Dirichlet Process Gaussian-mixture model: An application to localizing coalescing binary neutron stars with gravitational-wave observations
Can't claim much credit for this - the first two authors (Walter Del Pozzo and Christopher Berry) did most of the work, and I'm no physicist! From a machine learning point of view this paper is pretty straight forward however - readings from the LIGO detectors are run through a model that uses the known physics/noise to generate posterior samples of where binary neutron stars are. They then used my variational Dirichlet Process Gaussian Mixture Model code to fit a probability distribution, from which they can extract a credible region for the location of the stars. This could then be used, for instance, to drive a search to find a matching observation in the medium of light.
Ignoring the fact that giant machines that cost hundreds of millions are associated, which is always cool (and I have to beg for a computer with a GPU in...), what I really like about this paper is the rigorous testing. Simulated data is used to verify the model, with the Kolmogorov–Smirnov test used to demonstrate that the fitted distribution genuinely fits the distribution of the data (On which note, my DPGMM code is rock solid - this is the evidence!). ML papers rarely go this far. Maybe this can be attributed to ML being a new field, but ML systems are now making decisions that change lives - we need both theory and this level of rigour if we are to avoid catastrophe. Getting there is going to be a challenge though, as there is a genuine hostility towards it in some circles. And ignorance. Yes, doing things the proper way is more work, but it is also the future.
Anyway, that's enough of me hollering from my soap box - here is the final published version:
Dirichlet Process Gaussian-mixture model: An application to localizing coalescing binary neutron stars with gravitational-wave observations
which was published in the Monthly Notices of the Royal Astronomical Society
Additionally, here is the arXiv link to it:
Dirichlet Process Gaussian-mixture model: An application to localizing coalescing binary neutron stars with gravitational-wave observations
Ignoring the fact that giant machines that cost hundreds of millions are associated, which is always cool (and I have to beg for a computer with a GPU in...), what I really like about this paper is the rigorous testing. Simulated data is used to verify the model, with the Kolmogorov–Smirnov test used to demonstrate that the fitted distribution genuinely fits the distribution of the data (On which note, my DPGMM code is rock solid - this is the evidence!). ML papers rarely go this far. Maybe this can be attributed to ML being a new field, but ML systems are now making decisions that change lives - we need both theory and this level of rigour if we are to avoid catastrophe. Getting there is going to be a challenge though, as there is a genuine hostility towards it in some circles. And ignorance. Yes, doing things the proper way is more work, but it is also the future.
Anyway, that's enough of me hollering from my soap box - here is the final published version:
Dirichlet Process Gaussian-mixture model: An application to localizing coalescing binary neutron stars with gravitational-wave observations
which was published in the Monthly Notices of the Royal Astronomical Society
Additionally, here is the arXiv link to it:
Dirichlet Process Gaussian-mixture model: An application to localizing coalescing binary neutron stars with gravitational-wave observations