Statistical analysis of photometric data - Astronomy

In summary, the conversation discusses an assignment where the class is creating a program to convert Hipparcos data from magnitudes to flux in order to plot a frequency histogram and approximate a normal distribution. The reason for converting the data is because magnitudes are logarithmic and would not accurately represent a normal distribution. The conversation also touches on the definition of flux and how it is determined in this context.
  • #1
big man
254
1

Homework Statement


My class is doing an assignment where we have to create a programme to convert the Hipparcos data from magnitudes to flux so that it when you plot a frequency histogram of the data you will have an approximation of a normal distribution. I've completed this OK, but I was wondering why this is the case?

Why do you need to convert the data from magnitude to flux? Is it because magnitudes are logarithmic and so wouldn't get an accurate normal distribution with a reasonable spread up to +- 5 std from the mean?

That's all I can think of. Am I on the right chain of thought here? Any advice on this would be extremely helpful and appreciated.

Thanks
 
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  • #2
I think you're right in your reasoning, but maybe somebody else can offer further insights. In the mean time, at least this bumps the thread. So when you say flux, do you mean light intensity measured as the power per unit area arriving here (on Earth)?
 
  • #3
In a strict sense it won't be the actual flux (in terms of its value), but it is still flux as per your definition. In fact I probably should have said flux density.

It is determined by rearranging the standard apparent magnitude formula of:

m=-2.5log(F/Fo)
 

What is statistical analysis of photometric data in astronomy?

Statistical analysis of photometric data in astronomy is a method used to analyze and interpret the brightness or intensity measurements of celestial objects. This involves using mathematical techniques and statistical models to extract meaningful information from large datasets of photometric observations.

Why is statistical analysis important in astronomy?

Statistical analysis is important in astronomy because it allows us to make accurate and reliable conclusions about the properties and behavior of celestial objects. By analyzing large datasets of photometric data, we can uncover patterns and trends that may not be visible to the naked eye, and use this information to better understand the universe.

What are some common statistical methods used in analyzing photometric data?

Some common statistical methods used in analyzing photometric data include regression analysis, time series analysis, and hypothesis testing. Regression analysis helps to identify relationships between variables, while time series analysis is used to study the changes in brightness or intensity over time. Hypothesis testing is used to determine the significance of any observed patterns or differences in the data.

What are some challenges in statistical analysis of photometric data in astronomy?

One of the main challenges in statistical analysis of photometric data in astronomy is dealing with large and complex datasets. This requires specialized software and expertise to handle and analyze the data effectively. Additionally, there may be noise or errors in the data due to factors such as atmospheric conditions or instrumental limitations, which can affect the accuracy of the results.

How is statistical analysis of photometric data used in current astronomical research?

Statistical analysis of photometric data is used in a wide range of current astronomical research, including studies on exoplanets, star formation, galaxy evolution, and cosmology. It is also used to analyze data from space-based telescopes, such as the Hubble Space Telescope and the Kepler Space Telescope, and to aid in the discovery of new celestial objects and phenomena.

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