Searching For Black Holes Using Auto Differentiation


  • William Doyle University of Portsmouth



GravAD, Gravitational Waves, Automatic Differentiation, JAX, Signal-to-Noise Ratio, LIGO, Compact Binary Coalescence, Just-in-Time Compilation, Simulated Annealing, Adaptive Moment Estimation, Stochastic Gradient Descent, IMRPhenomD, Black Holes;, Matched Filtering; Template Bank;


This study presents GravAD, a novel approach for detecting gravitational waves using automatic differentiation and JAX. GravAD demonstrates comparable signal-to-noise ratio and mass values to established LIGO pipelines with a significant reduction in the number of templates. Limitations include the inability to handle binary neutron star systems and some lower-mass black holes. Leveraging JAX’s acceleration, GravAD offers potential as a rapid preliminary tool for gravitational wave detection. Future work includes further optimisation of functions, exploration of alternative optimisation algorithms, real-time data analysis adaptation, and expanding the scope to handle a broader range of astrophysical sources.


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2023-07-06 — Updated on 2023-07-20


How to Cite

Doyle, W. (2023). Searching For Black Holes Using Auto Differentiation. Emerging Minds Journal for Student Research, 1, 17–38. (Original work published July 6, 2023)