| Name: Nikitha Jithendran |
| Affiliation: Physical Research Laboratory |
| Conference ID: ASI2026_754 |
| Title: Development and Validation of a Modular Python Data-Reduction Pipeline for the PARAS-2 High-Resolution Spectrograph |
| Abstract Type: Poster |
| Abstract Category: Facilities, Technologies and Data science |
| Author(s) and Co-Author(s) with Affiliation: K.J.Nikitha(Physical Research Laboratory, Ahmedabad, Gujarat-380009,India), Rishikesh Sharma(Physical Research Laboratory, Ahmedabad, Gujarat-380009,India), Shubhendra Nath Das(Physical Research Laboratory, Ahmedabad, Gujarat-380009,India), Abhijit Chakraborty(Physical Research Laboratory, Ahmedabad, Gujarat-380009,India) |
| Abstract: The PARAS-2 high-resolution fiber-fed spectrograph at the PRL Mt. Abu Observatory is designed to enable precision radial-velocity studies for exoplanet detection and stellar astrophysics. To support routine operations and reproducible science output, we have developed a comprehensive, modular Python-based data-reduction and radial-velocity extraction pipeline tailored to the instrument architecture and observing modes of PARAS-2.
The pipeline implements an end-to-end processing framework incorporating bias calibration, order tracing, optimal and non-optimal spectral extraction, wavelength calibration, drift monitoring, and RV analysis. The architecture emphasizes transparency, logging, and configurability, enabling benchmarking against legacy IDL workflow and facilitating long-term instrument health tracking. During the Python migration, particular attention was devoted to ensuring numerical consistency with the IDL implementation — especially in data type propagation and implicit algebraic operations — which were systematically audited and standardized through cross-comparison diagnostics.
Validation using commissioning and early-science datasets demonstrates consistent RV recovery with the earlier IDL pipeline, alongside improved automation, processing efficiency, and calibration repeatability. I will present the design philosophy, key algorithmic components, and performance assessment of the pipeline, which strengthens the data-analysis capability of PARAS-2 and contributes to an indigenous, maintainable software ecosystem for precision spectroscopy.
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