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?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3847/2041-8213/add681
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3847/2041-8213/add681
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, this data manipulation can bias key astrophysical inferences, such as binary precession, and compound unpredictably when combining multiple observations. Alternative bias-free methods, like joint noise-signal inference, remain too computationally expensive for large-scale execution. 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.
Related Topics
Concepts
Gaussian noise
Noise (video)
Characterization (materials science)
Gaussian
Gravitational wave
Maximum likelihood
Statistical physics
Mathematics
Statistics
Estimation theory
Physics
Applied mathematics
Computer science
Algorithm
Astrophysics
Artificial intelligence
Optics
Quantum mechanics
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3847/2041-8213/add681
- OA Status
- gold
- Cited By
- 3
- References
- 59
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4410856420Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3847/2041-8213/add681Digital 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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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-29Full publication date if available
- Authors
-
Ronan Legin, M. Isi, Kaze W. K. Wong, Yashar Hezaveh, Laurence Perreault-LevasseurList of authors in order
- Landing page
-
https://doi.org/10.3847/2041-8213/add681Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.3847/2041-8213/add681Direct OA link when available
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Gaussian noise, Noise (video), Characterization (materials science), Gaussian, Gravitational wave, Maximum likelihood, Statistical physics, Mathematics, Statistics, Estimation theory, Physics, Applied mathematics, Computer science, Algorithm, Astrophysics, Artificial intelligence, Optics, Quantum mechanics, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
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59Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 59 |
| abstract_inverted_index.a | 23, 84, 143, 164, 209, 223 |
| abstract_inverted_index.GW | 98, 167, 233 |
| abstract_inverted_index.We | 156, 184 |
| abstract_inverted_index.an | 130, 149 |
| abstract_inverted_index.as | 36, 57 |
| abstract_inverted_index.by | 160 |
| abstract_inverted_index.in | 115, 196, 231 |
| abstract_inverted_index.is | 10, 206 |
| abstract_inverted_index.it | 141 |
| abstract_inverted_index.of | 107, 152, 166, 199, 211 |
| abstract_inverted_index.on | 22, 163 |
| abstract_inverted_index.or | 181 |
| abstract_inverted_index.to | 30, 93, 102, 128, 147, 217 |
| abstract_inverted_index.we | 82, 100, 123 |
| abstract_inverted_index.400 | 170 |
| abstract_inverted_index.and | 12, 60, 113, 138, 202 |
| abstract_inverted_index.any | 215 |
| abstract_inverted_index.are | 19 |
| abstract_inverted_index.can | 51, 190 |
| abstract_inverted_index.for | 41, 78, 226 |
| abstract_inverted_index.key | 53 |
| abstract_inverted_index.the | 39, 42, 96, 104, 116, 153, 158, 179, 187, 192, 197, 204, 218, 236 |
| abstract_inverted_index.too | 75 |
| abstract_inverted_index.was | 37 |
| abstract_inverted_index.(GW) | 2 |
| abstract_inverted_index.Here | 81 |
| abstract_inverted_index.LIGO | 175 |
| abstract_inverted_index.This | 220 |
| abstract_inverted_index.bias | 52 |
| abstract_inverted_index.case | 40 |
| abstract_inverted_index.data | 49, 137 |
| abstract_inverted_index.even | 195 |
| abstract_inverted_index.from | 16, 135, 169, 177 |
| abstract_inverted_index.like | 70 |
| abstract_inverted_index.loud | 200 |
| abstract_inverted_index.mock | 171 |
| abstract_inverted_index.only | 120 |
| abstract_inverted_index.over | 208, 235 |
| abstract_inverted_index.real | 174 |
| abstract_inverted_index.seek | 101 |
| abstract_inverted_index.show | 185 |
| abstract_inverted_index.such | 56 |
| abstract_inverted_index.take | 83 |
| abstract_inverted_index.than | 88 |
| abstract_inverted_index.that | 7, 186, 203 |
| abstract_inverted_index.then | 94, 139 |
| abstract_inverted_index.this | 17, 48 |
| abstract_inverted_index.true | 105, 193 |
| abstract_inverted_index.when | 63 |
| abstract_inverted_index.with | 142 |
| abstract_inverted_index.work | 221 |
| abstract_inverted_index.apply | 95 |
| abstract_inverted_index.data. | 219 |
| abstract_inverted_index.e.g., | 26 |
| abstract_inverted_index.first | 117 |
| abstract_inverted_index.joint | 71 |
| abstract_inverted_index.learn | 103, 129 |
| abstract_inverted_index.model | 146 |
| abstract_inverted_index.noise | 9, 33, 109, 121, 132, 176 |
| abstract_inverted_index.avenue | 225 |
| abstract_inverted_index.basis, | 25 |
| abstract_inverted_index.binary | 58 |
| abstract_inverted_index.coming | 237 |
| abstract_inverted_index.either | 178 |
| abstract_inverted_index.employ | 124 |
| abstract_inverted_index.future | 232 |
| abstract_inverted_index.method | 159, 189 |
| abstract_inverted_index.models | 127 |
| abstract_inverted_index.place. | 118 |
| abstract_inverted_index.rather | 87 |
| abstract_inverted_index.remain | 74 |
| abstract_inverted_index.source | 229 |
| abstract_inverted_index.subset | 165 |
| abstract_inverted_index.Hanford | 182 |
| abstract_inverted_index.Obvious | 14 |
| abstract_inverted_index.assumes | 6 |
| abstract_inverted_index.bespoke | 28 |
| abstract_inverted_index.binary. | 45 |
| abstract_inverted_index.combine | 140 |
| abstract_inverted_index.decade. | 238 |
| abstract_inverted_index.handled | 21 |
| abstract_inverted_index.provide | 148 |
| abstract_inverted_index.recover | 191 |
| abstract_inverted_index.signals | 212 |
| abstract_inverted_index.through | 27 |
| abstract_inverted_index.without | 110, 213 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Although | 46 |
| abstract_inverted_index.Assuming | 119 |
| abstract_inverted_index.GW170817 | 43 |
| abstract_inverted_index.Gaussian | 11 |
| abstract_inverted_index.applying | 214 |
| abstract_inverted_index.cleaning | 216 |
| abstract_inverted_index.compound | 61 |
| abstract_inverted_index.detector | 136 |
| abstract_inverted_index.directly | 134 |
| abstract_inverted_index.estimate | 151 |
| abstract_inverted_index.famously | 38 |
| abstract_inverted_index.methods, | 69 |
| abstract_inverted_index.modeling | 90 |
| abstract_inverted_index.multiple | 65 |
| abstract_inverted_index.presence | 198 |
| abstract_inverted_index.proposed | 188 |
| abstract_inverted_index.provides | 222 |
| abstract_inverted_index.unbiased | 150, 207, 228 |
| abstract_inverted_index.validate | 157 |
| abstract_inverted_index.waveform | 145 |
| abstract_inverted_index.approach: | 86 |
| abstract_inverted_index.bias-free | 68 |
| abstract_inverted_index.combining | 64 |
| abstract_inverted_index.different | 85 |
| abstract_inverted_index.diffusion | 126 |
| abstract_inverted_index.empirical | 131 |
| abstract_inverted_index.expensive | 77 |
| abstract_inverted_index.function. | 155 |
| abstract_inverted_index.glitches, | 201 |
| abstract_inverted_index.inference | 162, 205 |
| abstract_inverted_index.parameter | 3 |
| abstract_inverted_index.presuming | 111 |
| abstract_inverted_index.promising | 224 |
| abstract_inverted_index.typically | 5, 20 |
| abstract_inverted_index.Livingston | 180 |
| abstract_inverted_index.containing | 173 |
| abstract_inverted_index.departures | 15 |
| abstract_inverted_index.detectors. | 183 |
| abstract_inverted_index.effective, | 47 |
| abstract_inverted_index.estimation | 4 |
| abstract_inverted_index.execution. | 80 |
| abstract_inverted_index.explicitly | 89 |
| abstract_inverted_index.extracting | 227 |
| abstract_inverted_index.individual | 91 |
| abstract_inverted_index.inference, | 73 |
| abstract_inverted_index.likelihood | 154 |
| abstract_inverted_index.parameters | 168, 194 |
| abstract_inverted_index.performing | 161 |
| abstract_inverted_index.population | 210 |
| abstract_inverted_index.procedures | 29 |
| abstract_inverted_index.properties | 230 |
| abstract_inverted_index.transients | 34 |
| abstract_inverted_index.(glitches), | 35 |
| abstract_inverted_index.Alternative | 67 |
| abstract_inverted_index.Gaussianity | 112 |
| abstract_inverted_index.additivity, | 122 |
| abstract_inverted_index.inferences, | 55 |
| abstract_inverted_index.large-scale | 79 |
| abstract_inverted_index.likelihood, | 99 |
| abstract_inverted_index.precession, | 59 |
| abstract_inverted_index.score-based | 125 |
| abstract_inverted_index.stationary. | 13 |
| abstract_inverted_index.traditional | 97 |
| abstract_inverted_index.“clean” | 31 |
| abstract_inverted_index.case-by-case | 24 |
| abstract_inverted_index.distribution | 106, 133 |
| abstract_inverted_index.idealization | 18 |
| abstract_inverted_index.instrumental | 8, 108 |
| abstract_inverted_index.manipulation | 50 |
| abstract_inverted_index.neutron-star | 44 |
| abstract_inverted_index.noise-signal | 72 |
| abstract_inverted_index.non-Gaussian | 32 |
| abstract_inverted_index.observations | 234 |
| abstract_inverted_index.stationarity | 114 |
| abstract_inverted_index.astrophysical | 54 |
| abstract_inverted_index.deterministic | 144 |
| abstract_inverted_index.observations, | 172 |
| abstract_inverted_index.observations. | 66 |
| abstract_inverted_index.unpredictably | 62 |
| abstract_inverted_index.computationally | 76 |
| abstract_inverted_index.non-Gaussianities | 92 |
| abstract_inverted_index.Gravitational-wave | 1 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.95224231 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |