A/Prof Bram Slagmolen
Position |
Senior Fellow |
|---|---|
Department |
Centre for Gravitational Astrophysics |
Office phone |
2 61259094 |
Email |
|
Office |
Gravity Wave Building 1 80 |
Webpage |
https://cga.anu.edu.au |
Vibration control for optical interferometry
Develop an active vibraiton isolation platform to provide a quiet, small displacement environment for high precision inteferometry.
Prospects of future ground-based gravitational-wave detector network
In this project, we study the gravitational-wave astronomy and astrophysics science cases and observational prospects with future ground-based gravitational-wave observatories.
Dr Lilli (Ling) Sun, A/Prof Bram Slagmolen, Distinguished Prof David McClelland
Dual torsion pendulum for quantum noise limited sensing
Construct a small dual tosion pendulum which have their centre of mass co-incide and their rotational axis colinear. Inital diagnostics will be done using shadow sensors.
Calibrate gravitational wave detectors
For gravitational-wave detections and analyses, the raw outputs from the gravitational-wave detectors need to be converted into analysable data through some calibration apparatus. This project investigates new techniques to improve calibration accuracy and precision and better integrate the calibration bias into astrophysical analyses.
Dr Lilli (Ling) Sun, A/Prof Bram Slagmolen, Distinguished Prof Susan Scott
Fibre optic sensor arrays for vibrometry and acoustic sensing
By leveraging hybrid digital-optical methods, we develop new distributed and quasi-distributed fibre-optic acoustic sensors. These acoustic sensors aim to measure vibration, strain and displacement all while localising the signal source along an optical fibre.
Dr Chathura Bandutunga , Dr Paul Sibley, A/Prof Bram Slagmolen
Quantum squeezed states for interferometric gravitational-wave detectors
Using non-classical light states on laser interferometric gravitational-wave detectors, to further enhance the best length measurement devices in the world.
Distinguished Prof David McClelland, Professor Daniel Shaddock, A/Prof Bram Slagmolen
Machine learning for optics and controls
Optical cavities are widely used in physics and precision measurement. This project will explore the use of modern machine learning methods for the control of suspended optical cavities.
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