<blockquote>
<p>How should I interpret the lomb scargle results shown in the picture(below picture)?</p>
</blockquote>
<p>That there are no specific periodicities present.</p>
<blockquote>
<p>Why does the maximum appear near zero(below picture)?</p>
</blockquote>
<p>Because your signal is dominated by a dc component plus slow (low frequency) drift.</p>

CC BY-SA 4.0
2021-03-01T21:12:01.407Z
2021-03-01T21:12:01.407Z
ProfRob
<p>Not being familiar with Lomb-Scargle (never having used it myself) the first thing I would say is that your graph is not really suitable for this kind of analysis.  If I graph it in my spreadsheet I get what looks more like a &quot;bumpy line&quot; not a periodic pattern :</p>
<p><a href="https://i.sstatic.net/Zsb0V.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Zsb0V.png" alt="enter image description here" /></a></p>
<p>The purpose of this algorithm is to take data that has a <em>periodic</em> pattern and find try and determine the frequency spectrum of that <em>periodic</em> data.  If you use data that is not periodic or does not properly exhibit a range of data values that allow discovery of the period then you unsurprisingly do not get sensible results.  So what I'm saying is that I <em>suspect</em> your data is not going to give you useful results with this algorithm.  I think you need more data points over a longer timescale.</p>
<p>Note my limited understanding is based on how similar algorithms are used for analyzing light-curve data.  This is well explained in some detail in <a href="https://arxiv.org/abs/1703.09824" rel="nofollow noreferrer">this paper on Arxiv</a>.  THe first section has no mathematics at all and illustrates a typical use case of the algorithm.  Make particular note that you need data covering more than than the period of the light curve to get a sensible result.  Note also the way the first &quot;maximum peak&quot; in the power spectrum is the period.  Figure (1) is that paper shows the raw data (a jumbled mess covering multiple periods) and figure (2) shows the way the period is identified and how the raw data can be graphed using this discovered period to properly align out of phase values.  I am dubious that your data is suitable, possibly because the underlying data is not periodic or is only a partial window of a period.</p>
<p>Section 8 of that paper has several pieces of advice about effectively using this algorithm, so I would recommend you read that even if you have to skip the mathematics in the middle of the paper.</p>

CC BY-SA 4.0
2021-03-02T02:13:52.493Z
oh.Thank you for your reply. I will read that paper !
Yongho Lee
CC BY-SA 4.0
2021-03-01T18:41:58.080Z
2021-03-01T18:41:58.080Z
StephenG - Help Ukraine
<p>I used <a href="https://en.wikipedia.org/wiki/Least-squares_spectral_analysis" rel="nofollow noreferrer">lomb scargle</a> for periodic analysis.</p>
<p>The time interval used is constant at 500 seconds.
<a href="https://i.sstatic.net/7I5Ep.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/7I5Ep.png" alt="enter image description here" /></a></p>
<p>#My data (x=time, y=intensity values) is :</p>
<p>x= (
00:28,
00:37,
00:45,
00:53,
01:02,
01:10,
01:19,
01:27,
01:36,
01:45,
01:53,
02:01,
02:11,
02:19  )</p>
<p>y=(18264.682317  ,18720.708081  ,18476.409461  ,19592.046320</p>
<p>,19166.021184 , 18984.852405 , 20971.112057  ,20946.024653</p>
<p>21590.194169 , 21771.175308 , 21474.776018 , 20156.969939</p>
<p>21688.756080 , 22570.152358  )</p>
<p>--&gt; The time interval of x data is equal to 500 seconds.</p>
<p>and lomb scargle code is:</p>
<pre><code>frequency,power = LombScargle(x,y).autopower(nyquist_factor=1)
ax.plot(frequency,power,'-')
        #ax4.axvline(x=best_frequency)
ax.set_ylabel('(4-2) band R₁(2.5) Intensity - Lomb Scargle Power')
</code></pre>
<ol>
<li><p>How should I interpret the lomb scargle results shown in the picture(below picture)?</p>
</li>
<li><p>Why does the maximum appear near zero(below picture)?</p>
</li>
</ol>

CC BY-SA 4.0
2021-02-28T08:29:34.477Z
What is lomb scargle? For readers like me who have no idea, could you add a link or reference? *Thanks!*
uhoh
CC BY-SA 4.0
2021-02-28T11:10:32.037Z
Welcome to astronomy SE! As @uhoh states, a bit of editing your question would help.
B--rian
<astrophysics><python><astropy>
How should I interpret this result obtained using lomb scargle?
Yongho Lee
# How should I interpret this result obtained using lomb scargle?

(Asked by: username_0 on 2021-02-28)

I used [lomb scargle](https://en.wikipedia.org/wiki/Least-squares_spectral_analysis) for periodic analysis.

The time interval used is constant at 500 seconds.
[![enter image description here](https://i.sstatic.net/7I5Ep.png)](https://i.sstatic.net/7I5Ep.png)

#My data (x=time, y=intensity values) is :

x= (
00:28,
00:37,
00:45,
00:53,
01:02,
01:10,
01:19,
01:27,
01:36,
01:45,
01:53,
02:01,
02:11,
02:19 )

y=(18264.682317 ,18720.708081 ,18476.409461 ,19592.046320

,19166.021184 , 18984.852405 , 20971.112057 ,20946.024653

21590.194169 , 21771.175308 , 21474.776018 , 20156.969939

21688.756080 , 22570.152358 )

--> The time interval of x data is equal to 500 seconds.

and lomb scargle code is:

```
frequency,power = LombScargle(x,y).autopower(nyquist_factor=1)
ax.plot(frequency,power,'-')
        #ax4.axvline(x=best_frequency)
ax.set_ylabel('(4-2) band R₁(2.5) Intensity - Lomb Scargle Power')

```

1. How should I interpret the lomb scargle results shown in the picture(below picture)?
2. Why does the maximum appear near zero(below picture)?

**Tags:** astrophysics, python, astropy

> **Comments:**
> **username_1**: What is lomb scargle? For readers like me who have no idea, could you add a link or reference? *Thanks!*  
> **username_2**: Welcome to astronomy SE! As @username_1 states, a bit of editing your question would help.  

---

## 2 answers

### Answer by username_3 on 2021-03-01. Score: 2

> How should I interpret the lomb scargle results shown in the picture(below picture)?

That there are no specific periodicities present.

> Why does the maximum appear near zero(below picture)?

Because your signal is dominated by a dc component plus slow (low frequency) drift.


---

### Answer by username_4 on 2021-03-01. Score: 2

Not being familiar with Lomb-Scargle (never having used it myself) the first thing I would say is that your graph is not really suitable for this kind of analysis. If I graph it in my spreadsheet I get what looks more like a "bumpy line" not a periodic pattern :

[![enter image description here](https://i.sstatic.net/Zsb0V.png)](https://i.sstatic.net/Zsb0V.png)

The purpose of this algorithm is to take data that has a *periodic* pattern and find try and determine the frequency spectrum of that *periodic* data. If you use data that is not periodic or does not properly exhibit a range of data values that allow discovery of the period then you unsurprisingly do not get sensible results. So what I'm saying is that I *suspect* your data is not going to give you useful results with this algorithm. I think you need more data points over a longer timescale.

Note my limited understanding is based on how similar algorithms are used for analyzing light-curve data. This is well explained in some detail in [this paper on Arxiv](https://arxiv.org/abs/1703.09824). THe first section has no mathematics at all and illustrates a typical use case of the algorithm. Make particular note that you need data covering more than than the period of the light curve to get a sensible result. Note also the way the first "maximum peak" in the power spectrum is the period. Figure (1) is that paper shows the raw data (a jumbled mess covering multiple periods) and figure (2) shows the way the period is identified and how the raw data can be graphed using this discovered period to properly align out of phase values. I am dubious that your data is suitable, possibly because the underlying data is not periodic or is only a partial window of a period.

Section 8 of that paper has several pieces of advice about effectively using this algorithm, so I would recommend you read that even if you have to skip the mathematics in the middle of the paper.

> **Comments:**
> **username_0**: oh.Thank you for your reply. I will read that paper !  

---

