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Sagittarius debris in SDSS stripe 82

Sagittarius debris in SDSS stripe 82. Zhu Ling ( 朱玲 ) & Martin. C. Smith Center for Astrophysics, Tsinghua university KIAA at Peking University. Motivation. Sagittarius dwarf , being accreted by the Milky Way, forms lengthy tidal streams wrap entirely around the Milky way.

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Sagittarius debris in SDSS stripe 82

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  1. Sagittarius debris in SDSS stripe 82 Zhu Ling (朱玲) & Martin. C. Smith Center for Astrophysics, Tsinghua university KIAA at Peking University

  2. Motivation • Sagittarius dwarf, being accreted by the Milky Way, forms lengthy tidal streams wrap entirely around the Milky way. • It can provide important constrain to the shape, orientation, and mass of the Milky way dark matter halo. • Velocity dispersion of the streams is an Important constraint. • Relatively large sample of sag debris in a small region selected from SDSS stripe 82. • What constrains can this sample provide for the simulation. Majewski et al 2003 Niederste-Ostholt et al 2008

  3. Star overdensity traces the sag streams • Star overdensity traces the sag streams. • The stream passes through S82, S86, but not S76, maybe S79 • Over 25,000 stars in stripe 82 have spectra, also about 10,000 in 79 and 86 in SDSS dr7. • Overdensity ofBHBs, BSs, K/M giants and MSTOs may trace sag streams (yanny et al 2000, yanny et al 2009). 76 79 82 86 Belokurov et al 2006

  4. Select sample from S82 • Aim: A BHB+BS sample as clean as possible. • Selection Steps: 1) Color selection (A-color stars=BHB + BS + A type MS + BMP) 2) Cut in RA 3) Cut in g0 and surface gravity. (distance) BHB: Blue Horizontal Branch stars BS: Blue Stragglers BMP: Blue metal poor stars MS: main sequence stars

  5. 1) color selection - 0.3 < g-r < 0 & 0.8 < u-g < 1.5 (yanny et al 2001) A-color stars = BHBs + BSs + *** u-g g-r

  6. 2) cut in RA • Lots of smooth halo stars exist. their velocity distribution centered at 0. • Sag stream only passes a small RA region. • Sag debris have a systematic velocity offset. radial velocity RA

  7. 3) Cut in distance g=18.7 d=40kpc g=18.3 d~ 10 kpc • BHB: log(g) < 3.8, 17.2 < g < 18.7 typical absolute mag g0=0.7 (with dispersion ~ 0.2 mag) • BS: log(g)> 3.8, 18.3 < g < 19.2 typical absolute mag g0 2.7 (with dispersion ~ 1 mag) • faint: with no log(g) measurement, g > 18.7. apparant magnitude g g=17.2 d=20kpc 288 have log(g) radial velocity 15<RA<50: 416 RA Surface gravity log(g)

  8. Velocity distribution • We can not kick out the smooth halo stars which exist at the same RA & distance. • Another structure exists: a more compact, distant stream (Yanny et al. 2009, Newberg et al. 2009) • To the sag stream, a velocity gradient exist along RA, not along distance. • mean= -137 km/s. • sigma=14.7 (19.3) km/s • large error bar avg :13 km/s BHB: 76 BS: 91 faint: 123 Radialvelocity BHB+BS+fiant BSs and faint stars have been shifted 2 mag left, so magnitude represents distance. Radialvelocity g

  9. BHBs VS BSs BHB: 76 sigma: 9.5 km/s (11.4) BS: 91 sigma: 15 km/s (18.7) BHB: 76 BS: 91

  10. BHBS VS BSs BHB BS surface temperature metallicity BS: colder, higher metallicity BHB: hotter, lower metallicity

  11. Compare with previous result Manaco et al 2007 RGB Majewski et al 2004 M giants BS ~ 15 km/s BHB ~ 9.5 km/s 8.3+-0.9 km/s 10.4+-1.3 km/s BHB+BS+faint ~ 14.7 km/s

  12. How can the data constrain the simulation Law et al 2010

  13. How the velocity dispersion varies in the simulation Radial velocity Velocity dispersion Fellhauer et al 2006

  14. Summary • We select BHBs + BSs sample of sag debris in SDSS dr7. • BHBs and BSs show different velocity dispersion in this region. • BHBs: 76 stars.Velocity dispersion: 9.5 km/s • BSs : 91 stars. Velocity dispersion: 15 km/s • BHB+BS+faint: 76+91+123 stars. Velocity dispersion: 14.7 km/s. • BSs are metal richer than BHBs, and BSs are with little lower temperature. • The velocity dispersion of this sample are larger than previous results. • Analysis of simulation data shows that the velocity dispersion varies along the stream. • There are also a large number of red giants in the data, which we are looking into • We are improving the analysis by using Markov Chain Monte Carlo techniques

  15. Thank you Thank you

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