During GSoC 2024, I made several key contributions to the sunpy-soar project:

  1. Initial Implementation of Metadata for Remote Sensing Instrument(merged):
  • PR #118: This was my initial pull request where I established join operations for tables and implemented metadata for wavelength and detector for remote sensing instruments.

2. Gallery Examples and How-to Guide for recent implementations(merged):

  • PR #127: In this pull request, I added gallery examples and a how-to guide showcasing the newly implemented wavelength and detector metadata.

3. Error Handling for SOAR Server Downtime(merged):

  • PR #135: This update involved catching server errors thrown by SOAR when it’s down, enhancing the robustness of the system.

4. Distance Filtering Query Support(merged):

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Implementing the NIST database

  • The Einstein A coefficient is now used directly for calculating the non-equilibrium linestrength, given that it is calculated anyway for non-equilibrium spectra where it isn’t already present, rather than removing the temperature-dependent component of the reference linestrength, which was found to result in some atomic spectra not appearing. This also removes the need to calculate the reference linestrength for databanks where it’s not already present.
  • Removed some redundnant code and miscellaneous fixes and improvements.

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Bidirectional Recurrent Neural Networks

Introduction

In our ongoing objective to enhance the accuracy of Active Galactic Nuclei (AGN) light curve interpolation, we've previously explored various traditional and machine learning methods. Building on this foundation, this post introduces a sophisticated approach involving a Bidirectional Recurrent Neural Network (BRNN) coupled with an interpretative neural network layer, aimed at capturing the dynamics of AGN light curves more effectively.

Understanding Bidirectional Recurrent Neural Networks (BRNNs)

BRNNs are an extension of traditional Recurrent Neural Networks (RNNs), designed to improve model performance by processing data in both forward and reverse directions. This dual-path architecture allows the network to retain information from both past and future contexts simultaneously, which is particularly beneficial for predicting sequences with complex dependencies, like those found in AGN light curves.

Implementing an Interpretative Neural Network Layer

To make the outputs of the BRNN more comprehensible and useful, we integrate an additional neural network layer specifically for filling missing gaps. This layer translates the complex, non-linear relationships learned by the BRNN into clearer, more interpretable patterns. 

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