Shivaraj Kandhasamy

Shivaraj Kandhasamy

Contact: +91 20 2560 4468

E-mail: shivaraj [at] iucaa [dot] in

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My research interests and activities primarily focus on gravitational-wave (GW) detection, detector calibration and characterization, and detector performance improvement.

In GW detection, I am interested in developing and improving search methods and data analysis pipelines to detect stochastic GW backgrounds and extract more robust astrophysical information from observed data. These efforts are particularly important as the field moves toward the anticipated detection of GW backgrounds.

Detector calibration involves converting the measured laser power at the interferometer output into GW strain. Since calibration uncertainties directly affect astrophysical parameter estimation and scientific interpretation, there is a growing need to achieve sub-percent-level calibration uncertainties in current and future GW detectors. Meeting this goal requires both improved detector modeling and more accurate calibration references. My research interests encompass both of these areas.

GW detector characterization focuses on understanding and improving detector performance over both short and long timescales. Short-duration transient disturbances can affect the detection of compact binary coalescence (CBC) signals and burst GW signals, while long-term variations can limit the sensitivity to stochastic and continuous-wave (CW) signals. Identifying the sources of these disturbances and mitigating their impact is therefore crucial for maximizing the scientific reach of GW observatories. In particular, I am interested in understanding environmental influences, such as seismic motion, and the role of feedback control systems in detector performance. In addition, I am also interested in developing a time-domain simulation environment capable of emulating a full-scale LIGO interferometer. Such a platform could be used for training new personnel in detector operation, control, and monitoring, as well as for developing and testing advanced control strategies, including both conventional and machine-learning-based approaches.