![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8979231%2Fd5e5697871587c1424eecb6e9905e49b%2FScreenshot%202025-06-28%20at%203.47.17PM.png?generation=1751105909982686&alt=media\")
Distributions of Star and Planet Physical Parameters
\nRs (Stellar Radius): Most stars have a radius between 0.8 and 1.5 times that of our Sun. It's a right-skewed distribution.
\nMs (Stellar Mass): Similar to radius, most stars are around the mass of our Sun (1.0), with a distribution skewed towards slightly more massive stars.
\nTs (Stellar Temperature): A nice, roughly normal distribution centered around 5800K, which is very similar to our Sun. This means we're mostly dealing with Sun-like stars.
\nMp (Planetary Mass): This is heavily right-skewed. Most planets in the dataset are \"small\" (less than ~2 Earth masses), but there's a long tail of much more massive planets. This could be important for modeling.
\nP (Orbital Period): Appears to be a fairly uniform distribution between ~3 and ~7 days. These are all \"hot\" planets, orbiting very close to their stars.
\nsma (Semi-major Axis): A distribution centered around 10-12 stellar radii. This is the orbital distance.
\ni (Inclination): Tightly clustered between 87 and 90 degrees. This is exactly what we'd expect. An inclination of 90 degrees is a perfect edge-on orbit. We can only observe a transit if the inclination is very close to 90.
![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8979231%2Fe9e449057ab7d5fea77e875feea1b476%2FScreenshot%202025-06-28%20at%203.47.25PM.png?generation=1751105939815816&alt=media\")
Ground Truth Spectrum for Planet ID: 34983
\nThis is the Signal: This is what we are trying to recover. The y-axis shows the \"transit depth,\" which is the fraction of the star's light blocked by the planet.
\nThe \"Wiggles\" are Everything: The plot is not a flat line. The bumps and dips, especially in the red AIRS-CH0 data, are the chemical fingerprints of the planet's atmosphere. For example, the big bump around 3.3 µm and the dip near 2.7 µm indicate that the atmosphere absorbs light differently at those wavelengths. This is the information we need to extract from the noisy raw data.
\nInstrument Separation: We can clearly see the single FGS1 data point (blue) at 0.7 µm is distinct from the detailed AIRS-CH0 spectrum (red) which starts at ~1.95 µm.
\"Differential\" Photometry\"
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8979231%2F25fc5ecf34d995034dbd6c80835d2fed%2FScreenshot%202025-06-28%20at%203.47.40PM.png?generation=1751106000403376&alt=media\")
AIRS-CH0 (Bottom Plot):
\nOut-of-Transit: The flat sections before and after the dip are when the telescope is just observing the star by itself.
\nIngress/Egress: The sharp downward and upward slopes are the beginning (ingress) and end (egress) of the planet passing in front of the star.
\nIn-Transit: The bottom of the \"U\" is the period when the entire planet is in front of the star, blocking a constant amount of light.
\nNoise & Artifacts: Notice the many sharp, positive spikes. These are almost certainly cosmic ray hits on the detector. They are a secondary noise source we must remove.
\nFGS1 (Top Plot): The signal here is more subtle but just as important.
The Transit: If you look closely at the cropped images, you can see a very shallow, wide dip. The FGS1 instrument is capturing the transit in visible light, and the transit depth here is different from the infrared one in AIRS-CH0.
\nSystematic Trend: The entire FGS1 light curve has a gentle, long-term curve. It's not perfectly flat outside of the transit. This is a \"systematic effect\" or \"drift,\" likely due to tiny temperature or pointing changes in the instrument over the long observation. This drift must also be modeled and removed.
\nArtifacts: The FGS1 data also has a couple of very large cosmic ray spikes.
Cleaned pipeline:
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F8979231%2F239e598e48ccda1f5eab3920ba7cf816%2FScreenshot%202025-06-28%20at%203.47.49PM.png?generation=1751106064878065&alt=media\")
Step 1 (Top Plot): Our sigma clipping (red dots) has perfectly identified and ignored the cosmic ray artifacts. The baseline fit (orange line) has correctly identified the stable, out-of-transit flux level. This process is robust.
\nStep 2 (Bottom Plot): This is the result of our hard work. A beautiful, normalized light curve. The y-axis is now directly interpretable: the flux is 1.0 when the star is unobscured. The depth of the transit is now trivial to measure: it's simply 1.0 - (the flux at the bottom of the dip). In this case, the transit depth is roughly 1.0 - 0.982 = 0.018.
link to notebook: https://www.kaggle.com/code/lordpatil/perfect-eda-doesn-t-exist
", "messages": [ { "id": 3234793, "postDate": "2025-06-28T10:25:23.247Z", "content": "
Distributions of Star and Planet Physical Parameters
\nRs (Stellar Radius): Most stars have a radius between 0.8 and 1.5 times that of our Sun. It's a right-skewed distribution.
\nMs (Stellar Mass): Similar to radius, most stars are around the mass of our Sun (1.0), with a distribution skewed towards slightly more massive stars.
\nTs (Stellar Temperature): A nice, roughly normal distribution centered around 5800K, which is very similar to our Sun. This means we're mostly dealing with Sun-like stars.
\nMp (Planetary Mass): This is heavily right-skewed. Most planets in the dataset are \"small\" (less than ~2 Earth masses), but there's a long tail of much more massive planets. This could be important for modeling.
\nP (Orbital Period): Appears to be a fairly uniform distribution between ~3 and ~7 days. These are all \"hot\" planets, orbiting very close to their stars.
\nsma (Semi-major Axis): A distribution centered around 10-12 stellar radii. This is the orbital distance.
\ni (Inclination): Tightly clustered between 87 and 90 degrees. This is exactly what we'd expect. An inclination of 90 degrees is a perfect edge-on orbit. We can only observe a transit if the inclination is very close to 90.
Ground Truth Spectrum for Planet ID: 34983
\nThis is the Signal: This is what we are trying to recover. The y-axis shows the \"transit depth,\" which is the fraction of the star's light blocked by the planet.
\nThe \"Wiggles\" are Everything: The plot is not a flat line. The bumps and dips, especially in the red AIRS-CH0 data, are the chemical fingerprints of the planet's atmosphere. For example, the big bump around 3.3 µm and the dip near 2.7 µm indicate that the atmosphere absorbs light differently at those wavelengths. This is the information we need to extract from the noisy raw data.
\nInstrument Separation: We can clearly see the single FGS1 data point (blue) at 0.7 µm is distinct from the detailed AIRS-CH0 spectrum (red) which starts at ~1.95 µm.
\"Differential\" Photometry\"
\n
AIRS-CH0 (Bottom Plot):
\nOut-of-Transit: The flat sections before and after the dip are when the telescope is just observing the star by itself.
\nIngress/Egress: The sharp downward and upward slopes are the beginning (ingress) and end (egress) of the planet passing in front of the star.
\nIn-Transit: The bottom of the \"U\" is the period when the entire planet is in front of the star, blocking a constant amount of light.
\nNoise & Artifacts: Notice the many sharp, positive spikes. These are almost certainly cosmic ray hits on the detector. They are a secondary noise source we must remove.
\nFGS1 (Top Plot): The signal here is more subtle but just as important.
The Transit: If you look closely at the cropped images, you can see a very shallow, wide dip. The FGS1 instrument is capturing the transit in visible light, and the transit depth here is different from the infrared one in AIRS-CH0.
\nSystematic Trend: The entire FGS1 light curve has a gentle, long-term curve. It's not perfectly flat outside of the transit. This is a \"systematic effect\" or \"drift,\" likely due to tiny temperature or pointing changes in the instrument over the long observation. This drift must also be modeled and removed.
\nArtifacts: The FGS1 data also has a couple of very large cosmic ray spikes.
Cleaned pipeline:
\n
Step 1 (Top Plot): Our sigma clipping (red dots) has perfectly identified and ignored the cosmic ray artifacts. The baseline fit (orange line) has correctly identified the stable, out-of-transit flux level. This process is robust.
\nStep 2 (Bottom Plot): This is the result of our hard work. A beautiful, normalized light curve. The y-axis is now directly interpretable: the flux is 1.0 when the star is unobscured. The depth of the transit is now trivial to measure: it's simply 1.0 - (the flux at the bottom of the dip). In this case, the transit depth is roughly 1.0 - 0.982 = 0.018.
link to notebook: https://www.kaggle.com/code/lordpatil/perfect-eda-doesn-t-exist
", "rawMarkdown": "\n\n**Distributions of Star and Planet Physical Parameters**\n\nRs (Stellar Radius): Most stars have a radius between 0.8 and 1.5 times that of our Sun. It's a right-skewed distribution.\nMs (Stellar Mass): Similar to radius, most stars are around the mass of our Sun (1.0), with a distribution skewed towards slightly more massive stars.\nTs (Stellar Temperature): A nice, roughly normal distribution centered around 5800K, which is very similar to our Sun. This means we're mostly dealing with Sun-like stars.\nMp (Planetary Mass): This is heavily right-skewed. Most planets in the dataset are \"small\" (less than ~2 Earth masses), but there's a long tail of much more massive planets. This could be important for modeling.\nP (Orbital Period): Appears to be a fairly uniform distribution between ~3 and ~7 days. These are all \"hot\" planets, orbiting very close to their stars.\nsma (Semi-major Axis): A distribution centered around 10-12 stellar radii. This is the orbital distance.\ni (Inclination): Tightly clustered between 87 and 90 degrees. This is exactly what we'd expect. An inclination of 90 degrees is a perfect edge-on orbit. We can only observe a transit if the inclination is very close to 90.\n\n\n\n**Ground Truth Spectrum for Planet ID: 34983**\n\n\nThis is the Signal: This is what we are trying to recover. The y-axis shows the \"transit depth,\" which is the fraction of the star's light blocked by the planet.\nThe \"Wiggles\" are Everything: The plot is not a flat line. The bumps and dips, especially in the red AIRS-CH0 data, are the chemical fingerprints of the planet's atmosphere. For example, the big bump around 3.3 µm and the dip near 2.7 µm indicate that the atmosphere absorbs light differently at those wavelengths. This is the information we need to extract from the noisy raw data.\nInstrument Separation: We can clearly see the single FGS1 data point (blue) at 0.7 µm is distinct from the detailed AIRS-CH0 spectrum (red) which starts at ~1.95 µm.\n\n\n**\"Differential\" Photometry\"**\n\n\n\nAIRS-CH0 (Bottom Plot): \n\nOut-of-Transit: The flat sections before and after the dip are when the telescope is just observing the star by itself.\nIngress/Egress: The sharp downward and upward slopes are the beginning (ingress) and end (egress) of the planet passing in front of the star.\nIn-Transit: The bottom of the \"U\" is the period when the entire planet is in front of the star, blocking a constant amount of light.\nNoise & Artifacts: Notice the many sharp, positive spikes. These are almost certainly cosmic ray hits on the detector. They are a secondary noise source we must remove.\nFGS1 (Top Plot): The signal here is more subtle but just as important.\n\nThe Transit: If you look closely at the cropped images, you can see a very shallow, wide dip. The FGS1 instrument is capturing the transit in visible light, and the transit depth here is different from the infrared one in AIRS-CH0.\nSystematic Trend: The entire FGS1 light curve has a gentle, long-term curve. It's not perfectly flat outside of the transit. This is a \"systematic effect\" or \"drift,\" likely due to tiny temperature or pointing changes in the instrument over the long observation. This drift must also be modeled and removed.\nArtifacts: The FGS1 data also has a couple of very large cosmic ray spikes.\n\n**Cleaned pipeline:**\n\n\n\nStep 1 (Top Plot): Our sigma clipping (red dots) has perfectly identified and ignored the cosmic ray artifacts. The baseline fit (orange line) has correctly identified the stable, out-of-transit flux level. This process is robust.\nStep 2 (Bottom Plot): This is the result of our hard work. A beautiful, normalized light curve. The y-axis is now directly interpretable: the flux is 1.0 when the star is unobscured. The depth of the transit is now trivial to measure: it's simply 1.0 - (the flux at the bottom of the dip). In this case, the transit depth is roughly 1.0 - 0.982 = 0.018.\n\nlink to notebook: https://www.kaggle.com/code/lordpatil/perfect-eda-doesn-t-exist", "votes": 23 }, { "id": 3236806, "postDate": "2025-06-30T16:40:22.237Z", "content": "\n\nRs (Stellar Radius): Most stars have a radius between 0.8 and 1.5 times that of our Sun. It's a right-skewed distribution.
\n
\n Ms (Stellar Mass): Similar to radius, most stars are around the mass of our Sun (1.0), with a distribution skewed towards slightly more massive stars.
\n Ts (Stellar Temperature): A nice, roughly normal distribution centered around 5800K, which is very similar to our Sun. This means we're mostly dealing with Sun-like stars.
Hmm, are the competition organizers looking for a second Earth?
", "rawMarkdown": ">Rs (Stellar Radius): Most stars have a radius between 0.8 and 1.5 times that of our Sun. It's a right-skewed distribution.\nMs (Stellar Mass): Similar to radius, most stars are around the mass of our Sun (1.0), with a distribution skewed towards slightly more massive stars.\nTs (Stellar Temperature): A nice, roughly normal distribution centered around 5800K, which is very similar to our Sun. This means we're mostly dealing with Sun-like stars.\n\nHmm, are the competition organizers looking for a second Earth?", "votes": 4 }, { "id": 3254080, "postDate": "2025-07-25T19:01:37.687Z", "content": "I was scared by the data until I'd done the EDA…Now I'm terrified.
", "rawMarkdown": "I was scared by the data until I'd done the EDA...Now I'm terrified.", "votes": 1 }, { "id": 3235490, "postDate": "2025-06-29T10:00:52.083Z", "content": "very nice walkthrough :)!
", "rawMarkdown": "very nice walkthrough :)!\n", "votes": 2 }, { "id": 3253495, "postDate": "2025-07-24T19:32:35.167Z", "content": "Great EDA! Thanks for sharing.
\nI have a question - is the transit depth the difference between out-of-transit and in-transit: a) right after entering in-transit (red), b) maximum in-transit delta at mid point (yellow)? or c) average in-transit flux?
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F22052344%2F92baba4968afb1b0c11c29fe1c870bcb%2Fimg.png?generation=1753385402600006&alt=media\")
Hi ! The transit depth is indeed the difference between out-of-transit and in transit, the out of transit should be the continuum. while the in transit is the trough. Without the presence of limb darkening, we will normally expect any point in the trough to be a good estimation of the dip, However, with limb darkening, it has a round trough, which makes it hard to estimate the true transit depth.
Thank you for your comment! I'm still not sure how to deal with limb darkening though. Should I correct it \"upwards\" (red) or \"downwards\" (yellow)? Or closer to an average?
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F22052344%2F3706bfe4bd1f6040aacd1cc6f63def32%2Fimg.png?generation=1753457825579728&alt=media\")
Hello, can you explain a little more about the data? train.csv : what does the wl_1.. 283 mean, is it the flux or strength of that wavelength for the planet? in the simulation data, one planet will only associate with one star ? based on what rules or physics the simulation data is generated? does the task of prediction of the wavelength of a planet atmosphere has is just a reverse to find how the simulated data generated, then you can predict the spectrum?
", "rawMarkdown": "Hello, can you explain a little more about the data? train.csv : what does the wl_1.. 283 mean, is it the flux or strength of that wavelength for the planet? in the simulation data, one planet will only associate with one star ? based on what rules or physics the simulation data is generated? does the task of prediction of the wavelength of a planet atmosphere has is just a reverse to find how the simulated data generated, then you can predict the spectrum?", "replies": [ { "id": 3240428, "postDate": "2025-07-03T19:30:11.443Z", "content": "Hello, Please find my responses below:
\nThey are the transit depth: the fraction of starlight blocked by the planet's atmosphere at a specific wavelength. A higher value means the atmosphere absorbs more light at that color. The pattern of these values is the chemical \"fingerprint\" you're trying to find.
Yes. Each planet_id represents a unique planet orbiting a single host star. However, some planets may have multiple observation files
The simulation is highly realistic, combining two main parts:
Astronomy: A planet transiting a star, with an atmosphere that absorbs light based on its chemical makeup.
\nInstrument Effects: Realistic telescope noise, such as detector drift, cosmic ray hits, and the \"up-the-ramp\" reading pattern, which you must remove.
Thanks for the explanation.
", "rawMarkdown": "Thanks for the explanation. " } ] } ] } ], "comments": [ { "id": 3236806, "author_name": "Pavel Orlov", "author_url": "", "post_date": "2025-06-30T16:40:22.237000", "content": "\n\nRs (Stellar Radius): Most stars have a radius between 0.8 and 1.5 times that of our Sun. It's a right-skewed distribution.
\n
\n Ms (Stellar Mass): Similar to radius, most stars are around the mass of our Sun (1.0), with a distribution skewed towards slightly more massive stars.
\n Ts (Stellar Temperature): A nice, roughly normal distribution centered around 5800K, which is very similar to our Sun. This means we're mostly dealing with Sun-like stars.
Hmm, are the competition organizers looking for a second Earth?
", "votes": 4, "replies": [] }, { "id": 3254080, "author_name": "DennisSakva", "author_url": "", "post_date": "2025-07-25T19:01:37.687000", "content": "I was scared by the data until I'd done the EDA…Now I'm terrified.
", "votes": 1, "replies": [] }, { "id": 3235490, "author_name": "Gordon Yip", "author_url": "", "post_date": "2025-06-29T10:00:52.083000", "content": "very nice walkthrough :)!
", "votes": 2, "replies": [] }, { "id": 3253495, "author_name": "Natan Labarrère", "author_url": "", "post_date": "2025-07-24T19:32:35.167000", "content": "Great EDA! Thanks for sharing.
\nI have a question - is the transit depth the difference between out-of-transit and in-transit: a) right after entering in-transit (red), b) maximum in-transit delta at mid point (yellow)? or c) average in-transit flux?
\n
Hi ! The transit depth is indeed the difference between out-of-transit and in transit, the out of transit should be the continuum. while the in transit is the trough. Without the presence of limb darkening, we will normally expect any point in the trough to be a good estimation of the dip, However, with limb darkening, it has a round trough, which makes it hard to estimate the true transit depth.
Thank you for your comment! I'm still not sure how to deal with limb darkening though. Should I correct it \"upwards\" (red) or \"downwards\" (yellow)? Or closer to an average?
\n
Hello, can you explain a little more about the data? train.csv : what does the wl_1.. 283 mean, is it the flux or strength of that wavelength for the planet? in the simulation data, one planet will only associate with one star ? based on what rules or physics the simulation data is generated? does the task of prediction of the wavelength of a planet atmosphere has is just a reverse to find how the simulated data generated, then you can predict the spectrum?
", "votes": 0, "replies": [ { "id": 3240428, "author_name": "Chirag Patil", "author_url": "", "post_date": "2025-07-03T19:30:11.443000", "content": "Hello, Please find my responses below:
\nThey are the transit depth: the fraction of starlight blocked by the planet's atmosphere at a specific wavelength. A higher value means the atmosphere absorbs more light at that color. The pattern of these values is the chemical \"fingerprint\" you're trying to find.
Yes. Each planet_id represents a unique planet orbiting a single host star. However, some planets may have multiple observation files
The simulation is highly realistic, combining two main parts:
Astronomy: A planet transiting a star, with an atmosphere that absorbs light based on its chemical makeup.
\nInstrument Effects: Realistic telescope noise, such as detector drift, cosmic ray hits, and the \"up-the-ramp\" reading pattern, which you must remove.
Thanks for the explanation.
", "votes": 0, "replies": [] } ] } ] } ], "raw_markdown_by_id": { "3234793": "\n\n**Distributions of Star and Planet Physical Parameters**\n\nRs (Stellar Radius): Most stars have a radius between 0.8 and 1.5 times that of our Sun. It's a right-skewed distribution.\nMs (Stellar Mass): Similar to radius, most stars are around the mass of our Sun (1.0), with a distribution skewed towards slightly more massive stars.\nTs (Stellar Temperature): A nice, roughly normal distribution centered around 5800K, which is very similar to our Sun. This means we're mostly dealing with Sun-like stars.\nMp (Planetary Mass): This is heavily right-skewed. Most planets in the dataset are \"small\" (less than ~2 Earth masses), but there's a long tail of much more massive planets. This could be important for modeling.\nP (Orbital Period): Appears to be a fairly uniform distribution between ~3 and ~7 days. These are all \"hot\" planets, orbiting very close to their stars.\nsma (Semi-major Axis): A distribution centered around 10-12 stellar radii. This is the orbital distance.\ni (Inclination): Tightly clustered between 87 and 90 degrees. This is exactly what we'd expect. An inclination of 90 degrees is a perfect edge-on orbit. We can only observe a transit if the inclination is very close to 90.\n\n\n\n**Ground Truth Spectrum for Planet ID: 34983**\n\n\nThis is the Signal: This is what we are trying to recover. The y-axis shows the \"transit depth,\" which is the fraction of the star's light blocked by the planet.\nThe \"Wiggles\" are Everything: The plot is not a flat line. The bumps and dips, especially in the red AIRS-CH0 data, are the chemical fingerprints of the planet's atmosphere. For example, the big bump around 3.3 µm and the dip near 2.7 µm indicate that the atmosphere absorbs light differently at those wavelengths. This is the information we need to extract from the noisy raw data.\nInstrument Separation: We can clearly see the single FGS1 data point (blue) at 0.7 µm is distinct from the detailed AIRS-CH0 spectrum (red) which starts at ~1.95 µm.\n\n\n**\"Differential\" Photometry\"**\n\n\n\nAIRS-CH0 (Bottom Plot): \n\nOut-of-Transit: The flat sections before and after the dip are when the telescope is just observing the star by itself.\nIngress/Egress: The sharp downward and upward slopes are the beginning (ingress) and end (egress) of the planet passing in front of the star.\nIn-Transit: The bottom of the \"U\" is the period when the entire planet is in front of the star, blocking a constant amount of light.\nNoise & Artifacts: Notice the many sharp, positive spikes. These are almost certainly cosmic ray hits on the detector. They are a secondary noise source we must remove.\nFGS1 (Top Plot): The signal here is more subtle but just as important.\n\nThe Transit: If you look closely at the cropped images, you can see a very shallow, wide dip. The FGS1 instrument is capturing the transit in visible light, and the transit depth here is different from the infrared one in AIRS-CH0.\nSystematic Trend: The entire FGS1 light curve has a gentle, long-term curve. It's not perfectly flat outside of the transit. This is a \"systematic effect\" or \"drift,\" likely due to tiny temperature or pointing changes in the instrument over the long observation. This drift must also be modeled and removed.\nArtifacts: The FGS1 data also has a couple of very large cosmic ray spikes.\n\n**Cleaned pipeline:**\n\n\n\nStep 1 (Top Plot): Our sigma clipping (red dots) has perfectly identified and ignored the cosmic ray artifacts. The baseline fit (orange line) has correctly identified the stable, out-of-transit flux level. This process is robust.\nStep 2 (Bottom Plot): This is the result of our hard work. A beautiful, normalized light curve. The y-axis is now directly interpretable: the flux is 1.0 when the star is unobscured. The depth of the transit is now trivial to measure: it's simply 1.0 - (the flux at the bottom of the dip). In this case, the transit depth is roughly 1.0 - 0.982 = 0.018.\n\nlink to notebook: https://www.kaggle.com/code/lordpatil/perfect-eda-doesn-t-exist", "3236806": ">Rs (Stellar Radius): Most stars have a radius between 0.8 and 1.5 times that of our Sun. It's a right-skewed distribution.\nMs (Stellar Mass): Similar to radius, most stars are around the mass of our Sun (1.0), with a distribution skewed towards slightly more massive stars.\nTs (Stellar Temperature): A nice, roughly normal distribution centered around 5800K, which is very similar to our Sun. This means we're mostly dealing with Sun-like stars.\n\nHmm, are the competition organizers looking for a second Earth?", "3254080": "I was scared by the data until I'd done the EDA...Now I'm terrified.", "3235490": "very nice walkthrough :)!\n", "3253495": "Great EDA! Thanks for sharing.\nI have a question - is the transit depth the difference between out-of-transit and in-transit: a) right after entering in-transit (red), b) maximum in-transit delta at mid point (yellow)? or c) average in-transit flux?\n", "3240420": "Hello, can you explain a little more about the data? train.csv : what does the wl_1.. 283 mean, is it the flux or strength of that wavelength for the planet? in the simulation data, one planet will only associate with one star ? based on what rules or physics the simulation data is generated? does the task of prediction of the wavelength of a planet atmosphere has is just a reverse to find how the simulated data generated, then you can predict the spectrum?" } }