arXiv subjects

Wathela Alhassan

Publications and source records attributed to Wathela Alhassan.

4 recordsLinked to original sources

arXiv2026

Infrasound Newtonian Noise Estimation at the Einstein Telescope Candidate Site Sos Enattos

We investigate the seasonal variability of atmospheric infrasound and its contribution to Newtonian noise (NN) at the Sos Enattos site, a leading candidate location for the Einstein Telescope (ET). Infrasound data recorded at three stations -- SOE0 (surface), SOE1 ($-84$ m), and SOE3 ($-160$ m) -- are analyzed over multiple seasons to characterize both temporal variability and the depth dependence of the acoustic field. The amplitude spectral density (ASD) at 1 Hz exhibits a clear seasonal modulation, with winter levels exceeding summer values by 10--15 dB, primarily driven by variations in wind conditions. Using the measured pressure spectra, we estimate the corresponding NN contribution within a standard atmospheric coupling framework. At the surface station (SOE0), the median characteristic strain reaches $\sim 10^{-22}$ at 1 Hz, whereas at the deepest underground station (SOE3) it decreases to $\sim 10^{-27}$, corresponding to a suppression of approximately five orders of magnitude. Across all stations and environmental conditions, the inferred NN remains well below the ET-D design sensitivity curve in the 1--10 Hz frequency band. These results demonstrate the strong attenuation of infrasound-induced NN with depth and confirm that atmospheric infrasound does not constitute a limiting noise source for underground gravitational-wave detectors at this site.

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arXiv2025

PyMerger: Detecting Binary Black Hole merger from Einstein Telescope Using Deep Learning

We present PyMerger, a Python tool for detecting binary black hole (BBH) mergers from the Einstein Telescope (ET), based on a Deep Residual Neural Network model (ResNet). ResNet was trained on data combined from all three proposed sub-detectors of ET (TSDCD) to detect BBH mergers. Five different lower frequency cutoffs ($F_{\text{low}}$): 5 Hz, 10 Hz, 15 Hz, 20 Hz, and 30 Hz, with match-filter Signal-to-Noise Ratio ($MSNR$) ranges: 4-5, 5-6, 6-7, 7-8, and >8, were employed in the data simulation. Compared to previous work that utilized data from single sub-detector data (SSDD), the detection accuracy from TSDCD has shown substantial improvements, increasing from $60\%$, $60.5\%$, $84.5\%$, $94.5\%$ to $78.5\%$, $84\%$, $99.5\%$, $100\%$, and $100\%$ for sources with $MSNR$ of 4-5, 5-6, 6-7, 7-8, and >8, respectively. The ResNet model was evaluated on the first Einstein Telescope mock Data Challenge (ET-MDC1) dataset, where the model demonstrated strong performance in detecting BBH mergers, identifying 5,566 out of 6,578 BBH events, with optimal SNR starting from 1.2, and a minimum and maximum $D_{L}$ of 0.5 Gpc and 148.95 Gpc, respectively. Despite being trained only on BBH mergers without overlapping sources, the model achieved high BBH detection rates. Notably, even though the model was not trained on BNS and BHNS mergers, it successfully detected 11,477 BNS and 323 BHNS mergers in ET-MDC1, with optimal SNR starting from 0.2 and 1, respectively, indicating its potential for broader applicability.

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arXiv2022

Detection of Einstein Telescope gravitational wave signals from binary black holes using deep learning

The expected volume of data from the third-generation gravitational waves (GWs) Einstein Telescope (ET) detector would make traditional GWs search methods such as match filtering impractical. This is due to the large template bank required and the difficulties in waveforms modelling. In contrast, machine learning (ML) algorithms have shown a promising alternative for GWs data analysis, where ML can be used in developing semi-automatic and automatic tools for the detection, denoising and parameter estimation of GWs sources. Compared to second generation detectors, ET will have a wider accessible frequency band but also a lower noise. The ET will have a detection rate for Binary Black Holes (BBHs) and Binary Neutron Stars (BNSs) of order 1e5 - 1e6 per year and 7e4 per year respectively. In this work, we explore the possibility and efficiency of using convolutional neural networks (CNNs) for the detection of BBHs mergers in synthetic GWs signals buried in gaussian noise. The data was generated according to the ETs parameters using open-source tools. Without performing data whitening or applying bandpass filtering, we trained four CNN networks with the state-of-the-art performance in computer vision, namely VGG, ResNet and DenseNet. ResNet has significantly better performance, detecting BBHs sources with SNR of 8 or higher with 98.5% accuracy, and with 92.5%, 85%, 60% and 62% accuracy for sources with SNR range of 7-8, 6-7, 5-6 and 4-5 respectively. ResNet, in qualitative evaluation, was able to detect a BBHs merger at 60 Gpc with 4.3 SNR. It was also shown that, using CNN for BBHs merger on long time series data is computationally efficient, and can be used for near-real-time detection.

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arXiv2018

The FIRST Classifier: Compact and Extended Radio Galaxy Classification using Deep Convolutional Neural Networks

Upcoming surveys with new radio observatories such as the Square Kilometer Array will generate a wealth of imaging data containing large numbers of radio galaxies. Different classes of radio galaxies can be used as tracers of the cosmic environment, including the dark matter density field, to address key cosmological questions. Classifying these galaxies based on morphology is thus an important step toward achieving the science goals of next generation radio surveys. Radio galaxies have been traditionally been classified as Fanaroff-Riley (FR) I and II, although some exhibit more complex 'bent' morphologies arising from environmental factors or intrinsic properties. In this work we present the FIRST Classifier, an on-line system for automated classification of Compact and Extended radio sources. We developed the FIRST Classifier based on a trained Deep Convolutional Neural Network Model to automate the morphological classification of compact and extended radio sources observed in the FIRST radio survey. Our model achieved an overall accuracy of 97% and a recall of 98%, 100%, 98% and 93% for Compact, BENT, FRI and FRII galaxies respectively. The current version of the FIRST classifier is able to predict the morphological class for a single source or for a list of sources as Compact or Extended (FRI, FRII and BENT).

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