Unmodelled clustering methods for gravitational wave populations of compact binary mergers

Powell, J.; Stevenson, S. P.; Mandel, Ilya; Tiňo, Peter

doi:10.1093/mnras/stz1938

Cited by 21 publications

(12 citation statements)

References 60 publications

(88 reference statements)

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“…Numerous attempts have been made to leverage GW observations for characterizing the branching fractions between these channels or constraining uncertain physical processes governing these channels (Stevenson et al 2015;Rodriguez et al 2016b;Farr et al 2017a,b;Mandel et al 2017;Stevenson et al 2017a;Talbot & Thrane 2017;Vitale et al 2017;Zevin et al 2017;Barrett et al 2018;Taylor & Gerosa 2018;Arca Sedda & Benacquista 2019;Powell et al 2019;Roulet & Zaldarriaga 2019;Wysocki et al 2019;Abbott et al 2020c;Antonini & Gieles 2020a;Arca Sedda et al 2020;Baibhav et al 2020;Bavera et al 2020a;Bouffanais et al 2020;Farmer et al 2020;Fishbach & Holz 2020;Hall et al 2020;Kimball et al 2020a,b;Roulet et al 2020;Safarzadeh 2020;Wong et al 2020Wong et al , 2021Bhagwat et al 2021). However, due to the high complexity and dimensionality of the problem, these studies often restrict themselves to targeting a single channel or a small subset of channels.…”

Section: Introductionmentioning

confidence: 99%

One Channel to Rule Them All? Constraining the Origins of Binary Black Holes Using Multiple Formation Pathways

Zevin

Bavera

Berry

et al. 2021

ApJ

292

259

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The second LIGO-Virgo catalog of gravitational-wave (GW) transients has more than quadrupled the observational sample of binary black holes. We analyze this catalog using a suite of five state-of-the-art binary black hole population models covering a range of isolated and dynamical formation channels and infer branching fractions between channels as well as constraints on uncertain physical processes that impact the observational properties of mergers. Given our set of formation models, we find significant differences between the branching fractions of the underlying and detectable populations, and that the diversity of detections suggests that multiple formation channels are at play. A mixture of channels is strongly preferred over any single channel dominating the detected population: an individual channel does not contribute to more than 70% of the observational sample of binary black holes. We calculate the preference between the natal spin assumptions and common-envelope efficiencies in our models, favoring natal spins of isolated black holes of 0.1, and marginally preferring common-envelope efficiencies of 2.0 while strongly disfavoring highly inefficient common envelopes. We show that it is essential to consider multiple channels when interpreting GW catalogs, as inference on branching fractions and physical prescriptions becomes biased when contributing formation scenarios are not considered or incorrect physical prescriptions are assumed. Although our quantitative results can be affected by uncertain assumptions in model predictions, our methodology is capable of including models with updated theoretical considerations and additional formation channels.

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Section: Introductionmentioning

confidence: 99%

One Channel to Rule Them All? Constraining the Origins of Binary Black Holes Using Multiple Formation Pathways

Zevin

Bavera

Berry

et al. 2021

ApJ

292

259

View full text Add to dashboard Cite

show abstract

“…Alternative approaches consist in model-independent inference based on clustering of source parameters (e.g. Mandel et al 2015Mandel et al , 2017Powell et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Constraining the Fraction of Binary Black Holes Formed in Isolation and Young Star Clusters with Gravitational-wave Data

Bouffanais

Mapelli

Gerosa

et al. 2019

ApJ

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Ten binary black-hole mergers have already been detected during the first two observing runs of advanced LIGO and Virgo, and many more are expected to be observed in the near future. This opens the possibility for gravitational-wave astronomy to better constrain the properties of black hole binaries, not only as single sources, but as a whole astrophysical population. In this paper, we address the problem of using gravitational-wave measurements to estimate the proportion of merging black holes produced either via isolated binaries or binaries evolving in young star clusters. To this end, we use a Bayesian hierarchical modeling approach applied to catalogs of merging binary black holes generated using state-of-the-art population synthesis and N-body codes. In particular, we show that, although current advanced LIGO/Virgo observations only mildly constrain the mixing fraction f ∈ [0, 1] between the two formation channels, we expect to narrow down the fractional errors on f to 10 − 20% after a few hundreds of detections.

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“…These studies will shed new light on the stellar evolution processes that may lead to the formation of these astrophysical objects, or whether these objects are formed from a mixture of different populations [39]. Agnostic studies that involve Gaussian mixture models are ideally suited to enable data-driven analyses [40]. Signal denoising Gravitational wave signals are contaminated by environmental and instrumental noise sources that are complex to model and difficult to remove.…”

Section: Machine Learning For Gravitational Wave Data Analysismentioning

confidence: 99%

Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

Huerta,

Zhao

2021

Preprint

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We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the Universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos and cosmic rays cover a wide range of wavelengths and time scales. Combining and processing these datasets that vary in volume, speed and dimensionality requires new modes of instrument coordination, funding and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets, have powered the rise of artificial intelligence. This digital revolution now powers a multi-billion dollar industry, with far-reaching implications in technology and society. In this chapter we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in Multi-Messenger Astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017, to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data.

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Unmodelled clustering methods for gravitational wave populations of compact binary mergers

Cited by 21 publications

References 60 publications

One Channel to Rule Them All? Constraining the Origins of Binary Black Holes Using Multiple Formation Pathways

One Channel to Rule Them All? Constraining the Origins of Binary Black Holes Using Multiple Formation Pathways

Constraining the Fraction of Binary Black Holes Formed in Isolation and Young Star Clusters with Gravitational-wave Data

Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

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