Diagnostic Gif

Intro

We can start by importing our favourite package: elk!

import elk
import numpy as np

%config InlineBackend.figure_format = "retina" # Not required, only applicable for Jupyter Notebooks

Lightcurve Setup

Now let’s do a simple ensemble light curve fit to NGC~129

We just want a single light curve, and we don’t care which sector it is from, so we will use the just_one_lc command

c = elk.ensemble.EnsembleLC(identifier='NGC 419',
                            location='23.58271, +61.1236',
                            radius=.046,
                            cluster_age=7.75,
                            cutout_size=99,
                            just_one_lc=True,
                            verbose=True)

c.create_output_table()

NGC 419 has 4 observations
Starting Quality Tests for Observation: 0

100%|███████████████████████| 9801/9801 [13:30<00:00, 12.09it/s]

  Passed Quality Tests 
Found a lightcurve that passed quality tests - exiting since `self.just_one_lc=True`

Plotting the lightcurve itself

Let’s quickly grab the 3rd light curve since that sector is usually looking lovely this time of year

lc = c.lcs[0]

The plotting of the light curve itself is rather straightforward!

lc.plot()

(<Figure size 640x480 with 1 Axes>,
 <Axes: title={'center': 'Lightcurve for Sector 18'}, xlabel='Time $\\rm [days]$', ylabel='Flux $[e / {\\rm s}]$'>)

Now lets plot the periodogram

lc.plot_periodogram()

(<Figure size 640x480 with 1 Axes>,
 <Axes: title={'center': 'Periodogram for Sector 18'}, xlabel='Frequency $[1 / {\\rm days}]$', ylabel='Power'>)

Ther are 5 peaks in this LSP. What are they?

lc.stats['peak_freqs'][:lc.stats['n_peaks']]

array([0.31, 0.4 , 0.74, 0.81, 0.87])

Now, lets investigate the spacial pixel map for this light curve, and look at which pixels in the aperture contribute powers to each peak.

We can do this by using the lc.diagnose_lc_periodogram function. We need to specify a path to put the gif frames, and the gif, and how we want to identify this gif.

The first index in the return is the gif itself, and the second index is a SIMBAD query for all the sources in the pixels for each peak.

lc.diagnose_lc_periodogram(output_path='Corrected_LCs/', identifier='NGC 419')[0]

simbad_query=lc.diagnose_lc_periodogram(output_path='Corrected_LCs/', identifier='NGC 419')[1]

Lets try and look at the highest peak, the one at 0.81 days

simbad_query[3]

Row index=3

MAIN_ID	RA	DEC	V__vartyp	V__Vmax	V__R_Vmax	V__magtyp	V__UpVmin	V__Vmin	V__R_Vmin	V__UpPeriod	V__period	V__R_period	OTYPE	FLUX_V	peak_freq	peak_lower	peak_upper
	"h:m:s"	"d:m:s"		mag				mag			day			mag
str27	str13	str13	str16	float32	str1	str1	str1	float32	str1	str1	float64	str2	str6	float32	float64	float64	float64
Gaia DR3 509970402263876864	01 34 31.7910	+61 06 23.019	PULS	--		G		--			1.238034		PulsV*	--	0.81	0.78	0.83

The peak has a frequency of 0.81 days, and there is a pulsating variable at that location corresponding to that peak with a period of 1.23 days\(^{-1}\). Lets remember that frequency = 1/period, and 1/1.23 = 0.81, so it is very likely that this pulsating variable is the cause of the peak in the LSP

1/1.23

0.8130081300813008

Note

This tutorial was generated from a Jupyter notebook that can be found here.