{
  "id": 357854,
  "title": "PyCBC Gravitational Wave Toolkit.",
  "url": "/competitions/g2net-detecting-continuous-gravitational-waves/discussion/357854",
  "author_name": "Marília Prata",
  "post_date": "2022-10-05T20:32:43.791000",
  "votes": 5,
  "comment_count": 0,
  "views": 0,
  "content": "<h1>PyCBC open-source Gravitational- Wave Astronomy Toolkit.</h1>\n<p>\"PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals\"</p>\n<p>Authors: C. M. Biwer, Collin D. Capano, Soumi De, Miriam Cabero, Duncan A. Brown, Alexander H. Nitz, V. Raymond - <a href=\"https://doi.org/10.1088/1538-3873/aaef0b\" target=\"_blank\">https://doi.org/10.1088/1538-3873/aaef0b</a></p>\n<p>\"The authors introduced new modules in the open-source PyCBC gravitational- wave astronomy toolkit that implement Bayesian inference for compact-object binary mergers. We review the Bayesian inference methods implemented and describe the structure of the modules. We demonstrate that the PyCBC Inference modules produce unbiased estimates of the parameters of a simulated population of binary black hole mergers. We show that the posterior parameter distributions obtained used our new code agree well with the published estimates for binary black holes in the first LIGO-Virgo observing run.\"</p>\n<p><a href=\"https://doi.org/10.1088/1538-3873/aaef0b\" target=\"_blank\">https://doi.org/10.1088/1538-3873/aaef0b</a></p>\n<p><a href=\"https://arxiv.org/abs/1807.10312\" target=\"_blank\">https://arxiv.org/abs/1807.10312</a></p>\n<h1>PyCBC Tutorials</h1>\n<p><a href=\"https://pycbc.org/pycbc/latest/html/frame.html#introduction\" target=\"_blank\">https://pycbc.org/pycbc/latest/html/frame.html#introduction</a></p>\n<h1>PyCBC on Kaggle</h1>\n<p>Simulated GW  By Giba<br>\n<a href=\"https://www.kaggle.com/code/titericz/simulated-gw\" target=\"_blank\">https://www.kaggle.com/code/titericz/simulated-gw</a></p>\n<p>PyCBC: Making Images By AlexNitz<br>\n<a href=\"https://www.kaggle.com/code/alexnitz/pycbc-making-images\" target=\"_blank\">https://www.kaggle.com/code/alexnitz/pycbc-making-images</a></p>\n<p>PyCBC: Making Images By Rodolphe Lampe<br>\n<a href=\"https://www.kaggle.com/code/rodolphelampe/pycbc-making-images\" target=\"_blank\">https://www.kaggle.com/code/rodolphelampe/pycbc-making-images</a></p>",
  "messages": [
    {
      "id": 1973776,
      "postDate": "2022-10-05T20:32:43.793Z",
      "content": "<h1>PyCBC open-source Gravitational- Wave Astronomy Toolkit.</h1>\n<p>\"PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals\"</p>\n<p>Authors: C. M. Biwer, Collin D. Capano, Soumi De, Miriam Cabero, Duncan A. Brown, Alexander H. Nitz, V. Raymond - <a href=\"https://doi.org/10.1088/1538-3873/aaef0b\" target=\"_blank\">https://doi.org/10.1088/1538-3873/aaef0b</a></p>\n<p>\"The authors introduced new modules in the open-source PyCBC gravitational- wave astronomy toolkit that implement Bayesian inference for compact-object binary mergers. We review the Bayesian inference methods implemented and describe the structure of the modules. We demonstrate that the PyCBC Inference modules produce unbiased estimates of the parameters of a simulated population of binary black hole mergers. We show that the posterior parameter distributions obtained used our new code agree well with the published estimates for binary black holes in the first LIGO-Virgo observing run.\"</p>\n<p><a href=\"https://doi.org/10.1088/1538-3873/aaef0b\" target=\"_blank\">https://doi.org/10.1088/1538-3873/aaef0b</a></p>\n<p><a href=\"https://arxiv.org/abs/1807.10312\" target=\"_blank\">https://arxiv.org/abs/1807.10312</a></p>\n<h1>PyCBC Tutorials</h1>\n<p><a href=\"https://pycbc.org/pycbc/latest/html/frame.html#introduction\" target=\"_blank\">https://pycbc.org/pycbc/latest/html/frame.html#introduction</a></p>\n<h1>PyCBC on Kaggle</h1>\n<p>Simulated GW  By Giba<br>\n<a href=\"https://www.kaggle.com/code/titericz/simulated-gw\" target=\"_blank\">https://www.kaggle.com/code/titericz/simulated-gw</a></p>\n<p>PyCBC: Making Images By AlexNitz<br>\n<a href=\"https://www.kaggle.com/code/alexnitz/pycbc-making-images\" target=\"_blank\">https://www.kaggle.com/code/alexnitz/pycbc-making-images</a></p>\n<p>PyCBC: Making Images By Rodolphe Lampe<br>\n<a href=\"https://www.kaggle.com/code/rodolphelampe/pycbc-making-images\" target=\"_blank\">https://www.kaggle.com/code/rodolphelampe/pycbc-making-images</a></p>",
      "rawMarkdown": "#PyCBC open-source Gravitational- Wave Astronomy Toolkit.\n\n\"PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals\"\n\nAuthors: C. M. Biwer, Collin D. Capano, Soumi De, Miriam Cabero, Duncan A. Brown, Alexander H. Nitz, V. Raymond - https://doi.org/10.1088/1538-3873/aaef0b\n\n\"The authors introduced new modules in the open-source PyCBC gravitational- wave astronomy toolkit that implement Bayesian inference for compact-object binary mergers. We review the Bayesian inference methods implemented and describe the structure of the modules. We demonstrate that the PyCBC Inference modules produce unbiased estimates of the parameters of a simulated population of binary black hole mergers. We show that the posterior parameter distributions obtained used our new code agree well with the published estimates for binary black holes in the first LIGO-Virgo observing run.\"\n\nhttps://doi.org/10.1088/1538-3873/aaef0b\n\nhttps://arxiv.org/abs/1807.10312\n\n#PyCBC Tutorials\n\nhttps://pycbc.org/pycbc/latest/html/frame.html#introduction\n\n#PyCBC on Kaggle\n\nSimulated GW  By Giba\nhttps://www.kaggle.com/code/titericz/simulated-gw\n\nPyCBC: Making Images By AlexNitz\nhttps://www.kaggle.com/code/alexnitz/pycbc-making-images\n\nPyCBC: Making Images By Rodolphe Lampe\nhttps://www.kaggle.com/code/rodolphelampe/pycbc-making-images",
      "votes": 5
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "1973776": "#PyCBC open-source Gravitational- Wave Astronomy Toolkit.\n\n\"PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals\"\n\nAuthors: C. M. Biwer, Collin D. Capano, Soumi De, Miriam Cabero, Duncan A. Brown, Alexander H. Nitz, V. Raymond - https://doi.org/10.1088/1538-3873/aaef0b\n\n\"The authors introduced new modules in the open-source PyCBC gravitational- wave astronomy toolkit that implement Bayesian inference for compact-object binary mergers. We review the Bayesian inference methods implemented and describe the structure of the modules. We demonstrate that the PyCBC Inference modules produce unbiased estimates of the parameters of a simulated population of binary black hole mergers. We show that the posterior parameter distributions obtained used our new code agree well with the published estimates for binary black holes in the first LIGO-Virgo observing run.\"\n\nhttps://doi.org/10.1088/1538-3873/aaef0b\n\nhttps://arxiv.org/abs/1807.10312\n\n#PyCBC Tutorials\n\nhttps://pycbc.org/pycbc/latest/html/frame.html#introduction\n\n#PyCBC on Kaggle\n\nSimulated GW  By Giba\nhttps://www.kaggle.com/code/titericz/simulated-gw\n\nPyCBC: Making Images By AlexNitz\nhttps://www.kaggle.com/code/alexnitz/pycbc-making-images\n\nPyCBC: Making Images By Rodolphe Lampe\nhttps://www.kaggle.com/code/rodolphelampe/pycbc-making-images"
  }
}