
Worked on the FRBs/FRB repository to enhance analytics capabilities and streamline data workflows for fast radio burst (FRB) research. Developed new Python scripts in Jupyter Notebook and Shell for likelihood evaluation, scanning, parameter optimization, and redshift probability computation from dispersion measure, enabling more efficient and reproducible data analysis. Improved repository maintainability by reorganizing files and introducing a dedicated crossmatching folder to better reflect updated processing workflows. Addressed repository clutter by removing unnecessary artifacts, which reduced continuous integration noise. The work demonstrated depth in scientific computing, data analysis, and astrophysics, aligning technical improvements with long-term project goals for maintainability and speed.
May 2025 monthly summary for FRBs/FRB repository. Focused on delivering analytics capabilities and improving maintainability. Implemented new Python scripts for FRB data analysis (likelihood evaluation, scanning, and parameter optimization) and a script to compute redshift probability from DM; reorganized repository with a crossmatching folder and file moves; cleaned up artifacts by removing a dummy.txt file. These efforts accelerate data analysis, improve reproducibility, and reduce CI noise.
May 2025 monthly summary for FRBs/FRB repository. Focused on delivering analytics capabilities and improving maintainability. Implemented new Python scripts for FRB data analysis (likelihood evaluation, scanning, and parameter optimization) and a script to compute redshift probability from DM; reorganized repository with a crossmatching folder and file moves; cleaned up artifacts by removing a dummy.txt file. These efforts accelerate data analysis, improve reproducibility, and reduce CI noise.

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