Astronomical Data Analysis

Astronomical Data Analysis

What is data analysis?

Data analysis is a process of Inspecting, Cleansing, Transforming and Modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making.
In simple words, a method in which data is collected and organized so that one can derive helpful information from it.


What is Astronomical data?

When we are dealing with astronomical data, it is quite different with other kind of data. The data obtained in astronomy is often what is called noise in other fields. As a result, we have to deal with situations in which the data are incomplete, inconclusive and often hidden. Dealing with these situations requires some amount of imagination and a willingness to try different approaches.

There are 4 types of Astronomical data,

  • Image data – an image is a two-dimensional array of pixels, where each pixel value
  • Spectra – simple spectra show us how the energy of the light emitted by an object is distributed among the different possible wavelengths.
  • Data cubes – a data cube is like a two dimensional image contains a whole spectrum received from the region of the sky within that pixel. that gives us in effect a three-dimensional object: a data cube

  • Catalog data – catalogues are properties of different types of astronomical objects.

These different data usually come with meta-data, that is, descriptive information about the data. For example, Astronomical images typically include information about the circumstances of when and how the image was taken (what telescope, what time, what exposure time, what pointing?), and about where in the sky the object in question is located.

Tools for Astronomical Data Analysis

The main problem with astronomical data is the size of data. Therefore these are very hard to maintain without a machine help. All this implies that digital data analysis skills are part of the essential skill sets of modern astronomers.

When it comes to the tools for working with these various kinds of data, we can distinguish two types.

  • Application software is a software written for a specific set of tasks.
    • E.g. SAOImage, DS9 for images and TOPCAT for operation involvin tables
  • Programming language is a tool for writing your own custom applications. You can write your own custom application that can be used for the specific analysis problem you need to solve
    • e.g. Interactive Data Language (IDL), Python, R programming language

Astronomical data analysis starts with basic mathematical operations. When you go from the magnitude to the flux emitted by an astronomical object, you will need the “x to the power of n” operation.

Data points come in sets: the pixel data for an image corresponds to a two-dimensional arrangement, while a list of properties for astronomical objects will correspond to a one dimensional chain of values.

For this data handling astronomers must have a knowledge in data structures, data types, control structures and such other programming features. Knowledge of how to create various kinds of plots, diagrams or histograms is essential.

If we have obtained the data by downloading a file, we will need to know about proper input/output operations. For certain data formats, such as the ubiquitous FITS image files, or for astronomical tables in FITS or VOTable format, there are special functions that read the data in a way that makes it particularly easy to start working with them.

When we access astronomical data bases, we need to tell the data base which specific set of data we would like to access. In order to do so, we must submit a data base query, or query for short, to the data base. A number of astronomical data bases are organized in the shape of a Virtual Observatory (VO) The query language for the VO is the Astronomical Data Query Language (ADQL),

Astronomy isn’t only about observing. In the end, we want to understand the objects we observe. That involves creating simplified models for these objects. If a star is a gigantic ball of plasma, held together by its own gravity and heated up by nuclear fusion reactions in its core, then if we create a simulation of such an object, using our knowledge of the laws of physics, the resulting model should have similar properties to a real star which we can check using observations.

References-:

  • A beginner’s guide to working with astronomical data by Markus Possel
  • Statistical methods in astronomy by James p. Long and Rafael s. de Souza

-Pasan Muthugala-

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