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Johann Fernandes

Publications and source records attributed to Johann Fernandes.

3 recordsLinked to original sources

Eccentricity in Disguise? Insights from GW231123 and Numerically Simulated Binary Black Hole Merger Signals

GW231123 is a gravitational-wave signal originating from the merger of a black hole binary with total mass $\sim 250 M_{\odot}$, the largest ever detected by the LIGO-Virgo-Kagra Collaboration. Remarkably, under standard priors, the system features among the fastest-spinning binary components confidently measured in binary mergers, $ \chi_{1,2} \gtrsim 0.7$ at $90\%$ one-dimensional credibility, according to the most accurate model employed. As typical binary mergers result in remnants with $\chi \sim 0.7$, such spin values are challenging to obtain even from previous (hierarchical) mergers. These inferred properties rely on waveform models lacking eccentric corrections in the merger-ringdown stage. Here, we show that binaries retaining significant eccentricity up to merger can be misinterpreted as near-extremally spinning when non-circular corrections are neglected. Binary-agnostic ringdown analysis instead provides unbiased estimates of the remnant properties, provided that a robust estimate of the signal peak can be obtained. We re-analyse GW231123 using available eccentric numerical-relativity catalogues, finding that although eccentric templates can provide a good fit to the data, quasi-spherical templates are still favoured. Ringdown analyses confirm a secondary likelihood peak correlated with large eccentricity values, but improved eccentric models will be required to assess the reliability of this interpretation. Finally, analysing GW231123 under population-informed parametric priors confirms the exceptional nature of this event within the current black hole binary population.

gr-qc

Binary Black Hole inspirals cannot hide their eccentricity

The events detected by the LIGO Virgo KAGRA collaboration over a period of 10 years have yielded a treasure trove of signals from compact binary coalescences. None of these events have shown a confident signature of eccentricity. With upgrades to the existing network and potential next generation gravitational wave detectors, we will be able to see much further into the universe increasing the likelihood of detecting eccentric systems. We improve upon the phenomenological approach of providing eccentricity constraints using an effective chirp mass model in the time frequency domain. We introduce an improved pixel collection method along with a likelihood based sampling approach inspired by Bayesian parameter estimation. Our approach constructs a likelihood from the product of energies collected across different eccentric harmonics in the time frequency representation. This formulation enables coarse but meaningful constraints on orbital eccentricity. Additionally, we incorporate information from the energy ratios between eccentric harmonics, further refining the eccentricity estimates. We test our approach on 500 non spinning equal mass eccentric systems and demonstrate that we can constrain the eccentricity within 0.2 around the true value. Moreover, our approach can deliver these constraints in 5 minutes on a machine with 50 cores. These results demonstrate that our phenomenological approach provides fast and reasonably accurate eccentricity estimates, making it a promising tool for rapid gravitational wave data analysis.

gr-qc

Improving the detection significance of gravitational wave transient searches with CNN models

Gravitational wave (GW) transient searches rely on signal-noise discriminators to distinguish astrophysical signals from noise artefacts. These discriminators are typically tuned towards expected signal morphologies, which may limit their effectiveness as detector sensitivity improves and more complex signals, such as from core collapse supernovae or compact binary mergers featuring precession, higher-order harmonics, or eccentricity, become detectable. In this work, we use a Convolutional Neural Network-based approach to classify noise transients from astrophysical transients, aiming to enhance the sensitivity of existing searches. We evaluate our method on two matched filter based searches, PyCBC-IMBH and PyCBC-HM tuned for Intermediate Mass Black Hole (IMBH) binary systems. Our approach improves the sensitive volume-time reach of these searches by approximately 30% at a false alarm rate of once per 100 years. Finally, we apply our method to the first four chunks of the first half of the third observation run and demonstrate a marked improvement in significance. In particular, we significantly improve the first IMBH binary GW event GW190521 with an IFAR exceeding 42000 years.

gr-qc