Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization Article Swipe
Ronan Legin
,
M. Isi
,
Kaze W. K. Wong
,
Yashar Hezaveh
,
Laurence Perreault-Levasseur
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.19956
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.19956
Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from this idealization are typically handled on a case-by-case basis, e.g., through bespoke procedures to ``clean'' non-Gaussian noise transients (glitches), as was famously the case for the GW170817 neutron-star binary. Although effective, manipulating the data in this way can introduce biases in the inference of key astrophysical properties, like binary precession, and compound in unpredictable ways when combining multiple observations; alternative procedures free of the same biases, like joint inference of noise and signal properties, have so far proved too computationally expensive to execute at scale. Here we take a different approach: rather than explicitly modeling individual non-Gaussianities to then apply the traditional GW likelihood, we seek to learn the true distribution of instrumental noise without presuming Gaussianity and stationarity in the first place. Assuming only noise additivity, we employ score-based diffusion models to learn an empirical noise distribution directly from detector data and then combine it with a deterministic waveform model to provide an unbiased estimate of the likelihood function. We validate the method by performing inference on a subset of GW parameters from 400 mock observations, containing real LIGO noise from either the Livingston or Hanford detectors. We show that the proposed method can recover the true parameters even in the presence of loud glitches, and that the inference is unbiased over a population of signals without applying any cleaning to the data. This work provides a promising avenue for extracting unbiased source properties in future GW observations over the coming decade.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.19956
- https://arxiv.org/pdf/2410.19956
- OA Status
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- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4404313587Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2410.19956Digital Object Identifier
- Title
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Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood CharacterizationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-25Full publication date if available
- Authors
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Ronan Legin, M. Isi, Kaze W. K. Wong, Yashar Hezaveh, Laurence Perreault-LevasseurList of authors in order
- Landing page
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https://arxiv.org/abs/2410.19956Publisher landing page
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https://arxiv.org/pdf/2410.19956Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2410.19956Direct OA link when available
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Noise (video), Gaussian noise, Gaussian, Characterization (materials science), Maximum likelihood, Gravitational wave, Statistics, Mathematics, Physics, Acoustics, Statistical physics, Computer science, Astrophysics, Artificial intelligence, Algorithm, Optics, Image (mathematics), Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.method | 179, 209 |
| abstract_inverted_index.models | 147 |
| abstract_inverted_index.place. | 138 |
| abstract_inverted_index.proved | 93 |
| abstract_inverted_index.rather | 107 |
| abstract_inverted_index.scale. | 100 |
| abstract_inverted_index.signal | 88 |
| abstract_inverted_index.source | 249 |
| abstract_inverted_index.subset | 185 |
| abstract_inverted_index.Hanford | 202 |
| abstract_inverted_index.Obvious | 13 |
| abstract_inverted_index.assumes | 5 |
| abstract_inverted_index.bespoke | 27 |
| abstract_inverted_index.biases, | 81 |
| abstract_inverted_index.binary. | 44 |
| abstract_inverted_index.combine | 160 |
| abstract_inverted_index.decade. | 258 |
| abstract_inverted_index.execute | 98 |
| abstract_inverted_index.handled | 20 |
| abstract_inverted_index.provide | 168 |
| abstract_inverted_index.recover | 211 |
| abstract_inverted_index.signals | 232 |
| abstract_inverted_index.through | 26 |
| abstract_inverted_index.without | 130, 233 |
| abstract_inverted_index.Although | 45 |
| abstract_inverted_index.Assuming | 139 |
| abstract_inverted_index.GW170817 | 42 |
| abstract_inverted_index.Gaussian | 10 |
| abstract_inverted_index.applying | 234 |
| abstract_inverted_index.cleaning | 236 |
| abstract_inverted_index.compound | 67 |
| abstract_inverted_index.detector | 156 |
| abstract_inverted_index.directly | 154 |
| abstract_inverted_index.estimate | 171 |
| abstract_inverted_index.famously | 37 |
| abstract_inverted_index.modeling | 110 |
| abstract_inverted_index.multiple | 73 |
| abstract_inverted_index.presence | 218 |
| abstract_inverted_index.proposed | 208 |
| abstract_inverted_index.provides | 242 |
| abstract_inverted_index.unbiased | 170, 227, 248 |
| abstract_inverted_index.validate | 177 |
| abstract_inverted_index.waveform | 165 |
| abstract_inverted_index.``clean'' | 30 |
| abstract_inverted_index.approach: | 106 |
| abstract_inverted_index.combining | 72 |
| abstract_inverted_index.different | 105 |
| abstract_inverted_index.diffusion | 146 |
| abstract_inverted_index.empirical | 151 |
| abstract_inverted_index.expensive | 96 |
| abstract_inverted_index.function. | 175 |
| abstract_inverted_index.glitches, | 221 |
| abstract_inverted_index.inference | 58, 84, 182, 225 |
| abstract_inverted_index.introduce | 54 |
| abstract_inverted_index.parameter | 2 |
| abstract_inverted_index.presuming | 131 |
| abstract_inverted_index.promising | 244 |
| abstract_inverted_index.typically | 4, 19 |
| abstract_inverted_index.Livingston | 200 |
| abstract_inverted_index.containing | 193 |
| abstract_inverted_index.departures | 14 |
| abstract_inverted_index.detectors. | 203 |
| abstract_inverted_index.effective, | 46 |
| abstract_inverted_index.estimation | 3 |
| abstract_inverted_index.explicitly | 109 |
| abstract_inverted_index.extracting | 247 |
| abstract_inverted_index.individual | 111 |
| abstract_inverted_index.likelihood | 174 |
| abstract_inverted_index.parameters | 188, 214 |
| abstract_inverted_index.performing | 181 |
| abstract_inverted_index.population | 230 |
| abstract_inverted_index.procedures | 28, 76 |
| abstract_inverted_index.properties | 250 |
| abstract_inverted_index.transients | 33 |
| abstract_inverted_index.(glitches), | 34 |
| abstract_inverted_index.Gaussianity | 132 |
| abstract_inverted_index.additivity, | 142 |
| abstract_inverted_index.alternative | 75 |
| abstract_inverted_index.likelihood, | 119 |
| abstract_inverted_index.precession, | 65 |
| abstract_inverted_index.properties, | 62, 89 |
| abstract_inverted_index.score-based | 145 |
| abstract_inverted_index.stationary. | 12 |
| abstract_inverted_index.traditional | 117 |
| abstract_inverted_index.case-by-case | 23 |
| abstract_inverted_index.distribution | 126, 153 |
| abstract_inverted_index.idealization | 17 |
| abstract_inverted_index.instrumental | 7, 128 |
| abstract_inverted_index.manipulating | 47 |
| abstract_inverted_index.neutron-star | 43 |
| abstract_inverted_index.non-Gaussian | 31 |
| abstract_inverted_index.observations | 254 |
| abstract_inverted_index.stationarity | 134 |
| abstract_inverted_index.astrophysical | 61 |
| abstract_inverted_index.deterministic | 164 |
| abstract_inverted_index.observations, | 192 |
| abstract_inverted_index.observations; | 74 |
| abstract_inverted_index.unpredictable | 69 |
| abstract_inverted_index.computationally | 95 |
| abstract_inverted_index.non-Gaussianities | 112 |
| abstract_inverted_index.Gravitational-wave | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |