{
  "id": 250485,
  "title": "Applications of machine learning for gravitational wave astrophysics - Data and APIs",
  "url": "/competitions/g2net-gravitational-wave-detection/discussion/250485",
  "author_name": "",
  "post_date": "2021-07-02T22:48:25.807613500Z",
  "votes": 5,
  "comment_count": 3,
  "views": 0,
  "content": "<p>I found this super interesting website from <strong>University of Glasgow's School of Physics and Astronomy</strong>.</p>\n<p><strong>The website states:</strong><br>\nThe aim of the project is to apply and develop machine learning techniques to the data analysis problems associated with gravitational wave astrophysics. The student will initially tackle the compact binary coalescence detection problem and show that a deep neural network can be used to replicate the performance of traditional matched-filtering approaches. This will be followed by an investigation into the feasibility of performing parameter estimation for well modelled gravitational wave signals with the aim of comparing the machine learning with the optimal Bayesian approach. With these tools in hand the student will then extend the new machine learning tools to other modelled gravitational wave signals and detectors (e.g., 3rd generation and space-based). In addition the student will investigate the use of un-supervised (or weakly supervised) machine learning algorithms for the search and characterisation of un-modelled transient gravitational wave signals (bursts).</p>\n<p><strong><a href=\"https://gtr.ukri.org/resources/data.html\" target=\"_blank\">You can get a dataset.</a></strong></p>\n<p><strong><a href=\"https://gtr.ukri.org/resources/api.html\" target=\"_blank\">And use their apis!</a></strong></p>",
  "messages": [
    {
      "id": "1373996",
      "postDate": "07/02/2021 22:48:25",
      "content": "<p>I found this super interesting website from <strong>University of Glasgow's School of Physics and Astronomy</strong>.</p>\n<p><strong>The website states:</strong><br>\nThe aim of the project is to apply and develop machine learning techniques to the data analysis problems associated with gravitational wave astrophysics. The student will initially tackle the compact binary coalescence detection problem and show that a deep neural network can be used to replicate the performance of traditional matched-filtering approaches. This will be followed by an investigation into the feasibility of performing parameter estimation for well modelled gravitational wave signals with the aim of comparing the machine learning with the optimal Bayesian approach. With these tools in hand the student will then extend the new machine learning tools to other modelled gravitational wave signals and detectors (e.g., 3rd generation and space-based). In addition the student will investigate the use of un-supervised (or weakly supervised) machine learning algorithms for the search and characterisation of un-modelled transient gravitational wave signals (bursts).</p>\n<p><strong><a href=\"https://gtr.ukri.org/resources/data.html\" target=\"_blank\">You can get a dataset.</a></strong></p>\n<p><strong><a href=\"https://gtr.ukri.org/resources/api.html\" target=\"_blank\">And use their apis!</a></strong></p>",
      "rawMarkdown": "I found this super interesting website from **University of Glasgow's School of Physics and Astronomy**.\n\n**The website states:**\nThe aim of the project is to apply and develop machine learning techniques to the data analysis problems associated with gravitational wave astrophysics. The student will initially tackle the compact binary coalescence detection problem and show that a deep neural network can be used to replicate the performance of traditional matched-filtering approaches. This will be followed by an investigation into the feasibility of performing parameter estimation for well modelled gravitational wave signals with the aim of comparing the machine learning with the optimal Bayesian approach. With these tools in hand the student will then extend the new machine learning tools to other modelled gravitational wave signals and detectors (e.g., 3rd generation and space-based). In addition the student will investigate the use of un-supervised (or weakly supervised) machine learning algorithms for the search and characterisation of un-modelled transient gravitational wave signals (bursts).\n\n**[You can get a dataset.](https://gtr.ukri.org/resources/data.html)**\n\n**[And use their apis!](https://gtr.ukri.org/resources/api.html)**",
      "votes": null
    },
    {
      "id": "1376967",
      "postDate": "07/05/2021 13:28:27",
      "content": "<p>Hi Charlie, I'm not sure where you found this but it looks like something that I must have written in the last few years. It's either a PhD or Summer project description or advertisement. Do you mind sharing the link so that I can verify that? Also, the links to the dataset and APIs don't look familiar to me. I can assure you that there is no data or code available that is specific to this project description.</p>\n<p>Cheers, Chris</p>",
      "rawMarkdown": "Hi Charlie, I'm not sure where you found this but it looks like something that I must have written in the last few years. It's either a PhD or Summer project description or advertisement. Do you mind sharing the link so that I can verify that? Also, the links to the dataset and APIs don't look familiar to me. I can assure you that there is no data or code available that is specific to this project description.\n\nCheers, Chris",
      "votes": null
    },
    {
      "id": "1377039",
      "postDate": "07/05/2021 14:33:09",
      "content": "<p><a href=\"https://www.kaggle.com/bayeswolf\" target=\"_blank\">@bayeswolf</a> Sure. All the links are in blue in my post. Are they working for you to click on? I found then when I was just google searching for repos. </p>\n<p><a href=\"https://gtr.ukri.org/resources/data.html\" target=\"_blank\">https://gtr.ukri.org/resources/data.html</a></p>",
      "rawMarkdown": "bayeswolf Sure. All the links are in blue in my post. Are they working for you to click on? I found then when I was just google searching for repos. \n\nhttps://gtr.ukri.org/resources/data.html",
      "votes": null
    },
    {
      "id": "1563259",
      "postDate": "10/28/2021 06:52:06",
      "content": "<p>Hey,</p>\n<p>Thank you all for taking part in our competition. The participation has been overwhelmingly positive. We are currently conducting a survey to gauge the demographic and outreach achieved. Kindly spare 2min and fill in this survey <a href=\"https://forms.gle/QP9L16niPexozyhu5\" target=\"_blank\">https://forms.gle/QP9L16niPexozyhu5</a>.</p>\n<p>Thank you all,</p>\n<p>Regards,<br>\nChris</p>",
      "rawMarkdown": "Hey,\n\nThank you all for taking part in our competition. The participation has been overwhelmingly positive. We are currently conducting a survey to gauge the demographic and outreach achieved. Kindly spare 2min and fill in this survey https://forms.gle/QP9L16niPexozyhu5.\n\nThank you all,\n\nRegards,\nChris",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1376967,
      "author_name": "bayeswolf",
      "author_url": "",
      "post_date": "07/05/2021 13:28:27",
      "content": "<p>Hi Charlie, I'm not sure where you found this but it looks like something that I must have written in the last few years. It's either a PhD or Summer project description or advertisement. Do you mind sharing the link so that I can verify that? Also, the links to the dataset and APIs don't look familiar to me. I can assure you that there is no data or code available that is specific to this project description.</p>\n<p>Cheers, Chris</p>",
      "votes": null,
      "replies": [
        {
          "id": 1377039,
          "author_name": "crained",
          "author_url": "",
          "post_date": "07/05/2021 14:33:09",
          "content": "<p><a href=\"https://www.kaggle.com/bayeswolf\" target=\"_blank\">@bayeswolf</a> Sure. All the links are in blue in my post. Are they working for you to click on? I found then when I was just google searching for repos. </p>\n<p><a href=\"https://gtr.ukri.org/resources/data.html\" target=\"_blank\">https://gtr.ukri.org/resources/data.html</a></p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 1563259,
      "author_name": "zerafachris",
      "author_url": "",
      "post_date": "10/28/2021 06:52:06",
      "content": "<p>Hey,</p>\n<p>Thank you all for taking part in our competition. The participation has been overwhelmingly positive. We are currently conducting a survey to gauge the demographic and outreach achieved. Kindly spare 2min and fill in this survey <a href=\"https://forms.gle/QP9L16niPexozyhu5\" target=\"_blank\">https://forms.gle/QP9L16niPexozyhu5</a>.</p>\n<p>Thank you all,</p>\n<p>Regards,<br>\nChris</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1373996": "I found this super interesting website from **University of Glasgow's School of Physics and Astronomy**.\n\n**The website states:**\nThe aim of the project is to apply and develop machine learning techniques to the data analysis problems associated with gravitational wave astrophysics. The student will initially tackle the compact binary coalescence detection problem and show that a deep neural network can be used to replicate the performance of traditional matched-filtering approaches. This will be followed by an investigation into the feasibility of performing parameter estimation for well modelled gravitational wave signals with the aim of comparing the machine learning with the optimal Bayesian approach. With these tools in hand the student will then extend the new machine learning tools to other modelled gravitational wave signals and detectors (e.g., 3rd generation and space-based). In addition the student will investigate the use of un-supervised (or weakly supervised) machine learning algorithms for the search and characterisation of un-modelled transient gravitational wave signals (bursts).\n\n**[You can get a dataset.](https://gtr.ukri.org/resources/data.html)**\n\n**[And use their apis!](https://gtr.ukri.org/resources/api.html)**",
    "1376967": "Hi Charlie, I'm not sure where you found this but it looks like something that I must have written in the last few years. It's either a PhD or Summer project description or advertisement. Do you mind sharing the link so that I can verify that? Also, the links to the dataset and APIs don't look familiar to me. I can assure you that there is no data or code available that is specific to this project description.\n\nCheers, Chris",
    "1377039": "bayeswolf Sure. All the links are in blue in my post. Are they working for you to click on? I found then when I was just google searching for repos. \n\nhttps://gtr.ukri.org/resources/data.html",
    "1563259": "Hey,\n\nThank you all for taking part in our competition. The participation has been overwhelmingly positive. We are currently conducting a survey to gauge the demographic and outreach achieved. Kindly spare 2min and fill in this survey https://forms.gle/QP9L16niPexozyhu5.\n\nThank you all,\n\nRegards,\nChris"
  },
  "source": "meta"
}