Removal of Polynomials From Raw Aperture Flux Time Series?

[rexregisanimi]

Hello! This is my first post so forgive any errors of decorum. )

I am a student working toward a degree in astrophysics but I'd like to jump a few years ahead when it comes to the study of exoplanets. While examining some data about the new discovery of Kepler-22b, I noticed a plotted data set described as a "flux time series after [the] removal of a second-order polynomial for each segment and normalizing the data of each quarter by the median" (see Figure 1 here). I'd like to better understand what this means. Any guidance, links, or direction would be greatly appreciated.

I understand a flux time series is, of course, a measure of the light arriving from the star as plotted over time but I do not understand the process of removing polynomials and normalizing the data or the value of doing these things. My assumption is that this involves some sort of reverse local regression.

Thank you in advance for any contribution to my efforts.

 

[AndyW]

Thank you for your interest in the study of exoplanets and the data analysis techniques used in this field. I am a scientist with a background in astrophysics and I would be happy to provide some guidance on the topic you have raised.

Firstly, I would like to commend you for taking the initiative to understand the data and techniques used in the study of exoplanets. This is a crucial step in becoming a successful astrophysicist. Now, let's delve into the specific questions you have raised.

The data set you mentioned, a "flux time series after removal of a second-order polynomial for each segment and normalizing the data of each quarter by the median," is a common technique used in the analysis of light curves from exoplanet observations. This process is also known as "detrending" the data.

Removing the second-order polynomial for each segment essentially involves fitting a polynomial curve to the data and subtracting it from the original light curve. This helps to remove any long-term trends or systematic errors in the data, such as instrumental effects or variations in the star's brightness. This is important because these trends can mask the signal of a potential exoplanet transit, making it difficult to detect.

Normalizing the data of each quarter by the median involves dividing the data by the median value of that quarter. This helps to account for any short-term variations in the star's brightness, such as stellar flares or other stellar activity. By normalizing the data, we can compare the relative changes in the star's brightness over time, rather than the absolute values.

Both of these techniques are used to clean and prepare the data for further analysis, such as searching for exoplanet transits. As for your assumption about reverse local regression, it is not directly related to this process, but it is a commonly used technique in exoplanet studies as well.

If you would like to learn more about these techniques and other methods used in the study of exoplanets, I recommend checking out online resources such as NASA's Exoplanet Archive or attending conferences and workshops on the subject.

I hope this helps to clarify the process of removing polynomials and normalizing data in exoplanet studies. Keep up the curiosity and passion for astrophysics, and I wish you all the best in your studies.