arXiv subjects

Maxime Fays

Publications and source records attributed to Maxime Fays.

4 recordsLinked to original sources

arXiv2025

Short-Duration Gravitational Wave Burst Detection using Convolutional Neural Network

Detecting unmodeled gravitational wave (GW) bursts presents significant challenges due to the lack of accurate waveform templates required for matched-filtering techniques. A primary difficulty lies in distinguishing genuine signals from transient noise. Machine learning approaches, particularly convolutional neural networks (CNNs), offer promising alternatives for this classification problem. This paper presents a CNN-based pipeline for detecting short GW bursts (duration $< 10~\mathrm{s}$), adapted from an existing framework designed for longer-duration events. The CNN has been trained on core-collapse supernova (CCSN) gravitational waveform models injected into simulated Gaussian noise. The network successfully identifies these signals and generalizes to CCSN waveforms not included in the training set, showing the potential of U-Net architectures for detecting short-duration gravitational wave transients across diverse astrophysical scenarios.

gr-qc↗

arXiv2022

A machine learning algorithm for minute-long Burst searches

Minute-long Gravitational Wave (GW) transients are events lasting from few to hundreds of seconds. In opposition to compact binary mergers, their GW signals cover a wide range of poorly understood astrophysical phenomena such as accretion disk instabilities and magnetar flares. The lack of accurate and rapidly generated gravitational-wave emission models prevents the use of matched filtering methods. Such events are thus probed through the template-free excess-power method, consisting in searching for a local excess of power in the time-frequency space correlated between detectors. The problem can be viewed as a search for high-value clustered pixels within an image, which has been generally tackled by deep learning algorithms such as Convolutional Neural Networks (CNNs). In this work, we use a CNN as a anomaly detection tool for the long-duration searches. We show that it can reach a pixel-wise detection despite trained with minimal assumptions, while being able to retrieve both astrophysical signals and noise transients originating from instrumental coupling within the detectors. We also note that our neural network can extrapolate and connect partially disjoint signal tracks in the time-frequency plane.

gr-qc↗

arXiv2021

IWAVE -- An Adaptive Filter Approach to Phase Lock and the Dynamic Characterisation of Pseudo-Harmonic Waves

We present a novel adaptive filtering approach to the dynamic characterisation of waves of varying frequency and amplitude embedded in arbitrary noise backgrounds. This method, known as IWAVE, possesses critical advantages over conventional techniques making it a useful new tool in the dynamic characterisation of a wide range of data containing embedded oscillating signals. After a review of existing techniques, we present the IWAVE algorithm, derive its key characteristics, and provide tests of its performance using simulated and real world data.

physics.ins-det↗

arXiv2021

Search for sub-solar primordial black holes in low mass ratio binaries with LIGO-Virgo O2 data and implications for the primordial black hole dark matter fraction

We perform a search for binary black hole mergers with one sub-solar mass (SSM) black hole and a primary component above $\sim 2 M_\odot$ in data from the second observing run (O2) of the LIGO-Virgo detectors. Our analysis extends the parameter space explored by previous LIGO-Virgo Collaboration searches for binaries containing SSM components into a region of parameter space motivated by broad mass distributions of primordial black holes (PBHs) exhibiting a peak around $[2-3] M^{}_\odot$, which can arise from the reduction of the equation of state during the QCD phase transition in the early Universe. Four candidate events are found passing a signal-to-noise ratio (SNR) threshold of 8 and a false alarm rate (FAR) threshold of 2 per year, although none are statistically significant enough to constitute a confident detection. Assuming a null result for the search, we derive PBH model-independent 90\% confidence upper limits on the PBH merger rates by estimating the sensitive volume-time of the search using simulated gravitational-wave signal injections. We interpret these observational limits using a representative broad PBH mass function bearing imprints of the thermal history of the early Universe and considering both early and late PBH binary formation channels. The resulting constraints on the PBH dark-matter fraction, $f^{}_{\rm PBH}$, depend on the assumed mass function and merger-rate prescription. For all considerations, the upper limits remain above $f^{}_{\rm PBH}=1$, indicating the O2 data from LIGO-Virgo are not sensitive enough to place meaningful constraints within the assumptions of the PBH mass model and PBH binary mergers.

astro-ph.CO↗