So far we have employed three basic types:
- Box Least Square Periodogram -- optimized to detect periodic transits, by fitting the time series to a repeating "box"-shaped light curve.
- Generalized Lomb Scargle Periodogram -- an approximation of the Fourier Transform for unevenly spaced time sampling. It identifies periodic signals that are simple combinations of sines and cosines.
- Phase Dispersion Method Periodogram (aka Plavchan Method) -- binless phase-dispersion minimization algorithm that identifies periods with coherent phased light curves (i.e., least “dispersed”). There is no assumption about the underlying shape of the periodic signal.
Let's continue our focus on the overall picture and avoid getting into the weeds. We will come back to how these periodograms work later.
How do you classify a given source?
Some characteristics to look for:
- light curve shape
- period
- depth of transit (EBs & planets) or amplitude of variation (rotational/pulsational variables)
- stellar parameters: color, magnitude, location in the HR diagram, cluster membership, chemical abundance
Let's discuss these parameters one by one.
Light Curve Shape
Variables often have signature light curve shapes. Eclipsing binaries are one such example that we will explore today.
Let's look at the light curve of the detached eclipsing binary, Algol.