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PreprintCC BY 4.0arXiv

Normalizing Flow Emulator for Gravitational Wave Detection Probability in the LIGO/Virgo/KAGRA O4 Observing Run

  • Julius Gassert
  • Ignacio Magaña Hernandez
  • Ariel J. Amsellem
  • Antonella Palmese
  • Barnabas Póczos
Open at sourcePDFDOICite

Abstract

Efficient and accurate estimation of the detection probability of a gravitational wave event given its source parameters remains a challenging problem because it conventionally requires expensive Monte Carlo injection campaigns. We present a machine learning-based approach using Normalizing Flows (NFs) to estimate the detection probability of gravitational wave events for a LIGO/Virgo/KAGRA O4-like detector network configuration. We utilize the publicly available injection dataset as training and test data. We test our method and implementation by evaluating the raw NF performance as a generative model based on its ability to recover the correct parameter distributions of detected events. In addition, we compare the detection efficiencies predicted by the NF emulator with those from Monte Carlo importance sampling and find good agreement. Furthermore, estimating the detection efficiency for a wide variety of population models, including those with sharp features, can be more precise than estimates obtained via Monte Carlo importance sampling. We show that the NF emulator can be used in population analysis of gravitational wave sources, similar to previous neural network approaches. The training code and trained models from this work are publicly available.

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Cite

BibTeXRIS
BibTeX
@misc{gassert2026normalizing,
  title = {{Normalizing Flow Emulator for Gravitational Wave Detection Probability in the LIGO/Virgo/KAGRA O4 Observing Run}},
  author = {Julius Gassert and Ignacio Magaña Hernandez and Ariel J. Amsellem and Antonella Palmese and Barnabas Póczos},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.00306},
  eprint = {2610.00306},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.00306},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Normalizing Flow Emulator for Gravitational Wave Detection Probability in the LIGO/Virgo/KAGRA O4 Observing Run
AU  - Julius Gassert
AU  - Ignacio Magaña Hernandez
AU  - Ariel J. Amsellem
AU  - Antonella Palmese
AU  - Barnabas Póczos
PY  - 2026
DA  - 2026/10/01
PB  - arXiv
DO  - 10.48550/arxiv.2610.00306
UR  - https://arxiv.org/abs/2610.00306
AB  - Efficient and accurate estimation of the detection probability of a gravitational wave event given its source parameters remains a challenging problem because it conventionally requires expensive Monte Carlo injection campaigns. We present a machine learning-based approach using Normalizing Flows (NFs) to estimate the detection probability of gravitational wave events for a LIGO/Virgo/KAGRA O4-like detector network configuration. We utilize the publicly available injection dataset as training and test data. We test our method and implementation by evaluating the raw NF performance as a generative model based on its ability to recover the correct parameter distributions of detected events. In addition, we compare the detection efficiencies predicted by the NF emulator with those from Monte Carlo importance sampling and find good agreement. Furthermore, estimating the detection efficiency for a wide variety of population models, including those with sharp features, can be more precise than estimates obtained via Monte Carlo importance sampling. We show that the NF emulator can be used in population analysis of gravitational wave sources, similar to previous neural network approaches. The training code and trained models from this work are publicly available.
ER  -