The Experimental Gravity Group organizes its work into five long‑term research pillars. Each pillar has its own page describing the scientific focus, methods, and current projects. Project pages provide short summaries now, with space to add figures, photos, and related publications as they are curated.

LIGO & Experimental Gravity Infrastructure

Build and upgrade the hardware and controls that make gravitational-wave interferometers work: lasers, optics, vacuum, suspension, and commissioning tools for current and next‑generation detectors.

  • LIGO Adaptive Optics — Develop adaptive optics techniques to correct thermal and alignment distortions in LIGO interferometers, improving sensitivity at high laser power.
  • LIGO India — A third LIGO detector at Aundha Nagnath, India that will transform gravitational-wave source localization, double binary neutron star detection rates, and enable pre-merger alerts for multi-messenger astronomy.
  • LIGO Voyager — Design and prototype cryogenic silicon interferometer technology for the next major LIGO upgrade — 200 kg silicon test masses at 123 K with 2 µm laser light, targeting 5× sensitivity improvement and 125× event rate.
  • Coating Thermal Noise & Thin Films — Measure and reduce coating thermal noise — the dominant sensitivity limit in LIGO's most critical frequency band — through material characterization, computational optimization, and novel coating architectures including crystalline AlGaAs.

Quantum Measurement & Control

Develop quantum resources and measurement strategies—squeezing, non‑Gaussian states, and quantum links—to push precision sensing beyond standard quantum limits.

  • Waveguide squeezed light source — Build integrated nonlinear waveguide sources of squeezed light for compact, robust quantum-enhanced interferometry.
  • Quantum Control for Metrology using non-Gaussian states — Optimal state injection and readout for precision measurement — preparing non-Gaussian quantum states (cat, GKP, photon-subtracted) that break through the Gaussian squeezing ceiling and extracting maximum Fisher information from them.
  • Vacuum Beam Guide — Build and characterize a long-baseline vacuum beam guide as a quantum link between laboratories for interferometry and quantum networking.
  • Phase-Sensitive Optomechanical Amplifier (PSOMA) — An optomechanical amplifier that uses phase-sensitive gain to boost gravitational-wave signals below the standard quantum limit, targeting the radiation-pressure-dominated low-frequency band.
  • Quantum Neural Networks for Optimal Coherent Control — Parameterized quantum circuits and photonic neural networks that learn optimal coherent control strategies for precision measurement, closing the gap between achieved sensitivity and fundamental quantum bounds.

Quantum Gravity & Foundational Physics

Design tabletop experiments that probe quantum aspects of gravity and test foundational questions at the interface of quantum mechanics and spacetime.

  • Tabletop tests of quantum gravity — Design tabletop optomechanical experiments that probe quantum aspects of gravity and potential deviations from standard quantum mechanics.
  • Precision Optomechanical Platforms — Kilogram-scale mirrors with attometer-level readout sensitivity, repurposed from gravitational-wave detection to probe quantum mechanics at macroscopic scales and test quantum-gravity predictions.
  • Computational Experiment Design — Using optimization and computational search to design experiments that maximize sensitivity to quantum-gravity signatures, systematically exploring interferometer topologies and measurement protocols.

Frontier Photonics & Energy Systems

Create high‑power photonic systems where coherence, damage thresholds, and feedback control are central challenges, with applications from advanced interferometers to energy‑relevant laser platforms.

AI for Experimental Physics

Apply machine learning and control to complex experiments: faster lock acquisition, robust operation, automated diagnostics, and design optimization for precision instruments.

  • RL for classical feedback control in LIGO — Apply reinforcement learning to design and tune classical feedback controllers that keep LIGO interferometers stably locked.
  • Generative Optical Design — Gradient-based optimization over universal interferometer models to autonomously discover detector topologies that outperform human designs.
  • Neural Network Noise Cleaning — Convolutional and compound neural networks for nonlinear noise mitigation, data quality improvement, and real-time pre-merger early warning.
  • Digital-Twin Diagnostics & Forecasting — High-fidelity simulation models of gravitational-wave detectors that enable training ML controllers, accelerating commissioning, and predicting failures before they impact observations.
  • Searching for Unmodeled Signals — Machine learning methods for detecting gravitational-wave transients that don't match existing signal templates, targeting unknown astrophysical phenomena and anomalous events.