arXiv subjects

Theophilus Ansah-Narh

Publications and source records attributed to Theophilus Ansah-Narh.

11 recordsLinked to original sources

arXiv2026

Full Characterisation of the Polarisation Primary Beam of the GRAO 32-m Telescope

Direction-dependent instrumental polarisation is a major systematic limitation in high-fidelity single-dish radio polarimetry, yet a unified characterisation of beam leakage and the conditioning of polarisation recovery is lacking for the Ghana Radio Astronomy Observatory (GRAO) 32-m telescope. We aim to establish a quantitative, direction-dependent polarimetric beam model at 5.0 and 6.7 GHz. High-resolution \texttt{GRASP} electromagnetic simulations of the nominal telescope configuration are used to derive the full complex Jones response and corresponding Mueller matrices, from which beam shape, beam squint, instrumental Stokes leakage, and intrinsic cross-polarisation ratio (IXR) are evaluated across the primary beam. The Stokes $I$ beams have half-power beam widths of $396.4$ and $295.3$ arcsec at 5.0 and 6.7 GHz, respectively, with main-beam efficiencies of $56.4$ and $55.6$ per cent. At 5.0 GHz, the circular-polarisation beam squint is $9.96$ arcsec ($2.5$ per cent of the HPBW), whereas no statistically significant squint is detected at 6.7 GHz. Leakage from Stokes $I$ into linear polarisation remains below $0.1$ per cent within the half-power beam but increases substantially towards the sidelobes. IXR reaches approximately $80$ dB on axis and decreases with angular offset. These results establish the intrinsic electromagnetic polarimetric response of the GRAO 32-m telescope and provide a quantitative baseline for direction-dependent calibration and subsequent observational validation.

astro-ph.IM↗

arXiv2026

Unsupervised Detection of Groundwater Storage Anomalies in Ghana Using GRACE Satellite Data

Groundwater variability in Ghana remains poorly characterized due to limited long-term in-situ observations. This study investigates groundwater storage anomalies using GRACE-derived data from 2004-2024 combined with statistical analysis and unsupervised machine learning. Groundwater anomalies were standardized using Z-scores, while an ensemble-based Isolation Forest framework was applied for anomaly detection. The results revealed substantial temporal variability, with persistent groundwater deficits during 2004-2009 followed by increasing positive anomalies after 2018. A total of 12 anomalous months were identified, comprising 5 deficit and 7 surplus events, with the strongest anomalies associated with groundwater deficits. Spatial analysis showed more frequent deficit anomalies in northern Ghana and stronger surplus occurrence in southern regions. Comparison with statistical thresholds further indicated that the machine learning framework captured additional subtle deviations beyond conventional threshold-based methods. Overall, the integration of GRACE observations with unsupervised anomaly detection provides a practical framework for groundwater monitoring in data-scarce environments.

cs.ET↗

arXiv2026

Digital Beam Pattern Optimisation for the GRAO 32-m Telescope: A Comparative Analysis of FIR Filter Design Methods

The scientific utility of large single-dish radio telescopes depends critically on the stability and fidelity of their beam patterns, which govern angular resolution, sensitivity, and polarimetric accuracy. For the 32-m Ghana Radio Astronomy Observatory (GRAO) antenna, electromagnetic simulations reveal residual sidelobes, structural diffraction, and cross-polar leakage that limit performance in high-dynamic-range and polarisation-sensitive observations. To address these limitations, we develop a finite-impulse-response (FIR) spatial filtering framework that reformulates beam optimisation as a digital signal processing problem. By exploiting the equivalence between angular displacement and spatial frequency, classical FIR design methods, window-based and Parks-McClellan algorithms are adapted to operate directly on simulated Jones fields. This approach enables controlled suppression of high spatial frequency artefacts responsible for sidelobes and polarisation mixing, while preserving the telescope's diffraction-limited resolution. Applied to the GRAO 5 GHz beam model, the method achieves substantial reductions in near-in sidelobe ripple, improves beam smoothness, and lowers cross-polar leakage below -30 dB at boresight. These improvements translate into enhanced calibration stability and polarimetric precision, strengthening the telescope's capacity for Very Long Baseline Interferometry, spectral-line surveys, and pulsar timing. Beyond GRAO, the method provides a generalisable, non-invasive, and computationally efficient pathway for beam control applicable to other single-dish and phased-array instruments. The results establish digital spatial filtering as a practical complement to conventional optical or mechanical optimisation, advancing the integration of electromagnetic modelling and signal processing in next-generation radio astronomical instrumentation.

astro-ph.IM↗

arXiv2026

Bayesian estimation of spectral parameters of the 6.7-GHz methanol maser G339.884-1.259 from GRAO observations

Accurate decomposition of methanol maser spectra is essential for understanding high-mass star-forming regions, especially in complex blended spectra where small differences alter physical interpretation. Conventional Gaussian fitting often fails to capture non-Gaussian structure and lacks uncertainty quantification. We develop a Bayesian spectral decomposition framework using Gaussian, Lorentzian, and Voigt profiles with Markov Chain Monte Carlo sampling, enabling model comparison and uncertainty estimation. Applied to the 6.7\,GHz methanol maser G339.884$-$1.259 observed with the Ghana Radio Astronomy Observatory, our method reveals seven velocity-coherent components. The Voigt model is statistically preferred, yielding the lowest AIC and BIC ($\approx 1.98 \times 10^{4}$ and $1.99 \times 10^{4}$), the smallest RMSE ($\approx 11.1$ Jy), and the highest $R^{2}$ (0.985). Purely Gaussian or Lorentzian models leave systematic residuals. Elevated reduced $χ^{2}_ν$ values indicate unresolved substructure and non-ideal noise. Bayesian inference provides a robust framework for maser spectral analysis, extendable to other molecular lines and combinable with high-resolution interferometry.

astro-ph.IM↗

arXiv2026

Anomaly Detection in Soil Heavy Metal Contamination Using Unsupervised Learning for Environmental Risk Assessment

Soil contamination by heavy metals poses a persistent environmental and public health concern in rapidly urbanising regions of Ghana, particularly at unregulated waste disposal sites. This study applies an unsupervised machine learning framework to detect and characterise anomalous heavy metal contamination patterns in soils from twelve waste sites and residential controls in the Central Region, of Ghana. Concentrations of eight metals (As, Cd, Cr, Cu, Hg, Ni, Pb, Zn) were analysed alongside standard health risk indices, including the Hazard Index (HI) and Incremental Lifetime Cancer Risk (ILCR). Isolation Forest and PCA reconstruction error each identified $12$ anomalous samples ($15.4\%$ of $78$ samples), while DBSCAN detected no density-isolated noise points. A consensus approach isolated six robust anomalies ($7.7\%)$, all spatially concentrated at a single site (S3). Anomalies exhibited approximately $70$--$80\%$ higher mean HI values than normal samples, with all consensus anomalies exceeding the HI$=1$ threshold. PCA reconstruction error showed a strong positive association with HI ($r \approx 0.8$), indicating consistency between multivariate deviation and health risk. Three distinct anomaly types were identified: extreme Cu enrichment at S3, anomalously low Ni at S4/S5, and moderate multi-metal (Pb--Zn) co-elevation at S9--S12. The results demonstrate that unsupervised machine learning provides granular, objective insight beyond aggregate indices, enabling targeted site prioritisation and risk-informed environmental management.

cs.LG↗

arXiv2026

Unsupervised Electrofacies Classification and Porosity Characterization in the Offshore Keta Basin Using Wireline Logs

This study presents an unsupervised machine learning workflow for electrofacies analysis in the offshore Keta Basin, Ghana, where core data are scarce. Six standard wireline logs from Well~C were analysed over a depth interval comprising approximately $11{,}195$ samples. K-means clustering was applied in multivariate log space, with the clustering structure evaluated using inertia and silhouette diagnostics. Four clusters were identified, supported by an average silhouette coefficient of approximately $0.50$, indicating moderate but meaningful separation. The resulting electrofacies exhibit systematic, depth-continuous patterns associated with variations in clay content, porosity, and rock framework properties, forming a geological continuum from shale-dominated to cleaner sandstone-dominated units. The results demonstrate that log-only, unsupervised clustering supported by quantitative metrics provides a robust and reproducible framework for subsurface characterisation. The proposed workflow offers a practical tool for early-stage formation evaluation in frontier offshore basins and a foundation for future integrated studies.

cs.AI↗

arXiv2026

An Optimised Greedy-Weighted Ensemble Framework for Financial Loan Default Prediction

Accurate prediction of loan defaults is a central challenge in credit risk management, particularly in modern financial datasets characterised by nonlinear relationships, class imbalance, and evolving borrower behaviour. Traditional statistical models and static ensemble methods often struggle to maintain reliable performance under such conditions. This study proposes an Optimised Greedy-Weighted Ensemble framework for loan default prediction that dynamically allocates model weights based on empirical predictive performance. The framework integrates multiple machine learning classifiers, with their hyperparameters first optimised using Particle Swarm Optimisation. Model predictions are then combined via a regularised greedy weighting mechanism. At the same time, a neural-network-based meta-learner is employed within stacked-ensemble to capture higher-order relationships among model outputs. Experiments conducted on the Lending Club dataset demonstrate that the proposed framework improves predictive performance compared with individual classifiers. The BlendNet ensemble achieved the strongest results with an AUC of 0.80, a macro-average F1-score of 0.73, and a default recall of 0.81. Calibration analysis further shows that tree-based ensembles such as Extra Trees and Gradient Boosting provide the most reliable probability estimates, while the stacked ensemble offers superior ranking capability. Feature analysis using Recursive Feature Elimination identifies revolving utilisation, annual income, and debt-to-income ratio as the most influential predictors of loan default. These findings demonstrate that performance-driven ensemble weighting can improve both predictive accuracy and interpretability in credit risk modelling. The proposed framework provides a scalable data-driven approach to support institutional credit assessment, risk monitoring, and financial decision-making.

cs.LG↗

arXiv2026

The Ghana Radio Astronomy Observatory

The Ghana Radio Astronomy Observatory (GRAO) marks a pivotal advance in African radio astronomy through the successful transformation of a decommissioned 32 m satellite communication antenna into a scientifically capable, VLBI-ready radio telescope. Strategically located near the equator at Kutunse, Ghana, the telescope offers nearly full-sky coverage (-77 degrees to +88 degrees declination), making it a valuable asset for time-domain astronomy, transient surveys, and global VLBI networks. This work documents the technical evolution of the facility, including beam-waveguide optics, dual-polarization C-band receivers (5 and 6.7 GHz), and recent backend upgrades culminating in the integration of a hydrogen maser, wideband ROACH2 system, and enhanced control and pointing infrastructure. We report early science results from high-resolution spectral-line observations of 6.7 GHz Class II methanol masers, pulse timing of PSR J0835-4510 (Vela), and successful VLBI fringe detections on intercontinental baselines. Simulations and commissioning tests confirm high aperture efficiency (>77%), low sidelobe levels, and robust time stability across the signal chain. These outcomes validate the GRAO's readiness for both standalone and networked operations. As the first operational node in West Africa contributing to the African VLBI Network, GRAO plays a critical role in advancing the continent's participation in global radio astronomy, capacity building, and the preparatory phase of the Square Kilometre Array.

astro-ph.IM↗

arXiv2025

Decoding the Radio Sky: Bayesian Ensemble Learning and SVD-Based Feature Extraction for Automated Radio Galaxy Classification

The classification of radio galaxies is central to understanding galaxy evolution, active galactic nuclei dynamics, and the large-scale structure of the universe. However, traditional manual techniques are inadequate for processing the massive, heterogeneous datasets generated by modern radio surveys. In this study, we present a probabilistic machine learning framework that integrates Singular Value Decomposition (SVD) for feature extraction with Bayesian ensemble learning to achieve robust, scalable radio galaxy classification. The SVD approach effectively reduces dimensionality while preserving key morphological structures, enabling efficient representation of galaxy features. To mitigate class imbalance and avoid the introduction of artefacts, we incorporate a Local Neighbourhood Encoding strategy tailored to the astrophysical distribution of galaxy types. The resulting features are used to train and optimize several baseline classifiers: Logistic Regression, Support Vector Machines, LightGBM, and Multi-Layer Perceptrons within bagging, boosting, and stacking ensembles governed by a Bayesian weighting scheme. Our results demonstrate that Bayesian ensembles outperform their traditional counterparts across all metrics, with the Bayesian stacking model achieving a classification accuracy of 99.0% and an F1-score of 0.99 across Compact, Bent, Fanaroff-Riley Type I (FR-I), and Type II (FR-II) sources. Interpretability is enhanced through SHAP analysis, which highlights the principal components most associated with morphological distinctions. Beyond improving classification performance, our framework facilitates uncertainty quantification, paving the way for more reliable integration into next-generation survey pipelines. This work contributes a reproducible and interpretable methodology for automated galaxy classification in the era of data-intensive radio astronomy.

astro-ph.IM↗

arXiv2023

Label Assisted Autoencoder for Anomaly Detection in Power Generation Plants

One of the critical factors that drive the economic development of a country and guarantee the sustainability of its industries is the constant availability of electricity. This is usually provided by the national electric grid. However, in developing countries where companies are emerging on a constant basis including telecommunication industries, those are still experiencing a non-stable electricity supply. Therefore, they have to rely on generators to guarantee their full functionality. Those generators depend on fuel to function and the rate of consumption gets usually high, if not monitored properly. Monitoring operation is usually carried out by a (non-expert) human. In some cases, this could be a tedious process, as some companies have reported an exaggerated high consumption rate. This work proposes a label assisted autoencoder for anomaly detection in the fuel consumed by power generating plants. In addition to the autoencoder model, we added a labelling assistance module that checks if an observation is labelled, the label is used to check the veracity of the corresponding anomaly classification given a threshold. A consensus is then reached on whether training should stop or whether the threshold should be updated or the training should continue with the search for hyper-parameters. Results show that the proposed model is highly efficient for reading anomalies with a detection accuracy of $97.20\%$ which outperforms the existing model of $96.1\%$ accuracy trained on the same dataset. In addition, the proposed model is able to classify the anomalies according to their degree of severity.

cs.LG↗

arXiv2022

Predicting Fuel Consumption in Power Generation Plants using Machine Learning and Neural Networks

The instability of power generation from national grids has led industries (e.g., telecommunication) to rely on plant generators to run their businesses. However, these secondary generators create additional challenges such as fuel leakages in and out of the system and perturbations in the fuel level gauges. Consequently, telecommunication operators have been involved in a constant need for fuel to supply diesel generators. With the increase in fuel prices due to socio-economic factors, excessive fuel consumption and fuel pilferage become a problem, and this affects the smooth run of the network companies. In this work, we compared four machine learning algorithms (i.e. Gradient Boosting, Random Forest, Neural Network, and Lasso) to predict the amount of fuel consumed by a power generation plant. After evaluating the predictive accuracy of these models, the Gradient Boosting model out-perform the other three regressor models with the highest Nash efficiency value of 99.1%.

cs.LG↗