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title: Detecting outliers in the latest catalog of quasars

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# Detecting outliers in the latest catalog of quasars 

## General Overview
- [Quasars](https://en.wikipedia.org/wiki/Quasar) are a class [active galactic nuclei](https://en.wikipedia.org/wiki/Active_galactic_nucleus).  Basically, a type of galaxy with a supermassive and hungry   black hole in the center which accretes material from the host galaxy and out-shines the whole galaxy with an astounding bright light. 
- To get a general overview about quasars you can watch [this](https://youtu.be/AZfHV2_9fdY) or similar videos.
- The project is actually a research type, based on the course project document. 
- Please have a look at [Effectively using unsupervised machine learning in next generation astronomical surveys](https://arxiv.org/abs/1911.06823). 
- They basically compared various outlier/anomaly detection techniques for identifying outlier galaxies.
-  We can use similar approach, but not necessarily  similar methods, to obtain outlier quasars. 
-  Check out the methods they used: 
    - Isolation forest
    - Random forest
    - PCA 
    - Fisher Vector
- Decide if you want to use the same methods or you have another method in mind. 
- Each group member should use a separate method 
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## Proposal (0st draft)
- Given: 
    - Quasar catalog including measured properties of more than 750k objects in [dr16q](https://www.sdss.org/dr16/algorithms/qso_catalog/) 
    - 1D image of each quasars as their spectrum available in [SDSS](https://www.sdss.org/dr16/spectro)
    
- We use:
    - 4 outlier detection methods (**TBD**) 
    - to find outlier quasars in the main catalog.
    -  Quasars should be classified first based on the known classes of quasars like obscured, unobscured, red, blue. 
    - Outliers in each class should be identified. 
    
- Evaluation: 
    - Finally, outliers in each class should be visually inspected by looking their spectra to see why they have been labeled as outliers. 

