This tool relies on the Ultra-faint Galaxy Likelihood (ugali) toolkit for simulating the spatial and color-magnitude distributions of stars in dwarf galaxies. Stellar velocities are simulated via a numerical integration of the Eddington formula.
Installation is not the easiest thing in the world, but you can check out the travis.yml.
DEIMOSon KeckIMACSon MagellanGIRAFFEon VLTAAOmega/2dFon AATM2FSon MagellanGMACSfor GMT
The output of the dsphsim executable is an ascii table with the following columns.
| Column | Unit | Description |
|---|---|---|
| RA | deg | Right ascension |
| DEC | deg | Declination |
| MAG_G | mag | DES g-band magnitude |
| MAG_I | mag | DES i-band magnitude |
| ANGSEP | deg | Angular separtion from dwarf centroid |
| RPROJ | kpc | Projected radial separation from dwarf centroid |
| SNR | Simulated signal-to-noise ratio for spectroscopy | |
| VTRUE | km/s | True simulated random velocity drawn from the underlying distribution |
| VSTAT | km/s | Adjustment to true velocity from statistical measurement uncertainty related to the brightness of each star. |
| VSYS | km/s | Adjustment to true velocity from instrumental systematic uncertainty |
| VMEAS | km/s | Velocity measured by instrument VMEAS = VTRUE+VSTAT+VSYS |
| VMEASERR | km/s | Measured statistical velocity error |
| VSYSERR | km/s | Assumed instrumental systematic velocity error |
| VERR | km/s | Quadrature sum of VMEASERR and VSYSERR VERR = sqrt(VMEASERR2+VSYSERR2) |
The code used to generate these data products can be found in dsphsim/simulator.py.
This table can be read into a numpy array using numpy.genfromtxt:
import numpy as np
filename = "<your_filename.dat>"
data = np.genfromtxt(filename,names=True,dtype=None)