{
  "id": 591278,
  "title": "🚀 ArielML: A Python Library for Exoplanet Transit Analysis (Built for Learning!)",
  "url": "/competitions/ariel-data-challenge-2025/discussion/591278",
  "author_name": "Ridwan Jalali",
  "post_date": "2025-07-26T18:44:04.397000",
  "votes": 42,
  "comment_count": 5,
  "views": 0,
  "content": "<h1>🚀 ArielML: A Python Library for Ariel Transit Analysis (Built for Learning!)</h1>\n<p><img src=\"https://raw.githubusercontent.com/jalalirs/arielml/main/assets/data_inspector_screenshot.png\" alt=\"\"></p>\n<p><strong>TL;DR</strong>: I created <code>arielml</code>, a Python library with GUI tools for exploring the Ariel dataset. It's not groundbreaking science, but it might help newcomers like me understand the data better!</p>\n<p><strong>🔗 Repository</strong>: <a href=\"https://github.com/jalalirs/arielml\" target=\"_blank\">https://github.com/jalalirs/arielml</a> </p>\n<hr>\n<h2>🙋‍♂️ Background (Full Disclosure!)</h2>\n<p>Hi everyone! I'm <strong>not</strong> an astronomer, and I'm definitely <strong>not</strong> an expert in Gaussian processes or exoplanet science. However, I've been following this field as an enthusiast for a while now. While attending the <strong>BEACON conference in Iceland</strong>, I heard about this competition and thought \"hey, this is a great opportunity to practice with real-world exoplanet detection problems - let me dive in!\" 🌌</p>\n<p>So please take everything I share with a grain of salt - this is very much a learning exercise for me, and I'm sure there are much better approaches out there!</p>\n<hr>\n<h2>🛠️ What is ArielML?</h2>\n<p><code>arielml</code> is a Python library I built to help me (and hopefully others) explore and understand the Ariel dataset. It's <strong>heavily inspired</strong> by some excellent work from the community, particularly:</p>\n<ol>\n<li><p><strong>NeurIPS: Non-ML Transit Curve Fitting</strong> by <a href=\"https://www.kaggle.com/vitalykudelya\" target=\"_blank\">@vitalykudelya</a>  <br>\n📎 <a href=\"https://www.kaggle.com/code/vitalykudelya/neurips-non-ml-transit-curve-fitting/notebook\" target=\"_blank\">https://www.kaggle.com/code/vitalykudelya/neurips-non-ml-transit-curve-fitting/notebook</a></p></li>\n<li><p><strong>Ariel 2nd place submission notebook</strong> by <a href=\"https://www.kaggle.com/jeroencottaar\" target=\"_blank\">@jeroencottaar</a>  <br>\n📎 <a href=\"http://kaggle.com/code/jeroencottaar/ariel-2nd-place-submission-notebook\" target=\"_blank\">http://kaggle.com/code/jeroencottaar/ariel-2nd-place-submission-notebook</a></p></li>\n<li><p><strong>Calibrating and Binning Ariel Data</strong> by <a href=\"https://www.kaggle.com/gordonyip\" target=\"_blank\">@gordonyip</a>  <br>\n📎 <a href=\"https://www.kaggle.com/code/gordonyip/calibrating-and-binning-ariel-data\" target=\"_blank\">https://www.kaggle.com/code/gordonyip/calibrating-and-binning-ariel-data</a></p></li>\n</ol>\n<p><strong>Important</strong>: ArielML is <strong>NOT</strong> an accurate implementation of these original works! I've simplified, modified, and probably broken things while trying to understand the concepts. The original notebooks are the authoritative sources - mine is just a learning tool! </p>\n<hr>\n<h2>🎯 Key Features</h2>\n<h3>📊 <strong>Data Inspector GUI</strong></h3>\n<p>The main feature is a PyQt-based GUI that lets you:</p>\n<ul>\n<li><strong>🔍 Load and visualize</strong> raw sensor data (FGS1 &amp; AIRS-CH0)</li>\n<li><strong>⚙️ Step through preprocessing</strong> (calibration, binning, spatial aggregation)</li>\n<li><strong>📈 Interactive plotting</strong> with mouse-over coordinates</li>\n<li><strong>🔄 Real-time parameter tuning</strong> with immediate visual feedback</li>\n<li><strong>📋 Multiple pipeline modes</strong>: Preprocessing, Baseline, and Bayesian<ul>\n<li><em>Note: Baseline pipeline requires both FGS1 and AIRS-CH0 instruments loaded</em></li></ul></li>\n</ul>\n<h3>🧮 <strong>Pipeline Implementations</strong></h3>\n<p><strong>Baseline Pipeline</strong> (simplified from the community notebooks):</p>\n<ul>\n<li>Preprocessing with configurable binning factors</li>\n<li>Phase detection for transit start/end</li>\n<li>Polynomial detrending with optimization</li>\n<li>Single transit depth for all wavelengths</li>\n<li><strong>Requires both FGS1 and AIRS-CH0 instruments</strong> to be loaded</li>\n</ul>\n<p><strong>Bayesian Pipeline</strong> (very basic):</p>\n<ul>\n<li>MCMC sampling for uncertainty quantification  </li>\n<li>Gaussian Process detrending</li>\n<li>Calibration models for bias correction</li>\n</ul>\n<h3>🎨 <strong>Visualization Tools</strong></h3>\n<ul>\n<li><strong>Light curve plots</strong> with transit phases highlighted</li>\n<li><strong>Detector images</strong> showing spatial patterns</li>\n<li><strong>Component analysis</strong> plots for preprocessing steps</li>\n<li><strong>Transit fit visualization</strong> with wavelength selection</li>\n<li><strong>Covariance matrix</strong> heatmaps</li>\n<li><strong>Ground truth comparison</strong> when available</li>\n</ul>\n<hr>\n<h2>🖼️ Screenshots</h2>\n<h3>Main Interface</h3>\n<p>The GUI provides tabs for different analysis modes:</p>\n<pre><code>┌─ Navigation ─┐  ┌─ Settings ─┐  ┌─ Visuals ─┐\n│ Planet ID    │  │ Pipeline   │  │ Plots    │\n│ Instrument   │  │  │  │ Results  │  \n│ Obs        │  │  │  │  │\n└──────────────┘  └────────────┘  └──────────┘\n</code></pre>\n<h3>Transit Fit Analysis</h3>\n<p>One feature I'm particularly happy with is the <strong>Transit Fit</strong> tab that lets you:</p>\n<ul>\n<li>🎚️ <strong>Wavelength slider</strong>: Explore transit depths across the spectrum</li>\n<li>📊 <strong>Interactive visualization</strong>: See original light curve, polynomial fit, and depth measurement</li>\n<li>🎯 <strong>Phase highlighting</strong>: Visual indicators for transit start/end</li>\n<li>📏 <strong>Depth annotation</strong>: Numerical values displayed on the plot</li>\n</ul>\n<hr>\n<h2>🚀 Getting Started</h2>\n<h3>Installation (Local)</h3>\n<pre><code>git  https://github.com/jalalirs/arielml\n arielml\npip install -r requirements.txt\npython tools/data_inspector.py\n</code></pre>\n<p><strong>📋 Full documentation and setup instructions</strong>: <a href=\"https://github.com/jalalirs/arielml\" target=\"_blank\">https://github.com/jalalirs/arielml</a></p>\n<hr>\n<h2>🎓 What I Learned</h2>\n<p>Working on this project taught me a lot about:</p>\n<ul>\n<li><strong>Exoplanet transit photometry</strong> (still learning!)</li>\n<li><strong>Data preprocessing pipelines</strong> for astronomical data</li>\n<li><strong>Gaussian processes</strong> for detrending (barely scratching the surface)</li>\n<li><strong>PyQt GUI development</strong> for scientific applications</li>\n<li><strong>Memory management</strong> for large datasets</li>\n</ul>\n<hr>\n<h2>⚠️ Limitations &amp; Disclaimers</h2>\n<ol>\n<li><strong>Not scientifically rigorous</strong>: This is a learning tool, not research-grade software</li>\n<li><strong>Simplified implementations</strong>: I've cut corners to focus on understanding concepts</li>\n<li><strong>Limited validation</strong>: Haven't thoroughly tested against known results</li>\n<li><strong>Performance</strong>: Not optimized for speed or memory efficiency</li>\n<li><strong>Documentation</strong>: Could definitely be better!</li>\n</ol>\n<hr>\n<h2>🤝 Community &amp; Feedback</h2>\n<p>I'd love to hear from the community:</p>\n<ul>\n<li>🐛 <strong>Found bugs?</strong> Please let me know!</li>\n<li>💡 <strong>Suggestions for improvement?</strong> Always welcome!</li>\n<li>📚 <strong>Better resources to learn from?</strong> Please share!</li>\n</ul>\n<hr>",
  "messages": [
    {
      "id": 3254569,
      "postDate": "2025-07-26T18:44:04.397Z",
      "content": "<h1>🚀 ArielML: A Python Library for Ariel Transit Analysis (Built for Learning!)</h1>\n<p><img src=\"https://raw.githubusercontent.com/jalalirs/arielml/main/assets/data_inspector_screenshot.png\" alt=\"\"></p>\n<p><strong>TL;DR</strong>: I created <code>arielml</code>, a Python library with GUI tools for exploring the Ariel dataset. It's not groundbreaking science, but it might help newcomers like me understand the data better!</p>\n<p><strong>🔗 Repository</strong>: <a href=\"https://github.com/jalalirs/arielml\" target=\"_blank\">https://github.com/jalalirs/arielml</a> </p>\n<hr>\n<h2>🙋‍♂️ Background (Full Disclosure!)</h2>\n<p>Hi everyone! I'm <strong>not</strong> an astronomer, and I'm definitely <strong>not</strong> an expert in Gaussian processes or exoplanet science. However, I've been following this field as an enthusiast for a while now. While attending the <strong>BEACON conference in Iceland</strong>, I heard about this competition and thought \"hey, this is a great opportunity to practice with real-world exoplanet detection problems - let me dive in!\" 🌌</p>\n<p>So please take everything I share with a grain of salt - this is very much a learning exercise for me, and I'm sure there are much better approaches out there!</p>\n<hr>\n<h2>🛠️ What is ArielML?</h2>\n<p><code>arielml</code> is a Python library I built to help me (and hopefully others) explore and understand the Ariel dataset. It's <strong>heavily inspired</strong> by some excellent work from the community, particularly:</p>\n<ol>\n<li><p><strong>NeurIPS: Non-ML Transit Curve Fitting</strong> by <a href=\"https://www.kaggle.com/vitalykudelya\" target=\"_blank\">@vitalykudelya</a>  <br>\n📎 <a href=\"https://www.kaggle.com/code/vitalykudelya/neurips-non-ml-transit-curve-fitting/notebook\" target=\"_blank\">https://www.kaggle.com/code/vitalykudelya/neurips-non-ml-transit-curve-fitting/notebook</a></p></li>\n<li><p><strong>Ariel 2nd place submission notebook</strong> by <a href=\"https://www.kaggle.com/jeroencottaar\" target=\"_blank\">@jeroencottaar</a>  <br>\n📎 <a href=\"http://kaggle.com/code/jeroencottaar/ariel-2nd-place-submission-notebook\" target=\"_blank\">http://kaggle.com/code/jeroencottaar/ariel-2nd-place-submission-notebook</a></p></li>\n<li><p><strong>Calibrating and Binning Ariel Data</strong> by <a href=\"https://www.kaggle.com/gordonyip\" target=\"_blank\">@gordonyip</a>  <br>\n📎 <a href=\"https://www.kaggle.com/code/gordonyip/calibrating-and-binning-ariel-data\" target=\"_blank\">https://www.kaggle.com/code/gordonyip/calibrating-and-binning-ariel-data</a></p></li>\n</ol>\n<p><strong>Important</strong>: ArielML is <strong>NOT</strong> an accurate implementation of these original works! I've simplified, modified, and probably broken things while trying to understand the concepts. The original notebooks are the authoritative sources - mine is just a learning tool! </p>\n<hr>\n<h2>🎯 Key Features</h2>\n<h3>📊 <strong>Data Inspector GUI</strong></h3>\n<p>The main feature is a PyQt-based GUI that lets you:</p>\n<ul>\n<li><strong>🔍 Load and visualize</strong> raw sensor data (FGS1 &amp; AIRS-CH0)</li>\n<li><strong>⚙️ Step through preprocessing</strong> (calibration, binning, spatial aggregation)</li>\n<li><strong>📈 Interactive plotting</strong> with mouse-over coordinates</li>\n<li><strong>🔄 Real-time parameter tuning</strong> with immediate visual feedback</li>\n<li><strong>📋 Multiple pipeline modes</strong>: Preprocessing, Baseline, and Bayesian<ul>\n<li><em>Note: Baseline pipeline requires both FGS1 and AIRS-CH0 instruments loaded</em></li></ul></li>\n</ul>\n<h3>🧮 <strong>Pipeline Implementations</strong></h3>\n<p><strong>Baseline Pipeline</strong> (simplified from the community notebooks):</p>\n<ul>\n<li>Preprocessing with configurable binning factors</li>\n<li>Phase detection for transit start/end</li>\n<li>Polynomial detrending with optimization</li>\n<li>Single transit depth for all wavelengths</li>\n<li><strong>Requires both FGS1 and AIRS-CH0 instruments</strong> to be loaded</li>\n</ul>\n<p><strong>Bayesian Pipeline</strong> (very basic):</p>\n<ul>\n<li>MCMC sampling for uncertainty quantification  </li>\n<li>Gaussian Process detrending</li>\n<li>Calibration models for bias correction</li>\n</ul>\n<h3>🎨 <strong>Visualization Tools</strong></h3>\n<ul>\n<li><strong>Light curve plots</strong> with transit phases highlighted</li>\n<li><strong>Detector images</strong> showing spatial patterns</li>\n<li><strong>Component analysis</strong> plots for preprocessing steps</li>\n<li><strong>Transit fit visualization</strong> with wavelength selection</li>\n<li><strong>Covariance matrix</strong> heatmaps</li>\n<li><strong>Ground truth comparison</strong> when available</li>\n</ul>\n<hr>\n<h2>🖼️ Screenshots</h2>\n<h3>Main Interface</h3>\n<p>The GUI provides tabs for different analysis modes:</p>\n<pre><code>┌─ Navigation ─┐  ┌─ Settings ─┐  ┌─ Visuals ─┐\n│ Planet ID    │  │ Pipeline   │  │ Plots    │\n│ Instrument   │  │  │  │ Results  │  \n│ Obs        │  │  │  │  │\n└──────────────┘  └────────────┘  └──────────┘\n</code></pre>\n<h3>Transit Fit Analysis</h3>\n<p>One feature I'm particularly happy with is the <strong>Transit Fit</strong> tab that lets you:</p>\n<ul>\n<li>🎚️ <strong>Wavelength slider</strong>: Explore transit depths across the spectrum</li>\n<li>📊 <strong>Interactive visualization</strong>: See original light curve, polynomial fit, and depth measurement</li>\n<li>🎯 <strong>Phase highlighting</strong>: Visual indicators for transit start/end</li>\n<li>📏 <strong>Depth annotation</strong>: Numerical values displayed on the plot</li>\n</ul>\n<hr>\n<h2>🚀 Getting Started</h2>\n<h3>Installation (Local)</h3>\n<pre><code>git  https://github.com/jalalirs/arielml\n arielml\npip install -r requirements.txt\npython tools/data_inspector.py\n</code></pre>\n<p><strong>📋 Full documentation and setup instructions</strong>: <a href=\"https://github.com/jalalirs/arielml\" target=\"_blank\">https://github.com/jalalirs/arielml</a></p>\n<hr>\n<h2>🎓 What I Learned</h2>\n<p>Working on this project taught me a lot about:</p>\n<ul>\n<li><strong>Exoplanet transit photometry</strong> (still learning!)</li>\n<li><strong>Data preprocessing pipelines</strong> for astronomical data</li>\n<li><strong>Gaussian processes</strong> for detrending (barely scratching the surface)</li>\n<li><strong>PyQt GUI development</strong> for scientific applications</li>\n<li><strong>Memory management</strong> for large datasets</li>\n</ul>\n<hr>\n<h2>⚠️ Limitations &amp; Disclaimers</h2>\n<ol>\n<li><strong>Not scientifically rigorous</strong>: This is a learning tool, not research-grade software</li>\n<li><strong>Simplified implementations</strong>: I've cut corners to focus on understanding concepts</li>\n<li><strong>Limited validation</strong>: Haven't thoroughly tested against known results</li>\n<li><strong>Performance</strong>: Not optimized for speed or memory efficiency</li>\n<li><strong>Documentation</strong>: Could definitely be better!</li>\n</ol>\n<hr>\n<h2>🤝 Community &amp; Feedback</h2>\n<p>I'd love to hear from the community:</p>\n<ul>\n<li>🐛 <strong>Found bugs?</strong> Please let me know!</li>\n<li>💡 <strong>Suggestions for improvement?</strong> Always welcome!</li>\n<li>📚 <strong>Better resources to learn from?</strong> Please share!</li>\n</ul>\n<hr>",
      "rawMarkdown": "# 🚀 ArielML: A Python Library for Ariel Transit Analysis (Built for Learning!)\n\n![](https://raw.githubusercontent.com/jalalirs/arielml/main/assets/data_inspector_screenshot.png)\n\n**TL;DR**: I created `arielml`, a Python library with GUI tools for exploring the Ariel dataset. It's not groundbreaking science, but it might help newcomers like me understand the data better!\n\n**🔗 Repository**: https://github.com/jalalirs/arielml \n\n---\n\n## 🙋‍♂️ Background (Full Disclosure!)\n\nHi everyone! I'm **not** an astronomer, and I'm definitely **not** an expert in Gaussian processes or exoplanet science. However, I've been following this field as an enthusiast for a while now. While attending the **BEACON conference in Iceland**, I heard about this competition and thought \"hey, this is a great opportunity to practice with real-world exoplanet detection problems - let me dive in!\" 🌌\n\nSo please take everything I share with a grain of salt - this is very much a learning exercise for me, and I'm sure there are much better approaches out there!\n\n---\n\n## 🛠️ What is ArielML?\n\n`arielml` is a Python library I built to help me (and hopefully others) explore and understand the Ariel dataset. It's **heavily inspired** by some excellent work from the community, particularly:\n\n1. **NeurIPS: Non-ML Transit Curve Fitting** by @vitalykudelya  \n   📎 https://www.kaggle.com/code/vitalykudelya/neurips-non-ml-transit-curve-fitting/notebook\n\n2. **Ariel 2nd place submission notebook** by @jeroencottaar  \n   📎 http://kaggle.com/code/jeroencottaar/ariel-2nd-place-submission-notebook\n\n3. **Calibrating and Binning Ariel Data** by @gordonyip  \n   📎 https://www.kaggle.com/code/gordonyip/calibrating-and-binning-ariel-data\n\n**Important**: ArielML is **NOT** an accurate implementation of these original works! I've simplified, modified, and probably broken things while trying to understand the concepts. The original notebooks are the authoritative sources - mine is just a learning tool! \n\n---\n\n## 🎯 Key Features\n\n### 📊 **Data Inspector GUI**\nThe main feature is a PyQt-based GUI that lets you:\n\n- **🔍 Load and visualize** raw sensor data (FGS1 & AIRS-CH0)\n- **⚙️ Step through preprocessing** (calibration, binning, spatial aggregation)\n- **📈 Interactive plotting** with mouse-over coordinates\n- **🔄 Real-time parameter tuning** with immediate visual feedback\n- **📋 Multiple pipeline modes**: Preprocessing, Baseline, and Bayesian\n  - *Note: Baseline pipeline requires both FGS1 and AIRS-CH0 instruments loaded*\n\n### 🧮 **Pipeline Implementations**\n\n**Baseline Pipeline** (simplified from the community notebooks):\n- Preprocessing with configurable binning factors\n- Phase detection for transit start/end\n- Polynomial detrending with optimization\n- Single transit depth for all wavelengths\n- **Requires both FGS1 and AIRS-CH0 instruments** to be loaded\n\n**Bayesian Pipeline** (very basic):\n- MCMC sampling for uncertainty quantification  \n- Gaussian Process detrending\n- Calibration models for bias correction\n\n### 🎨 **Visualization Tools**\n\n- **Light curve plots** with transit phases highlighted\n- **Detector images** showing spatial patterns\n- **Component analysis** plots for preprocessing steps\n- **Transit fit visualization** with wavelength selection\n- **Covariance matrix** heatmaps\n- **Ground truth comparison** when available\n\n---\n\n## 🖼️ Screenshots\n\n### Main Interface\nThe GUI provides tabs for different analysis modes:\n```\n┌─ Navigation ─┐  ┌─ Settings ─┐  ┌─ Visuals ─┐\n│ Planet ID    │  │ Pipeline   │  │ Plots    │\n│ Instrument   │  │ Parameters │  │ Results  │  \n│ Obs ID       │  │ Detrending │  │ Analysis │\n└──────────────┘  └────────────┘  └──────────┘\n```\n\n### Transit Fit Analysis\nOne feature I'm particularly happy with is the **Transit Fit** tab that lets you:\n- 🎚️ **Wavelength slider**: Explore transit depths across the spectrum\n- 📊 **Interactive visualization**: See original light curve, polynomial fit, and depth measurement\n- 🎯 **Phase highlighting**: Visual indicators for transit start/end\n- 📏 **Depth annotation**: Numerical values displayed on the plot\n\n---\n\n## 🚀 Getting Started\n\n### Installation (Local)\n```bash\ngit clone https://github.com/jalalirs/arielml\ncd arielml\npip install -r requirements.txt\npython tools/data_inspector.py\n```\n\n**📋 Full documentation and setup instructions**: https://github.com/jalalirs/arielml\n\n---\n\n## 🎓 What I Learned\n\nWorking on this project taught me a lot about:\n\n- **Exoplanet transit photometry** (still learning!)\n- **Data preprocessing pipelines** for astronomical data\n- **Gaussian processes** for detrending (barely scratching the surface)\n- **PyQt GUI development** for scientific applications\n- **Memory management** for large datasets\n\n---\n\n## ⚠️ Limitations & Disclaimers\n\n1. **Not scientifically rigorous**: This is a learning tool, not research-grade software\n2. **Simplified implementations**: I've cut corners to focus on understanding concepts\n3. **Limited validation**: Haven't thoroughly tested against known results\n4. **Performance**: Not optimized for speed or memory efficiency\n5. **Documentation**: Could definitely be better!\n\n---\n\n## 🤝 Community & Feedback\n\nI'd love to hear from the community:\n\n- 🐛 **Found bugs?** Please let me know!\n- 💡 **Suggestions for improvement?** Always welcome!\n- 📚 **Better resources to learn from?** Please share!\n\n---",
      "votes": 42
    },
    {
      "id": 3269408,
      "postDate": "2025-08-14T12:53:07.110Z",
      "content": "<p>OMG this is super cool!</p>",
      "rawMarkdown": "OMG this is super cool!",
      "votes": 1
    },
    {
      "id": 3256981,
      "postDate": "2025-07-29T19:49:28.087Z",
      "content": "<p>Thanks for sharing, this is a very clear explanation. Super useful!</p>",
      "rawMarkdown": "Thanks for sharing, this is a very clear explanation. Super useful!\n\n"
    },
    {
      "id": 3255224,
      "postDate": "2025-07-28T08:26:18.397Z",
      "content": "<p>Thanks you for your excellent work. This project really helps me, as a beginner, to understand the challenge.</p>",
      "rawMarkdown": "Thanks you for your excellent work. This project really helps me, as a beginner, to understand the challenge."
    },
    {
      "id": 3254877,
      "postDate": "2025-07-27T11:52:31.773Z",
      "content": "<p>Wow, cool!</p>",
      "rawMarkdown": "Wow, cool!"
    },
    {
      "id": 3254819,
      "postDate": "2025-07-27T09:47:28.150Z",
      "content": "<p>Impressive work!</p>",
      "rawMarkdown": "Impressive work!"
    }
  ],
  "comments": [
    {
      "id": 3269408,
      "author_name": "Gordon Yip",
      "author_url": "",
      "post_date": "2025-08-14T12:53:07.110000",
      "content": "<p>OMG this is super cool!</p>",
      "votes": 1,
      "replies": []
    },
    {
      "id": 3256981,
      "author_name": "Daniel Ospina",
      "author_url": "",
      "post_date": "2025-07-29T19:49:28.087000",
      "content": "<p>Thanks for sharing, this is a very clear explanation. Super useful!</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 3255224,
      "author_name": "Long2003",
      "author_url": "",
      "post_date": "2025-07-28T08:26:18.397000",
      "content": "<p>Thanks you for your excellent work. This project really helps me, as a beginner, to understand the challenge.</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 3254877,
      "author_name": "c-number",
      "author_url": "",
      "post_date": "2025-07-27T11:52:31.773000",
      "content": "<p>Wow, cool!</p>",
      "votes": 0,
      "replies": []
    },
    {
      "id": 3254819,
      "author_name": "Jeroen Cottaar",
      "author_url": "",
      "post_date": "2025-07-27T09:47:28.150000",
      "content": "<p>Impressive work!</p>",
      "votes": 0,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "3254569": "# 🚀 ArielML: A Python Library for Ariel Transit Analysis (Built for Learning!)\n\n![](https://raw.githubusercontent.com/jalalirs/arielml/main/assets/data_inspector_screenshot.png)\n\n**TL;DR**: I created `arielml`, a Python library with GUI tools for exploring the Ariel dataset. It's not groundbreaking science, but it might help newcomers like me understand the data better!\n\n**🔗 Repository**: https://github.com/jalalirs/arielml \n\n---\n\n## 🙋‍♂️ Background (Full Disclosure!)\n\nHi everyone! I'm **not** an astronomer, and I'm definitely **not** an expert in Gaussian processes or exoplanet science. However, I've been following this field as an enthusiast for a while now. While attending the **BEACON conference in Iceland**, I heard about this competition and thought \"hey, this is a great opportunity to practice with real-world exoplanet detection problems - let me dive in!\" 🌌\n\nSo please take everything I share with a grain of salt - this is very much a learning exercise for me, and I'm sure there are much better approaches out there!\n\n---\n\n## 🛠️ What is ArielML?\n\n`arielml` is a Python library I built to help me (and hopefully others) explore and understand the Ariel dataset. It's **heavily inspired** by some excellent work from the community, particularly:\n\n1. **NeurIPS: Non-ML Transit Curve Fitting** by @vitalykudelya  \n   📎 https://www.kaggle.com/code/vitalykudelya/neurips-non-ml-transit-curve-fitting/notebook\n\n2. **Ariel 2nd place submission notebook** by @jeroencottaar  \n   📎 http://kaggle.com/code/jeroencottaar/ariel-2nd-place-submission-notebook\n\n3. **Calibrating and Binning Ariel Data** by @gordonyip  \n   📎 https://www.kaggle.com/code/gordonyip/calibrating-and-binning-ariel-data\n\n**Important**: ArielML is **NOT** an accurate implementation of these original works! I've simplified, modified, and probably broken things while trying to understand the concepts. The original notebooks are the authoritative sources - mine is just a learning tool! \n\n---\n\n## 🎯 Key Features\n\n### 📊 **Data Inspector GUI**\nThe main feature is a PyQt-based GUI that lets you:\n\n- **🔍 Load and visualize** raw sensor data (FGS1 & AIRS-CH0)\n- **⚙️ Step through preprocessing** (calibration, binning, spatial aggregation)\n- **📈 Interactive plotting** with mouse-over coordinates\n- **🔄 Real-time parameter tuning** with immediate visual feedback\n- **📋 Multiple pipeline modes**: Preprocessing, Baseline, and Bayesian\n  - *Note: Baseline pipeline requires both FGS1 and AIRS-CH0 instruments loaded*\n\n### 🧮 **Pipeline Implementations**\n\n**Baseline Pipeline** (simplified from the community notebooks):\n- Preprocessing with configurable binning factors\n- Phase detection for transit start/end\n- Polynomial detrending with optimization\n- Single transit depth for all wavelengths\n- **Requires both FGS1 and AIRS-CH0 instruments** to be loaded\n\n**Bayesian Pipeline** (very basic):\n- MCMC sampling for uncertainty quantification  \n- Gaussian Process detrending\n- Calibration models for bias correction\n\n### 🎨 **Visualization Tools**\n\n- **Light curve plots** with transit phases highlighted\n- **Detector images** showing spatial patterns\n- **Component analysis** plots for preprocessing steps\n- **Transit fit visualization** with wavelength selection\n- **Covariance matrix** heatmaps\n- **Ground truth comparison** when available\n\n---\n\n## 🖼️ Screenshots\n\n### Main Interface\nThe GUI provides tabs for different analysis modes:\n```\n┌─ Navigation ─┐  ┌─ Settings ─┐  ┌─ Visuals ─┐\n│ Planet ID    │  │ Pipeline   │  │ Plots    │\n│ Instrument   │  │ Parameters │  │ Results  │  \n│ Obs ID       │  │ Detrending │  │ Analysis │\n└──────────────┘  └────────────┘  └──────────┘\n```\n\n### Transit Fit Analysis\nOne feature I'm particularly happy with is the **Transit Fit** tab that lets you:\n- 🎚️ **Wavelength slider**: Explore transit depths across the spectrum\n- 📊 **Interactive visualization**: See original light curve, polynomial fit, and depth measurement\n- 🎯 **Phase highlighting**: Visual indicators for transit start/end\n- 📏 **Depth annotation**: Numerical values displayed on the plot\n\n---\n\n## 🚀 Getting Started\n\n### Installation (Local)\n```bash\ngit clone https://github.com/jalalirs/arielml\ncd arielml\npip install -r requirements.txt\npython tools/data_inspector.py\n```\n\n**📋 Full documentation and setup instructions**: https://github.com/jalalirs/arielml\n\n---\n\n## 🎓 What I Learned\n\nWorking on this project taught me a lot about:\n\n- **Exoplanet transit photometry** (still learning!)\n- **Data preprocessing pipelines** for astronomical data\n- **Gaussian processes** for detrending (barely scratching the surface)\n- **PyQt GUI development** for scientific applications\n- **Memory management** for large datasets\n\n---\n\n## ⚠️ Limitations & Disclaimers\n\n1. **Not scientifically rigorous**: This is a learning tool, not research-grade software\n2. **Simplified implementations**: I've cut corners to focus on understanding concepts\n3. **Limited validation**: Haven't thoroughly tested against known results\n4. **Performance**: Not optimized for speed or memory efficiency\n5. **Documentation**: Could definitely be better!\n\n---\n\n## 🤝 Community & Feedback\n\nI'd love to hear from the community:\n\n- 🐛 **Found bugs?** Please let me know!\n- 💡 **Suggestions for improvement?** Always welcome!\n- 📚 **Better resources to learn from?** Please share!\n\n---",
    "3269408": "OMG this is super cool!",
    "3256981": "Thanks for sharing, this is a very clear explanation. Super useful!\n\n",
    "3255224": "Thanks you for your excellent work. This project really helps me, as a beginner, to understand the challenge.",
    "3254877": "Wow, cool!",
    "3254819": "Impressive work!"
  }
}