This project is a data-driven exploration of the cosmos through analyzing the SDSS large-scale astronomical survey to explore and classify celestial objects based on their properties. To facilitate the analysis, I performed all my work on SciServer cloud-based computing system that offers an extensive range of interconnected tools and services.
Source : Part of the visualization task in my main.ipynb notebook.
For the classification of celestial objects, I deployed three distinct ML models: k-Nearest Neighbors (KNN), Random Forest, and a Neural Network (MLP Classifier). Each model underwent meticulous fine-tuning, incorporating various regularization techniques and hyperparameter selections to maximize accuracy and dependability.
Clone the repository and install the dependencies.
This work is designed to be run from the SciServer compute environment. Please set up a SciServer account and upload main.ipynb into a new “container”.
Use the following code block to search the SDSS Data Release 16 database via the CasJobs REST API:
SELECT TOP 10000 p.objId,p.ra,p.dec,p.u,p.g,p.r,p.i,p.z, p.petror90_r,
s.specobjid, s.class, s.z as redshift, s.plate, s.mjd, s.fiberid
FROM PhotoObj AS p
JOIN SpecObj AS s ON s.bestobjid = p.objid
WHERE p.u BETWEEN 0 AND 19.6
AND p.g BETWEEN 0 AND 20 AND p.petror90_r > 10CasJobs.executeQuery(query, "dr16")