Concluding GSoC24
As the sun sets on the Google Summer of Code 2024, it's time to reflect on our exploration of Active Galactic Nuclei (AGN) light curve interpolation using advanced neural networks. Over the course of this project, we ventured into the complexities of AGN data, developing and refining models to better predict and understand the erratic behaviors of these celestial objects.
Overview of the Project
Our journey began with the goal of enhancing the accuracy of AGN light curve predictions. We employed custom Bidirectional Recurrent Neural Networks (BRNNs), coupled with an interpretative neural network layer, aiming to leverage both past and future context in our predictions.
Final Results
In our last phase, we meticulously tested our BRNN model against traditional linear interpolation and K-Nearest Neighbors (KNN) methods: