Wolfram Data Repository

Immediate Computable Access to Curated Contributed Data

Star Clusters

Source Notebook

A catalog of star clusters in the Milky Way

Details

The data is collected from the catalogs of Dias et al. (2002) (DAML), Kharchenko et al. (2013) (MWSC), Schmeja et al. (2014), Scholz et al. (2015), and Roser et al. (2016).
Clusters were cross-referenced among the source catalogs using SIMBAD and their sky separation to avoid duplications.
The default content is a Dataset contaning the following properties for each cluster:
"Name"name of the cluster
"AlternateNames"alternate names of the cluster
"ObjectTypes"types that apply to the cluster
"RightAscension"right ascension in the ICRS system
"Declination"declination in the ICRS system
"CoreRadius"angular distance from the cluster center to the point where the slope of the radial density profile flattens
"CentralRadius"angular distance from the cluster center to the point where the radial density profile flattens
"ClusterRadius"total visible radius of the cluster
"CoreMembers"number of stars inside the core radius
"CentralMembers"number of stars inside the central radius
"ClusterMembers"number of stars inside the cluster radius
"Distance"distance from the sun to the cluster
"LogAge"logarithm (base 10) of the age of the cluster
"LogAgeError"error in the logarithm of the age
"AgeEmployedStars"number of stars used to calculate the age
"ApparentDistanceModulus"distance parameter infer from isochrone fitting
"ProperMotionRightAscension"proper motion in right ascension times the cosine of declination
"ProperMotionDeclination"proper motion in declination
"ProperMotionError"root mean square error of the proper motion
"ProperMotionRightAscensionError"error in the proper motion in right ascension
"ProperMotionDeclinationError"error in the proper motion in declination
"RadialVelocity"radial velocity
"RadialVelocityError"error in radial velocity
"RadialVelocityEmployedStars"number of stars used to calculate the radial velocity
"HMagnitudeCorrection"correction in the magnitude H of the fitted isochrone
"BVColourExcess"colour excess in B-V
"JKsColourExcess"colour excess in J-Ks
"JHColourExcess"colour excess in J-H
"KingCoreRadius"core radius in the King model
"KingCoreRadiusError"error in the King core radius
"KingTidalRadius"tidal radius in the King model
"KingTidalRadiusError"error in the King tidal radius
"KingNormalizationFactor"normalization factor in the King model
"KingNormalizationFactorError"error in the normalization factor
"Metallicity"cluster metallicity[Fe/H]
"MetallicityError"error in metallicity
"MetallicityEmployedStars"number of stars used to calculate metallicity

Examples

Basic Examples

In[1]:=
ResourceData[\!\(\* TagBox["\"\<Star Clusters\>\"", #& , BoxID -> "ResourceTag-Star Clusters-Input", AutoDelete->True]\)]
Out[1]=

Visualizations (4) 

Lets plot the 3D distribution of star clusters by age. Select clusters with non-missing data:

In[2]:=
data = ResourceData[\!\(\* TagBox["\"\<Star Clusters\>\"", #& , BoxID -> "ResourceTag-Star Clusters-Input", AutoDelete->True]\)][ Select[! MissingQ[#Distance] && ! MissingQ[#LogAge] &]];

Now, transform the right ascension, declination, and distance of each clusters to Cartesian coordinates in the galactic system. To do this transformation, astropy Python library is used. First, generate the Python code:

In[3]:=
template = " from astropy import units as u from astropy.coordinates import SkyCoord c = SkyCoord(ra=[`ras`]*u.deg, dec=[`decs`]*u.deg, distance=[`dists`]*u.kpc, frame='icrs') g = c.transform_to('galactic') g.representation_type = 'cartesian' [g.u.value, g.v.value, g.w.value] ";
In[4]:=
pythonCode = StringTemplate[template][<| "ras" -> StringRiffle[ QuantityMagnitude[Normal[data[All, "RightAscension"]], "AngularDegrees"], ","], "decs" -> StringRiffle[ QuantityMagnitude[Normal[data[All, "Declination"]], "AngularDegrees"], ","], "dists" -> StringRiffle[ QuantityMagnitude[Normal[data[All, "Distance"]], "Kiloparsecs"], ","]|>];

Run the computation in Python using ExternalEvaluate:

In[5]:=
cartesianCoords = ExternalEvaluate["Python", pythonCode]
Out[5]=

Plot the spatial position of the Milky Way clusters:

In[6]:=
ListPointPlot3D[List /@ Transpose[Normal /@ cartesianCoords], PlotStyle -> ColorData["Rainbow"] /@ Rescale@Normal@data[All, "LogAge"], PlotLegends -> BarLegend[{"Rainbow", MinMax@data[All, "LogAge"]}, LegendLabel -> "LogAge"], AxesLabel -> {x, y, z}, PlotRange -> {{-30, 39}, {-17, 20}, All}]
Out[6]=

Truman Tapia, "Star Clusters" from the Wolfram Data Repository (2022)  

Data Resource History

  • Date Created:

Source Metadata

  • Citation:
    • Dias, W. S., Alessi, B. S., Moitinho, A., & Lépine, J. R. D. (2002). New catalogue of optically visible open clusters and candidates. Astronomy & Astrophysics, 389(3), 871-873.
    • Kharchenko, N. V., Piskunov, A. E., Schilbach, E., Röser, S., & Scholz, R. D. (2013). Global survey of star clusters in the Milky Way-II. The catalogue of basic parameters. Astronomy & Astrophysics, 558, A53.
    • Schmeja, S., Kharchenko, N. V., Piskunov, A. E., Röser, S., Schilbach, E., Froebrich, D., & Scholz, R. D. (2014). Global survey of star clusters in the Milky Way-III. 139 new open clusters at high Galactic latitudes. Astronomy & Astrophysics, 568, A51.
    • Scholz, R. D., Kharchenko, N. V., Piskunov, A. E., Röser, S., & Schilbach, E. (2015). Global survey of star clusters in the Milky Way-IV. 63 new open clusters detected by proper motions. Astronomy & Astrophysics, 581, A39.
    • Röser, S., Schilbach, E., & Goldman, B. (2016). Nine new open clusters within 500 pc from the Sun. Astronomy & Astrophysics, 595, A22.

Data Downloads

Publisher Information

  • Prepared for the Wolfram Data Repository By: Truman Tapia
  • Publisher of Record: Truman Tapia