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Definition of RJD/MJD/BJD

All times should be measured from the mid-point of exposure (or flux weighted time)

• MJD: Modified Julian Date : JD − 2400000.5   

• RJD: Reduced Julian Date : JD − 2400000

• BJD: Barycentric correction of JD for the line of sight being considered

RJD  = MJD+0.5

BJD - 2 400 000 = ~MJD+0.5 (to within ~8 minutes)

BJD = 2 400 000+0.5 + MJD

BJD - 2 400 000.5  = MJD (to within ~8 minutes)

General considerations regarding temperatures in the LBL and DTEMP

Stellar temperatures are relevant inputs to optimize the LBL outputs and these need some context to avoid overinterpretation of the requirements. Lets say one attempts to analyze an M dwarf, for example GJ436. The simbad temperatures vary quite a bit, with values ranging from 3200 to 3500, with even higher values (probably unrealistic). Lets say that your favorite temperature is 3250 K. 

The first place where temperature is relevant is the rparams for your LBL wrap. You can input a value as accurate as you want, so you may as well input :

rparams['OBJECT_TEFF'] = [3250]

In practice, this is used at the time of constructing the mask and determining the systemic velocity of the system. The codes rounds this value to the nearest 100 K (here it would be 3300 K) and cross-correlates the mask with a Goettingen model at that temperature (available per 100 K bins, hence the rounding). This cross-correlation of a mask of hitherto unknown systemic velocity with a model of (assumed) zero velocity allows us to find the systemic velocity and this is used when producing the RDB tables at the end. If you entered a temperature that is off (say 4000 K), this would affect mildly the systemic velocity (wrong convective blueshift for example), but would have not other impact. Overall, put the best estimate of the stellar temperature and even an estimate from spectral type is fine.

The other temperature input is the one regarding the DTEMP. As explained in Artigau et al., the gradient of temperatures are determined as a function of temperature. There's nothing wrong with having more than one DTEMP temperature gradient. We are still trying to understand the physical meaning of the DTEMP values computed at temperatures that differ from the bulk temperature of the star. Conceptually, on a mid-M dwarf, one could look for spectral features of a brighter (say 5000 K) star appearing on the spectral and may want to compute the DTEMP at 5000K on a cooler object. Note that this is still work-in-progress on our side. So overall, for a T=3250K object, you could have the DTEMP computed at 3000 K and 3500 K and, out of curiosity, you could also compute the  DTEMP at 5000K. Your rparams would therefore include :

rparams['RESPROJ_TABLES'] = dict()

rparams['RESPROJ_TABLES']['DTEMP3000'] = 'temperature_gradient_3000.fits'

rparams['RESPROJ_TABLES']['DTEMP3500'] = 'temperature_gradient_3500.fits'

rparams['RESPROJ_TABLES']['DTEMP5000'] = 'temperature_gradient_5000.fits'