1 / 28

Statistics of Visual Binaries and Star Formation History

Statistics of Visual Binaries and Star Formation History. Oleg Malkov Institute of Astronomy Rus. Acad. Sci. (INASAN) Faculty of Physics, Moscow State University malkov@inasan.ru. Contents. Introduction Initial distributions Evolutionary stages Observational data for comparison

brendalewis
Download Presentation

Statistics of Visual Binaries and Star Formation History

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Statistics of Visual Binaries and Star Formation History Oleg Malkov Institute of Astronomy Rus. Acad. Sci. (INASAN) Faculty of Physics, Moscow State University malkov@inasan.ru MSA-2017

  2. Contents • Introduction • Initial distributions • Evolutionary stages • Observational data for comparison • Results of comparison • Conclusions MSA-2017

  3. Introduction • Most stars formed as part of a binary or multiple systems. • In order to understand the star formation process, it is vital to characterize distributions of physical parameters in the history of the Galaxy. • In the solar neighborhood limit, few hundred parsec of distance, most of the binary systems are visual binaries. • We begin from the assumption that all stars born in a binary system. • Evolutionary stage is calculated as a function of system age and component masses. • Observational selection effects are involved. • Thus, we modeled visual binaries in the solar neighborhood and compare our calculations with observations. MSA-2017

  4. Initial distributions MSA-2017

  5. Spatial distribution • Uniform • Barometric: GALACTIC_DISC_VERTICAL_SCALE_PC = 200 • Barometric: GALACTIC_DISC_VERTICAL_SCALE_PC = • 50 for mass>10, • 10-0.832*log(mass)+2.531 for 1<=mass<=10, • 340 for mass<1, • (Gilmore and Reid 1983, Kroupa 1992, Reed 2000) • No radial gradient MSA-2017

  6. Number of pairs simulated • Sphereradius = 500 pc • Pairs are simulated until their number in a 100-pc-sphere reaches 500,000 • It corresponds to the observed stellar density in the solar vicinity, about 0.12-0.14 stars per cubic pc MSA-2017

  7. Pairing scenarios: masses. 1 • Select (two) fundamental parameters among m1, m2, m1+m2, m2/m1, m1-m2, m1*m2, … • The following scenarios are used • m1, m2 (RP, random pairing) • m1, m2/m1 (PCP, primary constrained pairing) • m1+m2, m2/m1 (SCP, split-core pairing) • m1+m2, m1 (TPP, total and primary pairing) MSA-2017

  8. Pairing scenarios: masses. 2 • Select method of treating low-mass companions (m2<mmin): • Accept (even stars with a planetary companion are considered to be in a binary system) • Reject (the primary star becomes a single star) • Redraw (it is rejected, and a new companion star is drawn) • Method of treating low-mass companions (m2<mmin): Accept MSA-2017

  9. Mass distribution • Masses are distributed according to a power-law function N(m) ~ mα from 0.08 to 100 msun • Salpeter IMF: α=-2.35 • Kroupa IMF: • α=-1.3 for m<0.5 msun • α=-2.3 for m≥0.5 msun • Later: vary slopes and inflection points of Kroupa IMF MSA-2017

  10. Total mass and mass ratio distributions • m1+m2 (strictly speaking, it does not precisely equal protobinary cloud mass): is distributed like masses of individual stars • f(q) ~ qβ, where β = 0, -0.5, +0.5 • Later: add twins (q=1) MSA-2017

  11. Semi-major axis distribution • f(a) ~ aλ, where λ=-1, -1.5, -2 • Lower limit is 10 Rsun, upper limit is 106 Rsun • λ = -1: uniform logarithmic distribution along five orders of magnitude • Later: • lower limit depends on stellar mass, amin=amin(RocheLobe(m)) • upper limit amax=amax(height scale z, mass, eccentricity) MSA-2017

  12. Eccentricity distribution • f(e) = 2e • f(e) = δ(0) • f(e) = 1 MSA-2017

  13. Star formation rate • Constant star formation rate from 0 to DISCAGE = 14 Gyr • Declining star formation rate from 0 to DISCAGE = 14 Gyr: SFR(t)=15e-(t/τ), where τ=7Gyr • Verification: the function produces • current SFR = 3.6 msun/yr, which is correct • integral mass 8*1010msun, which is equal to Galaxy mass MSA-2017

  14. Other parameters • Metallicity: normal Fe/H distribution with mean=-0.1 and dispersion 0.3 • Random distributions for: mean anomaly, sin(inclination), position angle, periastron longitude • Interstellar extinction Av=0 MSA-2017

  15. Evolution MSA-2017

  16. Evolution stage (mass, age) • BD • Pre-MS • MS • RG • WD • NS • BH MSA-2017

  17. Evolution stage (mass, age) MSA-2017

  18. Evolution stage (mass, age) MSA-2017

  19. HR diagram MSA-2017

  20. Observational data for comparison: WDS+CCDM+TDSC binaries with TGAS parallaxes MSA-2017

  21. Selection criteria • Main component belongs to MS • Secondary component is not degenerate • Separation ρ > 1 arcsec • Primary brightness V1 < 10m • Secondary brightness V2 < 11m • Brightness difference (V2-V1) < 4m • Distance d < 500 pc (π > 2 mas) • Altogether 1028 systems MSA-2017

  22. Numbers/distributions for comparison • Number of selected stars • Distributions over V1, V2, V2-V1, ρ”, ρRsun, π“ (χ2 test) • Overall χ2 value, based on distributions of independent, original parameters (V1, V2-V1, ρ”, π“) MSA-2017

  23. An example: TPP (m1+m2, m1), Kroupa IMF,f(a) ~ a-1.5, f(e) = 1 ρ” V2-V1 V1 π" MSA-2017

  24. Results of comparison MSA-2017

  25. Resume. 1 • Results weakly depend on eccentricity distribution. • It is difficult to make conclusions on mass ratio (q) distribution. MSA-2017

  26. Scenario, IMF f(a) ~ aλ MSA-2017

  27. Resume. 2 • PCP (m1, q), RP (m1, m2) and SCP (m1+m2, q) scenarios show a good agreement with observations. • Kroupa IMF is slightly more preferable than Salpeter IMF. • Semi-major axis distributions f(a) ~ aλ, where λ=-1 and -1.5, look very promising, and will be analyzed in detail. Distribution with λ=-2 should be omitted from further consideration. MSA-2017

  28. Acknowledgments • Co-authors • RFFR 15-02-04053 • Audience for your attention MSA-2017

More Related