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首张黑洞照片被公布,怎么拍出来的?

发布者: 千缘 | 发布时间: 2021-1-7 23:59| 查看数: 112| 评论数: 0|



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.

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。

However, this isn't a real photograph, but a computer graphic rendering -- an artistic interpretation of what a black hole might look like.

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.

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。

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.

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。

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.

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。

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.

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.

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。

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.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.

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。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。

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.

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。

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 --

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--

an object so dense that it sucks up anything that ventures too close--even light. But what happens if we were to zoom in even further? Is it possible to see something that, by definition, is impossible to see?

它的密度极大,高到它能吸进 周围所有东西, 甚至光。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?

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.

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。

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,

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,

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.

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?

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?

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.

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。

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.

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。

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.

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.

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。

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.

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。

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.

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."

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。”

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.

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。

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.

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。

This data is then processed in a lab right here in Massachusetts。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?

接下来,这份数据会在麻省的实验室进行处理。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?

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.

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。

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.

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。

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.

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。

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.

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。

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.

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。

However, not all images are created equal. Some of those images look more like what we think of as images than others.

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。

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.

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.

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。

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.

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。

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.

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。

Although I'd love to go on about all the details of this algorithm, luckily for you, I don't have the time.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.

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。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。

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.

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,

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,

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.

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?

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?

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?

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?

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.

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.

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。

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.

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。

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.

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.

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。

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.

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。

And we use commonly seen puzzle pieces to piece together an image that also fits our telescope measurements.

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?

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?

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?

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?

Let's try another set of puzzle pieces from astronomical, non-black hole objects。OK,we get a similar-looking image.

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。

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,

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。

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.

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.

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。

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.

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。

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.

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。

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.

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。

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.

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……

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.

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.

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。

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.

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。

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.

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。

This is what will make it soon possible to achieve something once thought impossible.

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。

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.Thank you.

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。谢谢大家。


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