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.

Mitigating data gaps in Gamma-ray bursts (GRBs) light curves (LCs) holds immense value for its application in cosmological research because it provides more precise measurements of the parameter of interest of the two-dimensional Dainotti relation which is a relation among the end time of the plateau emission, Ta, its respective luminosity, La which is calculated from the fluxes at the end of the plateau, Fa. This study extends the work done by arXiv:2305.12126; arXiv:2412.20091v4 on the 545 GRB sample by introducing six different models: Deep Gaussian Process (DGP), Temporal Convolutional Network (TCN), Hybrid model of Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM), Bayesian Neural Network (BNN), Polynomial Curve Fitting and Isotonic Regression. Our findings demonstrate that Isotonic Regression achieves the highest uncertainty reduction for all three parameters (36.3% for log Ta, 36.1% for log Fa, and 43.6% for {\alpha}) outperforming all the other models. The CNN- LSTM model shows consistent improvements across all GRB parameters with the lowest outlier rate for {\alpha} (0.550%), surpassing the performance of the LSTM model in arXiv:2412.20091v4. The DGP model offers reliable uncertainty reduction across all parameters and improves upon the single-layer GP baseline. These advancements are essential for using GRBs as theoretical model discriminators via the parameters of their LCs and standard candles in cosmology, investigating theoretical models, and predicting GRB redshifts through recent cutting-edge machine-learning analysis (arXiv:2411.10736,arXiv:2405.02263; arXiv:2410.13985).