![\"\"](\"https://i.ibb.co/W0cVVFT/Screenshot-2023-01-27-at-4-28-29-PM.png\")
One thing I have observed is that the detector is often logically split into 3 pieces (for downstream modelling):
\nAs our sensor array data is listed 0 through 5159 (with xyz coordinates), it would be helpful to be able to map the provided sensor IDs directly into the 3 sections of the IceCube Detector. Is this something you can provide?
\nThe below images/pages are from the Deep Learning in Physics exemplified by the Reconstruction of Muon-Neutrino Events in IceCube paper and the Deep Learning in Physics exemplified by the Reconstruction of Muon-Neutrino Events in IceCube paper respectively and illustrate both the geometries and the transformation.
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![\"\"](\"https://i.ibb.co/yS4M8j1/Screenshot-2023-01-27-at-4-30-44-PM.png\")
One thing I have observed is that the detector is often logically split into 3 pieces (for downstream modelling):
\nAs our sensor array data is listed 0 through 5159 (with xyz coordinates), it would be helpful to be able to map the provided sensor IDs directly into the 3 sections of the IceCube Detector. Is this something you can provide?
\nThe below images/pages are from the Deep Learning in Physics exemplified by the Reconstruction of Muon-Neutrino Events in IceCube paper and the Deep Learning in Physics exemplified by the Reconstruction of Muon-Neutrino Events in IceCube paper respectively and illustrate both the geometries and the transformation.
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You can get the 3 groups (IC78, Veto, DeepCore) from the sensor geometry CSV. I used this:
\nsensors = pd.read_csv(INPUT_PATH / )\nsensors[] = \n\n i ((sensors) // ):\n start, end = i * , (i * ) + \n sensors.loc[start:end, ] = i\n\n i (, ):\n start_veto, end_veto = i * , (i * ) + \n start_core, end_core = end_veto + , (i * ) + \nVeto Cap/DeepCore is strings 78-86. The top 10 DOMs are the Veto cap, and the lower 50 DOMs are DeepCore
", "rawMarkdown": "You can get the 3 groups (IC78, Veto, DeepCore) from the sensor geometry CSV. I used this:\n\n```python\nsensors = pd.read_csv(INPUT_PATH / \"sensor_geometry.csv\")\nsensors[\"string\"] = 0\n\nfor i in range(len(sensors) // 60):\n start, end = i * 60, (i * 60) + 60\n sensors.loc[start:end, \"string\"] = i\n\n if i in range(78, 86):\n start_veto, end_veto = i * 60, (i * 60) + 10\n start_core, end_core = end_veto + 1, (i * 60) + 60\n```\nVeto Cap/DeepCore is strings 78-86. The top 10 DOMs are the Veto cap, and the lower 50 DOMs are DeepCore", "votes": null }, { "id": "2118297", "postDate": "01/27/2023 22:54:44", "content": "Wonderful! Thank you so much.
", "rawMarkdown": "Wonderful! Thank you so much.", "votes": null }, { "id": "2119450", "postDate": "01/28/2023 19:14:39", "content": "Wow, I totally missed that the deep core strings have a huge gap!
\nBut yes, I highly recommend using something like:
\nstring_id = sensor_id // \ndepth_id = sensor_id % \nThen you can get deep strings with your preference of sensor_id >= 78*60, or string_id >= 78. And NOW I know to check like so:
\nmain_sensor = (string_id < )\ndeep_veto = (string_id >= ) & (depth_id < )\ndeep_core = (string_id >= ) & (depth_id >= )\nI found one set of data on the ice cube site containing data for an event. That data does not contain sensor string 0 - if you happen to find and use actual data you need to add +1 to get the sensor string id I saw in that real data set.
", "rawMarkdown": "I found one set of data on the ice cube site containing data for an event. That data does not contain sensor string 0 - if you happen to find and use actual data you need to add +1 to get the sensor string id I saw in that real data set.", "votes": null }, { "id": "2119764", "postDate": "01/29/2023 04:48:06", "content": "Good to know. I did notice there was quite a bit of public IceCube data and even a library to interact with it.
\nI’m on my phone RN, but I’ll try to post links here tomorrow.
", "rawMarkdown": "Good to know. I did notice there was quite a bit of public IceCube data and even a library to interact with it.\n\nI’m on my phone RN, but I’ll try to post links here tomorrow.", "votes": null } ], "comments": [ { "id": 2118287, "author_name": "anjum48", "author_url": "", "post_date": "01/27/2023 22:31:26", "content": "You can get the 3 groups (IC78, Veto, DeepCore) from the sensor geometry CSV. I used this:
\nsensors = pd.read_csv(INPUT_PATH / )\nsensors[] = \n\n i ((sensors) // ):\n start, end = i * , (i * ) + \n sensors.loc[start:end, ] = i\n\n i (, ):\n start_veto, end_veto = i * , (i * ) + \n start_core, end_core = end_veto + , (i * ) + \nVeto Cap/DeepCore is strings 78-86. The top 10 DOMs are the Veto cap, and the lower 50 DOMs are DeepCore
", "votes": null, "replies": [ { "id": 2118297, "author_name": "dschettler8845", "author_url": "", "post_date": "01/27/2023 22:54:44", "content": "Wonderful! Thank you so much.
", "votes": null, "replies": [] }, { "id": 2119450, "author_name": "roberthatch", "author_url": "", "post_date": "01/28/2023 19:14:39", "content": "Wow, I totally missed that the deep core strings have a huge gap!
\nBut yes, I highly recommend using something like:
\nstring_id = sensor_id // \ndepth_id = sensor_id % \nThen you can get deep strings with your preference of sensor_id >= 78*60, or string_id >= 78. And NOW I know to check like so:
\nmain_sensor = (string_id < )\ndeep_veto = (string_id >= ) & (depth_id < )\ndeep_core = (string_id >= ) & (depth_id >= )\nI found one set of data on the ice cube site containing data for an event. That data does not contain sensor string 0 - if you happen to find and use actual data you need to add +1 to get the sensor string id I saw in that real data set.
", "votes": null, "replies": [ { "id": 2119764, "author_name": "dschettler8845", "author_url": "", "post_date": "01/29/2023 04:48:06", "content": "Good to know. I did notice there was quite a bit of public IceCube data and even a library to interact with it.
\nI’m on my phone RN, but I’ll try to post links here tomorrow.
", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "2118255": "pellerphys\n\nOne thing I have observed is that the detector is often logically split into 3 pieces (for downstream modelling):\n* The main IceCube array (Referred to as IC78)\n* The upper DeepCore array\n* The lower DeepCore array\n\nAs our sensor array data is listed 0 through 5159 (with xyz coordinates), **it would be helpful to be able to map the provided sensor IDs directly into the 3 sections of the IceCube Detector. Is this something you can provide?**\n\n