<p>It would have been be great if you had shared the original data. As @AtmosphericPrisonEscape mentioned it could well be that you are seeing only some kind of <a href="https://en.wikipedia.org/wiki/Aliasing" rel="nofollow noreferrer">aliazing effect</a> in the plot, i.e. the underlying data actually looks the same for all four light curve dips.</p>
<p>First off, there is a related question: <a href="https://astronomy.stackexchange.com/questions/38735/how-to-recognize-exoplanet-transit?rq=1">How to recognize exoplanet transit</a> where <a href="https://github.com/KeplerGO/lightkurve" rel="nofollow noreferrer">lightcurve</a> is used in Python.</p>
<p>If you are working in <strong>Python</strong>, you may also look e.g. at the <a href="https://github.com/hpparvi/PyTransit" rel="nofollow noreferrer">PyTransit github project</a> which seems rather well maintained. I am not affiliated with the author nor have I tested it, but it is worth a try.</p>
<p>In <strong>R</strong>, you may try to have a first look at your time series using the <a href="https://cran.r-project.org/web/packages/zoo/" rel="nofollow noreferrer"><code>zoo</code></a> library and follow e.g. the <a href="http://web.stanford.edu/class/earthsys214/notes/series.html" rel="nofollow noreferrer">introductory tutorial</a>.</p>

CC BY-SA 4.0
2021-06-04T08:43:11.953Z
2021-06-04T09:21:42.723Z
2021-06-04T09:21:42.723Z
B--rian
<p>My assignment is to identify how many planets are in this curve and what the orbital period is. I'm just confused on the way it looks, as most curves I've seen up to this date are more smooth and connected in appearance</p>

<p><a href="https://i.sstatic.net/bGIi8.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/bGIi8.png" alt="Transit Method"></a></p>

CC BY-SA 4.0
2019-03-04T16:24:52.550Z
Try to zoom in into one of the transit events, then you'll see more smooth data. Is this real data? Usually you either Fourier-Transform your data to see how many periods are in the data if the times are equidistantly spaced, or you need to perform a Lomb-Scargle analysis if it's non-equidistantly spaced.
AtmosphericPrisonEscape
<data-analysis><planetary-transits>
Challenge with a Transit Method Curve
Kayy Wang
# Challenge with a Transit Method Curve

(Asked by: username_0 on 2019-03-04)

My assignment is to identify how many planets are in this curve and what the orbital period is. I'm just confused on the way it looks, as most curves I've seen up to this date are more smooth and connected in appearance

[![Transit Method](https://i.sstatic.net/bGIi8.png)](https://i.sstatic.net/bGIi8.png)

**Tags:** data-analysis, planetary-transits

> **Comments:**
> **username_1**: Try to zoom in into one of the transit events, then you'll see more smooth data. Is this real data? Usually you either Fourier-Transform your data to see how many periods are in the data if the times are equidistantly spaced, or you need to perform a Lomb-Scargle analysis if it's non-equidistantly spaced.  

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## 1 answers

### Answer by username_2 on 2021-06-04. Score: 1

It would have been be great if you had shared the original data. As @username_1 mentioned it could well be that you are seeing only some kind of [aliazing effect](https://en.wikipedia.org/wiki/Aliasing) in the plot, i.e. the underlying data actually looks the same for all four light curve dips.

First off, there is a related question: [How to recognize exoplanet transit](https://astronomy.stackexchange.com/questions/38735/how-to-recognize-exoplanet-transit?rq=1) where [lightcurve](https://github.com/KeplerGO/lightkurve) is used in Python.

If you are working in **Python**, you may also look e.g. at the [PyTransit github project](https://github.com/hpparvi/PyTransit) which seems rather well maintained. I am not affiliated with the author nor have I tested it, but it is worth a try.

In **R**, you may try to have a first look at your time series using the [`zoo`](https://cran.r-project.org/web/packages/zoo/) library and follow e.g. the [introductory tutorial](http://web.stanford.edu/class/earthsys214/notes/series.html).


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