第1799期:Scientists Release Improved Version of First Black Hole Image

Scientists have released a more detailed version of the first image of a black hole. That first image, released four years ago, showed a blurry, round-shaped orange object. Now, researchers have used machine learning methods to create an improved picture. The new image was recently published in the Astrophysical Journal Letters. The same shape remains as in the first image, but it has a narrower ring and sharper resolution. Scientists have said the black hole in the image sits at the center of a galaxy called M87, more than 53 million light-years from Earth. A light year is the distance light travels in a year --- about 9.5 trillion kilometers. The mass of the black hole is 6.5 billion times greater than that of Earth’s sun.科学家们发布了第一张黑洞图像的更详细版本。四年前发布的第一张图片显示了一个模糊的圆形橙色物体。现在，研究人员已经使用机器学习方法来创建改进的图片。这张新图片最近发表在《天体物理学杂志快报》上。与第一张图片中的形状相同，但它的环更窄，分辨率更清晰。科学家们表示，图像中的黑洞位于一个名为 M87 的星系中心，距离地球超过 5300 万光年。一光年是光在一年内传播的距离——大约 9.5 万亿公里。黑洞的质量是地球太阳的 65 亿倍。A network of radio telescopes around the world gathered the data used to make the image. But even with many telescopes working together, holes remained in the data. In the latest study, scientists depended on the same data, but used machine learning methods to fill in the missing information. The resulting picture looks similar to the image, but with a thinner “doughnut” and a darker center, the researchers said. “For me, it feels like we’re really seeing it for the first time,” said the lead writer of the study, Lia Medeiros. She is an astrophysicist at the Institute for Advanced Study in New Jersey. She said it was the first time the team had used machine learning to fill in the data holes.世界各地的射电望远镜网络收集了用于制作图像的数据。但即使有许多望远镜一起工作，数据中仍然存在漏洞。在最新的研究中，科学家们依赖于相同的数据，但使用机器学习方法来填补缺失的信息。研究人员说，由此产生的图片看起来与图像相似，但“甜甜圈”更薄，中心更暗。 “对我来说，感觉就像我们第一次真正看到它，”该研究的主要作者 Lia Medeiros 说。她是新泽西高等研究院的天体物理学家。她说，这是该团队第一次使用机器学习来填补数据漏洞。With a clearer picture, researchers hope to learn more about the black hole’s properties and gravity in future studies. Medeiros said the team also plans to use machine learning on other images of space objects. This could include the black hole at the center of our galaxy, the Milky Way. The study's four writers are members of the Event Horizon Telescope (EHT) project. It is an international effort begun in 2012 with the goal of directly observing a black hole's nearby environment. A black hole's event horizon is the point beyond which anything - stars, planets, gas, dust and all forms of electromagnetic radiation – can escape. Dimitrios Psaltis is an astrophysicist at Georgia Institute of Technology in Atlanta, Georgia. He told Reuters news agency the main reason the first image had many gaps is because of where the observing telescopes sit. The telescopes operate from the tops of mountains and “are few and far apart from each other,” Psaltis said.有了更清晰的图像，研究人员希望在未来的研究中更多地了解黑洞的特性和引力。 Medeiros 说，该团队还计划在其他空间物体图像上使用机器学习。这可能包括我们银河系中心的黑洞，即银河系。该研究的四位作者是事件视界望远镜 (EHT) 项目的成员。这是 2012 年开始的一项国际努力，目标是直接观察黑洞附近的环境。黑洞的事件视界是任何东西——恒星、行星、气体、尘埃和所有形式的电磁辐射——都可以逃逸的点。 Dimitrios Psaltis 是佐治亚州亚特兰大市佐治亚理工学院的天体物理学家。他告诉路透社，第一张图片有很多空隙的主要原因是观测望远镜所在的位置。 Psaltis 说，这些望远镜在山顶上运行，“数量很少，而且彼此之间距离很远”。As a result, the telescope system has a lot of 'holes' and scientists can now use machine learning methods to fill in those gaps, he added. "The image we report in the new paper is the most accurate representation of the black hole image that we can obtain with our globe-wide telescope," Psaltis said.因此，望远镜系统有很多“漏洞”，科学家现在可以使用机器学习方法来填补这些空白，他补充说。 Psaltis 说：“我们在新论文中报道的图像是我们用全球望远镜可以获得的最准确的黑洞图像。”