How to take a picture of a black hole | Katie Bouman

3,361,362 views ・ 2017-04-28

TED


请双击下面的英文字幕来播放视频。

翻译人员: Anney Ye 校对人员: Yolanda Zhang
00:13
In the movie "Interstellar,"
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在电影《星际穿越》中,
我们得以近距离观察一个超级黑洞。
00:15
we get an up-close look at a supermassive black hole.
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00:18
Set against a backdrop of bright gas,
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在明亮气体构成的背景下,
00:20
the black hole's massive gravitational pull
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黑洞的巨大引力
00:22
bends light into a ring.
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将光线弯曲成环形。
00:24
However, this isn't a real photograph,
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但是,(电影中的)这一幕 并不是一张真正的照片,
00:26
but a computer graphic rendering --
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而是电脑合成的效果——
00:28
an artistic interpretation of what a black hole might look like.
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它只是一个对于黑洞 可能样子的艺术表现。
00:32
A hundred years ago,
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一百多年前,
00:33
Albert Einstein first published his theory of general relativity.
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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,
00:37
In the years since then,
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00:38
scientists have provided a lot of evidence in support of it.
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科学家们又对此提供了许多佐证。
00:41
But one thing predicted from this theory, black holes,
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但相对论中所预测的一点,黑洞,
00:44
still have not been directly observed.
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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,
00:47
Although we have some idea as to what a black hole might look like,
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却从未真正拍摄过它。
00:50
we've never actually taken a picture of one before.
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不过,这个现状可能很快就会改变。
00:53
However, you might be surprised to know that that may soon change.
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00:57
We may be seeing our first picture of a black hole in the next couple years.
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在接下来几年内,我们或许就能 见到第一张黑洞的图片。
01:01
Getting this first picture will come down to an international team of scientists,
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这一重任会落在一个由 各国科学家组成的团队上,
01:05
an Earth-sized telescope
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同时需要一个 地球大小的天文望远镜,
01:07
and an algorithm that puts together the final picture.
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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,
01:10
Although I won't be able to show you a real picture of a black hole today,
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01:13
I'd like to give you a brief glimpse into the effort involved
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我还是想让你们大致了解一下
01:16
in getting that first picture.
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得到第一张(黑洞)图片 所需要的努力。
01:19
My name is Katie Bouman,
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我叫凯蒂·伯曼,
01:20
and I'm a PhD student at MIT.
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是麻省理工学院的一名博士生。
01:23
I do research in a computer science lab
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我在计算机科学实验室中进行
01:25
that works on making computers see through images and video.
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让电脑解析图片和视频信息的研究。
01:28
But although I'm not an astronomer,
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尽管我并不是个天文学家,
今天我还是想向大家展示
01:31
today I'd like to show you
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我是怎样在这个项目中贡献 自己的一份力量的。
01:32
how I've been able to contribute to this exciting project.
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01:35
If you go out past the bright city lights tonight,
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如果你远离城市的灯光,
你可能有幸看到银河系
01:38
you may just be lucky enough to see a stunning view
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01:40
of the Milky Way Galaxy.
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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
01:42
And if you could zoom past millions of stars,
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01:44
26,000 light-years toward the heart of the spiraling Milky Way,
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2.6万光年以外的银河系中心,
01:48
we'd eventually reach a cluster of stars right at the center.
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我们就能抵达(银河系)中央的 一群恒星。
01:51
Peering past all the galactic dust with infrared telescopes,
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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。
01:55
astronomers have watched these stars for over 16 years.
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但是天文学家们所看不到的东西 才是最为壮观的。
01:59
But it's what they don't see that is the most spectacular.
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02:02
These stars seem to orbit an invisible object.
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这些恒星似乎是在围绕一个 隐形的物体旋转。
02:05
By tracking the paths of these stars,
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通过观测这些星星的移动路径,
天文学家们得出结论,
02:08
astronomers have concluded
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02:09
that the only thing small and heavy enough to cause this motion
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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
02:12
is a supermassive black hole --
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就是超级黑洞——
02:14
an object so dense that it sucks up anything that ventures too close --
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它的密度极大,高到它能吸进 周围所有东西,
02:18
even light.
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甚至光。
02:20
But what happens if we were to zoom in even further?
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那么,如果我们继续放大下去, 会发生什么?
02:23
Is it possible to see something that, by definition, is impossible to see?
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是不是就可能看见一些, 理论上不可能看到的东西呢?
02:28
Well, it turns out that if we were to zoom in at radio wavelengths,
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事实上,如果我们以 无线电波长放大,
02:31
we'd expect to see a ring of light
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我们会看到一圈光线,
02:33
caused by the gravitational lensing of hot plasma
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是由围绕着黑洞的
等离子体引力透镜产生的。
02:36
zipping around the black hole.
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02:37
In other words,
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换句话说,
这个黑洞,在背后明亮物质的衬托下,
02:39
the black hole casts a shadow on this backdrop of bright material,
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留下一个圆形的暗影。
02:42
carving out a sphere of darkness.
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02:44
This bright ring reveals the black hole's event horizon,
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而它周围那明亮的光环 指示了黑洞边境的位置。
02:47
where the gravitational pull becomes so great
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在这里,引力作用变得无比巨大,
大到就连光线都无法逃离。
02:50
that not even light can escape.
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02:51
Einstein's equations predict the size and shape of this ring,
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爱因斯坦用公式推测了 这个环的大小和形状,
02:54
so taking a picture of it wouldn't only be really cool,
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所以,给光环拍照不仅很酷,
02:57
it would also help to verify that these equations hold
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还能帮助我们检验这些公式在
03:00
in the extreme conditions around the black hole.
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黑洞周围的极端环境下是否成立。
03:02
However, this black hole is so far away from us,
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不过,这个黑洞离我们太过遥远,
03:05
that from Earth, this ring appears incredibly small --
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从地球上看,它非常,非常小——
03:08
the same size to us as an orange on the surface of the moon.
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大概就和月球上的一个橘子一样大。
03:12
That makes taking a picture of it extremely difficult.
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这导致给它拍照变得无比艰难。
03:16
Why is that?
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为什么呢?
03:18
Well, it all comes down to a simple equation.
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一切都源于一个简单的等式。
03:21
Due to a phenomenon called diffraction,
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由于衍射现象,
我们所能看到的
03:24
there are fundamental limits
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03:25
to the smallest objects that we can possibly see.
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最小物体是有限制的。
03:28
This governing equation says that in order to see smaller and smaller,
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这个等式指出,当想要看到的 东西越来越小时,
03:32
we need to make our telescope bigger and bigger.
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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,
03:35
But even with the most powerful optical telescopes here on Earth,
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其分辨率甚至不足以
03:38
we can't even get close to the resolution necessary
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03:40
to image on the surface of the moon.
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让我们得到月球表面的图片。
03:42
In fact, here I show one of the highest resolution images ever taken
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事实上,这里是一张有史以来 从地球上拍摄的最高清的
03:46
of the moon from Earth.
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月球图片。
03:47
It contains roughly 13,000 pixels,
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它包含约1.3万个像素,
03:50
and yet each pixel would contain over 1.5 million oranges.
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而每一个像素里包含超过150万个橘子。
03:55
So how big of a telescope do we need
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所以,我们需要多大的望远镜
03:57
in order to see an orange on the surface of the moon
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才能看到月球表面的橘子,
以及,那个黑洞呢?
04:00
and, by extension, our black hole?
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04:02
Well, it turns out that by crunching the numbers,
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事实上,通过计算,
我们可以轻易得出所需的 望远镜的大小,
04:04
you can easily calculate that we would need a telescope
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就和整个地球一样大。
04:07
the size of the entire Earth.
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(笑声)
04:08
(Laughter)
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而如果我们能够建造出这个 地球大小的望远镜,
04:09
If we could build this Earth-sized telescope,
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就能够分辨出那指示着视界线的
04:12
we could just start to make out that distinctive ring of light
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独特的光环。
04:14
indicative of the black hole's event horizon.
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尽管在这张照片上,我们无法看到
04:17
Although this picture wouldn't contain all the detail we see
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电脑合成图上的那些细节,
04:20
in computer graphic renderings,
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它仍可以让我们对于
04:21
it would allow us to safely get our first glimpse
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04:23
of the immediate environment around a black hole.
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黑洞周围的环境有个大致的了解。
04:26
However, as you can imagine,
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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。
04:28
building a single-dish telescope the size of the Earth is impossible.
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04:31
But in the famous words of Mick Jagger,
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不过,米克·贾格尔有一句名言:
04:33
"You can't always get what you want,
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“你不可能永远心想事成,
04:35
but if you try sometimes, you just might find
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但如果你尝试了,说不定就 正好能找到
04:37
you get what you need."
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你所需要的东西。”
04:38
And by connecting telescopes from around the world,
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通过将遍布全世界的望远镜 连接起来,
04:41
an international collaboration called the Event Horizon Telescope
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“视界线望远镜”, 一个国际合作项目,诞生了。
04:44
is creating a computational telescope the size of the Earth,
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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
04:48
capable of resolving structure
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04:49
on the scale of a black hole's event horizon.
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黑洞视界线的结构。
04:51
This network of telescopes is scheduled to take its very first picture
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这个由无数小望远镜构成的网络 将会在明年拍下它的
04:55
of a black hole next year.
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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。
04:57
Each telescope in the worldwide network works together.
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05:00
Linked through the precise timing of atomic clocks,
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通过原子钟的准确时间相连,
05:03
teams of researchers at each of the sites freeze light
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各地的研究团队们通过收集
05:05
by collecting thousands of terabytes of data.
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上万千兆字节的数据来定位光线。
05:08
This data is then processed in a lab right here in Massachusetts.
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接下来,这份数据会在 麻省的实验室进行处理。
05:13
So how does this even work?
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那么,这一项目到底是 怎么运作的呢?
05:15
Remember if we want to see the black hole in the center of our galaxy,
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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?
05:19
we need to build this impossibly large Earth-sized telescope?
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现在,先假设我们可以
05:22
For just a second, let's pretend we could build
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05:24
a telescope the size of the Earth.
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将这个望远镜建造出来。
05:26
This would be a little bit like turning the Earth
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这可能有点像是把地球变成
一个巨大的球形迪斯科灯。
05:28
into a giant spinning disco ball.
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05:30
Each individual mirror would collect light
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每一面镜子都会收集光线,
05:32
that we could then combine together to make a picture.
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然后,我们就可以将这些光线 组合成图片。
05:35
However, now let's say we remove most of those mirrors
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但是,现在,假设我们将 大多数镜子移走,
只有几片留了下来。
05:38
so only a few remained.
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我们仍可以尝试将信息合成图片,
05:40
We could still try to combine this information together,
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但现在,图片中有很多洞。
05:43
but now there are a lot of holes.
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这几片留下来的镜子就代表了 地球上的几处天文望远镜。
05:45
These remaining mirrors represent the locations where we have telescopes.
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05:49
This is an incredibly small number of measurements to make a picture from.
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这对于制成一张图片来说, 还远远不够。
05:53
But although we only collect light at a few telescope locations,
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不过,尽管我们只在寥寥几处 地方收集光线,
05:57
as the Earth rotates, we get to see other new measurements.
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每当地球旋转时,我们便可以 得到新的信息。
06:00
In other words, as the disco ball spins, those mirrors change locations
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换言之,当迪斯科球旋转时, 镜子会改变位置,
06:04
and we get to observe different parts of the image.
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而我们就可以看到图片的各个部分。
06:07
The imaging algorithms we develop fill in the missing gaps of the disco ball
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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,
06:11
in order to reconstruct the underlying black hole image.
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从而建造出隐藏的黑洞图片。
06:14
If we had telescopes located everywhere on the globe --
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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,
06:17
in other words, the entire disco ball --
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那么这个算法并不算重要。
06:19
this would be trivial.
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06:20
However, we only see a few samples, and for that reason,
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但现在我们只有少量的样本,
06:23
there are an infinite number of possible images
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所以,可能有无数张图像
06:26
that are perfectly consistent with our telescope measurements.
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符合望远镜所测量到的信息。
06:29
However, not all images are created equal.
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但并不是每一张图片都一样。
06:32
Some of those images look more like what we think of as images than others.
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有些图片,比其他一些 看起来更像我们想象中的图片。
06:37
And so, my role in helping to take the first image of a black hole
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所以我在拍摄黑洞 这一项目中的任务是,
06:40
is to design algorithms that find the most reasonable image
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开发一种既可以找到最合理图像,
06:43
that also fits the telescope measurements.
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又能使图像符合望远镜 所测量到的信息的算法。
06:46
Just as a forensic sketch artist uses limited descriptions
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就像法医素描师通过有限的信息,
06:50
to piece together a picture using their knowledge of face structure,
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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
06:54
the imaging algorithms I develop use our limited telescope data
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06:57
to guide us to a picture that also looks like stuff in our universe.
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来生成一张看起来像是 宇宙里的东西的图片。
07:01
Using these algorithms, we're able to piece together pictures
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通过这些算法,我们能从散乱 而充满干扰的数据中
07:05
from this sparse, noisy data.
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合成一张图片。
07:07
So here I show a sample reconstruction done using simulated data,
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这里是一个用模拟数据 进行重现的例子:
07:12
when we pretend to point our telescopes
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我们假设将望远镜指向
银河系中心的黑洞。
07:14
to the black hole in the center of our galaxy.
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07:16
Although this is just a simulation, reconstruction such as this give us hope
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尽管这只是一个模拟,像这样的 重建工作给了我们
07:21
that we'll soon be able to reliably take the first image of a black hole
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真正给黑洞拍摄可行照片的希望,
07:24
and from it, determine the size of its ring.
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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,
07:28
Although I'd love to go on about all the details of this algorithm,
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07:31
luckily for you, I don't have the time.
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但你们很幸运,我没有这个时间。
07:33
But I'd still like to give you a brief idea
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可我仍然想大概让你们了解一下
07:35
of how we define what our universe looks like,
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我们是怎样定义宇宙的样子,
07:37
and how we use this to reconstruct and verify our results.
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以及是怎样以此来重建 和校验我们的结果的。
07:42
Since there are an infinite number of possible images
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由于有无数种可以完美解释
07:44
that perfectly explain our telescope measurements,
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望远镜测量结果的图片,
07:47
we have to choose between them in some way.
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我们需要找到一个方式进行挑选。
07:49
We do this by ranking the images
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我们会按照这些图片是
07:51
based upon how likely they are to be the black hole image,
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真正黑洞图片的可能性进行排序,
07:54
and then choosing the one that's most likely.
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然后选出可能性最高的那一张。
我这话到底是什么意思呢?
07:57
So what do I mean by this exactly?
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07:59
Let's say we were trying to make a model
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假设我们正在建立一个能够
08:01
that told us how likely an image were to appear on Facebook.
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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
08:05
We'd probably want the model to say
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08:06
it's pretty unlikely that someone would post this noise image on the left,
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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
08:10
and pretty likely that someone would post a selfie
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08:12
like this one on the right.
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画出一张图片一样,
中间那张图有点模糊,
08:14
The image in the middle is blurry,
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08:15
so even though it's more likely we'd see it on Facebook
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所以它被发表的可能性
比左边的噪点图像大,
08:18
compared to the noise image,
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08:19
it's probably less likely we'd see it compared to the selfie.
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但比右边自拍发表的可能性要小。
08:22
But when it comes to images from the black hole,
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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。
08:25
we're posed with a real conundrum: we've never seen a black hole before.
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08:28
In that case, what is a likely black hole image,
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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?
08:31
and what should we assume about the structure of black holes?
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08:33
We could try to use images from simulations we've done,
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我们或许能够使用模拟试验 得出的图片,
08:36
like the image of the black hole from "Interstellar,"
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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。
08:39
but if we did this, it could cause some serious problems.
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如果爱因斯坦的理论是错的怎么办?
08:42
What would happen if Einstein's theories didn't hold?
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08:45
We'd still want to reconstruct an accurate picture of what was going on.
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我们仍然想要得到一张 准确而真实的图片。
08:49
If we bake Einstein's equations too much into our algorithms,
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而如果我们在算法中掺入太多 爱因斯坦的公式,
08:52
we'll just end up seeing what we expect to see.
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最终只会看到我们所希望看到的。
08:55
In other words, we want to leave the option open
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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。
08:58
for there being a giant elephant at the center of our galaxy.
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09:00
(Laughter)
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(笑声)
不同类型的照片拥有 完全不同的特征。
09:02
Different types of images have very distinct features.
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我们可以轻松分辨出 一张黑洞模拟图
09:05
We can easily tell the difference between black hole simulation images
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09:08
and images we take every day here on Earth.
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和我们日常拍的照片的差别。
09:10
We need a way to tell our algorithms what images look like
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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。
09:14
without imposing one type of image's features too much.
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09:17
One way we can try to get around this
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做到这一点的一种方法是,
09:19
is by imposing the features of different kinds of images
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向算法展示拥有不同特征的图片,
09:22
and seeing how the type of image we assume affects our reconstructions.
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然后看看这些图片会怎样 影响重建的结果。
09:27
If all images' types produce a very similar-looking image,
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如果不同类型的图片都产生出了 差不多的图像,
09:31
then we can start to become more confident
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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。
09:33
that the image assumptions we're making are not biasing this picture that much.
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09:37
This is a little bit like giving the same description
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这就有点像让来自不同国家的 三个法医素描师
09:40
to three different sketch artists from all around the world.
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根据同样的文字描述来作画。
09:43
If they all produce a very similar-looking face,
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如果他们画出的脸都差不多,
09:46
then we can start to become confident
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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。
09:48
that they're not imposing their own cultural biases on the drawings.
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09:51
One way we can try to impose different image features
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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。
09:55
is by using pieces of existing images.
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09:58
So we take a large collection of images,
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所以,我们将大量的图像
10:00
and we break them down into their little image patches.
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分解成无数小图片,
然后像拼图一样处理这些小图片。
10:03
We then can treat each image patch a little bit like pieces of a puzzle.
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10:07
And we use commonly seen puzzle pieces to piece together an image
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我们用其中常见的拼图碎片 来组合成一张
10:11
that also fits our telescope measurements.
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符合望远镜所测量数据的完整图片。
不同类型的图片拥有 完全不同的拼图碎片。
10:15
Different types of images have very distinctive sets of puzzle pieces.
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10:18
So what happens when we take the same data
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所以,当我们使用相同的数据和
10:21
but we use different sets of puzzle pieces to reconstruct the image?
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截然不同的拼图类型来 重现图像时,会发生什么呢?
10:25
Let's first start with black hole image simulation puzzle pieces.
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我们先从黑洞模拟类的拼图开始。
10:30
OK, this looks reasonable.
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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。
10:32
This looks like what we expect a black hole to look like.
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10:34
But did we just get it
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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?
10:36
because we just fed it little pieces of black hole simulation images?
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10:39
Let's try another set of puzzle pieces
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我们再来试试另一组拼图,
10:41
from astronomical, non-black hole objects.
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这组拼图由宇宙中不是黑洞的 各种天体构成。
10:44
OK, we get a similar-looking image.
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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,
10:47
And then how about pieces from everyday images,
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10:49
like the images you take with your own personal camera?
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就像你每天拿自己的相机 拍的那种照片?
10:53
Great, we see the same image.
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太好了,我们看到了和之前 一样的图像。
10:55
When we get the same image from all different sets of puzzle pieces,
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当我们通过不同类型的拼图 得出一样的图片时,
10:58
then we can start to become more confident
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我们就有充足的自信说
11:00
that the image assumptions we're making
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我们对图片进行的推测,
11:02
aren't biasing the final image we get too much.
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并没有引起最终结果的太大偏差。
11:05
Another thing we can do is take the same set of puzzle pieces,
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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,
11:09
such as the ones derived from everyday images,
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11:11
and use them to reconstruct many different kinds of source images.
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来得到不同类型的源图片。
11:15
So in our simulations,
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所以,在我们的模拟试验中,
11:16
we pretend a black hole looks like astronomical non-black hole objects,
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我们假设黑洞看起来像一个 非黑洞天体,
11:20
as well as everyday images like the elephant in the center of our galaxy.
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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
11:24
When the results of our algorithms on the bottom look very similar
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11:27
to the simulation's truth image on top,
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看起来和上面一排实际图片 十分相似时,
11:29
then we can start to become more confident in our algorithms.
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我们就能对我们的算法 有更多信心了。
11:32
And I really want to emphasize here
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在这里我想强调,
11:34
that all of these pictures were created
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此处所有的图片都是由
11:36
by piecing together little pieces of everyday photographs,
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拼接日常照片而得出的,
11:39
like you'd take with your own personal camera.
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就像你自己用相机拍的照片一样。
11:41
So an image of a black hole we've never seen before
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所以,一张我们从未见过的 黑洞的照片,
11:45
may eventually be created by piecing together pictures we see all the time
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最终却可能由我们日常 熟悉的图片构成:
11:49
of people, buildings, trees, cats and dogs.
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人,楼房,树,小猫,小狗……
11:51
Imaging ideas like this will make it possible for us
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想象这样的想法使拍摄第一张
11:54
to take our very first pictures of a black hole,
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黑洞的图片成为可能,
11:57
and hopefully, verify those famous theories
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同时使我们有望校验
11:59
on which scientists rely on a daily basis.
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科学家们每天所依靠的著名理论。
12:02
But of course, getting imaging ideas like this working
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但是,要想让如此充满想象力的 点子实际工作,
12:04
would never have been possible without the amazing team of researchers
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离不开这些我有幸一同工作的
出色的研究者团队。
12:08
that I have the privilege to work with.
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我仍然对此感到振奋:
12:10
It still amazes me
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12:11
that although I began this project with no background in astrophysics,
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虽然在项目开始时我没有任何 天文学背景知识,
12:14
what we have achieved through this unique collaboration
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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。
12:17
could result in the very first images of a black hole.
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像视界线望远镜这样大项目的成功
12:20
But big projects like the Event Horizon Telescope
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12:22
are successful due to all the interdisciplinary expertise
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是由来自不同学科的人们 用他们各自的专业知识,
12:25
different people bring to the table.
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一起创造的结果。
12:27
We're a melting pot of astronomers,
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我们是一个由天文学家,物理学家,
12:29
physicists, mathematicians and engineers.
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数学家和工程学家构成的大熔炉。
12:31
This is what will make it soon possible
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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。
12:34
to achieve something once thought impossible.
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12:36
I'd like to encourage all of you to go out
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在此我想鼓励你们所有人,走出去,
推动科学的边际,
12:39
and help push the boundaries of science,
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12:41
even if it may at first seem as mysterious to you as a black hole.
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尽管刚开始它看起来可能 和一个黑洞一样神秘。
12:45
Thank you.
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谢谢大家。
12:46
(Applause)
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(掌声)
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