arXiv subjects

S. Naqi

Publications and source records attributed to S. Naqi.

2 recordsLinked to original sources

arXiv2025

Multi-Model Framework for Reconstructing Gamma-Ray Burst Light Curves

Mitigating data gaps in Gamma-ray bursts (GRBs) light curves (LCs) is crucial for cosmological research, enhancing the precision of parameters, assuming perfect satellite conditions for complete LC coverage with no gaps. This analysis improves the applicability of the two-dimensional Dainotti relation, which connects the rest-frame end time of the plateau emission (Ta) and its luminosity (La), derived from the fluxes (Fa). The study expands on a previous 521 GRB sample by incorporating seven models: Deep Gaussian Process (DGP), Temporal Convolutional Network (TCN), Hybrid CNN with Bidirectional Long Short-Term Memory (CNN-BiLSTM), Bayesian Neural Network (BNN), Polynomial Curve Fitting, Isotonic Regression, and Quartic Smoothing Spline (QSS). Results indicate that QSS significantly reduces uncertainty across parameters: 43.5% for log Ta, 43.2% for log Fa, and 48.3% for alpha, outperforming the other models where alpha denotes the slope post-plateau based on Willingale 2007 functional form. The Polynomial Curve Fitting model demonstrates moderate uncertainty reduction across parameters, while CNN-BiLSTM has the lowest outlier rate for alpha at 0.77%. These models broaden the application of machine-learning techniques in GRB LC analysis, enhancing uncertainty estimation and parameter recovery, and complement traditional methods like the Attention U-Net and Multilayer Perceptron (MLP). These advancements highlight the potential of GRBs as cosmological probes, supporting their role in theoretical model discrimination via LC parameters, serving as standard candles, and facilitating GRB redshift predictions through advanced machine-learning approaches.

astro-ph.HE↗

arXiv2024

Gamma-Ray Burst Light Curve Reconstruction: A Comparative Machine and Deep Learning Analysis

Gamma-Ray Bursts (GRBs), observed at high-z, are probes of the evolution of the Universe and can be used as cosmological tools. Thus, we need correlations with small dispersion among key parameters. To reduce such a dispersion, we mitigate gaps in light curves (LCs), including the plateau region, key to building the two-dimensional Dainotti relation between the end time of plateau emission (Ta) and its luminosity (La). We reconstruct LCs using nine models: Multi-Layer Perceptron (MLP), Bi-Mamba, Fourier Transform, Gaussian Process-Random Forest Hybrid (GP-RF), Bidirectional Long Short-Term Memory (Bi-LSTM), Conditional GAN (CGAN), SARIMAX-based Kalman filter, Kolmogorov-Arnold Networks (KANs), and Attention U-Net. These methods are compared to the Willingale model (W07) over a sample of 521 GRBs. MLP and Attention U-Net outperform other methods, with MLP reducing the plateau parameter uncertainties by 37.2% for log Ta, 38.0% for log Fa, and 41.2% for alpha (the post-plateau slope in the W07 model), achieving the lowest 5-fold cross-validation (CV) mean squared error (MSE) of 0.0275. Attention U-Net achieved the lowest uncertainty of parameters, a 37.9% reduction in log Ta, a 38.5% reduction in log Fa and a 41.4% reduction in alpha, but with a higher MSE of 0.134. Although Attention U-Net achieves the largest uncertainty reduction, the MLP attains the lowest test MSE while maintaining comparable uncertainty performance, making it the more reliable model. The other methods yield MSE values ranging from 0.0339 to 0.174. These improvements in parameter precision are needed to use GRBs as standard candles, investigate theoretical models, and predict GRB redshifts through machine learning.

astro-ph.HE↗