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| --- | ||
| title: Characterization of Transiting Exoplanets from Transmission Spectroscopy | ||
| layout: gsoc_proposal | ||
| project: EXXA | ||
| year: 2024 | ||
| organization: | ||
| - Florida | ||
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| --- | ||
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| ## Description | ||
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| Transmission spectroscopy provides a powerful tool to decode the chemical composition of the atmospheres of transiting exoplanets. This project will bring in modern machine learning (ML) methods to advance the theoretical framework for exoplanet parameter retrievals and test it using synthetic spectroscopic data. A tangible output of this work will be a publicly available code that takes a spectrum of a transiting planet and returns a reliable determination, with corresponding uncertainties, of its atmospheric chemical composition. | ||
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| ## Duration | ||
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| Total project length: 175 hours | ||
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| ## Task ideas | ||
| * Training a regression ML model using publicly available spectroscopic database | ||
| * Uncertainty quantification on the retrieved parameters | ||
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| ## Expected results | ||
| * publicly available code to be used for spectral analysis of exoplanet observations from the JWST and Ariel missions | ||
| * Benchmarking the results against existing Bayesian retrieval models (TauREx, Transit) | ||
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| ## Requirements | ||
| * Python, previous experience in machine learning | ||
| * Basic knowledge of astronomy and observations is useful but not required | ||
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| ## Mentors | ||
| * [Katia Matcheva]((mailto:[email protected])) (University of Florida) | ||
| * [Konstantin Matchev]((mailto:[email protected])) (University of Florida) | ||
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| ## Links | ||
| * <a href="https://arxiv.org/abs/2206.14633">Paper 1</a> | ||
| * <a href="https://proceedings.mlr.press/v220/yip23a.html">Paper 2</a> | ||
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| Please **DO NOT** contact mentors directly by email. Instead, please email [[email protected]](mailto:[email protected]) with Project Title and **include your CV** and **test results**. The mentors will then get in touch with you. | ||
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This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,42 +1,41 @@ | ||
| --- | ||
| title: Characterization of Transiting Exoplanets from Transmission Spectroscopy | ||
| title: TBC | ||
| layout: gsoc_proposal | ||
| project: EXXA | ||
| project: TBC | ||
| year: 2024 | ||
| organization: | ||
| - University of Florida | ||
| - YourInstitute | ||
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| --- | ||
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| ## Description | ||
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| Transmission spectroscopy provides a powerful tool to decode the chemical composition of the atmospheres of transiting exoplanets. This project will bring in modern machine learning (ML) methods to advance the theoretical framework for exoplanet parameter retrievals and test it using synthetic spectroscopic data. A tangible output of this work will be a publicly available code that takes a spectrum of a transiting planet and returns a reliable determination, with corresponding uncertainties, of its atmospheric chemical composition. | ||
| Proposal TBC | ||
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| ## Duration | ||
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| Total project length: 175 hours | ||
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| ## Task ideas | ||
| * Training a regression ML model using publicly available spectroscopic database | ||
| * Uncertainty quantification on the retrieved parameters | ||
| * Add task idea | ||
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| ## Expected results | ||
| * publicly available code to be used for spectral analysis of exoplanet observations from the JWST and Ariel missions | ||
| * Benchmarking the results against existing Bayesian retrieval models (TauREx, Transit) | ||
| * Add expected result 1 | ||
| * Add expected result 2 etc | ||
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| ## Requirements | ||
| * Python, previous experience in machine learning | ||
| * Basic knowledge of astronomy and observations is useful but not required | ||
| Add requirements here | ||
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| ## Project difficulty level | ||
| Easy/Medium/Hard | ||
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| ## Mentors | ||
| * [Katia Matcheva]((mailto:[email protected])) (University of Florida) | ||
| * [Konstantin Matchev]((mailto:[email protected])) (University of Florida) | ||
| * [Mentor Name](mailto:[email protected]) (Mentor institute) | ||
| * [Mentor Name 2](mailto:[email protected]) (Mentor institute 2) | ||
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| ## Links | ||
| * <a href="https://arxiv.org/abs/2206.14633">Paper 1</a> | ||
| * <a href="https://proceedings.mlr.press/v220/yip23a.html">Paper 2</a> | ||
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| Please **DO NOT** contact mentors directly by email. Instead, please email [[email protected]](mailto:human-ai@cern.ch) with Project Title and **include your CV** and **test results**. The mentors will then get in touch with you. | ||
| Please **DO NOT** contact mentors directly by email. Instead, please email [[email protected]](mailto:ml4-sci@cern.ch) with Project Title and **include your CV** and **test results**. The mentors will then get in touch with you. | ||
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