Q best of binary star
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The JKTEBOP code is a tool for fitting the q best of binary star curves of detached eclipsing binary star and transiting planetary systems in order to derive the physical properties of the component objects. If you have any problems then please check this FAQ first. If you don't find an answer to your question then feel free to contact me at the email address on my main page. Do you have a users manual? JKTEBOP returns a rubbish solution I get "singular matrix in gaussj" error messages Warning about log or cubic limb darkening What is a good value for the integration ring parameter?
Sorry, but this is unlikely to q best of binary star. In the next "few years" I am intending to completely rewrite JKTEBOP in a newer programming language and much q best of binary star capability, including the possibility to deal with multiple light curves and radial velocity curves. JKTEBOP will be gracefully retired, and therefore a user manual will be of lesser importance than writing the new code. An eclipsing binary star system normally shows two eclipses: The eclipse depths depend on the temperatures of the two stars: The primary eclipse is therefore at inferior conjunction and the secondary eclipse is at superior conjunction.
Thus primary eclipse occurs at phase 0. The ratio of the radii, the surface brightness ratio, the light ratio and the mass ratio are therefore:. There are quite a few eclipsing binary systems where the more massive star has evolved to have a lower temperature and a much larger radius than the less massive star.
In this situation star A the hotter star is actually the smaller and less massive star. This is because J is less than 1. The result is q best of binary star k, lrat and q are greater than 1.
The more massive star may still produce more light than the less massive star thus confusing spectroscopic analysesbut it is still star B because it is cooler than star A. If you put the secondary shallower eclipse at phase 0. The only thing you need to do is be careful to remember that star A is actually star B and vice versa. In some cases JKTEBOP does not converge and the output parameters are either completely mad, or simply the initial parameters with bad errorbars.
This sometimes also causes error messages such as:. In almost all cases this error message is caused by problems with the input data and parameters, so should be quite easy to sort out. If you have one of these problems then check the following things:. If none q best of binary star the above helps, then fix all of your input parameters i. Make any appropriate changes to the input parameters, and try again. Once you have got manually got close to the solution, you can try fitting for one parameter, then two, then three, etc.
This is a minor problem with flux normalisation for two of the limb darkening laws. These three laws are fully integrated.
I have since added logarithmic and cubic limb darkening to JKTEBOP, but have not yet got round to deriving the proper flux normalisation including gravity darkening and asphericity. If you have very good data e. The solution will take a bit longer, but will have very good numerical accuracy. This is a "feature" rather than a "bug". In TASK3 it is possible to fit directly for the reflection coefficients, whereas in TASK2 the input values are ignored and predicted values are used instead.
So it is possible for input files with identical parameters to return different light curves, which might surprise some users. There are two options available if this is causing you a problem. Secondly, you can comment out three specific lines in the TASK2 subroutine and recompile the code. If you are unsure about which lines to comment out then please contact me.
The other tasks require one more line in the input file, after the Reference time of primary minimum, which TASK3 does not require. Otherwise the different input files are of identical format. If you are unsure about what to do, generate empty input files for each task and compare them to see how they differ.
In TASK8, q best of binary star fit which has more than this number of iterations is assumed to have failed. Failed fits trigger this warning message on the screen, but are not recorded in the.
They can therefore be q best of binary star if they do not occur very often. If you are getting a large number of these warning messages then this usually means q best of binary star the data are not good enough to constrain all of the fitted parameters. Sometimes this is due to poor data quality so may be unavoidable but it can also be due to trying to fit for too many parameters. You can therefore try to fit for just the most important parameters and see if this makes the results better.
This means that the limb darkening coefficients are physically impossible, because the limb of the star is predicted to be producing negative light. This normally happens if you are trying to fit for one or both limb darkening coefficients, and the best-fitting values are unreasonable. JKTEBOP does not force the limb darkening to be physically reasonable, as this causes problems for deriving errorbars.
The implication is that your data are not q best of binary star enough to fit for the limb darkening coefficients in the way you have attempted. The solution is to fix one or both of the limb darkening coefficients to reasonable values. This means that the limb darkening coefficients are physically unreasonable because they predict an overall limb brightening the limb of the star is brighter than the centre.
As above, the implication is that your data are not good enough to fit for the limb darkening coefficients in the way you have attempted. TASK1 originally produced a range of limb darkening coefficients via interpolation in tables of theoretical coefficients. The fractional radii are the true radii of the components divided by the orbital semimajor axis.
The relevant line in the input file would therefore look something like this:. If you plot the best fit from the. This is a choice made during the implementation process, and arises because I did not want to JKTEBOP to have to output multiple best fits when multiple polynomials were used.
If you check the. This gives the difference between each datapoint and the best-fit model value for the q best of binary star. If you plot columns 2 and 5 in the. This does create a small complication when plotting your results.
If there is a gap in the data, then ideally we want to show how the fit varies through the gap even if there are no datapoints there.
This is most obviously true if your data cover only half a transit but you want to see q best of binary star shape of the whole transit. There are two ways of dealing with this:.
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