In the movie "Interstellar," we get an up-close look at a supermassive black hole. Set against a backdrop of bright gas, the black hole's massive gravitational pull bends light into a ring. However, this isn't a real photograph, but a computer graphic rendering -- an artistic interpretation of what a black hole might look like. 在电影《星际穿越》中， 我们得以近距离观察一个超级黑洞。 在明亮气体构成的背景下， 黑洞的巨大引力 将光线弯曲成环形。 但是，（电影中的）这一幕 并不是一张真正的照片， 而是电脑合成的效果—— 它只是一个对于黑洞 可能样子的艺术表现。
A hundred years ago, Albert Einstein first published his theory of general relativity. In the years since then, scientists have provided a lot of evidence in support of it. But one thing predicted from this theory, black holes, still have not been directly observed. Although we have some idea as to what a black hole might look like, we've never actually taken a picture of one before. However, you might be surprised to know that that may soon change. We may be seeing our first picture of a black hole in the next couple years. Getting this first picture will come down to an international team of scientists, an Earth-sized telescope and an algorithm that puts together the final picture. Although I won't be able to show you a real picture of a black hole today, I'd like to give you a brief glimpse into the effort involved in getting that first picture. 一百多年前， 阿尔伯特·爱因斯坦 第一次发表了广义相对论学说。 在之后的数年里， 科学家们又对此提供了许多佐证。 但相对论中所预测的一点，黑洞， 却始终无法被直接观察到。 尽管我们大致知道一个黑洞 看起来应该是什么样， 却从未真正拍摄过它。 不过，这个现状可能很快就会改变。 在接下来几年内，我们或许就能 见到第一张黑洞的图片。 这一重任会落在一个由 各国科学家组成的团队上， 同时需要一个 地球大小的天文望远镜， 以及一个可以让我们合成出 最终图片的算法。 尽管今天我不能让你们 见到真正的黑洞图片， 我还是想让你们大致了解一下 得到第一张（黑洞）图片 所需要的努力。
My name is Katie Bouman, and I'm a PhD student at MIT. I do research in a computer science lab that works on making computers see through images and video. But although I'm not an astronomer, today I'd like to show you how I've been able to contribute to this exciting project. 我叫凯蒂·伯曼， 是麻省理工学院的一名博士生。 我在计算机科学实验室中进行 让电脑解析图片和视频信息的研究。 尽管我并不是个天文学家， 今天我还是想向大家展示 我是怎样在这个项目中贡献 自己的一份力量的。
If you go out past the bright city lights tonight, you may just be lucky enough to see a stunning view of the Milky Way Galaxy. And if you could zoom past millions of stars, 26,000 light-years toward the heart of the spiraling Milky Way, we'd eventually reach a cluster of stars right at the center. Peering past all the galactic dust with infrared telescopes, astronomers have watched these stars for over 16 years. But it's what they don't see that is the most spectacular. These stars seem to orbit an invisible object. By tracking the paths of these stars, astronomers have concluded that the only thing small and heavy enough to cause this motion is a supermassive black hole -- an object so dense that it sucks up anything that ventures too close -- even light. 如果你远离城市的灯光， 你可能有幸看到银河系 那令人震撼的美景。 而如果你可以穿过百万星辰， 将镜头放大到 2.6万光年以外的银河系中心， 我们就能抵达（银河系）中央的 一群恒星。 天文学家们已经穿过星尘，使用红外望远镜 观察了这些恒星整整十六年。 但是天文学家们所看不到的东西 才是最为壮观的。 这些恒星似乎是在围绕一个 隐形的物体旋转。 通过观测这些星星的移动路径， 天文学家们得出结论， 体积足够小，而质量又大到能导致 恒星们如此运动的唯一物体 就是超级黑洞—— 它的密度极大，高到它能吸进 周围所有东西， 甚至光。
But what happens if we were to zoom in even further? Is it possible to see something that, by definition, is impossible to see? Well, it turns out that if we were to zoom in at radio wavelengths, we'd expect to see a ring of light caused by the gravitational lensing of hot plasma zipping around the black hole. In other words, the black hole casts a shadow on this backdrop of bright material, carving out a sphere of darkness. This bright ring reveals the black hole's event horizon, where the gravitational pull becomes so great that not even light can escape. Einstein's equations predict the size and shape of this ring, so taking a picture of it wouldn't only be really cool, it would also help to verify that these equations hold in the extreme conditions around the black hole. 那么，如果我们继续放大下去， 会发生什么？ 是不是就可能看见一些， 理论上不可能看到的东西呢？ 事实上，如果我们以 无线电波长放大， 我们会看到一圈光线， 是由围绕着黑洞的 等离子体引力透镜产生的。 换句话说， 这个黑洞，在背后明亮物质的衬托下， 留下一个圆形的暗影。 而它周围那明亮的光环 指示了黑洞边境的位置。 在这里，引力作用变得无比巨大， 大到就连光线都无法逃离。 爱因斯坦用公式推测了 这个环的大小和形状， 所以，给光环拍照不仅很酷， 还能帮助我们检验这些公式在 黑洞周围的极端环境下是否成立。
However, this black hole is so far away from us, that from Earth, this ring appears incredibly small -- the same size to us as an orange on the surface of the moon. That makes taking a picture of it extremely difficult. Why is that? Well, it all comes down to a simple equation. Due to a phenomenon called diffraction, there are fundamental limits to the smallest objects that we can possibly see. This governing equation says that in order to see smaller and smaller, we need to make our telescope bigger and bigger. But even with the most powerful optical telescopes here on Earth, we can't even get close to the resolution necessary to image on the surface of the moon. In fact, here I show one of the highest resolution images ever taken of the moon from Earth. It contains roughly 13,000 pixels, and yet each pixel would contain over 1.5 million oranges. 不过，这个黑洞离我们太过遥远， 从地球上看，它非常，非常小—— 大概就和月球上的一个橘子一样大。 这导致给它拍照变得无比艰难。 为什么呢？ 一切都源于一个简单的等式。 由于衍射现象， 我们所能看到的 最小物体是有限制的。 这个等式指出，当想要看到的 东西越来越小时， 望远镜需要变得更大。 但即使是地球上功能最强大的 光学望远镜， 其分辨率甚至不足以 让我们得到月球表面的图片。 事实上，这里是一张有史以来 从地球上拍摄的最高清的 月球图片。 它包含约1.3万个像素， 而每一个像素里包含超过150万个橘子。
So how big of a telescope do we need in order to see an orange on the surface of the moon and, by extension, our black hole? Well, it turns out that by crunching the numbers, you can easily calculate that we would need a telescope the size of the entire Earth. If we could build this Earth-sized telescope, we could just start to make out that distinctive ring of light indicative of the black hole's event horizon. Although this picture wouldn't contain all the detail we see in computer graphic renderings, it would allow us to safely get our first glimpse of the immediate environment around a black hole. 所以，我们需要多大的望远镜 才能看到月球表面的橘子， 以及，那个黑洞呢？ 事实上，通过计算， 我们可以轻易得出所需的 望远镜的大小， 就和整个地球一样大。 而如果我们能够建造出这个 地球大小的望远镜， 就能够分辨出那指示着视界线的 独特的光环。 尽管在这张照片上，我们无法看到 电脑合成图上的那些细节， 它仍可以让我们对于 黑洞周围的环境有个大致的了解。
However, as you can imagine, building a single-dish telescope the size of the Earth is impossible. But in the famous words of Mick Jagger, "You can't always get what you want, but if you try sometimes, you just might find you get what you need." And by connecting telescopes from around the world, an international collaboration called the Event Horizon Telescope is creating a computational telescope the size of the Earth, capable of resolving structure on the scale of a black hole's event horizon. This network of telescopes is scheduled to take its very first picture of a black hole next year. Each telescope in the worldwide network works together. Linked through the precise timing of atomic clocks, teams of researchers at each of the sites freeze light by collecting thousands of terabytes of data. This data is then processed in a lab right here in Massachusetts. 但是，正如你预料， 想建造一个地球大小的射电望远镜 是不可能的。 不过，米克·贾格尔有一句名言： “你不可能永远心想事成， 但如果你尝试了，说不定就 正好能找到 你所需要的东西。” 通过将遍布全世界的望远镜 连接起来， “视界线望远镜”， 一个国际合作项目，诞生了。 这个项目通过电脑制作一个 地球大小的望远镜， 能够帮助我们找到 黑洞视界线的结构。 这个由无数小望远镜构成的网络 将会在明年拍下它的 第一张黑洞图片。 在这个网络中，每一个望远镜 都与其他所有望远镜一同工作。 通过原子钟的准确时间相连， 各地的研究团队们通过收集 上万千兆字节的数据来定位光线。 接下来，这份数据会在 麻省的实验室进行处理。
So how does this even work? Remember if we want to see the black hole in the center of our galaxy, we need to build this impossibly large Earth-sized telescope? For just a second, let's pretend we could build a telescope the size of the Earth. This would be a little bit like turning the Earth into a giant spinning disco ball. Each individual mirror would collect light that we could then combine together to make a picture. However, now let's say we remove most of those mirrors so only a few remained. We could still try to combine this information together, but now there are a lot of holes. These remaining mirrors represent the locations where we have telescopes. This is an incredibly small number of measurements to make a picture from. But although we only collect light at a few telescope locations, as the Earth rotates, we get to see other new measurements. In other words, as the disco ball spins, those mirrors change locations and we get to observe different parts of the image. The imaging algorithms we develop fill in the missing gaps of the disco ball in order to reconstruct the underlying black hole image. If we had telescopes located everywhere on the globe -- in other words, the entire disco ball -- this would be trivial. However, we only see a few samples, and for that reason, there are an infinite number of possible images that are perfectly consistent with our telescope measurements. However, not all images are created equal. Some of those images look more like what we think of as images than others. And so, my role in helping to take the first image of a black hole is to design algorithms that find the most reasonable image that also fits the telescope measurements. 那么，这一项目到底是 怎么运作的呢？ 大家是否记得，如果要看到 银河系中心的那个黑洞， 我们需要一个地球大小的望远镜？ 现在，先假设我们可以 将这个望远镜建造出来。 这可能有点像是把地球变成 一个巨大的球形迪斯科灯。 每一面镜子都会收集光线， 然后，我们就可以将这些光线 组合成图片。 但是，现在，假设我们将 大多数镜子移走， 只有几片留了下来。 我们仍可以尝试将信息合成图片， 但现在，图片中有很多洞。 这几片留下来的镜子就代表了 地球上的几处天文望远镜。 这对于制成一张图片来说， 还远远不够。 不过，尽管我们只在寥寥几处 地方收集光线， 每当地球旋转时，我们便可以 得到新的信息。 换言之，当迪斯科球旋转时， 镜子会改变位置， 而我们就可以看到图片的各个部分。 我们开发的生成图片的算法 可以将迪斯科球上的空缺部分填满， 从而建造出隐藏的黑洞图片。 如果我们能在地球上每一处 都装上望远镜， 或者说能有整个迪斯科球， 那么这个算法并不算重要。 但现在我们只有少量的样本， 所以，可能有无数张图像 符合望远镜所测量到的信息。 但并不是每一张图片都一样。 有些图片，比其他一些 看起来更像我们想象中的图片。 所以我在拍摄黑洞 这一项目中的任务是， 开发一种既可以找到最合理图像， 又能使图像符合望远镜 所测量到的信息的算法。
Just as a forensic sketch artist uses limited descriptions to piece together a picture using their knowledge of face structure, the imaging algorithms I develop use our limited telescope data to guide us to a picture that also looks like stuff in our universe. Using these algorithms, we're able to piece together pictures from this sparse, noisy data. So here I show a sample reconstruction done using simulated data, when we pretend to point our telescopes to the black hole in the center of our galaxy. Although this is just a simulation, reconstruction such as this give us hope that we'll soon be able to reliably take the first image of a black hole and from it, determine the size of its ring. Although I'd love to go on about all the details of this algorithm, luckily for you, I don't have the time. 就像法医素描师通过有限的信息， 结合自己对于人脸结构的认知 画出一张画像一样， 我正在开发的图片算法， 是使用望远镜提供的有限数据 来生成一张看起来像是 宇宙里的东西的图片。 通过这些算法，我们能从散乱 而充满干扰的数据中 合成一张图片。 这里是一个用模拟数据 进行重现的例子： 我们假设将望远镜指向 银河系中心的黑洞。 尽管这只是一个模拟，像这样的 重建工作给了我们 真正给黑洞拍摄可行照片的希望， 之后便可以决定其光环的大小。 虽然我很想继续描绘 这个算法的细节， 但你们很幸运，我没有这个时间。
But I'd still like to give you a brief idea of how we define what our universe looks like, and how we use this to reconstruct and verify our results. Since there are an infinite number of possible images that perfectly explain our telescope measurements, we have to choose between them in some way. We do this by ranking the images based upon how likely they are to be the black hole image, and then choosing the one that's most likely. 可我仍然想大概让你们了解一下 我们是怎样定义宇宙的样子， 以及是怎样以此来重建 和校验我们的结果的。 由于有无数种可以完美解释 望远镜测量结果的图片， 我们需要找到一个方式进行挑选。 我们会按照这些图片是 真正黑洞图片的可能性进行排序， 然后选出可能性最高的那一张。
So what do I mean by this exactly? Let's say we were trying to make a model that told us how likely an image were to appear on Facebook. We'd probably want the model to say it's pretty unlikely that someone would post this noise image on the left, and pretty likely that someone would post a selfie like this one on the right. The image in the middle is blurry, so even though it's more likely we'd see it on Facebook compared to the noise image, it's probably less likely we'd see it compared to the selfie. 我这话到底是什么意思呢？ 假设我们正在建立一个能够 指出一张图出现在脸书上的 可能性的模型。 我们希望这个模型能指出 不太可能有人会上传最左边的图像， 而像右边那样的自拍照 画出一张图片一样， 中间那张图有点模糊， 所以它被发表的可能性 比左边的噪点图像大， 但比右边自拍发表的可能性要小。
But when it comes to images from the black hole, we're posed with a real conundrum: we've never seen a black hole before. In that case, what is a likely black hole image, and what should we assume about the structure of black holes? We could try to use images from simulations we've done, like the image of the black hole from "Interstellar," but if we did this, it could cause some serious problems. What would happen if Einstein's theories didn't hold? We'd still want to reconstruct an accurate picture of what was going on. If we bake Einstein's equations too much into our algorithms, we'll just end up seeing what we expect to see. In other words, we want to leave the option open for there being a giant elephant at the center of our galaxy. 但是当模型的主角变成 黑洞的照片时， 一个难题出现了：我们从未 见过真正的黑洞。 在这样的情况下， 什么样的图才更像黑洞， 而我们又该怎样假设黑洞的结构呢？ 我们或许能够使用模拟试验 得出的图片， 比如《星际穿越》里的那张黑洞图。 但这样做可能会引起 一些严重的问题。 如果爱因斯坦的理论是错的怎么办？ 我们仍然想要得到一张 准确而真实的图片。 而如果我们在算法中掺入太多 爱因斯坦的公式， 最终只会看到我们所希望看到的。 换句话说，我们想保留在银河系中心 看到一头大象这样的可能性。
Different types of images have very distinct features. We can easily tell the difference between black hole simulation images and images we take every day here on Earth. We need a way to tell our algorithms what images look like without imposing one type of image's features too much. One way we can try to get around this is by imposing the features of different kinds of images and seeing how the type of image we assume affects our reconstructions. If all images' types produce a very similar-looking image, then we can start to become more confident that the image assumptions we're making are not biasing this picture that much. 不同类型的照片拥有 完全不同的特征。 我们可以轻松分辨出 一张黑洞模拟图 和我们日常拍的照片的差别。 我们需要在不过度提供某类图片 特征的情况下， 告诉我们的算法，一张正常的图片 应该是什么样。 做到这一点的一种方法是， 向算法展示拥有不同特征的图片， 然后看看这些图片会怎样 影响重建的结果。 如果不同类型的图片都产生出了 差不多的图像， 那么我们便可以更有信心了， 我们对图片的假设并没有 导致结果出现太大偏差。
This is a little bit like giving the same description to three different sketch artists from all around the world. If they all produce a very similar-looking face, then we can start to become confident that they're not imposing their own cultural biases on the drawings. One way we can try to impose different image features is by using pieces of existing images. So we take a large collection of images, and we break them down into their little image patches. We then can treat each image patch a little bit like pieces of a puzzle. And we use commonly seen puzzle pieces to piece together an image that also fits our telescope measurements. 这就有点像让来自不同国家的 三个法医素描师 根据同样的文字描述来作画。 如果他们画出的脸都差不多， 那么我们就能比较确信， 他们各自的文化背景 并没有影响到他们的画。 将不同图片的特征赋予 （算法）的一个方法 就是使用现有的图片的碎片特征。 所以，我们将大量的图像 分解成无数小图片， 然后像拼图一样处理这些小图片。 我们用其中常见的拼图碎片 来组合成一张 符合望远镜所测量数据的完整图片。
Different types of images have very distinctive sets of puzzle pieces. So what happens when we take the same data but we use different sets of puzzle pieces to reconstruct the image? Let's first start with black hole image simulation puzzle pieces. OK, this looks reasonable. This looks like what we expect a black hole to look like. But did we just get it because we just fed it little pieces of black hole simulation images? Let's try another set of puzzle pieces from astronomical, non-black hole objects. OK, we get a similar-looking image. And then how about pieces from everyday images, like the images you take with your own personal camera? Great, we see the same image. When we get the same image from all different sets of puzzle pieces, then we can start to become more confident that the image assumptions we're making aren't biasing the final image we get too much. 不同类型的图片拥有 完全不同的拼图碎片。 所以，当我们使用相同的数据和 截然不同的拼图类型来 重现图像时，会发生什么呢？ 我们先从黑洞模拟类的拼图开始。 这张图看起来还比较合理。 它比较符合我们预料中黑洞的样子。 但我们得到这个结果 是否仅仅是因为我们拿的是 黑洞模拟拼图呢？ 我们再来试试另一组拼图， 这组拼图由宇宙中不是黑洞的 各种天体构成。 很好，我们得到了一幅相似的图片。 那如果我们拿日常照片的拼图 会怎么样呢， 就像你每天拿自己的相机 拍的那种照片？ 太好了，我们看到了和之前 一样的图像。 当我们通过不同类型的拼图 得出一样的图片时， 我们就有充足的自信说 我们对图片进行的推测， 并没有引起最终结果的太大偏差。
Another thing we can do is take the same set of puzzle pieces, such as the ones derived from everyday images, and use them to reconstruct many different kinds of source images. So in our simulations, we pretend a black hole looks like astronomical non-black hole objects, as well as everyday images like the elephant in the center of our galaxy. When the results of our algorithms on the bottom look very similar to the simulation's truth image on top, then we can start to become more confident in our algorithms. And I really want to emphasize here that all of these pictures were created by piecing together little pieces of everyday photographs, like you'd take with your own personal camera. So an image of a black hole we've never seen before may eventually be created by piecing together pictures we see all the time of people, buildings, trees, cats and dogs. Imaging ideas like this will make it possible for us to take our very first pictures of a black hole, and hopefully, verify those famous theories on which scientists rely on a daily basis. 我们能做的另一件事是， 用同一组拼图， 比如源自日常图片的那一种， 来得到不同类型的源图片。 所以，在我们的模拟试验中， 我们假设黑洞看起来像一个 非黑洞天体， 以及在银河系中心的一头大象。 当下面一排算法算出的图片 看起来和上面一排实际图片 十分相似时， 我们就能对我们的算法 有更多信心了。 在这里我想强调， 此处所有的图片都是由 拼接日常照片而得出的， 就像你自己用相机拍的照片一样。 所以，一张我们从未见过的 黑洞的照片， 最终却可能由我们日常 熟悉的图片构成： 人，楼房，树，小猫，小狗…… 想象这样的想法使拍摄第一张 黑洞的图片成为可能， 同时使我们有望校验 科学家们每天所依靠的著名理论。
But of course, getting imaging ideas like this working would never have been possible without the amazing team of researchers that I have the privilege to work with. It still amazes me that although I began this project with no background in astrophysics, what we have achieved through this unique collaboration could result in the very first images of a black hole. But big projects like the Event Horizon Telescope are successful due to all the interdisciplinary expertise different people bring to the table. We're a melting pot of astronomers, physicists, mathematicians and engineers. This is what will make it soon possible to achieve something once thought impossible. I'd like to encourage all of you to go out and help push the boundaries of science, even if it may at first seem as mysterious to you as a black hole. 但是，要想让如此充满想象力的 点子实际工作， 离不开这些我有幸一同工作的 出色的研究者团队。 我仍然对此感到振奋： 虽然在项目开始时我没有任何 天文学背景知识， 我们通过这一独特合作 所达成的成就， 可能导致世界上第一幅 黑洞照片的诞生。 像视界线望远镜这样大项目的成功 是由来自不同学科的人们 用他们各自的专业知识， 一起创造的结果。 我们是一个由天文学家，物理学家， 数学家和工程学家构成的大熔炉。 这就是我们能够很快达成 一个看起来不可能达成的 成就的原因。 在此我想鼓励你们所有人，走出去， 推动科学的边际， 尽管刚开始它看起来可能 和一个黑洞一样神秘。
Thank you. 谢谢大家。
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