How artificial intelligence is changing astronomy（原文链接）
——Machine learning has become an essential piece of astronomers’ toolkits.
人工智能如何改变天文学（英文翻译）
——机器学习已经成为天文学家工具包中必不可少的一部分。
原文：
When most people picture an astronomer, they think of a lone person sitting on top of a mountain, peering into a massive telescope. Of course, that image is out of date: Digital cameras have long since done away with the need to actually look though a telescope.
But now the face of astronomy is changing again. With the advent of more powerful computers and sky surveys that generate unimaginable quantities of data, artificial intelligence is the go-to tool for the keen researcher of space. But where is all of this data coming from? And how can computers help us learn about the universe?
翻译：
当大多数人想到天文学家时，他们想到的是一个独自坐在山顶上的人，凝视着巨大的望远镜。事实上，这种印象已经过时了：数码相机早就不需要通过望远镜来进行观察了。
但如今，天文学又面临着新的变化。随着更强大的计算机和天空勘探的出现，产生了庞大的海量数据，人工智能则成为了热衷于太空研究的人的首选工具。但这些数据都是从哪里来的呢？计算机又如何帮助我们了解宇宙呢?
原文：
AI’s appetite for data
Chances are you’ve heard the terms “artificial intelligence” and “machine learning” thrown around recently, and while they are often used together, they actually refer to different things. Artificial intelligence (AI) is a term used to describe any kind of computational behavior that mimics the way humans think and perform tasks. Machine learning (ML) is a little more specific: It’s a family of technologies that learn to make predictions and decisions based on vast quantities of historical data. Crucially, ML creates models which exhibit behavior that is not pre-programmed, but learned from the data used to train it.
The facial recognition in your smartphone, the spam filter in your emails, and the ability of digital assistants like Siri or Alexa to understand speech are all examples of machine learning being used in the real world. Many of these technologies are now being used by astronomers to investigate the mysteries of space and time. Astronomy and machine learning are a match made in the heavens, because if there’s one thing astronomers have too much of — and ML models can’t get enough of — it’s data.
We’re all familiar with megabytes (MB), gigabytes (GB), and terabytes (TB), but data at that scale is old news in astronomy. These days, we’re interested in petabytes (PB). A petabyte is about one thousand TB, a million GB, or a billion MB. It would take around 10 PB of storage to hold every single feature-length movie ever made in 4K resolution — and it would take over a hundred years to watch them all.
The Vera C. Rubin Observatory, a new telescope under construction in Chile, will be tasked with mapping the entire night sky in unprecedented detail, every single night. Over a 10-year survey, Vera Rubin will produce about 60 PB of raw data — studying everything from asteroids in our solar system, to galaxies in the distant universe. No human being could ever hope to analyze all that data — and that’s from just one of the next-generation observatories being built, so the race is on among astronomers in every field to find new ways to leverage the power of AI.
翻译：
人工智能对数据的需求
最近你可能听说过“人工智能”（AI）和“机器学习”（ML）这两个术语，虽然它们经常被放在一起使用，但实际上它们指的是不同的东西。人工智能(AI)是一个术语，用于描述任何模仿人类思维和执行任务方式的计算行为。机器学习(ML)更具体一点：它是一组根据大量历史数据学习做出预测和决策的技术。至关重要的是，机器学习创建的模型显示的行为不是预先编程的，而是从用于训练它的数据中学习的。
智能手机中的面部识别、电子邮件中的垃圾邮件过滤器，以及Siri或Alexa等数字助理理解语音的能力，都是机器学习在现实世界中应用的例子。天文学家现在正在利用其中的许多技术来研究空间和时间的奥秘。天文学和机器学习实乃天作之合，因为如果有一样东西天文学家拥有的太多——而机器学习模型又往往不够的——那就是数据。
我们都熟悉兆字节(MB)、吉字节(GB)和兆字节(TB)，但这种规模的数据在天文学上已经不是什么新鲜事了。如今，我们天文学感兴趣的是拍字节(PB)。1拍字节大约是1000 TB、100万GB或10亿MB。要保存一部4K分辨率的长篇视频，大约需要10PB的存储空间——而要把它看完，可能需要100多年的时间。
Vera C. Rubin天文台是智利正在建造的新望远镜，它的任务是绘制每个晚上的全天星空图，图的精细度是前所未有的。通过一项超过10年的调查，天文台将产生约60 PB的原始数据，研究范围将包括太阳系内的小行星，及远在外太空的其他星系。没有人能指望分析所有的数据——而这只是正在建造的下一代天文台之一，所以各个领域的天文学家都在竞相寻找利用人工智能力量的新方法。
原文：
Planet hunters
One area of astronomy where AI has made a significant impact is in the search for exoplanets. There are many ways to look for their signals, but the most productive methods with current technology usually involve studying the variation of a star’s brightness over time. If a star’s light curve shows a characteristic dimming, it could be a sure sign of a planet transiting in front of the host star. Conversely, a phenomenon called gravitational microlensing can cause a large spike in a star’s brightness, when the exoplanet’s gravity acts as a lens and magnifies a more distant star along the line of sight. Detecting these dips and spikes means sifting through millions of light curves, studiously collected by space telescopes like NASA’s Kepler and TESS (Transiting Exoplanet Survey Satellite).
Using the huge libraries of observed light curves, astronomers have been able to develop ML-based models that can outperform humans in the search for exoplanets. But AI can do much more than just find exoplanets: It can also lead astronomers to new insights into how those techniques work.
In a paper published May 23 in Nature Astronomy, a team of researchers reported that ML algorithms had helped them discover a more elegant understanding of exoplanet microlensing, unifying multiple interpretations of how the exoplanet’s configuration with its host star might vary. The report came just months after researchers at DeepMind reported in Nature new AI-aided fundamental insights into mathematics.
Astronomers also hope that in the near future, machine learning will help them identify which planets might be habitable. Using next-generation observatories like the Nancy Grace Roman Telescope and James Webb Space Telescope (JWST), astronomers intend to use ML to detect water, ice, and snow on rocky planets.
翻译：
行星猎人
人工智能对天文学产生重大影响的一个领域是寻找系外行星。寻找它们的信号有很多方法，但以目前的技术，最有效的方法通常是研究恒星的亮度随时间的变化。如果一颗恒星的光曲线显现出有特征性的变暗，这可能是一颗行星在主星前面过境的确切迹象。相反，当沿着视线方向看远处的恒星时，系外行星的引力通常会起到透镜的作用，而这种称为引力透镜的现象会导致恒星亮度大幅上升。探测这些起伏现象意味着要筛选数百万条光曲线，这些光曲线则是由NASA的开普勒和TESS（凌星系外行星巡天卫星）等太空望远镜收集的数据。
利用观测到的光曲线巨大数据库，天文学家已经能够开发出基于机器学习的模型，这些模型在寻找系外行星方面比人类表现得更好。 但人工智能能做的远不止寻找系外行星：它还能让天文学家对这些技术的工作原理有新的认识。
在5月23日发表在《自然天文学》(Nature Astronomy)杂志上的一篇论文中，一组研究人员报告说，机器学习算法能帮助他们更加优雅地理解对系外行星微透镜效应的发现，从而统一了系外行星与其宿主恒星的构造如何变化的多种解释。就在几个月前，DeepMind 的研究人员在《自然》(Nature)杂志上发表了一篇关于人工智能辅助数学研究中新见解的报告。
天文学家还希望，在不久的将来，机器学习将帮助他们确定哪些行星可能适合居住。利用下一代天文台，如南希·罗曼望远镜和詹姆斯·韦伯太空望远镜(JWST)，天文学家打算用机器学习来探测岩石行星上的水、冰和雪。
原文：
Galactic forgeries
While many ML models are trained to distinguish between different types of data, others are intended to produce new data. These generative models are a subset of AI techniques that create artificial data products, such as images, based on some underlying understanding of the data used to train it.
The series of DALL-E models developed by the research company OpenAI — and the free-to-use imitator it inspired, DALL-E mini — have pushed this concept into the public eye. These models generate an image from any written prompt and have set the internet alight with their uncanny ability to construct images of, for instance, Garfield inserted into episodes of Seinfeld.
You might think that astronomers would be wary of any kind of fake imagery, but in recent years, researchers have turned to generative models in order to create galactic forgeries. A paper published Jan. 28 in Monthly Notices of the Royal Astronomical Society describes using the method to produce incredibly detailed images of fake galaxies, which can be used to test predictions from enormous simulations of the universe. They can also help develop and refine the data processing pipelines for next-generation surveys.
Some of these algorithms are so good that even professional astronomers can struggle to distinguish between the real and the fake. Take this recent entry into NASA’s Astronomy Picture of the Day webpage, which features dozens of synthetically generated images of objects in the night sky — and just one real image.
Searching for serendipity
AI is also primed to make discoveries that we cannot predict. There’s a long history of discoveries in astronomy that happened because someone was in the right place, at the right time. Uranus was discovered by chance when William Herschel was scanning the night sky for faint stars, Vesto Slipher measured the speed of spiral arms in what he thought were protoplanetary disks — eventually leading to the discovery of the expanding universe — and Jocelyn Bell Burnell’s famous detection of pulsars happened while she was analyzing measurements of quasars.
Perhaps soon, an AI could join these ranks of serendipitous discoverers though a field of techniques called anomaly detection. These algorithms are specifically trained to sift through mountains of images, light curves, and spectra, looking for the samples that don’t look like anything we’ve seen before. In the next generation of astronomy, with its petabytes of raw data from observatories like the Rubin and JWST, we can’t possibly imagine what these algorithms might find.
翻译：
拟造银河
虽然许多机器学习模型被训练来区分不同类型的数据，但有的模型旨在产生新的数据。这些生成模型是人工智能技术的一个子集，它们基于对训练数据的理解来创建一些人造的数据产品，例如图像。
由研究公司 OpenAI 开发的 DALL-E 系列模型，以及受其启发而产生的开源产品 DALL-E mini，将这一概念推向了公众的视野。这些模型可以根据任何书面提示生成图像，并以其构建图像的超凡能力在互联网上引起轰动，正如它所做的——将加菲猫插入到《宋飞正传》的剧集中。
你可能会认为天文学家会对任何形式的假图像保持警惕，但近年来，研究人员转向生成模型，以创建伪造的星系。1月28日发表在《皇家天文学会月刊》（Monthly Notices of the Royal Astronomical Society）上的一篇论文描述了如何利用这种方法制作出极其精细的模拟星系图像，这些图像可以用来测试对宇宙进行大规模模拟的预测。它们还有助于开发和完善下一代巡天的数据处理管道。
其中一些算法非常出色，甚至连专业天文学家都难以分辨真假。就拿最近美国国家航空航天局（NASA）的 "每日天文图片 "网页上的作品来说，其中有数十张合成的夜空天体图片，而真实的图片只有一张。
寻找意外发现
人工智能还可以做出我们无法预测的发现。在天文学历史上很长的一段时间里的发现，都是有人在正确的时间和地点做出的。比如天王星是威廉·赫歇尔（William Herschel）在扫描夜空中的暗星时偶然发现的，维斯托·斯莱弗（Vesto Slipher）测量了他认为是行星盘的旋臂速度——最终导致了宇宙膨胀的发现，而乔斯林·贝尔·伯内尔（Jocelyn Bell Burnell）著名的脉冲星探测则发生在她分析类星体测量结果的时候。
也许很快有一天，人工智能就能加入这些偶然发现者的行列中，通过一个名为异常检测的技术。这些算法经过专门训练，可以在堆积如山的图像、光曲线和光谱中进行筛选，寻找与我们之前见过的任何样本都不一样的样本。在下一代天文学中，像Rubin望远镜和 JWST 这样的天文台将提供数以 PB 的原始数据，我们则无法想象这些算法会在其中发现些什么。
