How artificial intelligence is changing astronomy（原文鏈接）
——Machine learning has become an essential piece of astronomers’ toolkits.
人工智能如何改變天文學（英文翻譯）
——機器學習已經成為天文學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 Of course, that image is out of date: Digital cameras have long since done away with the need to actually look though a telescope.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?
翻譯：
當大多數人想到天文學家時，他們想到的是一個獨自坐在山頂上的人，凝視著巨大的望遠鏡。事實上，這種印象已經過時了：數碼相機早就不需要通過望遠鏡來進行觀察了。
但如今，天文學又面臨著新的變化。隨著更強大的計算機和天空勘探的出現，產生了龐大的海量數據，人工智能則成為了熱衷于太空研究的人的首選工具。但這些數據都是從哪里來的呢？計算機又如何幫助我們了解宇宙呢?
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.
翻譯：
人工智能對數據的需求
最近你可能聽說過“人工智能”（AI）和“機器學習”（ML）這兩個術語，雖然它們經常被放在一起使用，但實際上它們指的是不同的東西。人工智能(AI)是一個術語，用于描述任何模仿人類思維和執行任務方式的計算行為。機器學習(ML)更具體一點：它是一組根據大量歷史數據學習做出預測和決策的技術。至關重要的是，機器學習創建的模型顯示的行為不是預先編程的，而是從用于訓練它的數據中學習的。
智能手機中的面部識別、電子郵件中的垃圾郵件過濾器，以及Siri或Alexa等數字助理理解語音的能力，都是機器學習在現實世界中應用的例子。天文學家現在正在利用其中的許多技術來研究空間和時間的奧秘。天文學和機器學習實乃天作之合，因為如果有一樣東西天文學家擁有的太多——而機器學習模型又往往不夠的——那就是數據。
我們都熟悉兆字節(MB)、吉字節(GB)和兆字節(TB)，但這種規模的數據在天文學上已經不是什么新鮮事了。如今，我們天文學感興趣的是拍字節(PB)。1拍字節大約是1000 TB、100萬GB或10億MB。要保存一部4K分辨率的長篇視頻，大約需要10PB的存儲空間——而要把它看完，可能需要100多年的時間。
Vera C. Rubin天文臺是智利正在建造的新望遠鏡，它的任務是繪制每個晚上的全天星空圖，圖的精細度是前所未有的。通過一項超過10年的調查，天文臺將產生約60 PB的原始數據，研究范圍將包括太陽系內的小行星，及遠在外太空的其他星系。沒有人能指望分析所有的數據——而這只是正在建造的下一代天文臺之一，所以各個領域的天文學家都在競相尋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.
翻譯：
行星獵人
人工智能對天文學產生重大影響的一個領域是尋找系外行星。尋找它們的信號有很多方法，但以目前的技術，最有效的方法通常是研究恒星的亮度隨時間的變化。如果一顆恒星的光曲線顯現出有特征性的變暗，這可能是一顆行星在主星前面過境的確切跡象。相反，當沿著視線方向看遠處的恒星時，系外行星的引力通常會起到透鏡的作用，而這種稱為引力透鏡的現象會導致恒星亮度大幅上升。探測這些起伏現象意味著要篩選數百萬條光曲線，這些光曲線則是由NASA的開普勒和TESS（凌星系外行星巡天衛星）等太空望遠鏡收集的數據。
利用觀測到的光曲線巨大數據庫，天文學家已經能夠開發出基于機器學習的模型，這些模型在尋找系外行星方面比人類表現得更好。 但人工智能能做的遠不止尋找系外行星：它還能讓天文學家對這些技術的工作原理有新的認識。
在5月23日發表在《自然天文學》(Nature Astronomy)雜志上的一篇論文中，一組研究人員報告說，機器學習算法能幫助他們更加優雅地理解對系外行星微透鏡效應的發現，從而統一了系外行星與其宿主恒星的構造如何變化的多種解釋。就在幾個月前，DeepMind 的研究人員在《自然》(Nature)雜志上發表了一篇關于人工智能輔助數學研究中新見解的報告。
天文學家還希望，在不久的將來，機器學習將幫助他們確定哪些行星可能適合居住。利用下一代天文臺，如南希·羅曼望遠鏡和詹姆斯·韋伯太空望遠鏡(JWST)，天文學家打算用機器學習來探測巖石行星上的水、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.
翻譯：
擬造銀河
雖然許多機器學習模型被訓練來區分不同類型的數據，但有的模型旨在產生新的數據。這些生成模型是人工智能技術的一個子集，它們基于對訓練數據的理解來創建一些人造的數據產品，例如圖像。
由研究公司 OpenAI 開發的 DALL-E 系列模型，以及受其啟發而產生的開源產品 DALL-E mini，將這一概念推向了公眾的視野。這些模型可以根據任何書面提示生成圖像，并以其構建圖像的超凡能力在互聯網上引起轟動，正如它所做的——將加菲貓插入到《宋飛正傳》的劇集中。
你可能會認為天文學家會對任何形式的假圖像保持警惕，但近年來，研究人員轉向生成模型，以創建偽造的星系。1月28日發表在《皇家天文學會月刊》（Monthly Notices of the Royal Astronomical Society）上的一篇論文描述了如何利用這種方法制作出極其精細的模擬星系圖像，這些圖像可以用來測試對宇宙進行大規模模擬的預測。它們還有助于開發和完善下一代巡天的數據處理管道。
其中一些算法非常出色，甚至連專業天文學家都難以分辨真假。就拿最近美國國家航空航天局（NASA）的 "每日天文圖片 "網頁上的作品來說，其中有數十張合成的夜空天體圖片，而真實的圖片只有一張。
尋找意外發現
人工智能還可以做出我們無法預測的發現。在天文學歷史上很長的一段時間里的發現，都是有人在正確的時間和地點做出的。比如天王星是威廉·赫歇爾（William Herschel）在掃描夜空中的暗星時偶然發現的，維斯托·斯萊弗（Vesto Slipher）測量了他認為是行星盤的旋臂速度——最終導致了宇宙膨脹的發現，而喬斯林·貝爾·伯內爾（Jocelyn Bell Burnell）著名的脈沖星探測則發生在她分析類星體測量結果的時候。
也許很快有一天，人工智能就能加入這些偶然發現者的行列中，通過一個名為異常檢測的技術。這些算法經過專門訓練，可以在堆積如山的圖像、光曲線和光譜中進行篩選，尋找與我們之前見過的任何樣本都不一樣的樣本。在下一代天文學中，像Rubin望遠鏡和 JWST 這樣的天文臺將提供數以 PB 的原始數據，我們則無法想象這些算法會在其中發現些什么。
