<p>I wrote a Python script that calculates the period of an unevenly sampled data set using the String-Length Minimization method.</p>
<p>You can read about that method here: <a href="https://articles.adsabs.harvard.edu/pdf/1983MNRAS.203..917D" rel="nofollow noreferrer">A period-finding method for sparse randomly spaced observations or “How long is a piece of string?”</a></p>
<p>Overall, my code works great! However, Now I'm wanting to calculate the uncertainty in the estimated period from the technique.</p>
<p>My initial thought was to look at section <strong>2.1 Effect of Random Errors</strong> from the paper. I wasn't entirely sure if that's what I wanted but I continued anyway. I wrote a script that does a monte carlo method so I could find the error parameter and ultimately calculate a δL value.</p>
<p>I found:</p>
<p>ε = 0.03</p>
<p>δL = 8.253</p>
<p>Both of those numbers seem reasonable at least compared to the examples in the paper.</p>
<p>After doing all that though, I'm not entirely sure if I'm even on the right track here. I tried adding the δL value to string lengths to see if that would make a difference to the calculated period but it didn't. I'm guessing I'm missing something or I'm on the completely wrong track.</p>
<p>Am I on the right track for calculating the uncertainty in period found for string-Length? If so, what steps should I take next? If not, what do you suggest?</p>
<p>Thanks!</p>

CC BY-SA 4.0
2023-04-13T06:41:17.137Z
What do you mean "you found" an $\epsilon$? That is - according to the paper you quote - a parameter to *define* the magnitude of your random errors, not an output of such calculation. Choosing an $\epsilon$ then will give you a $\delta L$ as a resulting error which then probably should be in accordance with the quoted error formula in that section.
planetmaker
CC BY-SA 4.0
2023-04-13T20:35:08.443Z
@planetmaker I ran a monte carlo and was able to calculate an ϵ. How else would I know what to put for ϵ in the equation? 

Am I on the right track here? Or am I way out of my way?
Zachary Kennedy
<observational-astronomy><star><data-analysis><light-curve><variable-star>
Finding the uncertainty in period for String-Length Minimization calculation
Zachary Kennedy
# Finding the uncertainty in period for String-Length Minimization calculation

(Asked by: username_0 on 2023-04-13)

I wrote a Python script that calculates the period of an unevenly sampled data set using the String-Length Minimization method.

You can read about that method here: [A period-finding method for sparse randomly spaced observations or “How long is a piece of string?”](https://articles.adsabs.harvard.edu/pdf/1983MNRAS.203..917D)

Overall, my code works great! However, Now I'm wanting to calculate the uncertainty in the estimated period from the technique.

My initial thought was to look at section **2.1 Effect of Random Errors** from the paper. I wasn't entirely sure if that's what I wanted but I continued anyway. I wrote a script that does a monte carlo method so I could find the error parameter and ultimately calculate a δL value.

I found:

ε = 0.03

δL = 8.253

Both of those numbers seem reasonable at least compared to the examples in the paper.

After doing all that though, I'm not entirely sure if I'm even on the right track here. I tried adding the δL value to string lengths to see if that would make a difference to the calculated period but it didn't. I'm guessing I'm missing something or I'm on the completely wrong track.

Am I on the right track for calculating the uncertainty in period found for string-Length? If so, what steps should I take next? If not, what do you suggest?

Thanks!

**Tags:** observational-astronomy, star, data-analysis, light-curve, variable-star

> **Comments:**
> **username_1**: What do you mean "you found" an $\epsilon$? That is - according to the paper you quote - a parameter to *define* the magnitude of your random errors, not an output of such calculation. Choosing an $\epsilon$ then will give you a $\delta L$ as a resulting error which then probably should be in accordance with the quoted error formula in that section.  
> **username_0**: @username_1 I ran a monte carlo and was able to calculate an ϵ. How else would I know what to put for ϵ in the equation?   Am I on the right track here? Or am I way out of my way?  

---

## 0 answers

