今日TED分享
如果你在網上搜索黑洞的照片
那麼你將會徜徉在一片美麗且令人震撼的照片海洋中
但這些圖片不過是我們對自然黑洞的最佳假設，因為就連光都無法穿過黑洞。
幸運的是，物理學家並不會因為缺乏光子就放棄。
0:12
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.
在電影「星際穿越」中，我們可以看到一個超大質量的黑洞。在黑暗的巨大的引力的背景下，將光線變成一個光環。 但這不是真正的成像，而是計算機的圖形呈現——藝術地解釋黑洞可能是什麼樣子的。
0:31
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.
一百年前，愛因斯坦首先發表了他的廣義相對論。 但這個理論預測到有一點是黑洞還沒有被直接觀察到。在此之後的幾年裡，科學家們也為此提供了很多證據。雖然我們對黑洞的樣子有一些想法，但是我們從來沒有真正地拍過一張照片。不過令人驚訝的是，這可能很快就會改變。我們可能會在未來幾年看到我們有史以來的第一張黑洞照片。 獲得第一張這樣的照片需要一個國際性的科學家團隊，一個地球大小般的望遠鏡和一個整理最終圖片的算法。雖然我今天無法向你們展示一個黑洞的真實情況，但我想讓你們能夠對這樣的照片有初步的認識和了解。
1:18
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.
我的名字是Katie Bouman，我是麻省理工學院的博士生。我在一個計算機科學實驗室進行研究，該實驗室致力於通過計算機觀看圖像和視頻。雖然我不是天文學家，但是我今天還是想向您們展示我是如何為這個令人興奮的項目做出貢獻的。
1:34
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.
如果你今晚走出燈光明亮的城市，你可能會幸運地看到銀河系壯麗的景色。如果你能放大數以百萬計的距離銀河系中心26000光年的恆星，就會在中心看到一簇星星。 天文學家們用紅外望遠鏡觀察銀河系已經超過16年了。但他們沒有看到的最為壯觀的，這些恆星似乎都環繞著一個看不見的物體在運動。天文學家通過跟蹤這些恆星的路徑，得出的結論是，唯一能夠引起這些恆星運動的東西是一個超大質量的黑洞——一個非常密集的物體，它吸收任何靠近的東西——甚至是光。
2:19
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.
如果我們進一步放大會怎麼樣？會不會看到一些按常理來說不可能看到的東西？事實證明，如果我們放大無線電波，我們會看到由熱等離子體壓縮黑洞周圍的引力透鏡效應引起的光環。換言之，黑洞在這個明亮的背景下投下陰影，影射出一個黑暗的球體。這個明亮的光環揭示了黑洞的視界線，在那裡引力變得如此巨大以至於連光都無法逃脫。愛因斯坦的方程式預測了光環的大小和形狀,所以能拍攝黑洞的照片不僅非常酷，還可以幫助驗證這些方程在黑洞周圍的極端條件下是否依然有用。
3:01
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.
然而，黑洞離我們太遠了，從地球上看這個環非常小，對我們來說，就像月球表面的橙子大小一樣。這使得拍攝非常困難。這是為什麼？這一切都歸結為一個簡單的方程式。由於衍射現象，我們可以看到的最小物體有根本的限制。這個方程式說，只有我們的望遠鏡越大才能看到越小的東西。但是即使是地球上最強大的光學望遠鏡，都不能接近月球表面圖像所需的解析度。事實上，在這裡，我展示了一個曾經從地球得到的月球最高解析度的圖像。它大約包含13,000個像素，但圖片中每個像素實際大小都超過了150萬個橙子。
3:54
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.
那麼為了在月球表面看到一個橙子和我們的黑洞，我們需要一個多大的望遠鏡？ 通過計算，我們需要一個整個地球般大小的望遠鏡。
4:07
(Laughter)
4:08
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.
如果我們可以建造這個地球般大小的望遠鏡，我們就可以分辨出那個代表黑洞視界線的獨特光環。雖然這張圖片不包含我們在計算機圖形效果圖中看到的所有細節，但它能夠讓我們直接觀察到黑洞周圍的環境。
4:25
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 sights freeze light by collecting thousands of terabytes of data. This data is then processed in a lab right here in Massachusetts.
但我們可以想像，建造一個地球大小的單筒望遠鏡是不可能的。 但是Mick Jagger說過：「你不可能總是得到你想要的東西，但如果你願意嘗試，你會得到你所需要的東西。」「 Event Horizon Telescope」國際聯合會正在通過連接來自世界各地的望遠鏡來創建一個能夠了解黑洞結構的地球般大小的計算望遠鏡。這個望遠鏡網計劃將在明年拍攝第一張黑洞的照片。通過原子鐘的精確定時，世界範圍內的每個望遠鏡共同工作，每個點的研究團隊將光線收集的數千兆字節的數據凍結起來。然後在麻薩諸塞州的實驗室處理這些數據。
5:12
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.
那麼這些是如何工作的呢？如果我們想看到銀河系中心的黑洞，我們需要建立一個不可思議的地球般大小的望遠鏡?第二，假如我們可以建造一個地球般大小的望遠鏡。這有點像把地球變成一個巨大的旋轉迪斯科舞會。每個鏡子都收集光線，然後我們把他們組合在一起來製作照片。現在假設我們移除大部分的鏡子只剩下幾個。我們會發現雖然有很多漏洞，但這些信息還是可以試著結合在一起。這是一個令人難以置信的小數量的測量圖片。 但是，儘管我們只在幾個望遠鏡位置收集光，隨著地球旋轉，我們仍然可以看到其他新的測量數據。換句話說，當迪斯科球旋轉時，鏡子會隨之改變位置，我們便可以觀察到圖像的不同部分。我們開發的以重建底層黑洞圖像的成像算法填補了迪斯科球的缺失間隙。如果我們在全球各地都有望遠鏡——換句話說，就是整個迪斯科舞會——這將是微不足道的。然而，我們只看到幾個樣本，因此，有無數可能的圖像與我們的望遠鏡測量完全一致。 但是，並不是所有的圖像都是相同的。其中一些圖像看起來和我們所想像的圖像更加相似。因此，在拍攝第一張黑洞圖像時，我的角色是幫助找到最適合望遠鏡測量的圖像的算法。
6:45
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.
就像一個法醫素描藝術家使用他們的知識結構利用有限的描述來拼湊的臉的圖片,我開發的成像算法利用我們有限的望遠鏡數據來引導我們的照片。這些算法可以使我們從這個稀疏，嘈雜的數據中拼出圖片。所以當我們假裝把望遠鏡指向我們星系中心的黑洞時，我使用模擬數據完成了一個樣本重建。雖然這只是一個模擬，但是像這樣的重建讓我們有很快能夠獲取黑洞第一個圖像的希望，並可以從中確定它的大小。我沒有太多的時間繼續討論關於這個算法的所有細節。
7:32
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.
但是我仍然想簡短的告訴你們我們是如何定義我們的宇宙，以及我們如何用它來重建和驗證我們的結果的。由於有無數的圖像完美地解釋了我們的望遠鏡測量，我們必須以某種方式在它們之間進行選擇。我們根據黑洞圖像的可能性排列，然後選擇最有可能的圖像來做到這一點。
7:56
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.
那麼這是什麼意思呢？ 假設我們正在嘗試製作一個模型，告訴我們一個圖像在Facebook上是如何出現的。 人們認為噪聲圖像不太可能是在左邊，大多數人會發布幻燈片右邊這種。中間的圖像是模糊，所以即使它是更有可能的，但是我們會看到它和facebook 上的噪聲圖像相比較，是不太可能的。
8:21
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.
但是當涉及到黑洞的圖像時，我們遇到了一個真正的難題：我們從未見過黑洞。在這種情況下，黑洞是什麼樣的，我們應該怎麼看待黑洞的結構呢？雖然我們可以嘗試使用我們所做的模擬圖像，例如「星際穿越」的黑洞形象，但是如果我們這樣做，可能會導致一些嚴重的問題。 如果愛因斯坦的理論不成立會發生什麼？我們仍然需要重建一個準確的圖片。如果我們將愛因斯坦的方程式太多地融入到我們的算法中，我們只會看到我們期望看到的結果。換句話說，我們希望脫離這個觀點，打開巨大神秘的銀河系中心。
8:59
(Laughter)
9:01
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.
不同類型的圖像是具有明顯不同特徵的。我們可以很容易地分辨出黑洞模擬圖像和我們每天在地球上拍攝的圖像。我們需要一種讓我們圖像看起來沒有強加其他內容的方法。我們可以嘗試解決的一個方法是向圖像強加不同類型的特徵，看看我們假設的圖像類型是如何影響我們的重建的。如果所有圖像的類型最終都產生非常相似的圖像，那麼我們可以相信我們製作的圖像假設是沒有對任何圖片有偏見的。
9:36
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.
這有點像向三個不同地方的素描藝術家提供相同的描述。如果他們最終都會產生一個非常相似的面孔，那麼我們可以相信他們沒有在圖紙上強加自己的文化偏見。我們可以在現有圖像中嘗試強加不同的特徵的一種方法。所以我們需要收集大量的圖像，把它們分解成小圖像補丁。然後，對每個圖像補丁進行拼圖的處理。使用常見的拼圖方式拼湊出一個適合望遠鏡測量的圖像。
10:14
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.
不同類型的圖像都有非常獨特的拼圖。當我們採用相同的數據，使用不同的拼圖方式來重構圖像時會發生什麼呢？ 我們先從黑洞圖像模擬拼圖開始。好的，這看起來很合理。 這看起來和我們期待的黑洞很像。但是我們應該如何通過黑洞模擬圖像得到它？讓我們從天文，非黑洞的對象中嘗試另一套拼圖。好的，我們得到一個類似的圖像。然後我們要如何拼湊這些日常圖像處理自己的照片一樣簡單？太好了，我們又看到了相同的圖像。所以當我們從所有不同的拼圖方式中獲得相同的圖像時，我們可以開始相信我們製作的圖像假設不會偏離我們得到的最終圖像太多。
11:04
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.
我們可以做的另一件事是採取同樣的拼圖，例如從日常圖像中得到的拼圖，並使用它們重建許多不同種類的源圖像。所以在我們的模擬中，我們假裝一個黑洞看起來像天文非黑洞物體，以及像我們星系中心的神秘的日常圖像。當我們算法底層的結果看起來與模擬的真實圖像非常相似時，我們可以更加相信我們的算法。我真的想在這裡強調，所有這些照片就像你用自己的個人相機一樣，都是通過拼接一些小件的日常照片創建的。所以我們從未見過的一個黑洞的形象最終可以通過拼接我們同一時間在人們、建築物、樹木、貓和狗看到的圖片來創建。這樣的想法將使我們能夠得到有史以來的第一張黑洞的照片，並希望能夠驗證科學家們所依賴的那些著名的理論。
12:01
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.
當然，如果我沒有機會與這樣驚人的研究團隊合作，是永遠不可能有這些想法的。至今都讓我感到驚訝的是，雖然我開始了這個沒有天體物理學背景的項目，但我們可以通過這種獨特的協作所取得有史以來黑洞的第一張圖像。「 Event Horizon Telescope」是所有跨學科的人的各自表現而獲得的成功。我們是天文學家，物理學家，數學家和工程師的熔爐。這將是很快就可能實現的事情。
12:35
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.
我想鼓勵大家出門幫助推動科學，即使它最初似乎對你來說是神秘的黑洞。
12:44
Thank you.
謝謝。