gwastro/pycbc: 1.18.0 release of PyCBC

Nitz, A.; Harry, I. W.; Brown, D.; Biwer, C.; Willis, Josh; Canton, T. Dal; Capano, C. D.; Pekowsky, L.; Dent, T.; Williamson, A. R.; Davies, G. S.; De, S.; Cabero, M.; Machenschalk, B.; Kumar, P.; Reyes, S. D.; Macleod, D. M.; dfinstad,; Pannarale, F.; Massinger, T. J.; Kumar, Sandeep; Tápai, M.; Singer, L. P.; Khan, S.; Fairhurst, S.; Nielsen, A. B.; Singh, Shashwat; shasvath,; Gadre, Bhooshan Uday Varsha

doi:10.5281/zenodo.4556907

Cited by 13 publications

(12 citation statements)

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“…We acknowledge use of the software package pycbc [91] and, for the computation of the SNR of mergers at LISA, gwent [55]. V.DL., G.F. and A.R.…”

Section: Acknowledgmentsmentioning

confidence: 99%

The Minimum Testable Abundance of Primordial Black Holes at Future Gravitational-Wave Detectors

De Luca,

Franciolini,

Pani

et al. 2021

Preprint

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The next generation of gravitational-wave experiments, such as Einstein Telescope, Cosmic Explorer and LISA, will test the primordial black hole scenario. We provide a forecast for the minimum testable value of the abundance of primordial black holes as a function of their masses for both the unclustered and clustered spatial distributions at formation. In particular, we show that these instruments may test abundances, relative to the dark matter, as low as 10 −10 .

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“…We acknowledge use of the software package pycbc [91] and, for the computation of the SNR of mergers at LISA, gwent [55]. V.DL., G.F. and A.R.…”

Section: Acknowledgmentsmentioning

confidence: 99%

The Minimum Testable Abundance of Primordial Black Holes at Future Gravitational-Wave Detectors

De Luca,

Franciolini,

Pani

et al. 2021

Preprint

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“…[124,125], as implemented in the Python package gwdet [126], which relies on computing the SNR of optimally oriented sources with the same intrinsic parameters. This is estimated using pyCBC [127], In red is a validation simulation (as in Fig. 3) with dH = 0.10 and whose distribution contains distinct features due to repeat mergers.…”

Section: Detection Probabilitymentioning

confidence: 99%

Deep learning and Bayesian inference of gravitational-wave populations: hierarchical black-hole mergers

Mould¹,

Gerosa²,

Taylor³

2022

Preprint

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The catalog of gravitational-wave events is growing, and so are our hopes of constraining the underlying astrophysics of stellar-mass black-hole mergers by inferring the distributions of, e.g., masses and spins. While conventional analyses parametrize this population with simple phenomenological models, we propose an innovative physics-first approach that compares gravitational-wave data against astrophysical simulations. We combine state-of-the-art deep-learning techniques with hierarchical Bayesian inference and exploit our approach to constrain the properties of repeated black-hole mergers from the gravitational-wave events in the most recent LIGO/Virgo catalog. Deep neural networks allow us to (i) construct a flexible population model that accurately emulates simulations of hierarchical mergers, (ii) estimate selection effects, and (iii) recover the branching ratios of repeatedmerger generations. Among our results we find that: the distribution of host-environment escape speeds favors values < 100 km s −1 but is relatively flat; first-generation black holes are born with a maximum mass that is compatible with current estimates from pair-instability supernovae; there is multimodal substructure in both the mass and spin distributions due to repeated mergers; and binaries with a higher-generation component make up at least 15% of the underlying population. The deep-learning pipeline we present is ready to be used in conjunction with realistic astrophysical population-synthesis predictions.

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“…To run the matched filter search we use the program pycbc_inspiral [38]. It is setup to use a SNR threshold of 5 in both detectors to create two sets of single detector triggers.…”

Section: G Matched Filteringmentioning

confidence: 99%

“…We compare this search to an equivalent matched filter search [38]. We find that the deep learning search still retains 92.4% of the sensitivity of a two-detector matched filter search when the latter is restricted to using the timing difference between the detectors as the only means for determining coincident events.…”

Section: Introductionmentioning

confidence: 99%

From One to Many: A Deep Learning Coincident Gravitational-Wave Search

Schäfer,

Nitz

2021

Preprint

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Gravitational waves from the coalescence of compact-binary sources are now routinely observed by Earth bound detectors. The most sensitive search algorithms convolve many different precalculated gravitational waveforms with the detector data and look for coincident matches between different detectors. Machine learning is being explored as an alternative approach to building a search algorithm that has the prospect to reduce computational costs and target more complex signals. In this work we construct a two-detector search for gravitational waves from binary black hole mergers using neural networks trained on non-spinning binary black hole data from a single detector. The network is applied to the data from both observatories independently and we check for events coincident in time between the two. This enables the efficient analysis of large quantities of background data by time-shifting the independent detector data. We find that while for a single detector the network retains 91.5% of the sensitivity matched filtering can achieve, this number drops to 83.9% for two observatories. To enable the network to check for signal consistency in the detectors, we then construct a set of simple networks that operate directly on data from both detectors. We find that none of these simple two-detector networks are capable of improving the sensitivity over applying networks individually to the data from the detectors and searching for time coincidences.

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gwastro/pycbc: 1.18.0 release of PyCBC

Cited by 13 publications

References 0 publications

The Minimum Testable Abundance of Primordial Black Holes at Future Gravitational-Wave Detectors

The Minimum Testable Abundance of Primordial Black Holes at Future Gravitational-Wave Detectors

Deep learning and Bayesian inference of gravitational-wave populations: hierarchical black-hole mergers

From One to Many: A Deep Learning Coincident Gravitational-Wave Search

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