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researcher

Michael J. Williams

7 papers hereh-index 5310 citations7 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author3
  • last author2

Across the 7 of 7 papers where every author was matched, so the position is known.

fields
  • astro-ph.HE3
  • astro-ph.IM2
  • gr-qc2
same name
  • Michael J. Williams — 10 papers, h 7
  • Michael J. Williams — 3 papers, h 4
  • Michael J. Williams — 3 papers, h 3
  • Michael J. Williams — 3 papers, h 2
  • Michael J. Williams — 2 papers, h 1
  • Michael J. Williams — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172026
most citedMatching matched filtering with deep networks in gravitational-wave astronomy

250 citations · 440 across the 6 of their papers we have counts for

collaborators
Showing astro-ph.HEShow all

3 papers · 1 filter

astro-ph.HE2026

Normalizing flows for density estimation in multi-detector gravitational-wave searches

Sam Insley, Michael J. Williams, Rahul Dhurkunde +1

Identifying compact binary coalescences buried within the non-Gaussian and non-stationary data taken by gravitational-wave interferometers requires sophisticated search pipelines,…

astro-ph.HE2023★ 1 cited

Unpacking merger jets: a Bayesian analysis of GW170817, GW190425 and electromagnetic observations of short gamma-ray bursts

Fergus Hayes, Ik Siong Heng, Gavin Lamb +3

We present a novel fully Bayesian analysis to constrain short gamma-ray burst jet structures associated with cocoon, wide-angle and simple top-hat jet models, as well as the binary…

astro-ph.HE2021★ 2 cited

A Bayesian Inference Framework for Gamma-Ray Burst Afterglow Properties

En-Tzu Lin, Fergus Hayes, Gavin P. Lamb +5

In the field of multi-messenger astronomy, Bayesian inference is commonly adopted to compare the compatibility of models given the observed data. However, to describe a physical sy…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.