神码ai人工智能写作机器人_机器学习人工智能确认了50个新行星

by Ryan Whitwam

瑞安·惠特瓦姆(Ryan Whitwam)

Spotting exoplanets isn’t as simple as pointing a telescope at the sky and picking out the planet-shaped things. The worlds orbiting distant stars are too dim and tiny for that, but we can detect them with the help of planet-hunting satellites like TESS and the dearly departed Kepler. These missions produce a lot of data that someone needs to evaluate, and researchers from the University of Warwick think they can speed it up with AI. To illustrate this, the team has developed a machine learning algorithm that just confirmed 50 exoplanets in observational data.

发现系外行星并不像将望远镜对准天空并挑选出行星状物体那样简单。 为此，世界上绕行的遥远恒星太暗且太小，但是我们可以借助TESS和离世快得多的开普勒等行星搜索卫星来探测到它们。 这些任务产生了很多人需要评估的数据，华威大学的研究人员认为他们可以使用AI加快速度。 为了说明这一点，研究小组开发了一种机器学习算法，该算法仅在观测数据中确认了50颗系外行星。

Astronomers have two methods at their disposal to detect exoplanets. There’s the radial velocity approach, which monitors stars for small counter-movements caused by the gravity of planets. A more sensitive technique, and the one employed by TESS and Kepler, relies on luminance variation in the host star. If a solar system’s plane is aligned correctly, its planets transit in front of the star from our perspective. By monitoring those dips in brightness, we can infer the presence of exoplanets with a high degree of certainty.

天文学家有两种方法可以探测系外行星。 存在径向速度方法，该方法可以监视恒星是否存在由行星引力引起的微小反向运动。 TESS和Kepler使用的一种更灵敏的技术依赖于主星的亮度变化。 如果太阳系的平面正确对齐，那么从我们的角度来看，其行星将在恒星前方移动。 通过监视亮度的下降，我们可以高度确定地推断系外行星的存在。

The problem with the transit method is that it produces a mountain of luminance data for stars, many of which will have no visible exoplanets. It takes a combination of computer analysis and human oversight to identify candidates and confirm their existence. The system developed at the University of Warwick is the first capable of taking candidate exoplanets and performing all the necessary analysis to either confirm their planet status or rule them out. Previous attempts to use AI, like Google’s TensorFlow-based algorithm, can only rank candidates by the likelihood they are real planets.

过渡方法的问题在于，它会产生大量的恒星亮度数据，其中许多恒星没有可见的系外行星。 它需要计算机分析和人工监督相结合来识别候选人并确认其存在。 华威大学开发的系统是第一个能够获取系外行星候选者并执行所有必要分析以确认其行星状态或将其排除的系统。 之前使用AI的尝试(例如Google的基于TensorFlow的算法 )只能根据候选者是真实行星的可能性对其进行排名。

The researchers didn’t just flip a switch and have an AI capable of sifting through data to spot planets. They had to train the neural network with data from confirmed exoplanets and false positives so it could identify those telltale signs in new data. The 50 exoplanets confirmed by the University of Warwick run the gamut from Neptune-sized gas giants to rocky worlds smaller than Earth. It’s particularly difficult to confirm smaller planets using the transit method, so that speaks to the accuracy of the AI.

研究人员不只是翻转开关，而是拥有能够筛选数据以发现行星的AI。 他们必须使用来自已确认的系外行星和假阳性的数据训练神经网络，以便能够识别新数据中的那些明显迹象。 华威大学(University of Warwick)确认的50颗系外行星，范围从海王星大小的天然气巨头到比地球还小的岩石世界。 使用过渡方法确认较小的行星特别困难，因此可以说是AI的准确性。

According to the new study, about a third of all confirmed exoplanets have been identified using a single analytical method, which is not ideal. Even if existing techniques are spotting all the observable exoplanets, we should have more options just for proper validation, say the scientists. They hope to see the new machine learning system evolve as it detects more planets, becoming an important part of the exoplanet exploration process.

根据这项新研究，使用单一分析方法已经确定了大约三分之一已确认的系外行星，这并不理想。 科学家们说，即使现有的技术发现了所有可观测的系外行星，我们也应该有更多的选择来进行正确的验证。 他们希望看到新的机器学习系统随着探测到更多的行星而发展，成为系外行星探索过程的重要组成部分。

Now read:

现在阅读：

• MIT: ‘Snowball Earth’ Came From Huge Drop in Sunlight

  麻省理工学院：“雪球地球”来自阳光下的巨大下落

• NASA: TESS Space Telescope Completes Primary Planet-Hunting Mission

  NASA：TESS太空望远镜完成了主要的星球狩猎任务

• New AI Calculates Distant Planet Orbits 100,000 Times Faster

  新的AI计算遥远行星的速度快100,000倍

Originally published at https://www.extremetech.com on August 26, 2020.

最初于 2020年8月26日 在 https://www.extremetech.com 上 发布 。