Machine Learning for Segmentation of Lensed Galaxies: Distinguishing Source Galaxies from Gravitational Lenses
| Title | Machine Learning for Segmentation of Lensed Galaxies: Distinguishing Source Galaxies from Gravitational Lenses |
|---|---|
| Summary | Machine Learning for Segmentation of Lensed Galaxies: Distinguishing Source Galaxies from Gravitational Lenses |
| Keywords | Semanitc Segmentation, Astronomical data, classification |
| TimeFrame | 2023-2024 |
| References | |
| Prerequisites | Good Programing skills, previous experience with CNN and deep learning, interest in astronomy data. |
| Author | |
| Supervisor | Tiago Cortinhal, Idriss Gouigah, Eren Erdal Aksoy, Margherita Grespan, Hareesh Thuruthipilly |
| Level | Master |
| Status | Draft |
Abstract: Lensed galaxies serve as cosmic magnifying glasses, offering a unique window into the distant universe. Accurate segmentation of lensed galaxy images, separating the source galaxy from the gravitational lens, is crucial for extracting meaningful scientific insights. This master's thesis aims to develop and train a machine learning model capable of segmenting lensed galaxy images, differentiating between the source galaxy and the gravitational lens. The dataset used for this research will comprise images from the Gravitational Lens Finding Challenge and data obtained from an astronomical survey. This thesis will be supervised in partnership with NCBJ (Poland).
Research Objectives:
1. Dataset Compilation: Preprocess a diverse dataset of lensed galaxy images, encompassing data from the Gravitational Lens Finding Challenge, an astronomical survey, and synthetic data.
2. Model Development: Investigate and implement state-of-the-art machine learning techniques, with a focus on convolutional neural networks (CNNs) and deep learning architectures, to design a model capable of accurately segmenting lensed galaxy images.
3. Source-Lens Separation: Develop a novel model architecture that can effectively differentiate between the source galaxy and the gravitational lens in lensed galaxy images, considering the unique challenges posed by gravitational lensing.
4. Training and Validation: Train the model on the curated dataset, employing data augmentation and regularization techniques. Validate the model's performance using cross-validation and a range of appropriate evaluation metrics, such as accuracy, precision, recall, and F1-score.
5. Generalization and Application: Assess the model's generalization abilities by testing it on different datasets, including data from various astronomical surveys. Evaluate its applicability in real-world astronomical observations and discuss potential applications.
Methodology:
1. Data Preparation: Collect lensed galaxy images from the Gravitational Lens Finding Challenge and the astronomical survey, and preprocess the data.
2. Model Design: Explore various deep learning architectures and techniques to develop a robust model tailored to segment lensed galaxy images and distinguish source galaxies from gravitational lenses.
3. Training and Validation: Train the model on the prepared dataset, optimizing hyperparameters and monitoring performance throughout the training process. Employ cross-validation to ensure robustness and apply the model to real data.
4. Generalization Testing: Evaluate the model's ability to generalize to different datasets, including those from the survey, to assess its practicality for broader astronomical research.
5. Comparative Analysis: Compare the proposed model's performance with existing methods, highlighting its strengths, weaknesses, and potential contributions to the field.
Expected Outcomes: This master's thesis aims to advance the field of astrophysics by providing a novel machine learning approach for accurate segmentation of lensed galaxy images, specifically focusing on separating source galaxies from gravitational lenses. The expected outcomes include:
1. A trained machine learning model capable of accurately segmenting lensed galaxy images.
2. Insights into the effectiveness of different deep learning architectures for this specific task.
3. An evaluation of the model's generalization capabilities to diverse astronomical datasets.
4. Recommendations for the practical application of the model in gravitational lensing studies, enhancing our understanding of the distant universe.
Resources:
https://github.com/LSST-strong-lensing/DESC-Lamp/tree/main
https://www.lsst.org/sites/default/files/docs/sciencebook/SB_12.pdf
