Difference between revisions of "Machine Learning for Segmentation of Lensed Galaxies: Distinguishing Source Galaxies from Gravitational Lenses"

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{{StudentProjectTemplate
 
{{StudentProjectTemplate
 
|Summary=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, classificqtion
+
|Keywords=Semanitc Segmentation, Astronomical data, classification
−
|Supervisor=Tiago Cortinhal, Idriss Gouigah, Eren Erdal Aksoy,  
+
|TimeFrame=2023-2024
 +
|References=For general understanding: https://www.youtube.com/watch?v=8PQO4P8pR8o&t=839s
 +
 
 +
Papers:
 +
 
 +
1. The strong gravitational lens finding challenge - Metcalf, R. B., Meneghetti, M., Avestruz, C., et al. 2019, A&A, 625, A119
 +
 
 +
2. Testing convolutional neural networks for finding strong gravitational lenses in KiDS  - Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2019a, MNRAS, 482, 807
 +
 
 +
3. Finding strong gravitational lenses through self-attention - Study based on the Bologna Lens Challenge - Thuruthipilly, H., Adam Zadrozny, Agnieszka Pollo, and Marek Biesiada.  A&A, 664:A4
 +
 
 +
4. The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples  - Pearson, J., Li, N., & Dye, S. 2019, MNRAS, 488, 991
 +
 
 +
5. Deep convolutional neural networks as strong gravitational lens detectors - Schaefer, C., Geiger, M., Kuntzer, T., & Kneib, J.-P. 2018, A&A, 611, A2
 +
 
 +
6. Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks - K. Rojas, E. Savary, B. Clément, M. Maus, F. Courbin, C. Lemon, J. H. H. Chan, G. Vernardos, R. Joseph, R. Cañameras, A. Galan, DOI: 0.1051/0004-6361/202142119
 +
|Prerequisites=Good Programing skills, previous experience with CNN and deep learning, interest in astronomy data.
 +
|Supervisor=Tiago Cortinhal, Idriss Gouigah, Eren Erdal Aksoy,Margherita Grespan, Hareesh Thuruthipilly
 
|Level=Master
 
|Level=Master
−
|Status=Draft
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|Status=Open
 
}}
 
}}
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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 na astronomical survey.
+
Abstract: Strong gravitational lensing (SGL) is a phenomenon in which a massive foreground objecting (lens)  distorting space-time bends the signal coming from background sources. On the sky, this is visible as arc-like structures or double images of the foreground object.
 +
Strong gravitational lenses (SGLs) are a powerful tool for addressing the dark matter problem and constraining cosmological parameters, while also serving as a natural telescope into otherwise too faint to observe celestial objects. The upcoming large-scale astronomical surveys, such as the Rubin Observatory Legacy Survey of Space and Time (LSST) and Euclid, are expected to uncover approximately 10^5 SGLs by analyzing datasets of unprecedented scale. However, detecting and accurately modelling these systems is a formidable task, necessitating the development of automated deep learning algorithms.
 +
This master's thesis focuses on the development and training of a machine learning model for the detection of SGLs and the segmentation of lensed galaxy images, enabling the differentiation between source (background) galaxies and gravitational lenses permitting the modelling of lensed systems. The dataset utilized comprises simulated images from the Gravitational Lens Finding Challenge, simulated data based on the Euclid survey and simulated data from the Legacy Survey of Space and Time (LSST).
 +
 
 
Research Objectives:
 
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.
+
1. Dataset Compilation: Preprocess a diverse dataset of lensed galaxy images, encompassing data from the Gravitational Lens Finding Challenge, and the data from the Legacy Survey of Space and Time (LSST).  
−
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.
+
2. Model Development: Investigate and implement state-of-the-art machine learning techniques, with a focus on convolutional neural networks (CNNs) and transformers to design a pipeline capable of classifying and subsequently segmenting the lensed galaxy images accurately to predict the parameters of the lensing system.
−
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.
+
 
 +
3. Strong-Lens classification: Develop a state-of-the-art model for identifying SGLs with a focus on reducing false positives (FP).
 +
 
 +
4. 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.
 +
 
 +
5. 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. Compare the performance of the model with the existing models from the literature.  
 +
 
 +
6. 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:
 
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.
+
1. Data Preparation: Collect lensed galaxy images from the Gravitational Lens Finding Challenge and the other two previously mentioned astronomical survey and preprocess the data.
 +
 
 +
2. Model Design: Explore various deep learning architectures and techniques to develop a robust model tailored to identify and 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.
 
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.
 
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.
 
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.
+
Expected Outcomes: This master's thesis aims to advance the field of astrophysics by providing a novel machine learning approach for identifying strong lenses and accurately segmenting the input image into the lens and source galaxies. The expected outcomes include:
−
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.
+
1. A trained machine learning model capable of identifying SGLs from the upcoming astronomical surveys such as Euclid and LSST.
 +
 
 +
2. A trained machine learning model capable of accurately segmenting lensed galaxy images which would be used for modelling the lensing system and estimating the physical parameters.
 +
 
 +
3. Insights into the effectiveness of different deep learning architectures for this specific task.
 +
 
 +
4. An evaluation of the model's generalization capabilities to diverse astronomical datasets.
 +
 
 +
5. Recommendations for the practical application of the model in gravitational lensing studies, enhancing our understanding of the distant universe.
 +
 
 +
 
 +
You can contact us at tiago.cortinhal@hh.se or idriss.gouigah@hh.se

Latest revision as of 12:18, 25 October 2023

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 For general understanding: https://www.youtube.com/watch?v=8PQO4P8pR8o&t=839s

Papers:

1. The strong gravitational lens finding challenge - Metcalf, R. B., Meneghetti, M., Avestruz, C., et al. 2019, A&A, 625, A119

2. Testing convolutional neural networks for finding strong gravitational lenses in KiDS - Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2019a, MNRAS, 482, 807

3. Finding strong gravitational lenses through self-attention - Study based on the Bologna Lens Challenge - Thuruthipilly, H., Adam Zadrozny, Agnieszka Pollo, and Marek Biesiada. A&A, 664:A4

4. The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples - Pearson, J., Li, N., & Dye, S. 2019, MNRAS, 488, 991

5. Deep convolutional neural networks as strong gravitational lens detectors - Schaefer, C., Geiger, M., Kuntzer, T., & Kneib, J.-P. 2018, A&A, 611, A2

6. Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks - K. Rojas, E. Savary, B. Clément, M. Maus, F. Courbin, C. Lemon, J. H. H. Chan, G. Vernardos, R. Joseph, R. Cañameras, A. Galan, DOI: 0.1051/0004-6361/202142119

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 Open


Abstract: Strong gravitational lensing (SGL) is a phenomenon in which a massive foreground objecting (lens) distorting space-time bends the signal coming from background sources. On the sky, this is visible as arc-like structures or double images of the foreground object. Strong gravitational lenses (SGLs) are a powerful tool for addressing the dark matter problem and constraining cosmological parameters, while also serving as a natural telescope into otherwise too faint to observe celestial objects. The upcoming large-scale astronomical surveys, such as the Rubin Observatory Legacy Survey of Space and Time (LSST) and Euclid, are expected to uncover approximately 10^5 SGLs by analyzing datasets of unprecedented scale. However, detecting and accurately modelling these systems is a formidable task, necessitating the development of automated deep learning algorithms. This master's thesis focuses on the development and training of a machine learning model for the detection of SGLs and the segmentation of lensed galaxy images, enabling the differentiation between source (background) galaxies and gravitational lenses permitting the modelling of lensed systems. The dataset utilized comprises simulated images from the Gravitational Lens Finding Challenge, simulated data based on the Euclid survey and simulated data from the Legacy Survey of Space and Time (LSST).

Research Objectives:

1. Dataset Compilation: Preprocess a diverse dataset of lensed galaxy images, encompassing data from the Gravitational Lens Finding Challenge, and the data from the Legacy Survey of Space and Time (LSST).

2. Model Development: Investigate and implement state-of-the-art machine learning techniques, with a focus on convolutional neural networks (CNNs) and transformers to design a pipeline capable of classifying and subsequently segmenting the lensed galaxy images accurately to predict the parameters of the lensing system.

3. Strong-Lens classification: Develop a state-of-the-art model for identifying SGLs with a focus on reducing false positives (FP).

4. 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.

5. 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. Compare the performance of the model with the existing models from the literature.

6. 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 other two previously mentioned astronomical survey and preprocess the data.

2. Model Design: Explore various deep learning architectures and techniques to develop a robust model tailored to identify and 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 identifying strong lenses and accurately segmenting the input image into the lens and source galaxies. The expected outcomes include:

1. A trained machine learning model capable of identifying SGLs from the upcoming astronomical surveys such as Euclid and LSST.

2. A trained machine learning model capable of accurately segmenting lensed galaxy images which would be used for modelling the lensing system and estimating the physical parameters.

3. Insights into the effectiveness of different deep learning architectures for this specific task.

4. An evaluation of the model's generalization capabilities to diverse astronomical datasets.

5. Recommendations for the practical application of the model in gravitational lensing studies, enhancing our understanding of the distant universe.


You can contact us at tiago.cortinhal@hh.se or idriss.gouigah@hh.se

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