摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑-研究人员详细介绍了机器学习的新数据。根据NewsRx记者从英国伦敦发回的新闻报道，研究表明：“正确的压力展宽对于模拟大气中的辐射传输至关重要，然而，对于预期存在于外行星大气中的许多奇异分子，缺乏数据。在此，我们探索现代机器学习方法，为外行星数据库中的大量分子大量产生压力展宽参数。”这项研究的财政支持者包括欧洲研究理事会(ERC)，STFC培训拨款。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting originating in London, United Kingdom, by NewsRx journalists, research stated, “Correct pressure broadening is essential for modelling radiative transfer in atmospheres, however data are lacking for th e many exotic molecules expected in exoplanetary atmospheres. Here we explore mo dern machine learning methods to mass produce pressure broadening parameters for a large number of molecules in the ExoMol data base.” Financial supporters for this research include European Research Council (ERC), STFC training grant.
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