gas morphology viewed face-on and in projection on the sky. Panel (c): face-on M32 dark matter density map (note the larger scale). Panel (d): stellar density map viewed in projection. In panel (b), a dashed ellipse marks the location of M31 s 10 kpc pseudo-ring. The dim, incomplete outer ring tentatively identified in infrared images (Gordon et al. 2006) is also reproduced. Black and white lines indicate the trajectories of Andromeda and M32, respectively, and include plus signs spaced every 500 Myr. Angular scales are calculated assuming a distance to M31 of 780 kpc. (A color version of this figure is available in the online journal.) LG Astrostatistics, May 2015 successfully replicated with the first selfconsistent model of M32 s passage through Andromeda s disk. Table 2 of existing distance measurements (Freedman 1989; Monachesi Velocity Properties of the Fiducial Orbit et al. 2011; Sarajedini et al. 2012). The prediction that M32 lies 10% closer to us than M31 may be testable with improvements in the calibration of red giant branch distance indicators. Contrary to previous models, which place the collision less than 200 Myr ago (Block et al. 2006; Gordon et al. 2006), in our simulation the passage occurred 800 Myr in the past. This Velocity Magnitude (km s 1 ) Current full v Current v transverse Andromeda (M31) M
6 roughly doubled by SDSS I More satellite galaxies have been discovered in the DES data (Bechtol et al., 2015; Koposov et al., 2015) Figure 1: Previously known Milky Way satellite galaxies (blue; McConnachie, 2012) and D Bechtol et al., 2015) in Galactic coordinates (Mollweide projection). The gr I A likelihood technique can combine spatial 1 2 Alex Drlica-Wagner and candidates Keith Bechtol(red; the logarithmic density of stars with r < 22 from SDSS and DES (full DES footprint outli and photometric information to increase on behalf of the Dark Energy Survey Collaboration DARK ENERGY 1 firstlaboratory year of DES observed a region of 1,800 deg2. Fermi National Accelerator sensitivity to faint satellite galaxies 2 SURVEY University of Wisconsin Madison A Likelihood-Based Search for Milky Way Satellite Galaxies Likelihood Technique Summary: We describe a maximum-likelihood technique to discover and characterize Milky Way satellite galaxies. Introduction Stellar Membershi Milky Way Satellites Galaxies Milky Way satellite galaxies: I Unique laboratories for galaxy formation I Building blocks for large-scale structure I Most dark matter dominated objects The Dark Energy Survey (DES): I 5,000 deg2 of the southern Galactic cap I 5 photometric bands (g, r, i, z, Y ) I 525 nights (spread over 5 years) I 5-year depth of r 24 mag Searching for Milky Way satellites: I The number of known satellites Figure 2: The model for a was satellite galaxy is composed of a spatial kernel (left) and a stellar isochrone roughly doubled by SDSS weighted by an initial mass function (center). The predicted number of observable stars incorporates spatial I More satellite galaxies have been discovvariations in the depth of the survey (right). ered in the DES data (Bechtol et al., 2015; Koposov et al., 2015) Figure 1: Previously known Milky Way satellite galaxies (blue; McConnachie, 2012) and DES dwarf galaxy candidates (red; Bechtol et al., 2015) in Galactic coordinates (Mollweide projection). The gray scale indicates I A likelihood technique combine spatial represents Assume that a can stellar catalog a Poisson realization the logarithmic density of stars with r < 22 from SDSS and DES (full DES footprint outlined in red). The and photometric information to increase firstforeground year of DES observed a region 1,800 deg2. of (1) ato field contribution including stars andof missensitivity faint satellite galaxies classified galaxies, and (2) a putative satellite galaxy Likelihood Technique I Build an empirical model for the local field contribution I Model the spatial distribution of satellite stars with an elliptical density kernel (Figure 2a) I Model the photometric distribution of satellite stars with a metal-poor isochrone weighted by a stellar initial mass function (Figure 2b) I Use the survey depth to predict the number of observable stars (Figure 2c) Figure 2: The model for a satellite galaxy is composed of a spatial kernel (left) and a stellar isochrone weighted by an initial function (center). The predicted number of observable stars incorporates spatial I Scan themass survey area maximizing the Poisson likelihood with variations in the depth of the survey (right). respect to satellite model parameters Assume that a stellar statistical catalog represents a Poisson realization I Calculate significance of putative satellites with a of (1) a field contribution including foreground stars and mistest satellite galaxy classifiedlikelihood galaxies, and ratio (2) a putative I Build an empirical model for the local field contribution The the likelihood procedure results in ana ellipprobability that each I Model spatial distribution of satellite stars with tical kernel of (Figure is adensity member the2a)putative satellite galaxy (Figure 3) I Model the photometric distribution of satellite stars with a metal-poor isochrone weighted by a stellar initial mass function (Figure 2b) star Figure 4: Comparison of DES p bership probabilities for the Ret galaxy and spectroscopic memb mined from velocities measured Radial velocities provide an a of stellar membership Stellar Membership I Photometric membership p be used to select targets fo observations (Simon et al et al., 2015) I Spectroscopic membership locities can be used to info the likelihood procedure Figure 4: Comparison of DES photometric membership probabilities for the Reticulum II satellite galaxy and spectroscopic membership as determined from velocities measured by M2FS. References Radial velocities provide an additional probe of stellar membership I Photometric membership probabilities Figure 3: Photometric member- can be used to select targets for spectroscopic ship probabilities for stars associobservations (Simon et al., 2015; Walker ated with Reticulum II galaxy. et al.,the 2015) I Spectroscopic membership from radial velocities can be used to inform and validate Bechtol, K. et al. 2015, accep Koposov, S. E. et al. 2015, ac McConnachie, A. W. 2012, A Simon, J. D. 2015, submitted Walker, M. G. 2015, submitte
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