Are all models wrong? Falsifying binary formation models in gravitational-wave astronomy using exceptional events

Creators: Passenger, Lachlan^{1, 2}; Thrane, Eric^{1, 2}; Lasky, Paul^{1, 2}; Payne, Ethan³; Stevenson, Simon^{2, 4}; Farr, Ben⁵

1. Monash University
2. ARC Centre of Excellence for Gravitational Wave Discovery
3. California Institute of Technology
4. Swinburne University of Technology
5. University of Oregon

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Abstract

As the catalogue of gravitational-wave transients grows, several entries appear ‘exceptional’ within the population. Tipping the scales with a total mass of ∼150M⊙⁠, GW190521 likely contained black holes in the pair-instability mass gap. The event GW190814, meanwhile, is unusual for its extreme mass ratio and the mass of its secondary component. A growing model-building industry has emerged to provide explanations for such exceptional events, and Bayesian model selection is frequently used to determine the most informative model. However, Bayesian methods can only take us so far. They provide no answer to the question: does our model provide an adequate explanation for exceptional events in the data? If none of the models we are testing provide an adequate explanation, then it is not enough to simply rank our existing models – we need new ones. In this paper, we introduce a method to answer this question with a frequentist p-value. We apply the method to different models that have been suggested to explain the unusually massive event GW190521: hierarchical mergers in active galactic nuclei and globular clusters. We show that some (but not all) of these models provide adequate explanations for exceptionally massive events like GW190521.

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Acknowledgement

We thank Thomas Callister, Matthew Mould and the referee for their helpful comments. This is LIGO document #P2400175. We acknowledge support from the Australian Research Council (ARC) Centres of Excellence CE170100004 and CE230100016, as well as ARC LE210100002, and ARC DP230103088. LP receives support from the Australian Government Research Training Program. SS is a recipient of an ARC Discovery Early Career Research Award (DE220100241). This material is based upon work supported by NSF’s LIGO Laboratory which is a major facility fully funded by the National Science Foundation. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants PHY-0757058 and PHY-0823459.

This research has made use of data or software obtained from the Gravitational Wave Open Science Center (gw-openscience.org), a service of LIGO Laboratory, the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. LIGO Laboratory and Advanced LIGO are funded by the United States National Science Foundation (NSF) as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. The construction and operation of KAGRA are funded by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS), National Research Foundation(NRF), Ministry of Science and ICT (MSIT) in Korea, and Academia Sinica (AS) and the Ministry of Science and Technology (MoST) in Taiwan.

Data Availability

The data underyling this article are publicly available at https://www.gw-openscience.org.

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