{ "id": 523588, "title": "is Unique Dataset ? -- Previous Year Challenges / Solution / Papers", "url": "/competitions/ariel-data-challenge-2024/discussion/523588", "author_name": "SeshuRaju 🧘‍♂️", "post_date": "2024-08-01T16:05:05.603000", "votes": 10, "comment_count": 2, "views": 0, "content": "

\"\"
\n[train/test]/[planet_id]/AIRS-CH0_signal.parquet: signal data from the AIRS-CH0 instrument. Each file contains 11,250 rows of images captured at constant time steps noted in axis_info.parquet file for details of the time steps. Each 32 x 356 image has been flattened into 11392 columns. You can un-flatten the data with numpy.reshape(11250, 32, 356). The instruments generate data as uint16. To restore the full dynamic range you must multiply the data by the matching gain value from [train/test]_adc_info.csv and then add the offset value, also from [train/test]_adc_info.csv.
\n[train/test]/[planet_id]/FGS1_signal.parquet : signal data from the FGS1 instrument. Each file contains 135,000 rows of images at 0.1 second time steps. Each 32x32 image has been flattened into 1024 columns. You can un-flatten the data with numpy.reshape(135000, 32, 32). Similar to AIR-CH0, the data is generated in uint16. To restore its original dynamic range you must multiply the data by the matching gain value from [train/test]_adc_info.csv and then add the offset value, also from [train/test]_adc_info.csv.

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Simulated Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL) data

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2 Stars x 2 instruments (CH0 and FGS1) x 1,000 exoplanets

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\n \n

CV Stategy : 403 exoplanets in train and cv ( time series split ) + 270 exoplanets in cv only ( complete time series )

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\n

Previous year Challenges - 2019 to 2023

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2023 - Solution Code + Paper

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\"\"

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Youtube Link

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\n

Publications

\n

All Publications

", "messages": [ { "id": 2943465, "postDate": "2024-08-01T16:05:05.603Z", "content": "

\"\"
\n[train/test]/[planet_id]/AIRS-CH0_signal.parquet: signal data from the AIRS-CH0 instrument. Each file contains 11,250 rows of images captured at constant time steps noted in axis_info.parquet file for details of the time steps. Each 32 x 356 image has been flattened into 11392 columns. You can un-flatten the data with numpy.reshape(11250, 32, 356). The instruments generate data as uint16. To restore the full dynamic range you must multiply the data by the matching gain value from [train/test]_adc_info.csv and then add the offset value, also from [train/test]_adc_info.csv.
\n[train/test]/[planet_id]/FGS1_signal.parquet : signal data from the FGS1 instrument. Each file contains 135,000 rows of images at 0.1 second time steps. Each 32x32 image has been flattened into 1024 columns. You can un-flatten the data with numpy.reshape(135000, 32, 32). Similar to AIR-CH0, the data is generated in uint16. To restore its original dynamic range you must multiply the data by the matching gain value from [train/test]_adc_info.csv and then add the offset value, also from [train/test]_adc_info.csv.

\n
\n

Simulated Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL) data

\n\n
\n

2 Stars x 2 instruments (CH0 and FGS1) x 1,000 exoplanets

\n
\n \n

CV Stategy : 403 exoplanets in train and cv ( time series split ) + 270 exoplanets in cv only ( complete time series )

\n
\n
\n

Previous year Challenges - 2019 to 2023

\n
\n

2023 - Solution Code + Paper

\n
\n

\"\"

\n

Youtube Link

\n
\n

Publications

\n

All Publications

", "rawMarkdown": "![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F761268%2Fc35dee14257bf3df92c020b85a19812b%2Fposter.webp?generation=1722528413804185&alt=media)\n**[train/test]/[planet_id]/AIRS-CH0_signal.parquet**: signal data from the AIRS-CH0 instrument. Each file contains 11,250 rows of images captured at constant time steps noted in axis_info.parquet file for details of the time steps. Each 32 x 356 image has been flattened into 11392 columns. You can un-flatten the data with numpy.reshape(11250, 32, 356). The instruments generate data as uint16. To restore the full dynamic range you must multiply the data by the matching gain value from [train/test]_adc_info.csv and then add the offset value, also from [train/test]_adc_info.csv.\n**[train/test]/[planet_id]/FGS1_signal.parquet** : signal data from the FGS1 instrument. Each file contains 135,000 rows of images at 0.1 second time steps. Each 32x32 image has been flattened into 1024 columns. You can un-flatten the data with numpy.reshape(135000, 32, 32). Similar to AIR-CH0, the data is generated in uint16. To restore its original dynamic range you must multiply the data by the matching gain value from [train/test]_adc_info.csv and then add the offset value, also from [train/test]_adc_info.csv.\n\n---\n\n# Simulated Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL) data\n- A dozen of the exoplanet simulations in the test set were based directly real exoplanets. All of those cases are ignored for scoring purposes.\n- **All Test set is also simulated as training dataset for scoring**\n\n---\n\n## 2 Stars x 2 instruments (CH0 and FGS1) x 1,000 exoplanets \n> * Mission will gather data on roughly **1,000 exoplanets** by observing them while they transit in front of their host stars\n\n> * **673 exoplanets in Train set** vs **800 exoplanets in Test set** - (**new 327 exoplanets in the test set ?**)\n\n> **CV Stategy** : **403 exoplanets in train and cv ( time series split ) + 270 exoplanets in cv only ( complete time series )**\n\n---\n\n# [Previous year Challenges](https://arielmission.space/index.php/data-challenges/) - 2019 to 2023\n\n---\n\n# [2023 - Solution Code](https://github.com/AstroAI-CfA/Ariel_Data_Challenge_2023_solution/tree/main) + [Paper](https://arxiv.org/pdf/2309.09337)\n\n---\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F761268%2F0273cbeceeab60a3cf30f9f4fcc9afae%2FScreenshot%202024-08-01%20at%209.43.43PM.png?generation=1722528845605285&alt=media) \n# [Youtube Link](https://www.youtube.com/watch?v=Kn4j8ffOQi8&ab_channel=ArielSpaceMission)\n\n---\n# Publications\n# [ All Publications ](https://arielmission.space/index.php/ariel-publications/)", "votes": 10 }, { "id": 2943655, "postDate": "2024-08-01T18:24:00.507Z", "content": "

Thank you

", "rawMarkdown": "Thank you ", "votes": 1 }, { "id": 2949832, "postDate": "2024-08-07T01:57:35.033Z", "content": "

Thank you for your sharing.🥳

", "rawMarkdown": "Thank you for your sharing.🥳", "votes": 1, "isDeleted": true } ], "comments": [ { "id": 2943655, "author_name": "Thopmann", "author_url": "", "post_date": "2024-08-01T18:24:00.507000", "content": "

Thank you

", "votes": 1, "replies": [] }, { "id": 2949832, "author_name": "", "author_url": "", "post_date": "2024-08-07T01:57:35.033000", "content": "

Thank you for your sharing.🥳

", "votes": 1, "replies": [] } ], "raw_markdown_by_id": { "2943465": "![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F761268%2Fc35dee14257bf3df92c020b85a19812b%2Fposter.webp?generation=1722528413804185&alt=media)\n**[train/test]/[planet_id]/AIRS-CH0_signal.parquet**: signal data from the AIRS-CH0 instrument. Each file contains 11,250 rows of images captured at constant time steps noted in axis_info.parquet file for details of the time steps. Each 32 x 356 image has been flattened into 11392 columns. You can un-flatten the data with numpy.reshape(11250, 32, 356). The instruments generate data as uint16. To restore the full dynamic range you must multiply the data by the matching gain value from [train/test]_adc_info.csv and then add the offset value, also from [train/test]_adc_info.csv.\n**[train/test]/[planet_id]/FGS1_signal.parquet** : signal data from the FGS1 instrument. Each file contains 135,000 rows of images at 0.1 second time steps. Each 32x32 image has been flattened into 1024 columns. You can un-flatten the data with numpy.reshape(135000, 32, 32). Similar to AIR-CH0, the data is generated in uint16. To restore its original dynamic range you must multiply the data by the matching gain value from [train/test]_adc_info.csv and then add the offset value, also from [train/test]_adc_info.csv.\n\n---\n\n# Simulated Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL) data\n- A dozen of the exoplanet simulations in the test set were based directly real exoplanets. All of those cases are ignored for scoring purposes.\n- **All Test set is also simulated as training dataset for scoring**\n\n---\n\n## 2 Stars x 2 instruments (CH0 and FGS1) x 1,000 exoplanets \n> * Mission will gather data on roughly **1,000 exoplanets** by observing them while they transit in front of their host stars\n\n> * **673 exoplanets in Train set** vs **800 exoplanets in Test set** - (**new 327 exoplanets in the test set ?**)\n\n> **CV Stategy** : **403 exoplanets in train and cv ( time series split ) + 270 exoplanets in cv only ( complete time series )**\n\n---\n\n# [Previous year Challenges](https://arielmission.space/index.php/data-challenges/) - 2019 to 2023\n\n---\n\n# [2023 - Solution Code](https://github.com/AstroAI-CfA/Ariel_Data_Challenge_2023_solution/tree/main) + [Paper](https://arxiv.org/pdf/2309.09337)\n\n---\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F761268%2F0273cbeceeab60a3cf30f9f4fcc9afae%2FScreenshot%202024-08-01%20at%209.43.43PM.png?generation=1722528845605285&alt=media) \n# [Youtube Link](https://www.youtube.com/watch?v=Kn4j8ffOQi8&ab_channel=ArielSpaceMission)\n\n---\n# Publications\n# [ All Publications ](https://arielmission.space/index.php/ariel-publications/)", "2943655": "Thank you ", "2949832": "Thank you for your sharing.🥳" } }