TEDxBeaconStreet
Katie Bouman: How to take a picture of a black hole
凯蒂·伯曼: 怎样拍摄一张黑洞的图片
Filmed:
Readability: 4.5
5,455,257 views
在银河系的中心，有一个靠吞噬高温气体为生的超级黑洞。它将一切接近它的东西吞噬殆尽——连光也不例外。我们看不见黑洞本身，但可以观察到黑洞表面视界线留下的投影，而一张黑洞影子的照片，可以帮助我们找到宇宙重要问题的答案。科学家们曾经认为，拍摄这样一张黑洞的照片需要地球大小的天文望远镜——直到凯蒂·伯曼和一支由天文学家组成的队伍想出了一个聪明的解决办法。
Katie Bouman - Imaging scientist
Katie Bouman is part of an international team of astronomers that's creating the world's largest telescope to take the very first picture of a black hole. Full bio
Katie Bouman is part of an international team of astronomers that's creating the world's largest telescope to take the very first picture of a black hole. Full bio
Double-click the English transcript below to play the video.
00:13
In the movie "Interstellar,"
0
1436
1860
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.
at a supermassive black hole.
1
3320
3327
在明亮气体构成的背景下，
00:18
Set against a backdrop of bright gas,
2
6671
2143
黑洞的巨大引力
00:20
the black hole's massive
gravitational pull
gravitational pull
3
8838
2118
将光线弯曲成环形。
00:22
bends light into a ring.
4
10980
1435
但是，（电影中的）这一幕
并不是一张真正的照片，
并不是一张真正的照片，
00:24
However, this isn't a real photograph,
5
12439
2109
而是电脑合成的效果——
00:26
but a computer graphic rendering --
6
14572
1786
它只是一个对于黑洞
可能样子的艺术表现。
可能样子的艺术表现。
00:28
an artistic interpretation
of what a black hole might look like.
of what a black hole might look like.
7
16382
3390
00:32
A hundred years ago,
8
20401
1166
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。
第一次发表了广义相对论学说。
00:33
Albert Einstein first published
his theory of general relativity.
his theory of general relativity.
9
21591
3601
在之后的数年里，
00:37
In the years since then,
10
25216
1439
科学家们又对此提供了许多佐证。
00:38
scientists have provided
a lot of evidence in support of it.
a lot of evidence in support of it.
11
26679
2973
但相对论中所预测的一点，黑洞，
00:41
But one thing predicted
from this theory, black holes,
from this theory, black holes,
12
29676
3084
却始终无法被直接观察到。
00:44
still have not been directly observed.
13
32784
2350
尽管我们大致知道一个黑洞
看起来应该是什么样，
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,
as to what a black hole might look like,
14
35158
3206
却从未真正拍摄过它。
00:50
we've never actually taken
a picture of one before.
a picture of one before.
15
38388
2779
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.
that that may soon change.
16
41191
4279
在接下来几年内，我们或许就能
见到第一张黑洞的图片。
见到第一张黑洞的图片。
00:57
We may be seeing our first picture
of a black hole in the next couple years.
of a black hole in the next couple years.
17
45494
4164
这一重任会落在一个由
各国科学家组成的团队上，
各国科学家组成的团队上，
01:01
Getting this first picture will come down
to an international team of scientists,
to an international team of scientists,
18
49682
3958
同时需要一个
地球大小的天文望远镜，
地球大小的天文望远镜，
01:05
an Earth-sized telescope
19
53664
1567
以及一个可以让我们合成出
最终图片的算法。
最终图片的算法。
01:07
and an algorithm that puts together
the final picture.
the final picture.
20
55255
2832
尽管今天我不能让你们
见到真正的黑洞图片，
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,
a real picture of a black hole today,
21
58111
3528
我还是想让你们大致了解一下
01:13
I'd like to give you a brief glimpse
into the effort involved
into the effort involved
22
61663
2911
得到第一张（黑洞）图片
所需要的努力。
所需要的努力。
01:16
in getting that first picture.
23
64598
1613
我叫凯蒂·伯曼，
01:19
My name is Katie Bouman,
24
67477
1437
是麻省理工学院的一名博士生。
01:20
and I'm a PhD student at MIT.
25
68938
2566
我在计算机科学实验室中进行
01:23
I do research in a computer science lab
26
71528
2027
让电脑解析图片和视频信息的研究。
01:25
that works on making computers
see through images and video.
see through images and video.
27
73579
3298
尽管我并不是个天文学家，
01:28
But although I'm not an astronomer,
28
76901
2162
今天我还是想向大家展示
01:31
today I'd like to show you
29
79087
1285
我是怎样在这个项目中贡献
自己的一份力量的。
自己的一份力量的。
01:32
how I've been able to contribute
to this exciting project.
to this exciting project.
30
80396
2903
如果你远离城市的灯光，
01:35
If you go out past
the bright city lights tonight,
the bright city lights tonight,
31
83323
2831
你可能有幸看到银河系
01:38
you may just be lucky enough
to see a stunning view
to see a stunning view
32
86178
2436
那令人震撼的美景。
01:40
of the Milky Way Galaxy.
33
88638
1493
而如果你可以穿过百万星辰，
将镜头放大到
将镜头放大到
01:42
And if you could zoom past
millions of stars,
millions of stars,
34
90155
2462
2.6万光年以外的银河系中心，
01:44
26,000 light-years toward the heart
of the spiraling Milky Way,
of the spiraling Milky Way,
35
92641
3755
我们就能抵达（银河系）中央的
一群恒星。
一群恒星。
01:48
we'd eventually reach
a cluster of stars right at the center.
a cluster of stars right at the center.
36
96420
3521
天文学家们已经穿过星尘，使用红外望远镜
01:51
Peering past all the galactic dust
with infrared telescopes,
with infrared telescopes,
37
99965
3206
观察了这些恒星整整十六年。
01:55
astronomers have watched these stars
for over 16 years.
for over 16 years.
38
103195
3867
但是天文学家们所看不到的东西
才是最为壮观的。
才是最为壮观的。
01:59
But it's what they don't see
that is the most spectacular.
that is the most spectacular.
39
107086
3589
这些恒星似乎是在围绕一个
隐形的物体旋转。
隐形的物体旋转。
02:02
These stars seem to orbit
an invisible object.
an invisible object.
40
110699
3066
通过观测这些星星的移动路径，
02:05
By tracking the paths of these stars,
41
113789
2323
天文学家们得出结论，
02:08
astronomers have concluded
42
116136
1294
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
恒星们如此运动的唯一物体
02:09
that the only thing small and heavy
enough to cause this motion
enough to cause this motion
43
117454
3129
就是超级黑洞——
02:12
is a supermassive black hole --
44
120607
1968
它的密度极大，高到它能吸进
周围所有东西，
周围所有东西，
02:14
an object so dense that it sucks up
anything that ventures too close --
anything that ventures too close --
45
122599
4178
甚至光。
02:18
even light.
46
126801
1494
那么，如果我们继续放大下去，
会发生什么？
会发生什么？
02:20
But what happens if we were
to zoom in even further?
to zoom in even further?
47
128319
3061
是不是就可能看见一些，
理论上不可能看到的东西呢？
理论上不可能看到的东西呢？
02:23
Is it possible to see something
that, by definition, is impossible to see?
that, by definition, is impossible to see?
48
131404
4733
02:28
Well, it turns out that if we were
to zoom in at radio wavelengths,
to zoom in at radio wavelengths,
49
136719
3244
事实上，如果我们以
无线电波长放大，
无线电波长放大，
我们会看到一圈光线，
02:31
we'd expect to see a ring of light
50
139987
1682
是由围绕着黑洞的
02:33
caused by the gravitational
lensing of hot plasma
lensing of hot plasma
51
141693
2411
等离子体引力透镜产生的。
02:36
zipping around the black hole.
52
144128
1829
换句话说，
02:37
In other words,
53
145981
1160
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,
on this backdrop of bright material,
54
147165
3171
留下一个圆形的暗影。
02:42
carving out a sphere of darkness.
55
150360
1842
而它周围那明亮的光环
指示了黑洞边境的位置。
指示了黑洞边境的位置。
02:44
This bright ring reveals
the black hole's event horizon,
the black hole's event horizon,
56
152226
3339
在这里，引力作用变得无比巨大，
02:47
where the gravitational pull
becomes so great
becomes so great
57
155589
2400
大到就连光线都无法逃离。
02:50
that not even light can escape.
58
158013
1626
爱因斯坦用公式推测了
这个环的大小和形状，
这个环的大小和形状，
02:51
Einstein's equations predict
the size and shape of this ring,
the size and shape of this ring,
59
159663
2859
所以，给光环拍照不仅很酷，
02:54
so taking a picture of it
wouldn't only be really cool,
wouldn't only be really cool,
60
162546
3208
还能帮助我们检验这些公式在
02:57
it would also help to verify
that these equations hold
that these equations hold
61
165778
2618
黑洞周围的极端环境下是否成立。
03:00
in the extreme conditions
around the black hole.
around the black hole.
62
168420
2466
不过，这个黑洞离我们太过遥远，
03:02
However, this black hole
is so far away from us,
is so far away from us,
63
170910
2558
从地球上看，它非常，非常小——
03:05
that from Earth, this ring appears
incredibly small --
incredibly small --
64
173492
3098
大概就和月球上的一个橘子一样大。
03:08
the same size to us as an orange
on the surface of the moon.
on the surface of the moon.
65
176614
3590
这导致给它拍照变得无比艰难。
03:12
That makes taking a picture of it
extremely difficult.
extremely difficult.
66
180758
2824
03:16
Why is that?
67
184645
1302
为什么呢？
03:18
Well, it all comes down
to a simple equation.
to a simple equation.
68
186512
3188
一切都源于一个简单的等式。
由于衍射现象，
03:21
Due to a phenomenon called diffraction,
69
189724
2416
我们所能看到的
03:24
there are fundamental limits
70
192164
1355
最小物体是有限制的。
03:25
to the smallest objects
that we can possibly see.
that we can possibly see.
71
193543
2670
03:28
This governing equation says
that in order to see smaller and smaller,
that in order to see smaller and smaller,
72
196789
3672
这个等式指出，当想要看到的
东西越来越小时，
东西越来越小时，
望远镜需要变得更大。
03:32
we need to make our telescope
bigger and bigger.
bigger and bigger.
73
200485
2587
但即使是地球上功能最强大的
光学望远镜，
光学望远镜，
03:35
But even with the most powerful
optical telescopes here on Earth,
optical telescopes here on Earth,
74
203096
3069
其分辨率甚至不足以
03:38
we can't even get close
to the resolution necessary
to the resolution necessary
75
206189
2419
让我们得到月球表面的图片。
03:40
to image on the surface of the moon.
76
208632
2198
事实上，这里是一张有史以来
从地球上拍摄的最高清的
从地球上拍摄的最高清的
03:42
In fact, here I show one of the highest
resolution images ever taken
resolution images ever taken
77
210854
3617
月球图片。
03:46
of the moon from Earth.
78
214495
1397
它包含约1.3万个像素，
03:47
It contains roughly 13,000 pixels,
79
215916
2557
而每一个像素里包含超过150万个橘子。
03:50
and yet each pixel would contain
over 1.5 million oranges.
over 1.5 million oranges.
80
218497
4050
03:55
So how big of a telescope do we need
81
223396
1972
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
on the surface of the moon
82
225392
2765
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?
83
228181
2214
事实上，通过计算，
04:02
Well, it turns out
that by crunching the numbers,
that by crunching the numbers,
84
230419
2340
我们可以轻易得出所需的
望远镜的大小，
望远镜的大小，
04:04
you can easily calculate
that we would need a telescope
that we would need a telescope
85
232783
2610
就和整个地球一样大。
04:07
the size of the entire Earth.
86
235417
1393
（笑声）
04:08
(Laughter)
87
236834
1024
而如果我们能够建造出这个
地球大小的望远镜，
地球大小的望远镜，
04:09
If we could build
this Earth-sized telescope,
this Earth-sized telescope,
88
237882
2119
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
that distinctive ring of light
89
240025
2925
独特的光环。
04:14
indicative of the black
hole's event horizon.
hole's event horizon.
90
242974
2183
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
all the detail we see
91
245181
2918
电脑合成图上的那些细节，
04:20
in computer graphic renderings,
92
248123
1506
它仍可以让我们对于
04:21
it would allow us to safely get
our first glimpse
our first glimpse
93
249653
2299
黑洞周围的环境有个大致的了解。
04:23
of the immediate environment
around a black hole.
around a black hole.
94
251976
2487
但是，正如你预料，
04:26
However, as you can imagine,
95
254487
1613
想建造一个地球大小的射电望远镜
是不可能的。
是不可能的。
04:28
building a single-dish telescope
the size of the Earth is impossible.
the size of the Earth is impossible.
96
256124
3624
不过，米克·贾格尔有一句名言：
04:31
But in the famous words of Mick Jagger,
97
259772
1887
“你不可能永远心想事成，
04:33
"You can't always get what you want,
98
261683
1791
但如果你尝试了，说不定就
正好能找到
正好能找到
04:35
but if you try sometimes,
you just might find
you just might find
99
263498
2187
你所需要的东西。”
04:37
you get what you need."
100
265709
1215
通过将遍布全世界的望远镜
连接起来，
连接起来，
04:38
And by connecting telescopes
from around the world,
from around the world,
101
266948
2464
04:41
an international collaboration
called the Event Horizon Telescope
called the Event Horizon Telescope
102
269436
3538
“视界线望远镜”，
一个国际合作项目，诞生了。
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，
地球大小的望远镜，
04:44
is creating a computational telescope
the size of the Earth,
the size of the Earth,
103
272998
3109
能够帮助我们找到
04:48
capable of resolving structure
104
276131
1537
黑洞视界线的结构。
04:49
on the scale of a black
hole's event horizon.
hole's event horizon.
105
277692
2199
这个由无数小望远镜构成的网络
将会在明年拍下它的
将会在明年拍下它的
04:51
This network of telescopes is scheduled
to take its very first picture
to take its very first picture
106
279915
3387
第一张黑洞图片。
04:55
of a black hole next year.
107
283326
1815
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。
都与其他所有望远镜一同工作。
04:57
Each telescope in the worldwide
network works together.
network works together.
108
285165
3338
通过原子钟的准确时间相连，
05:00
Linked through the precise timing
of atomic clocks,
of atomic clocks,
109
288527
2712
各地的研究团队们通过收集
05:03
teams of researchers at each
of the sights freeze light
of the sights freeze light
110
291263
2657
上万千兆字节的数据来定位光线。
05:05
by collecting thousands
of terabytes of data.
of terabytes of data.
111
293944
2962
接下来，这份数据会在
麻省的实验室进行处理。
麻省的实验室进行处理。
05:08
This data is then processed in a lab
right here in Massachusetts.
right here in Massachusetts.
112
296930
5017
那么，这一项目到底是
怎么运作的呢？
怎么运作的呢？
05:13
So how does this even work?
113
301971
1794
大家是否记得，如果要看到
银河系中心的那个黑洞，
银河系中心的那个黑洞，
05:15
Remember if we want to see the black hole
in the center of our galaxy,
in the center of our galaxy,
114
303789
3403
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?
Earth-sized telescope?
115
307216
2982
现在，先假设我们可以
05:22
For just a second,
let's pretend we could build
let's pretend we could build
116
310222
2232
将这个望远镜建造出来。
05:24
a telescope the size of the Earth.
117
312478
1842
这可能有点像是把地球变成
05:26
This would be a little bit
like turning the Earth
like turning the Earth
118
314344
2455
一个巨大的球形迪斯科灯。
05:28
into a giant spinning disco ball.
119
316823
1747
每一面镜子都会收集光线，
05:30
Each individual mirror would collect light
120
318594
2200
然后，我们就可以将这些光线
组合成图片。
组合成图片。
05:32
that we could then combine
together to make a picture.
together to make a picture.
121
320818
2597
但是，现在，假设我们将
大多数镜子移走，
大多数镜子移走，
05:35
However, now let's say
we remove most of those mirrors
we remove most of those mirrors
122
323439
2661
只有几片留了下来。
05:38
so only a few remained.
123
326124
1972
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,
this information together,
124
328120
2877
但现在，图片中有很多洞。
05:43
but now there are a lot of holes.
125
331021
1993
这几片留下来的镜子就代表了
地球上的几处天文望远镜。
地球上的几处天文望远镜。
05:45
These remaining mirrors represent
the locations where we have telescopes.
the locations where we have telescopes.
126
333038
4373
这对于制成一张图片来说，
还远远不够。
还远远不够。
05:49
This is an incredibly small number
of measurements to make a picture from.
of measurements to make a picture from.
127
337435
4079
不过，尽管我们只在寥寥几处
地方收集光线，
地方收集光线，
05:53
But although we only collect light
at a few telescope locations,
at a few telescope locations,
128
341538
3838
每当地球旋转时，我们便可以
得到新的信息。
得到新的信息。
05:57
as the Earth rotates, we get to see
other new measurements.
other new measurements.
129
345400
3423
换言之，当迪斯科球旋转时，
镜子会改变位置，
镜子会改变位置，
06:00
In other words, as the disco ball spins,
those mirrors change locations
those mirrors change locations
130
348847
3819
而我们就可以看到图片的各个部分。
06:04
and we get to observe
different parts of the image.
different parts of the image.
131
352690
2899
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，
可以将迪斯科球上的空缺部分填满，
06:07
The imaging algorithms we develop
fill in the missing gaps of the disco ball
fill in the missing gaps of the disco ball
132
355613
4018
从而建造出隐藏的黑洞图片。
06:11
in order to reconstruct
the underlying black hole image.
the underlying black hole image.
133
359655
3033
如果我们能在地球上每一处
都装上望远镜，
都装上望远镜，
06:14
If we had telescopes located
everywhere on the globe --
everywhere on the globe --
134
362712
2636
或者说能有整个迪斯科球，
06:17
in other words, the entire disco ball --
135
365372
1941
那么这个算法并不算重要。
06:19
this would be trivial.
136
367337
1284
但现在我们只有少量的样本，
06:20
However, we only see a few samples,
and for that reason,
and for that reason,
137
368645
3322
所以，可能有无数张图像
06:23
there are an infinite number
of possible images
of possible images
138
371991
2388
符合望远镜所测量到的信息。
06:26
that are perfectly consistent
with our telescope measurements.
with our telescope measurements.
139
374403
2964
但并不是每一张图片都一样。
06:29
However, not all images are created equal.
140
377391
3016
有些图片，比其他一些
看起来更像我们想象中的图片。
看起来更像我们想象中的图片。
06:32
Some of those images look more like
what we think of as images than others.
what we think of as images than others.
141
380849
4458
所以我在拍摄黑洞
这一项目中的任务是，
这一项目中的任务是，
06:37
And so, my role in helping to take
the first image of a black hole
the first image of a black hole
142
385331
3222
开发一种既可以找到最合理图像，
06:40
is to design algorithms that find
the most reasonable image
the most reasonable image
143
388577
2932
又能使图像符合望远镜
所测量到的信息的算法。
所测量到的信息的算法。
06:43
that also fits the telescope measurements.
144
391533
2222
06:46
Just as a forensic sketch artist
uses limited descriptions
uses limited descriptions
145
394727
3942
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，
画出一张画像一样，
06:50
to piece together a picture using
their knowledge of face structure,
their knowledge of face structure,
146
398693
3514
我正在开发的图片算法，
是使用望远镜提供的有限数据
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
use our limited telescope data
147
402231
3315
来生成一张看起来像是
宇宙里的东西的图片。
宇宙里的东西的图片。
06:57
to guide us to a picture that also
looks like stuff in our universe.
looks like stuff in our universe.
148
405570
4322
通过这些算法，我们能从散乱
而充满干扰的数据中
而充满干扰的数据中
07:01
Using these algorithms,
we're able to piece together pictures
we're able to piece together pictures
149
409916
3651
合成一张图片。
07:05
from this sparse, noisy data.
150
413591
2180
这里是一个用模拟数据
进行重现的例子：
进行重现的例子：
07:07
So here I show a sample reconstruction
done using simulated data,
done using simulated data,
151
415795
4529
我们假设将望远镜指向
07:12
when we pretend to point our telescopes
152
420348
1933
银河系中心的黑洞。
07:14
to the black hole
in the center of our galaxy.
in the center of our galaxy.
153
422305
2585
尽管这只是一个模拟，像这样的
重建工作给了我们
重建工作给了我们
07:16
Although this is just a simulation,
reconstruction such as this give us hope
reconstruction such as this give us hope
154
424914
4455
真正给黑洞拍摄可行照片的希望，
07:21
that we'll soon be able to reliably take
the first image of a black hole
the first image of a black hole
155
429393
3453
之后便可以决定其光环的大小。
07:24
and from it, determine
the size of its ring.
the size of its ring.
156
432870
2595
虽然我很想继续描绘
这个算法的细节，
这个算法的细节，
07:28
Although I'd love to go on
about all the details of this algorithm,
about all the details of this algorithm,
157
436118
3199
07:31
luckily for you, I don't have the time.
158
439341
2174
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
07:33
But I'd still like
to give you a brief idea
to give you a brief idea
159
441539
2001
我们是怎样定义宇宙的样子，
07:35
of how we define
what our universe looks like,
what our universe looks like,
160
443564
2302
以及是怎样以此来重建
和校验我们的结果的。
和校验我们的结果的。
07:37
and how we use this to reconstruct
and verify our results.
and verify our results.
161
445890
4466
由于有无数种可以完美解释
07:42
Since there are an infinite number
of possible images
of possible images
162
450380
2496
望远镜测量结果的图片，
07:44
that perfectly explain
our telescope measurements,
our telescope measurements,
163
452900
2365
我们需要找到一个方式进行挑选。
07:47
we have to choose
between them in some way.
between them in some way.
164
455289
2605
我们会按照这些图片是
07:49
We do this by ranking the images
165
457918
1838
真正黑洞图片的可能性进行排序，
07:51
based upon how likely they are
to be the black hole image,
to be the black hole image,
166
459780
2834
然后选出可能性最高的那一张。
07:54
and then choosing the one
that's most likely.
that's most likely.
167
462638
2482
我这话到底是什么意思呢？
07:57
So what do I mean by this exactly?
168
465144
2195
07:59
Let's say we were trying to make a model
169
467862
1978
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。
可能性的模型。
08:01
that told us how likely an image
were to appear on Facebook.
were to appear on Facebook.
170
469864
3183
我们希望这个模型能指出
08:05
We'd probably want the model to say
171
473071
1701
08:06
it's pretty unlikely that someone
would post this noise image on the left,
would post this noise image on the left,
172
474796
3557
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
would post a selfie
173
478377
2419
画出一张图片一样，
08:12
like this one on the right.
174
480820
1334
中间那张图有点模糊，
08:14
The image in the middle is blurry,
175
482178
1639
所以它被发表的可能性
08:15
so even though it's more likely
we'd see it on Facebook
we'd see it on Facebook
176
483841
2639
比左边的噪点图像大，
08:18
compared to the noise image,
177
486504
1360
但比右边自拍发表的可能性要小。
08:19
it's probably less likely we'd see it
compared to the selfie.
compared to the selfie.
178
487888
2960
但是当模型的主角变成
黑洞的照片时，
黑洞的照片时，
08:22
But when it comes to images
from the black hole,
from the black hole,
179
490872
2290
一个难题出现了：我们从未
见过真正的黑洞。
见过真正的黑洞。
08:25
we're posed with a real conundrum:
we've never seen a black hole before.
we've never seen a black hole before.
180
493186
3502
在这样的情况下，
什么样的图才更像黑洞，
什么样的图才更像黑洞，
08:28
In that case, what is a likely
black hole image,
black hole image,
181
496712
2291
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?
about the structure of black holes?
182
499027
2938
我们或许能够使用模拟试验
得出的图片，
得出的图片，
08:33
We could try to use images
from simulations we've done,
from simulations we've done,
183
501989
2632
比如《星际穿越》里的那张黑洞图。
08:36
like the image of the black hole
from "Interstellar,"
from "Interstellar,"
184
504645
2530
但这样做可能会引起
一些严重的问题。
一些严重的问题。
08:39
but if we did this,
it could cause some serious problems.
it could cause some serious problems.
185
507199
2938
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?
if Einstein's theories didn't hold?
186
510161
3380
我们仍然想要得到一张
准确而真实的图片。
准确而真实的图片。
08:45
We'd still want to reconstruct
an accurate picture of what was going on.
an accurate picture of what was going on.
187
513565
3961
而如果我们在算法中掺入太多
爱因斯坦的公式，
爱因斯坦的公式，
08:49
If we bake Einstein's equations
too much into our algorithms,
too much into our algorithms,
188
517550
3371
最终只会看到我们所希望看到的。
08:52
we'll just end up seeing
what we expect to see.
what we expect to see.
189
520945
2755
换句话说，我们想保留在银河系中心
08:55
In other words,
we want to leave the option open
we want to leave the option open
190
523724
2276
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.
at the center of our galaxy.
191
526024
2923
（笑声）
09:00
(Laughter)
192
528971
1057
不同类型的照片拥有
完全不同的特征。
完全不同的特征。
09:02
Different types of images have
very distinct features.
very distinct features.
193
530052
2989
我们可以轻松分辨出
一张黑洞模拟图
一张黑洞模拟图
09:05
We can easily tell the difference
between black hole simulation images
between black hole simulation images
194
533065
3548
和我们日常拍的照片的差别。
09:08
and images we take
every day here on Earth.
every day here on Earth.
195
536637
2276
我们需要在不过度提供某类图片
特征的情况下，
特征的情况下，
09:10
We need a way to tell our algorithms
what images look like
what images look like
196
538937
3104
告诉我们的算法，一张正常的图片
应该是什么样。
应该是什么样。
09:14
without imposing one type
of image's features too much.
of image's features too much.
197
542065
3249
做到这一点的一种方法是，
09:17
One way we can try to get around this
198
545865
1893
向算法展示拥有不同特征的图片，
09:19
is by imposing the features
of different kinds of images
of different kinds of images
199
547782
3062
然后看看这些图片会怎样
影响重建的结果。
影响重建的结果。
09:22
and seeing how the type of image we assume
affects our reconstructions.
affects our reconstructions.
200
550868
4130
09:27
If all images' types produce
a very similar-looking image,
a very similar-looking image,
201
555712
3491
如果不同类型的图片都产生出了
差不多的图像，
差不多的图像，
那么我们便可以更有信心了，
09:31
then we can start to become more confident
202
559227
2057
我们对图片的假设并没有
导致结果出现太大偏差。
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.
are not biasing this picture that much.
203
561308
4173
这就有点像让来自不同国家的
三个法医素描师
三个法医素描师
09:37
This is a little bit like
giving the same description
giving the same description
204
565505
2990
根据同样的文字描述来作画。
09:40
to three different sketch artists
from all around the world.
from all around the world.
205
568519
2996
如果他们画出的脸都差不多，
09:43
If they all produce
a very similar-looking face,
a very similar-looking face,
206
571539
2860
那么我们就能比较确信，
09:46
then we can start to become confident
207
574423
1793
他们各自的文化背景
并没有影响到他们的画。
并没有影响到他们的画。
09:48
that they're not imposing their own
cultural biases on the drawings.
cultural biases on the drawings.
208
576240
3616
将不同图片的特征赋予
（算法）的一个方法
（算法）的一个方法
09:51
One way we can try to impose
different image features
different image features
209
579880
3315
就是使用现有的图片的碎片特征。
09:55
is by using pieces of existing images.
210
583219
2441
09:58
So we take a large collection of images,
211
586214
2160
所以，我们将大量的图像
分解成无数小图片，
10:00
and we break them down
into their little image patches.
into their little image patches.
212
588398
2718
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.
a little bit like pieces of a puzzle.
213
591140
4285
我们用其中常见的拼图碎片
来组合成一张
来组合成一张
10:07
And we use commonly seen puzzle pieces
to piece together an image
to piece together an image
214
595449
4278
符合望远镜所测量数据的完整图片。
10:11
that also fits our telescope measurements.
215
599751
2452
10:15
Different types of images have
very distinctive sets of puzzle pieces.
very distinctive sets of puzzle pieces.
216
603040
3743
不同类型的图片拥有
完全不同的拼图碎片。
完全不同的拼图碎片。
所以，当我们使用相同的数据和
10:18
So what happens when we take the same data
217
606807
2806
截然不同的拼图类型来
重现图像时，会发生什么呢？
重现图像时，会发生什么呢？
10:21
but we use different sets of puzzle pieces
to reconstruct the image?
to reconstruct the image?
218
609637
4130
我们先从黑洞模拟类的拼图开始。
10:25
Let's first start with black hole
image simulation puzzle pieces.
image simulation puzzle pieces.
219
613791
4766
这张图看起来还比较合理。
10:30
OK, this looks reasonable.
220
618581
1591
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.
a black hole to look like.
221
620196
2694
但我们得到这个结果
10:34
But did we just get it
222
622914
1193
是否仅仅是因为我们拿的是
黑洞模拟拼图呢？
黑洞模拟拼图呢？
10:36
because we just fed it little pieces
of black hole simulation images?
of black hole simulation images?
223
624131
3314
我们再来试试另一组拼图，
10:39
Let's try another set of puzzle pieces
224
627469
1880
这组拼图由宇宙中不是黑洞的
各种天体构成。
各种天体构成。
10:41
from astronomical, non-black hole objects.
225
629373
2509
10:44
OK, we get a similar-looking image.
226
632914
2126
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，
会怎么样呢，
10:47
And then how about pieces
from everyday images,
from everyday images,
227
635064
2236
就像你每天拿自己的相机
拍的那种照片？
拍的那种照片？
10:49
like the images you take
with your own personal camera?
with your own personal camera?
228
637324
2785
10:53
Great, we see the same image.
229
641312
2115
太好了，我们看到了和之前
一样的图像。
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，
得出一样的图片时，
10:55
When we get the same image
from all different sets of puzzle pieces,
from all different sets of puzzle pieces,
230
643451
3366
我们就有充足的自信说
10:58
then we can start to become more confident
231
646841
2046
我们对图片进行的推测，
11:00
that the image assumptions we're making
232
648911
1966
并没有引起最终结果的太大偏差。
11:02
aren't biasing the final
image we get too much.
image we get too much.
233
650901
2921
我们能做的另一件事是，
用同一组拼图，
用同一组拼图，
11:05
Another thing we can do is take
the same set of puzzle pieces,
the same set of puzzle pieces,
234
653846
3253
比如源自日常图片的那一种，
11:09
such as the ones derived
from everyday images,
from everyday images,
235
657123
2489
来得到不同类型的源图片。
11:11
and use them to reconstruct
many different kinds of source images.
many different kinds of source images.
236
659636
3600
所以，在我们的模拟试验中，
11:15
So in our simulations,
237
663260
1271
我们假设黑洞看起来像一个
非黑洞天体，
非黑洞天体，
11:16
we pretend a black hole looks like
astronomical non-black hole objects,
astronomical non-black hole objects,
238
664555
3775
以及在银河系中心的一头大象。
11:20
as well as everyday images like
the elephant in the center of our galaxy.
the elephant in the center of our galaxy.
239
668354
3849
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
on the bottom look very similar
240
672227
3168
看起来和上面一排实际图片
十分相似时，
十分相似时，
11:27
to the simulation's truth image on top,
241
675419
2096
我们就能对我们的算法
有更多信心了。
有更多信心了。
11:29
then we can start to become
more confident in our algorithms.
more confident in our algorithms.
242
677539
3346
在这里我想强调，
11:32
And I really want to emphasize here
243
680909
1867
此处所有的图片都是由
11:34
that all of these pictures were created
244
682800
1934
拼接日常照片而得出的，
11:36
by piecing together little pieces
of everyday photographs,
of everyday photographs,
245
684758
2936
就像你自己用相机拍的照片一样。
11:39
like you'd take with your own
personal camera.
personal camera.
246
687718
2215
所以，一张我们从未见过的
黑洞的照片，
黑洞的照片，
11:41
So an image of a black hole
we've never seen before
we've never seen before
247
689957
3276
最终却可能由我们日常
熟悉的图片构成：
熟悉的图片构成：
11:45
may eventually be created by piecing
together pictures we see all the time
together pictures we see all the time
248
693257
3943
人，楼房，树，小猫，小狗……
11:49
of people, buildings,
trees, cats and dogs.
trees, cats and dogs.
249
697224
2745
想象这样的想法使拍摄第一张
11:51
Imaging ideas like this
will make it possible for us
will make it possible for us
250
699993
2645
黑洞的图片成为可能，
11:54
to take our very first pictures
of a black hole,
of a black hole,
251
702662
2619
同时使我们有望校验
11:57
and hopefully, verify
those famous theories
those famous theories
252
705305
2447
科学家们每天所依靠的著名理论。
11:59
on which scientists rely on a daily basis.
253
707776
2421
但是，要想让如此充满想象力的
点子实际工作，
点子实际工作，
12:02
But of course, getting
imaging ideas like this working
imaging ideas like this working
254
710221
2608
离不开这些我有幸一同工作的
12:04
would never have been possible
without the amazing team of researchers
without the amazing team of researchers
255
712853
3322
出色的研究者团队。
12:08
that I have the privilege to work with.
256
716199
1887
我仍然对此感到振奋：
12:10
It still amazes me
257
718110
1163
虽然在项目开始时我没有任何
天文学背景知识，
天文学背景知识，
12:11
that although I began this project
with no background in astrophysics,
with no background in astrophysics,
258
719297
3351
我们通过这一独特合作
所达成的成就，
所达成的成就，
12:14
what we have achieved
through this unique collaboration
through this unique collaboration
259
722672
2619
可能导致世界上第一幅
黑洞照片的诞生。
黑洞照片的诞生。
12:17
could result in the very first
images of a black hole.
images of a black hole.
260
725315
2759
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
the Event Horizon Telescope
261
728098
2698
是由来自不同学科的人们
用他们各自的专业知识，
用他们各自的专业知识，
12:22
are successful due to all
the interdisciplinary expertise
the interdisciplinary expertise
262
730820
2814
一起创造的结果。
12:25
different people bring to the table.
263
733658
1790
我们是一个由天文学家，物理学家，
12:27
We're a melting pot of astronomers,
264
735472
1706
12:29
physicists, mathematicians and engineers.
265
737202
2232
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
12:31
This is what will make it soon possible
266
739458
2554
一个看起来不可能达成的
成就的原因。
成就的原因。
12:34
to achieve something
once thought impossible.
once thought impossible.
267
742036
2853
在此我想鼓励你们所有人，走出去，
12:36
I'd like to encourage all of you to go out
268
744913
2256
推动科学的边际，
12:39
and help push the boundaries of science,
269
747193
2096
尽管刚开始它看起来可能
和一个黑洞一样神秘。
和一个黑洞一样神秘。
12:41
even if it may at first seem
as mysterious to you as a black hole.
as mysterious to you as a black hole.
270
749313
3901
谢谢大家。
12:45
Thank you.
271
753238
1174
（掌声）
12:46
(Applause)
272
754436
2397
Translated by Anney Ye
ABOUT THE SPEAKER
Katie Bouman - Imaging scientistKatie Bouman is part of an international team of astronomers that's creating the world's largest telescope to take the very first picture of a black hole.
Why you should listen
It is believed that the heart of the Milky Way hosts a four-million-solar-mass black hole feeding off a spinning disk of hot gas. An image of the shadow cast by the event horizon of the black hole could help to address a number of important scientific questions. For instance, does Einstein's theory of general relativity hold in extreme conditions? Unfortunately, the event horizon of this black hole appears so small in the sky that imaging it would require a single-dish radio telescope the size of the entire Earth. Although a single-dish telescope this large is unrealizable, by connecting disjoint radio telescopes located all around the globe, Katie Bouman and a team of astronomers are creating an Earth-sized computational telescope -- the Event Horizon Telescope -- that is capable of taking the very first up-close picture of a black hole.
Bouman is a PhD candidate in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT). The focus of her research is on using emerging computational methods to push the boundaries of interdisciplinary imaging. By combining techniques from both astronomy and computer science, Bouman has been working on developing innovative ways to combine the information from the Event Horizon Telescope network to produce the first picture of a black hole. Her work on imaging for the Event Horizon Telescope has been featured on BBC, The Boston Globe, The Washington Post, Popular Science and NPR.
Katie Bouman | Speaker | TED.com