BANYAN. II. Material

BANYAN. II. Additional MaterialScreenshot 2014-07-04 04.45.14

This page contains additional scientific material related to the Gagné et al. (2014) paper 2014ApJ…783..121G. All datasets are hosted on Figshare so that they have their own citeable DOIs. However, we ask that you always cite 2014ApJ…783..121G whenever you use any of those data sets.

1) A web tool for BANYAN II.

After the success of the BANYAN I web tool, we have decided to build another web tool for BANYAN II. It implements various modifications that were brought to our Bayesian classifier analysis, including rotated ellipsoids for the spatial and kinematic models of moving groups, the use of a Besançon Galaxy model for the old and young field population model, the use of prior probabilities different than unity to represent the expected population of a star for each hypothesis (taking account of the galactic latitude and the size of proper motion). As was the case with the BANYAN I web tool, no photometric treatment is available in the BANYAN II web tool.

2) Additional tables.

2.1) Known red or young, late-type field stars and brown dwarfs from the literature.

We have collected a list of 158 young or unusually red object in the literature that were not already bona fide members to a known moving group or association. The list is restricted to objects with spectral types later than M5. It is available in 5 formats : iWork Numbers (v3 2013 or 2009) for MAC OS X, Microsoft Excel, CSV and PDF.

DOI & Figshare link

2.2) Bona Fide members of nearby, young moving groups.

This table contains all known bona fide members to moving groups considered in Malo et al. (2013) and Gagné et al. (2014), as well as relevant photometric and kinematic information.

DOI & Figshare link

2.3) Statistical distance and radial velocity predictions for known young or red > M5 star and brown dwarf candidates to nearby, young moving groups.

This table is an electronic version of Table 4 in Gagné et al. (2014).

DOI & Figshare link

2.4) Bayesian membership probabilities to nearby, young moving groups for known young or red > M5 field stars and brown dwarfs.

This table is an electronic version of Table 5 in Gagné et al. (2014).

DOI & Figshare link

2.5) Properties of nearby, young moving groups.

This table is a merged electronic version of Tables 1 and 2 in Gagné et al. (2014) in the EPS, PDF, Numbers v3, Numbers 2009 and XLSX formats.

DOI & Figshare link

2.6) All tables in Gagné et al. (2014) in the TeX format.

This contains all tables in Gagné et al. (2014) in the latex TeX format.

DOI & Figshare link

3) Additional figures.

3.1) Proper motion convergence in nearby, young moving groups.

These figures contain a map of the position and proper motion of bona fide members in nearby, young moving groups. Each star’s proper motion vector is prolongated into a great circle on the celestial sphere, to show that they converge in two points (the apex and anti-apex), which are often close to the Solar’s apex and anti-apex. Thanks to Adric Riedel for help in the construction of these figures, and see some similar figures that he made.

DOI & Figshare link

3.2) Proper motion of known young or red > M5 field star and brown dwarf candidate members to nearby, young moving groups.

These figures compare the proper motion of each candidate member to nearby, young moving groups in Gagné et al. (2014) to figures described in Section 3.1.

DOI & Figshare link

3.3) J – Ks vs W1 color-magnitude diagram of known young or red > M5 star and brown dwarf candidate members to NYAs.

These figures compare the position of each candidate member (red) of nearby, young moving groups in Gagné et al. (2014) to the color-magnitude sequences in Figure 3 (left) in the same reference, as well as new candidate members in Gagné et al. (in prep.) (black dots, purple dots for those whose youth is confirmed). See also Joe Filippazzo’s BDNYC post on the photometric properties of young stars. The statistical distance predictions from BANYAN II were used when a parallax measurement was unavailable. An empty circle means that the magnitude has been corrected for binarity, and an upside-down triangle means that a parallax measurement was used.

DOI & Figshare link

3.4) H – W2 vs W1 color-magnitude diagram of known young or red > M5 star and brown dwarf candidate members to NYAs.

These figures compare the position of each candidate member to nearby, young moving groups in Gagné et al. (2014) to Figure 3 (right) in the same reference. The statistical distance predictions from BANYAN II were used when a parallax measurement was unavailable.

DOI & Figshare link

3.5) XYZ Galactic Positions and UVW spatial velocities of bona fide members in nearby, young moving groups.

These figures show the 3-dimensional XYZ and UVW positions of bona fide member to nearby, young moving groups, as well as their ellipsoidal spatial models in Gagné et al. (2014). They are similar to Figure 2 in the same work (only shown for TW Hydrae therein).

DOI & Figshare link

3.6) XYZ and UVW of known young or red > M5 field star and brown dwarf candidate members to nearby, young moving groups.

These figures show the expected 3-dimensional XYZ and UVW positions of candidate members to nearby, young moving groups in Gagné et al. (2014), compared to figures similar to those in Section 3.5. The statistical distance and radial velocity predictions from BANYAN II were used when such measurements were unavailable.

XYZ Figures : DOI
UVW Figures : DOI

3.9) Posterior 2-dimensional PDFs known young or red > M5 field star and brown dwarf candidate members to nearby, young moving groups.

These figures show the 2-dimensional posterior probability density functions as a function of radial velocity and distance for all candidate members presented in Gagné et al. (2014). They are similar to Figure 10 in the same reference.

DOI & Figshare link

3.10) Electronic versions of all figures in Gagné et al. (2014).

These packages contain all figures in Gagné et al. (2014) in either the EPS or PDF formats.

DOI & Figshare link

4) Additional data.

4.1) Prior probability distributions for the radial velocity and distance of nearby, young moving group’s members.

These IDL save files contain the RV and distance probability density functions for the spatial and kinematic models of nearby, young moving groups as defined in Gagné et al. 2014. They can be used to generate Fig.1 in this reference (see Figshare description for more information).

DOI & Figshare link

4.2) An IDL structure containing the parameters of nearby, young associations (see Table in section 2.5).

This idl savefile contains the parameters of all spatial and Kinematic models of nearby, young associations of Gagné et al. 2014. You can acess them with the following command in IDL : IDL> restore, ‘mg_parameters_Gagne2014.sav’, /verbose & help, params_all, /str.

DOI & Figshare link

5) IDL Routines.

5.1) Euler angles and rotation matrices.

These two IDL routines can be used to transform Euler angles to a rotation matrix, and vice-versa.

DOI & Figshare link

5.2) A procedure allowing to fit freely rotating 3D ellipsoids to a set of cartesian coordinates.

This IDL routine uses the krEllipsoidFit algorithm (from Ronn Kling and Jerry Lefever) to fit a 3D ellipsoid to a set of cartesian coordinates. The krEllipsoidFit IDL routine was not written by our team but rather by Ronn Kling, so please give him credit and cite his web page (www.rlkling.com) when using this package (there is no paper to be cited), in addition to the reference to the BANYAN II paper.

DOI & Figshare link

5.3) XYZUVW coordinates rotation.

This IDL routine can be used to rotate XYZUVW coordinates and their measurement errors in a new frame of reference, using either a rotation matrix or Euler angles.

DOI & Figshare link

5.4) XYZ and UVW calculation with measurement errors.

These IDL routines can be used to transform ra, dec, distance and the error on distance for any number of stars to XYZ coordinates and their errors, as well as ra, dec, distance, proper motion and radial velocity to UVW coordinates and their errors. This package includes a modification on the glactc.pro astrolib routine that is significantly faster with large sets of objects.

DOI & Figshare link

5.5) An IDL routine to estimate the field contamination probability of a moving group candidate.

This IDL routine can be used to transform the Bayesian probability of a candidate member to its field contamination probability (calculated from the Monte Carlo simulation presented in Section 5 of Gagné et al., 2014). The proper motion, sky position as well as radial velocity (if available) and distance (if available) must be input in order to compute the χ-factors that are used to adjust the expected field and NYA populations for a given object. Because of this, this routine can correctly make predictions for e.g. objects with very small proper motions, or objects in the Galactic plane.

DOI

5.6) An IDL routine to estimate the mass of late-type stars or brown dwarfs.

This IDL routine uses the 2MASS and WISE J, H, Ks, W1 and W2 apparent magnitudes and the distance of a given object, along with an estimated age range, to determine its most probable mass range using AMES-COND isochrones (Baraffe et al. (2003) in combination with CIFIST2011 BT-SETTL atmosphere models (Allard et al. 2013, Rajpurohit et al. 2013) in a likelihood analysis. Errors on the distance and photometry, when input, are propagated to the estimated mass range. This routine was used to estimate the masses of candidate members in Table 3 of Gagné et al. (2014). Please cite Gagné et al. 2014Baraffe et al. 2003Allard et al. 2013 and Rajpurohit et al. 2013 (see links on Figshare page) if you use this procedure.

DOI & Figshare link

5.7) An IDL routine to combine measurements with individual errors.

This IDL routine uses an optimal variance-weighted mean to combine measurements of the same physical quantity with individual errors. It corrects for over-dispersion to account for possible underestimations of individual measurements, as well as an intrinsically varying physical quantity. The routine does not correct for under-dispersion to avoid strange behaviours in cases where individual measurements are very close one to another. This IDL routines uses the method described here, and was used to combine radial velocity, proper motion and distance measurements in Gagné et al. (2014).

DOI & Figshare link

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