Gaia DR2 and/versus RAVE DR5: application for the semi-analytic thin disk model

Transcription

1 Gaia DR2 and/versus RAVE DR5: application for the semi-analytic thin disk model Speaker: Supervisor: Kseniia Sysoliatina (ARI) Prof. Dr. Andreas Just Gaia DR2 workshop Heidelberg,

2 Outline (1) Comparison of the RAVE DR5 and Gaia DR2 content: distances radial velocities (2) A semi-analytic thin disk model: Validation of findings from TGASxRAVE with Gaia_DR2xRAVE_DR5 Age-metallicity relation from the Gaia_DR2xRAVE_DR5 Red Clump sample

3 Radial Velocity Experiment (RAVE) RAVE kinematically unbiased spectroscopic survey of medium resolution (R~7800). Science with RAVE: Local and Global parameters of the Milky Way (MW escape speed, separation of thin and thick disks, velocity ellipsoid orientation, asymmetric drift, etc.) Substructure as probles of formation (signatures of in-falling stellar streams, role of the mergers in thick disk formation) Stellar astrophysics (variables, binary stars...) More on UK Schmidt Telescope, Anglo-Australian Observatory (Steinmetz+2006, see Kunder+2017 on DR5) Gaia_DR2xRAVE_DR5 ( , 93% of RAVE DR5) positions, proper motions G, G_BP, G_RP photometry geometric distances (Bailer-Jones+2018) radial velocities chemical abundances (Fe, Mg, Al, Si, Ti, Ni) stellar parameters (logg, Teff) spectrophotometric distances (McMillan+2017) radial velocitites

4 Radial Velocity Experiment (RAVE) RAVE kinematically unbiased spectroscopic survey of medium resolution (R~7800). Science with RAVE: Local and Global parameters of the Milky Way (MW escape speed, separation of thin and thick disks, velocity ellipsoid orientation, asymmetric drift, etc.) Substructure as probles of formation (signatures of in-falling stellar streams, role of the mergers in thick disk formation) Stellar astrophysics (variables, binary stars...) UK Schmidt Telescope, Anglo-Australian Observatory (Steinmetz+2006, see Kunder+2017 on DR5) More on Gaia_DR2xRAVE_DR5 (512, 971 = 93% of RAVE DR5) positions, proper motions G, G_BP, G_RP photometry geometric distances (Bailer-Jones+2018) radial velocities? chemical abundances (Fe, Mg, Al, Si, Ti, Ni) stellar parameters (logg, Teff) spectrophotometric distances (McMillan+2017) radial velocitites

5 Gaia DR2 and RAVE DR5 distances

6 Gaia DR2 and RAVE DR5 distances Bayesian method Gaia DR2 distances (Bailer-Jones+2018) Gaia DR2 parallaxes RAVE DR5 distances (McMillan+2017): TGAS parallaxes (logg,teff), [M/H] (J,H,K)2MASS Relative distance errors:

7 Gaia DR2 and RAVE DR5 distances Gaia DR2 and RAVE DR5 distances of comparable precision TGAS-based distances Geometric distances from Gaia DR2 are same or better quality then RAVE DR5 spectrophotometric distances!

8 Gaia DR2 and RAVE DR5 distances Gaia DR2 and RAVE DR5 distances of comparable precision TGAS-based distances Geometric distances from Gaia DR2 are same or better quality then RAVE DR5 spectrophotometric distances!

9 Gaia DR2 and RAVE DR5 distances Defining a «good distance subset»: ed/d RAVE_DR5 ed/d Gaia_DR2 Contains 9,873 stars (only 2% of the Gaia_DR2xRAVE_DR5 cross-match) Conclusions: RAVE DR5 distances are only useful: for a small subset of ~104 stars for giants (e.g., red clump) and upper main sequence mainly close to the Galactic plane In all other cases use Gaia DR2 distances OR wait for the update of RAVE (DR6 with new stellar parameters, abundances and distances constrained by Gaia DR2 parallaxes)

10 Good RAVE distances and Conclusions #1 Defining a «good distance subset»: ed/d RAVE_DR5 ed/d Gaia_DR2 Contains 9,873 stars (only 2% of the Gaia_DR2xRAVE_DR5 cross-match) Conclusions #1: RAVE DR5 distances are only useful: for a small subset of ~104 stars for giants (e.g., red clump) and upper main sequence mainly close to the Galactic plane In all other cases use Gaia DR2 distances OR wait for the update of RAVE (DR6 with new stellar parameters, abundances and distances constrained by Gaia DR2 parallaxes)

11 Gaia DR2 and RAVE DR5 radial velocities

12 Gaia DR2 and RAVE DR5 radial velocities See Soubiran+2018 and especially Katz+2018 on validation of Gaia DR2 radial velocitites. (RAVE Collaboration) (Katz+2018)

13 Gaia DR2 and RAVE DR5 radial velocities Gaia_DR2xRAVE_DR5 with Gaia radial velocitites available = 456, 316 (89% of RAVE DR5)

14 Gaia DR2 and RAVE DR5 radial velocities Something is wrong! Helpful questions: Are these stars misidentified? Where are these stars (d, l, b)? Are they faint sources? Do they have large radial velocity errors in RAVE? Defining a «bad radial velocity subset»: Def1: Vr, Gaia Vr, RAVE 3(eVr, Gaia+eVr, RAVE) Def2: Vr, RAVE > 200 km/s && Vr, Gaia < 100 km/s 24, 442 stars 2, 511

15 Gaia DR2 and RAVE DR5 radial velocities Something is wrong! Helpful questions: Are these stars misidentified? Where are these stars (d, l, b)? Are they faint sources? Do they have large radial velocity errors in RAVE? Defining a «bad radial velocity subset»: Def1: Vr, Gaia Vr, RAVE 3(eVr, Gaia+eVr, RAVE) Def2: Vr, RAVE > 200 km/s && Vr, Gaia < 100 km/s 24, 442 stars 2, 511

16 Gaia DR2 and RAVE DR5 radial velocities

17 What about completeness? RAVE completeness depends on apparent magnitude (Wojno+2017) Gaia DR2 radial velocity catalog: completeness of stars with G < 12.5 (Katz+2018)

18 Gaia_DR2(+Vr)xRAVE effective completeness

19 Conclusions #2 General remarks: A systematic offset between Gaia DR2 and RAVE DR5 radial velocities is very small, ~0.3 km/s Gaia DR2 radial velocities are ~3 times more precise (typical error is 0.3 km/s) There are stars with unreliable RAVE DR5 radial velocitites (low SNR) Gaia DR2 radial velocities catalog is more complete then RAVE DR5 and its completeness function is simpler Thus: Gaia DR2 radial velocities look more attractive If you use Gaia DR2 radial velocities with RAVE or other catalog, you should care about selection function/ possible kinematic biases

20 Gaia DR2 and a semi-analytic thin disk model: first results

21 (1): Semi-analytic Just&Jahreiß model Thin disk model and how disk it works SFR(t) Function with peak, decline after t>t_peak Vertical density profiles AVR(t) Power law, monotonous increase with age Just&Jahreiß 2010 Just&Gao 2011 Rybizki&Just 2015 JJ-model (Poisson's eq) Scale heights Ф( z ) Monotonous increase with age Metallicity distributions AMR(t) IMF 2-slope IMF, (3rd slope tested) Additional ingredients: - gas, thick disk, DM halo - constant thickness of the thin disk [Rybizki&Just 2015] Age distributions Velocity distribution function f( W )

22 Forward modelling paradigm JJ10 disk model stellar populations Extinction Completeness distances metallicities x ages disk heating stellar populations* Parallax errors Scatter in metallicity Data sample

23 Testing the model with TGASxRAVE JJ10 disk model Extinction map Bovy+2015 stellar populations distances metallicities x ages disk heating S_RAVE Wojno+2017 S_TGAS Bovy2017 TGAS parallax errors 0.15 dex scatter in [Fe/H] Sample selection (~ 19, 000 stars): - Local solar cylinder (r=300 pc, z < 1 kpc) with d_estimate = 1/parallax - SNR > 30, eparallax/parallax < 0.3 stellar populations* TGASxRAVE local sample

24 Testing the model with TGASxRAVE Distance error effect influences z > 500 pc Sysoliatina+2018 (submitted)

25 Testing the model with TGASxRAVE Sysoliatina+2018 (submitted)

26 Testing the model with TGASxRAVE Summary of TGASxRAVE forward modelling: Model/data star counts agreement: -8% in total (LMS -3.6%, UMS -6%, RGB -34.7%) Additional 10% uncertainty comes from choice of isochrones Near-plane populations are dynamically colder in the data Metal-rich populations ([Fe/H]>0.2) are not reproduced Sysoliatina+2018 (submitted)

27 Validation with the Gaia_DR2xRAVE_DR5

28 Validation with the Gaia_DR2xRAVE_DR5

29 Validation with the Gaia_DR2xRAVE_DR5 z = [-1000,-600] pc z = [-600,-400] pc z = [-400,-300] pc z = [-300,-200] pc z = [-200,-100] pc z = [-100,0] pc total

30 Validation with the Gaia_DR2xRAVE_DR5 z = [-1000,-600] pc z = [-600,-400] pc z = [-400,-300] pc z = [-300,-200] pc z = [-200,-100] pc z = [-100,0] pc total Near-plane open clusters?

31 Gaia DR2 K-dwarfs in the local 50-pc sphere Hyades cluster 4, 255 stars

32 Extended JJ-model, R=[4,12] kpc SFR + assumption about constant thin disk thickness AVR, age distributions... Model 1 Model 2 IMF = const. AMR(R) -?

33 Gaia_DR2xRAVE_DR5 red clump 4250 < Teff/K < < logg < 2.8 (Boeche+2014)

34 Gaia_DR2xRAVE_DR5 red clump Bovy+2015 extinction map 4250 < Teff/K < < logg < 2.8 (Boeche+2014)

35 Gaia_DR2xRAVE_DR5 red clump 4250 < Teff/K < < logg < 2.8 (Boeche+2014)

36 Gaia_DR2xRAVE_DR5 red clump 4250 < Teff/K < < logg < 2.8 (Boeche+2014) MG < 1.5(GBP-GRP) RAVE Mg, Fe available 35, 042 stars

37 Constructing AMR from red clump JJ10+Padova isochrones

38 Constructing AMR from red clump JJ10+Padova isochrones [Fe/H](t) = age-metallicity relation (2-3 iterations)

39 Constructing AMR from red clump JJ10+Padova isochrones [Fe/H](t) = age-metallicity relation (2-3 iterations)

40 Conclusions #3 Using the Gaia_RD2xRAVE local sample we confirm our findings based on TGASxRAVE cross-match: Overall star counts/density profiles show perfect consistency with the data. Near-plane populations are dynamically colder in the data. Check with the Gaia DR2 K-dwarfs in the 50-pc sphere (Hyades cluster removed) gives the same result. Basing on Gaia_DR2xRAVE red clump sample, AMR is derived for Galactocentric distances R=[7,9] kpc.

41 Summary 1. Gaia DR2 versus RAVE DR5 Distances RAVE DR5 spectrophotometric distances (McMillan+17) are only iseful for a small near-plane subset of ~104 stars (giants and upper main sequence) In all other cases use Gaia DR2 geometric distances (Bailer-Jones+2018) Radial velocities Gaia DR2 radial velocities are more precise then those of RAVE DR5 Show only small offset between Gaia DR2 and RAVE DR5 of ~0.3 km/s Are more reliable for a subset of Gaia_DR2xRAVE stars where RAVE SNR is low (< 10) Gaia DR2 radial velocity catalog have higher completeness then RAVE DR5 and it selection function is simpler (but kinematically unbiased or not?) General remarks: RAVE distances/stellar parameters and abundances will be updated soon, i.e. Gaia_DR2xRAVE_DR6 will be a highly useful sample for the Galactic studies When using some catalog N with parameters NOT provided by Gaia, check an overlap Gaia_DR2xN and use Gaia DR2 high-precision astrometry/radial velocities/photometry depending on your task Thin disk studies with the semi-analytic Just-Jahreiß model Local disk model is consistent with the Gaia_DR2xRAVE_DR5 (star counts, kinematics except of a near-plane discrepancy, etc ) Gaia_DR2xRAVE_DR5 RC sample is useful for reconstructing AMR(R)

42 Thank you!