Indian Students Selected for Caltech’s LIGO-SURF 2023

For this year (2023) Caltech selected three Indian Students for the LIGO-SURF program:

1) Kushal Jain

Project Title:  “Birefringence Fluctuations in silicon crystals”
Mentors: Yuta Michimura, Rana Adhikari

2) Advait Mehla

Project Title:  “Nonlinear Coherent Control for Nonlinear Measurements”
Mentors: Rana Adhikari, Yanbei Chen, Lee McCuller

3) Reuben Mathew

Project Title: “Increasing Laser Stabilization Speed”
Mentors: Radhika Bhatt, Rana Adhikari

 


Students selected in 2022

1) Deeksha Sabhari (Manipal Institute of Technology)

Project Title: Multicolor calibration scheme prototyping at 40m

Mentors: Anchal, Paco, Koji, Rana

 

Abstract: Advanced LIGO (aLIGO) achieves detection of gravitational waves (GWs) originating in distant astrophysical events by reducing the detector measurement noise below the signal strength in the frequency band they arrive. However, the lower the detector noise becomes, the more the uncertainty of a measurement outcome depends on systematic rather than statistical errors. With next-generation cryogenic GW detectors aiming to bring the detector noise floor further down, new calibration procedures ensuring statistical uncertainty limited measurements are required. At 40m, we are testing a new calibration procedure that uses stable oscillating auxiliary fields at a different wavelength in the 40m long arms as a reference to calibrate the differential arm length fluctuations of the interferometer. The project would be to undertake laying down a new beatnote detection path at the end station table(s) between the transmitted main laser and the seed laser of the auxiliary field. This setup needs to be completed with signal routing to data acquisition and calibration tests will be performed on the interferometer. [Pre-requisites from students; (medium) optical lab work experience, (optional, preferred) analog electronics, (optional) Python]

2) Hiya Gada (IIT Bombay)

Project Title:  Material emissivity characterization and analysis for test mass coatings

Mentors: Radhika, Chris, Rana

Abstract: For the 40m cryogenic upgrade, optimal choice of coatings for suspended optics is necessary to achieve efficient cooling to 123K. Resources for determining the emissivities of various optics, coatings, and shielding elements are essential for accurate radiative and conductive heat transfer models. However, public availability of these properties is limited, and their in-house experimental determination is proving necessary. This project would involve measuring the emissivity of various materials and refining such experimental procedure. Additionally, it may involve fabrication components such as application of dark coatings to test masses. This work would directly enhance thermal models and inform design choices for the cryogenic upgrade. [Pre-requisites: (medium) optical lab work experience, (medium) python, (optional, preferred) simulation/optimization experience.]

Students selected in 2021

1) Anirban Bairagi

Project Title: Beam tracking
Mentors: Tega, Yehonathan, Rana

 

Interferometric gravitational wave detectors must be precisely aligned to maintain low-noise operation. A misaligned laser beam due to seismic motion causes an unwanted coupling of angular mirror motion noise to the gravitational wave readout. Therefore, the laser beam position must be continuously monitored and corrected. Existing beam monitoring schemes are ridden with systematic errors. In this project, we will develop a real-time machine-learning algorithm for tracking a beam spot position using various image processing techniques.

2) Bhavini Jeloka

Project Title: System Identification and Optimal Control
Mentors: Rana, Hang, Gabriele

 

LIGO detects gravitational waves by operating a 4 km long arm length dual recycled Michelson interferometer. This requires several hundred control loops to keep the detector in an optimum state. Many such systems are complex as they are Multiple Inputs Multiple Outputs (MIMO) systems with cross-coupled sensors and actuators. Identifying the true system (plant) dynamics is important to develop an optimal control design for such complex systems. In this project, we will develop a method to identify the system automatically and obtain an optimal control module for it.