Astronomical Data Enhancement with DL

  Prior Research The domain of artificial intelligence's application in astronomy has been enriched by seminal studies, such as the one conducted by Dr. Pavlos Protopapas from Harvard University, Dr. Germán García-Jara from the Universidad de Chile, and Dr. Pablo A. Estévez, also from the Universidad de Chile. Their research, " Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks " leverages GANs for the augmentation of astronomical data, specifically targeting the enhancement of variable star classification. This work, published in the reputable Astrophysical Journal, showcases the potential of deep learning techniques to address the challenges posed by imbalanced and complex datasets in astronomy. Their methodology and findings not only advance our understanding of astronomical data processing but also lay a solid foundation for further exploration in this interdisciplinary field. This project aims to build upon th...

GSOC2024 ML/DL Starter Problem This is a simple toy problem meant as a pre-application exercise for the GSOC2024 project Astronomical data enhancement with DL. Overview Here we will have a more simplified case of the actual project with no time information. We have measurements of galaxies in different wavelengthts (i.e., broadband filters) in five different fields and the task is to bring them all onto a same wavelength footing. A simple notebook to read the galaxy data in the initial filters is in this repository (gsoc-ML-exercise/ReadCandels.md). Link to the problem published in the Open Astronomy GitHub:  Published Problem Instructions Clone this repo and checkout the branch gsco-ML-exercise. Write code. Required: A simple way to combine all five fields in optical and NIR filters and output one file in the requested wavelengths. Optional: Use ML or DL to do this combination. Optional: Add plots to show what you did makes sense. Optional: Use prior information in ML by grouping ...

Introduction As we stand on the brink of astronomical discovery, the role of data in unlocking the mysteries of the universe has never been more critical. In this digital age, the vast expanse of space is mirrored by the equally vast datasets collected by astronomers—datasets that hold the keys to understanding our place in the cosmos. However, the sheer diversity and complexity of this data often present significant challenges to its full utilization. This is where the synergy between artificial intelligence (AI) and astronomical research heralds a new dawn of discovery. This project leverages advanced data processing and deep learning models to address data inconsistencies, aiming for uniform analysis and minimal information loss. It represents a convergence of data science and astronomy, underscoring the importance of open-source development and collaboration within the astronomical community. In the embrace of OpenAstronomy, this project is not just about technical achievements; it...