Local Group Astrostatistics

Transcript

1. the Potential of the Milky Way adrian price-whelan kathryn johnston TIDAL STREAMS & david hogg
2. Tidal Streams Distances 10–100 kpc
3. Tidal Streams Distances 10–100 kpc ∼20 known around Milky Way (100 total?)
4. Tidal Streams Distances 10–100 kpc ∼20 known around Milky Way (100 total?) Sgr, Orphan, GD1, Pal 5 most prominent (Many other short streams, many w/ no progenitors)
5. Tidal Streams Stream morphology set by progenitor orbit Dynamically cold (small spreads in orbital properties) (and internal dynamics…)
6. Tidal Streams Stream morphology set by progenitor orbit Dynamically cold (small spreads in orbital properties) (and internal dynamics…) Can measure host galaxy potential
7. With near-future surveys we can measure 6D kinematics (l, b, D, µl, µb, v los )
8. RR Lyrae as dynamical tracers
9. RR Lyrae as dynamical tracers Bright, MV ~ 0.5
10. RR Lyrae as dynamical tracers Found in many streams (some may only have a few…) Bright, MV ~ 0.5
11. RR Lyrae as dynamical tracers Found in many streams (some may only have a few…) Bright, MV ~ 0.5 Easy to identify from light curve
12. RR Lyrae as dynamical tracers Found in many streams (some may only have a few…) Bright, MV ~ 0.5 Easy to identify from light curve Period-Luminosity relation for precise distances (2% uncertainty in relative distances)
13. “Orphan stream” (no uncertainty)
14. M giants (15-20%)
15. BHB stars (10%)
16. RR Lyrae with PL (2%)
17. RR Lyrae in Gaia post-launch estimates Transverse velocity error
18. post-launch estimates 15 km/s at 30 kpc RR Lyrae in Gaia
19. “Orphan stream” (no uncertainty)
20. HSTPROMO (~0.1 mas/yr)
21. Gaia (~0.03 mas/yr)
22. How do we use these data?
23. How do we use these data? We need fast generative models
24. How do we use these data? We need fast generative models N-body
25. Rewinder
26. potential center L1 L2 Stream formation
27. potential center L1 L2 Stream formation
28. t = 0 Price-Whelan et al. (2014) Rewinder L1 L2 stream stars progenitor
29. t = 0 Price-Whelan et al. (2014) Rewinder L1 L2
30. Rewinder t = -1 Price-Whelan et al. (2014)
31. Rewinder t = -1 evaluate likelihood Price-Whelan et al. (2014)
32. Rewinder t = -2 Price-Whelan et al. (2014)
33. Rewinder t = -2 evaluate likelihood Price-Whelan et al. (2014)
34. Rewinder t = -3 Price-Whelan et al. (2014)
35. ⌧ub K unbinding time leading/trailing tail mass vs. time any parametrization per star progenitor potential (l, b, d, µl, µb, vr) (l, b, d, µl, µb, vr) Price-Whelan et al. (2014) Rewinder M(t)
36. ⌧ub K unbinding time leading/trailing tail mass vs. time any parametrization per star progenitor potential (l, b, d, µl, µb, vr) (l, b, d, µl, µb, vr) marginalize out Price-Whelan et al. (2014) Rewinder M(t)
37. ⌧ub K unbinding time leading/trailing tail mass vs. time any parametrization per star progenitor potential (l, b, d, µl, µb, vr) (l, b, d, µl, µb, vr) marginalize out FFFUUUU Price-Whelan et al. (2014) Rewinder M(t)
38. −100 −50 0 50 X [kpc] −100 −50 0 X [kpc] x “DATA” 8 “RR Lyrae” stars Gaia velocity errors 2% distance errors + progenitor Price-Whelan et al. (2014)
39. 8 RR Lyrae stars Gaia velocity errors 2% distance error Price-Whelan et al. (2014)
40. This is fine as a test, but we are all liars.
41. We must move away from static, analytic potentials! With restrictive models, we will make uninterpretable, biased measurements
42. For example, Basis Function Expansions ⇢nlm = ˜ ⇢nl(r) Ylm(✓, ) nlm = ˜ nl(r) Ylm(✓, )
43. Current stream models can recover true parameters of true potential (from “observed” simulations)
44. Current stream models can recover true parameters of true potential (from “observed” simulations) Moving to more general potential models & real obs. uncertainties is going to hurt (but we have to do it)
45. Current stream models can recover true parameters of true potential (from “observed” simulations) Rewinder can do this, but more work to be done! Moving to more general potential models & real obs. uncertainties is going to hurt (but we have to do it)