6 A Simple Recipe

A simple guide designed to introduce inexperienced users to the steps involved in detecting periodicities is outlined below. Detailed descriptions of the individual PERIOD commands can be found in section 5 and in the on-line help facility (which also gives help on the individual prompts one is confronted with).

(1)

Create an ASCII data file containing the time-series.

(2)

Read ASCII data into PERIOD using INPUT.

(3)

or read OGIP FITS files into PERIOD using OGIP.

(4)

Detrend the data using the [M] option in DETREND (if the data show long term variations, use the [P] option instead).

(5)

Open a log file for the fits using OPEN.

(6)

Enter the PERIOD_PERIOD menu by typing PERIOD.

(7)

Select the slots which contain the time-series data and specify the output slots for the periodograms using SELECT.

(8)

Set the frequency search limits using FREQ. If you have no idea what the period is, accept the default values by typing 0 (or alternatively, by not typing FREQ in the first place).

(9)

Enable the significance calculation by typing SIG and specifying, say, 200 permutations.

(10)

Calculate the Lomb-Scargle periodogram by typing SCARGLE.

(11)

Now run PEAKS on the resulting periodogram, specifying the frequency range which contains the peak you wish to measure (you may enter 0,0 if you wish to process the entire range). Write the results to the log file.

(12)

Now reselect the time-series slots and different output slots for a new periodogram using SELECT.

(13)

Type CLEAN with 5 iterations and a loop gain of 0.2, for example. (Before doing this, you may wish to disable the significance calculation by typing SIG again, since the CLEAN algorithm can take a considerable amount of processing time).

(14)

Run PEAKS on the resulting periodogram and store the results.

(15)

Now quit the PERIOD_PERIOD sub-menu by typing QUIT.

(16)

Plot the periodograms using PLT. Check to see the validity of the highest peak selected.

(17)

Check the results in the log file using STATUS. In particular, look closely at the false alarm probabilities.

(18)

Fold the original data on the most likely period using FOLD.

(19)

Plot the folded data using PLT. If this looks sensible, the period may well be correct. Make a postscript file by typing epsf_l. To see the other options type ’?’ instead.

(20)

Output the periodograms and folded data to an ASCII file on disk using OUTPUT.

(21)

Exit PERIOD by typing QUIT.

The above description is intended only to be a very brief guide. Clearly, a great deal more experimentation is required before it can definitely be said that a period has been detected. For example, you should investigate other large peaks in the periodogram, try smaller or larger frequency ranges, or try one of the other periodicity-finding options (a useful comparison of a number of different techniques is given by Carbonell, Oliver and Ballester 1992 and Heck, Manfroid and Mersch 1985). Other analysis techniques might also be attempted, such as subtracting a sine curve from the data in order to investigate its effects on the harmonics and enable the detection of less-dominant periods.