Modern astronomy generates an unprecedented volume of data through the space and ground Telescope Observations and sky surveys capturing High-resolution images, spectra, and light curves of billions of celestial objects.

Astronomical data holds the key to mysteries of universe. But this data is huge for example only Large Synoptic Survey Telescope (LSST) collects 20 terabytes data every night. Dealing with this massive data requires advanced tools and techniques like

Big Data Analytics Extracting meaningful insights from immense datasets

Complex Calculations Simulating cosmic events, understanding phenomena.

Pattern Recognition Identifying hidden trends and anomalies.

The fusion of Machine Learning, Artificial Intelligence, and High-Performance Computing has a transformative impact on modern astronomy.

Machine Learning involves the use of algorithms and statistical models to enable computers to learn from data and improve their performance on a specific task without being explicitly programmed.

ML focuses on pattern recognition, predictions, and making informed decisions based on historical data.

ML algorithms include decision trees, neural networks, support vector machines, and more.

Artificial Intelligence aims to enable computers to perform tasks that would typically require human intelligence, such as reasoning, problem-solving, understanding natural language, and even making decisions.

While AI includes ML, it also involves other techniques like rule-based systems, expert systems, and symbolic reasoning.

High-Performance Computing refers to the use of powerful computers or clusters of computers to perform complex calculations and process large amounts of data at incredibly fast speeds.

HPC focuses on maximizing processing power, memory, and bandwidth to solve intricate problems efficiently.

At computational Astrophysics lab we apply Machine Learning, Artificial Intelligence, and High-Performance Computing on the following research areas: