Skip to content

Measuring the rotation curves of SDSS MaNGA galaxies

License

Notifications You must be signed in to change notification settings

alabarca531/RotationCurves

 
 

Repository files navigation

RotationCurves

Measuring the rotation curves of SDSS MaNGA galaxies.

‘rotation_curve_vX_X’ is configured to receive FITS files from the SDSS MaNGA Pipe3D catalog. MaNGA data can be downloaded from the DR14 MaNGA database (see instructions) and DR15 MaNGA database (see instructions). All MaNGA data follows the MaNGA datamodel. In addition, the NASA-Sloan-Atlas (NSA) catalog, nsa_v0_1_2, is used to cross reference for stellar mass. All data contained within the NSA catalog follows the NSA datamodel found at the NSA website.

File/Folder Structure

The required folder and file structure to run on a user's local machine is as follows (note that the parent directory can be named anything):

.
├── images                           # contains image files in a format dictated by
|   ├── collected_velocity_fields    #    the 'image_format' variable in 'rot_curve_main_vX_X'
|   ├── diagnostic_panels
|   ├── fitted_rotation_curves
|   ├── histograms
|   ├── masked_Ha_vel
|   ├── mass_curves
|   ├── rot_curves
|   ├── unmasked_Ha_vel
|   └── unmasked_v_band
├── manga_files                      # contains the MaNGA data files (separated by data release)
|   ├── dr14                         #    read in in the beginning of 'rotation_curve_vX_X'
|   └── dr15
├── rot_curve_data_files             # contains the output data files of 'rotation_curve_vX_X'
├── updated_vflag_files              # contains the text files with the galaxy's environmental classification
|                                    #    taken from 'void_finder'
├── rot_curve_main.py                # script files to be executed (note that these files must
├── rotation_curve_vX_X.py           #    be in the main folder)
├── dark_matter_mass_main.py
├── dark_matter_mass_vX_X.py
├── nsa_v0_1_2.fits                  # the NSA catalog used in matching galaxies and extracting the necessary data
├── master_file.txt                  # master data file that contains compiled information about each galaxy
└── master_file_ref.txt              # reference file describing the content of 'master_file.txt'

Output Data

The output of ‘rotation_curve_vX_X’ is two .txt files in ECSV format for each MaNGA data file housed in ‘/rot_curve_data_files’. The first text file is of the format “[MANGA PLATE]-[MANGA FIBER ID]_rot_curve_data” and contains the following quantities as a function of deprojected radius:

  • maximum velocity in units of km/s
  • error in maximum velocity in units of km/s
  • minimum velocity in units of km/s
  • error in minimum velocity in units of km/s
  • average (between the maximum and minimum) velocity in units of km/s
  • error in the average velocity in units of km/s
  • difference between the maximum and minimum velocity in units of km/s
  • error in the difference between the maximum and minimum velocity in units of km/s
  • total mass interior in units of solar masses
  • error in total mass interior in units of solar masses
  • stellar mass interior in units of solar masses
  • stellar component of the rotational velocity in units of km/s
  • error in the stellar component of the rotational velocity in units of km/s
  • dark matter mass interior in units of solar masses
  • error in dark matter mass interior in units of solar masses
  • dark matter component of the rotational velocity in units of km/s
  • error in the dark matter component of the rotational velocity in units of km/s.

The second text file is of the format "[MANGA PLATE]-[MANGA FIBER ID]_gal_stat_data” and contains a string identifier of the format “[MANGA PLATE]-[MANGA FIBER ID],” the flux of the brightest spaxel in the visual band, and its error both in units of ergs /s /cm^2.

About

Measuring the rotation curves of SDSS MaNGA galaxies

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 98.6%
  • Python 1.4%