DEVIKA CHANDIRAN

International Astronomical Search Collaboration (IASC)

• Gravitational Wave Modelling in Binary Systems: Understanding Orbital Decay and Energy Loss Through Simulations, Queen Mary University of London, To model gravitational wave emissions from binary neutron star systems using the quadrupole formula, focusing on how energy loss through gravitational radiation affects orbital decay, and comparing simulated results with LIGO observations., Developed simulations in Mathematica to model gravitational wave emission from binary neutron star systems., Applied the quadrupole formula to calculate gravitational wave strain and energy loss rates., Investigated the effect of orbital eccentricity on the energy loss and decay of binary orbits., Conducted comparative analysis between simulated gravitational waveforms and real-world observations, particularly the LIGO GW150914 event., Proposed enhancements to current gravitational wave detection models to improve simulation accuracy., Presented findings in departmental seminars and contributed to research discussions about improving gravitational wave models., The research deepened understanding of binary system dynamics and gravitational wave emissions, and highlighted the need for improvements in current models for more accurate future detections.
• Extraction of Astrophysical Information from Simulated Gravitational Waves, NSS College, Pandalam, To extract astrophysical data such as chirp mass, orbital period, and binary system distance from simulated gravitational wave signals using theoretical models and LIGO data., Analyzed simulated gravitational wave signals to determine key astrophysical properties., Calculated the chirp mass, orbital separation, and distance to binary neutron star systems using Kepler's laws and gravitational wave theory., Examined how gravitational wave signals evolve as the binary system approaches coalescence, focusing on the changes in wave amplitude and frequency., Applied theoretical equations to interpret the physical properties of binary systems from simulated LIGO data., Conducted hands-on work with gravitational wave data visualization and waveform analysis., This project demonstrated the effectiveness of using gravitational wave data to extract key astrophysical information, showcasing how simulated signals can be used to understand real-world binary systems and their dynamics.

• Data-Driven Astronomy, University of Sydney, Coursera, 09/2024, This course focuses on how to analyze large astronomical datasets using modern data science techniques. It covers data handling, processing, and interpreting from observatories, and introduces machine learning techniques for classifying galaxies., Given the large datasets collected by observatories like LIGO, this course enhances the skills required to process and visualize gravitational wave data, which is crucial in detecting wave patterns amidst noise. Machine learning techniques learned in this course are directly applicable to automating the detection and filtering of gravitational waves, a key skill for future research.
• The Data Scientist's Toolbox, Johns Hopkins University, Coursera, 09/2024, This course introduces data science tools such as Git for version control, R programming for statistical analysis, and markdown for documentation., Version control tools like Git are essential in large collaborative projects like LIGO, where researchers share code and data. This course enhances your ability to manage complex data analysis workflows, a skill crucial for handling the vast datasets in gravitational wave astronomy.