Raghav Wani

Master's Thesis Researcher

Collaborating with the SPOTLIGHT team, a GPU-powered HPC radio transient survey instrument at the GMRT designed for simultaneous detection and arc-sec localization of Fast Radio Bursts (FRBs) and pulars

• Developing a real-time multi-beam FRB injection module for raw uGMRT data to test the detection pipeline and generate training datasets for Machine Learning models
• Planning to work on GPU-based real-time RFI mitigation tailored for uGMRT data streams
• Exploring candidate selection and optimization strategies within the FRB detection pipeline
• Using various data analysis tools like PRESTO, Astro-Accelerate, IQRM, FETCH, Candies, etc.

Research Intern

• Built a tool to map signal-to-noise ratio across sky coordinates, aiding the development of a multibeam coincidence filter for SPOTLIGHT’s FRB detection pipeline.
• Worked on analyzing repeater Fast Radio Burst (FRB) data obtained from Ooty Radio Telescope (ORT).

Summer Research Intern

• Developed a reduced-bit real-time FRB injection module to test the SPOTLIGHT FRB detection pipeline.
• Led end-to-end engineering and astronomical tests for FRB detection, enhancing expertise in FRB observational parameters.
• Conducted multiple visits to the GMRT observatory for testing and pipeline validation.

Summer Research Fellow

Awarded the IASc-INSA-NASI Summer Research Fellowship 2023

• Image cleaning: Bias subtraction, flat-fielding, and cosmic ray correction.
• Photometric processing: Star detection, magnitude calculation, calibration, and light curve generation.
• Results: Successfully detected variable stars in the NGC 6709 open star cluster.

• Collaborating with Prof. Prasad Subramanian, IISER Pune and others to assemble and configure a NASA Radio JOVE radio telescope and a log-periodic antenna
• Initiative to observe and classify the low-frequency radio transient bursts from the Sun and Jupiter, as well as studying the local radio environment
• Tested the antenna receiver using a signal generator for varied frequencies and powers
• View first light from our single dipole antenna here

• Gained insights into the operation of gravitational-wave detectors, including LIGO, Virgo, and KAGRA
• Learned to access public gravitational-wave data and assess its quality for analysis
• Developed skills in searching for signals from compact binary coalescence events using appropriate search techniques
• Learned techniques to infer the properties of gravitational-wave sources through parameter estimation
• Engaged in practical tutorials and data challenges to apply learned concepts to real-data from detectors

• Gained hands-on exposure to basic signal processing concepts using MATLAB’s built-in tools
• Worked with seismic signals from multiple stations, focusing on tasks such as resampling, signal alignment, power spectrum analysis, time-frequency visualization (spectrograms and scalograms), and basic filtering (lowpass/highpass)
• Developed an understanding of key techniques and their applications in analyzing real-world data

• Gained foundational knowledge of time-frequency analysis for time-varying signals using MATLAB’s built-in functions
• Explored spectral estimation techniques, including short-time Fourier transform (STFT) for spectrograms and continuous wavelet transform (CWT) for scalograms
• Learned key concepts like windowing, overlap, and DFT points to improve spectral resolution and signal visualization

• Learned the basics of machine learning, focusing on classification models that categorize data into discrete classes
• Understood how machine learning algorithms learn patterns from data instead of relying on manually designed rules
• Gained hands-on experience with data import, feature extraction, model training, and prediction using a handwriting recognition example
• Used MATLAB's built-in tools and a web-based environment to implement classification models without needing extensive theoretical knowledge

• Developed a Molecular Dynamics simulation using the Verlet algorithm, modeling atomic interactions with the Lennard-Jones potential to study particle motion and system dynamics.
• Simulated the Ising Model to explore magnetic spin interactions on a lattice, employing Monte Carlo methods to analyze phase transitions and thermodynamic behavior.
• Implemented a reaction-diffusion model to generate Turing Patterns, solving the FitzHugh-Nagumo equation with finite difference methods to study self-organized pattern formation in biological and chemical systems.

• Under guidance of Dr. Ashish Arora, IISER Pune
• Studied the detailed derivation of the Chandrasekhar mass limit using concepts from Statistical Mechanics, Quantum Mechanics, Relativity, and Stellar Physics
• Explored the astrophysical implications of the mass limit for stellar evolution and the formation of compact objects