https://www.um.edu.mt/library/oar/handle/123456789/16096 2026-04-15T18:21:08Z https://www.um.edu.mt/library/oar/handle/123456789/144821 Title: A deep neural network based reverse radio spectrogram search algorithm Authors: Ma, Peter Xiangyuan; Croft, Steve; Lintott, Chris; Siemion, Andrew P. V. Abstract: Modern radio astronomy instruments generate vast amounts of data, and the increasingly challenging radio frequency interference (RFI) environment necessitates ever-more sophisticated RFI rejection algorithms. The ‘needle in a haystack’ nature of searches for transients and technosignatures requires us to develop methods that can determine whether a signal of interest has unique properties, or is a part of some larger set of pernicious RFI. In the past, this vetting has required onerous manual inspection of very large numbers of signals. In this paper, we present a fast and modular deep learning algorithm to search for lookalike signals of interest in radio spectrogram data. First, we trained a β-variational autoencoder on signals returned by an energy detection algorithm. We then adapted a positional embedding layer from classical transformer architecture to a embed additional metadata, which we demonstrate using a frequency-based embedding. Next we used the encoder component of the β-variational autoencoder to extract features from small (∼715 Hz, with a resolution of 2.79 Hz per frequency bin) windows in the radio spectrogram. We used our algorithm to conduct a search for a given query (encoded signal of interest) on a set of signals (encoded features of searched items) to produce the top candidates with similar features. We successfully demonstrate that the algorithm retrieves signals with similar appearance, given only the original radio spectrogram data. This algorithm can be used to improve the efficiency of vetting signals of interest in technosignature searches, but could also be applied to a wider variety of searches for ‘lookalike’ signals in large astronomical data sets. 2024-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/143141 Title: Propagating gravitational waves in teleparallel Gauss-Bonnet gravity Authors: Mishra, Shivam Kumar; Said, Jackson; Mishra, Bivudutta Abstract: Gravitational waves offer a key insight into the viability of classes of gravitational theories beyond general relativity. The observational constraints on their speed of propagation can provide strong constraints on generalized classes of broader gravitational frameworks. In this work, we reconsider the general class of Gauss-Bonnet theories in the context of teleparallel gravity, where the background geometry is expressed through torsion. We perform tensor perturbations on a flat Friedmann–Lemaître–Robertson–Walker background, and derive the gravitational wave propagation equation. We find that gravitational waves propagate at the speed of light in these classes of theories. We also derive the distance-duality relationship for radiation propagating in the gravitational wave and electromagnetic domains. 2025-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/143129 Title: Constraining big bang nucleosynthesis in 𝑓(𝑇, 𝐵) gravity through observational analysis Authors: Sultan, Abdul Malik; Fatima, Maryam; Said, Jackson; Batool, Aliya Abstract: In this article, we investigate the big bang nucleosynthesis scenario responsible for the birth of light elements in the context of 𝑓(𝑇 ,𝐵) gravity (where 𝑇 is the torsion scalar and 𝐵 is the boundary term). For this purpose, we consider four different models; [formula omitted, please refer to the original abstract on the article] to analyze the scenario. We calculate the deviation of the freeze-out temperature 𝑇𝑓 in comparison to that of the ΛCDM paradigm and compare it with the recent observational bound on [formula omitted, please refer to the original abstract on the article] to extract constraints for each model. We find that each model depict the production of light elements up to some constrained values of model parameters lying in the observational range. To observe the viability of our results, we scrutinize the Hubble parameter 𝐻 and conduct a Chi-square test to compare it with the observations. Each model remains compatible and consistent with the most recent observational data. Moreover, we obtain a reasonable level of consistency between the four models under investigation, and the Panteon+ sample together with ΛCDM model background. In the end we utilize the latest MCMC data analysis to find the viable constraints on free parameters with 1 − 𝜎, 2 − 𝜎, and 3 − 𝜎 level of confidence. 2025-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/143062 Title: Model-independent calibration of gamma-ray bursts with neural networks Authors: Mukherjee, Purba; Dainotti, Maria Giovanna; Dialektopoulos, Konstantinos F.; Said, Jackson; Mifsud, Jurgen Abstract: The Λ Cold Dark Matter (ΛCDM) cosmological model has been highly successful in predicting cosmic structure and evolution, yet recent precision measurements have highlighted discrepancies, especially in the Hubble constant inferred from local and early-Universe data. Gamma-ray bursts (GRBs) present a promising alternative for cosmological measurements, capable of reaching higher redshifts than traditional distance indicators. This work leverages GRBs to refine cosmological parameters independently of the ΛCDM framework. Using the Platinum compilation of long GRBs, we calibrate the Dainotti relations—empirical correlations among GRB luminosity properties—as standard candles through artificial neural networks (ANNs). We analyze both the 2D and 3D Dainotti calibration relations, leveraging an ANN-driven Markov Chain Monte Carlo approach to minimize scatter in the calibration parameters, thereby achieving a stable Hubble diagram. This ANN-based calibration approach offers advantages over Gaussian processes, avoiding issues such as kernel function dependence and overfitting. Our results emphasize the need for model-independent calibration approaches to address systematic challenges in GRB luminosity variability, ultimately extending the cosmic distance ladder in a robust way. By addressing redshift evolution and reducing systematic uncertainties, GRBs can serve as reliable high-redshift distance indicators, offering critical insights into current cosmological tensions. 2026-01-01T00:00:00Z