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

3,362,713 views ・ 2017-04-28

TED


Dvaput kliknite na engleske titlove ispod za reprodukciju videozapisa.

Prevoditelj: Katarina Mikjel Recezent: Sanda L
00:13
In the movie "Interstellar,"
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U filmu „Interstellar”
00:15
we get an up-close look at a supermassive black hole.
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možemo izbliza vidjeti supermasivnu crnu rupu.
00:18
Set against a backdrop of bright gas,
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Na pozadini koju stvara zagrijani plin
00:20
the black hole's massive gravitational pull
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ogromna gravitacijska sila te crne rupe
00:22
bends light into a ring.
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zakreće svjetlost u prsten.
00:24
However, this isn't a real photograph,
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Međutim, to nije stvarna fotografija,
00:26
but a computer graphic rendering --
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već računalno-grafički prikaz –
00:28
an artistic interpretation of what a black hole might look like.
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umjetnička interpretacija mogućeg izgleda crne rupe.
00:32
A hundred years ago,
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Prije sto godina
00:33
Albert Einstein first published his theory of general relativity.
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Albert Einstein objavio je svoju teoriju opće relativnosti.
00:37
In the years since then,
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U godinama koje su uslijedile
00:38
scientists have provided a lot of evidence in support of it.
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znanstvenici su pronašli mnogo dokaza u prilog toj teoriji.
00:41
But one thing predicted from this theory, black holes,
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No jedna stvar koju ta teorija predviđa, crne rupe,
00:44
still have not been directly observed.
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još nije izravno opažena.
00:47
Although we have some idea as to what a black hole might look like,
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Iako imamo predodžbe o tome kako bi crna rupa mogla izgledati,
00:50
we've never actually taken a picture of one before.
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nikad je zapravo nismo uspjeli fotografirati.
00:53
However, you might be surprised to know that that may soon change.
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Ipak, možda ćete se iznenaditi kad čujete da bi se to uskoro moglo promijeniti.
00:57
We may be seeing our first picture of a black hole in the next couple years.
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U sljedećih par godina mogli bismo vidjeti prvu fotografiju crne rupe.
01:01
Getting this first picture will come down to an international team of scientists,
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Tu prvu fotografiju dobit ćemo
zahvaljujući međunarodnom timu znanstvenika,
01:05
an Earth-sized telescope
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teleskopu veličine Zemlje
01:07
and an algorithm that puts together the final picture.
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i algoritmu koji će složiti konačnu sliku.
01:10
Although I won't be able to show you a real picture of a black hole today,
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Premda vam danas još ne mogu pokazati stvarnu fotografiju crne rupe,
01:13
I'd like to give you a brief glimpse into the effort involved
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htjela bih vam ukratko predstaviti napore uložene
01:16
in getting that first picture.
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u dobivanje te prve fotografije.
01:19
My name is Katie Bouman,
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Zovem se Katie Bouman
01:20
and I'm a PhD student at MIT.
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i doktorandica sam na MIT-u.
01:23
I do research in a computer science lab
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Istraživačica sam u laboratoriju računalnih znanosti,
01:25
that works on making computers see through images and video.
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gdje razvijamo računalni vid s pomoću slika i videozapisa.
01:28
But although I'm not an astronomer,
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No iako nisam astronom,
danas bih vam htjela pokazati
01:31
today I'd like to show you
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01:32
how I've been able to contribute to this exciting project.
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kako sam uspjela doprinijeti tom uzbudljivom projektu.
01:35
If you go out past the bright city lights tonight,
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Odmaknete li se večeras od jarkih gradskih svjetala,
01:38
you may just be lucky enough to see a stunning view
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možda budete imali sreće pa ugledate zapanjujući prizor
01:40
of the Milky Way Galaxy.
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galaktike Mliječne staze.
01:42
And if you could zoom past millions of stars,
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Kad biste mogli prozujati pored milijuna zvijezda
01:44
26,000 light-years toward the heart of the spiraling Milky Way,
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26 000 svjetlosnih godina prema središtu spiralne Mliječne staze,
01:48
we'd eventually reach a cluster of stars right at the center.
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naposljetku biste stigli do skupine zvijezda u samom središtu.
01:51
Peering past all the galactic dust with infrared telescopes,
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Gledajući infracrvenim teleskopima kroz galaktičku prašinu
01:55
astronomers have watched these stars for over 16 years.
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astronomi su promatrali te zvijezde više od 16 godina.
01:59
But it's what they don't see that is the most spectacular.
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Međutim, najspektakularnije je zapravo ono što ne vide.
02:02
These stars seem to orbit an invisible object.
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Čini se kao da te zvijezde kruže oko nevidljiva objekta.
02:05
By tracking the paths of these stars,
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Prateći putanje tih zvijezda
02:08
astronomers have concluded
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astronomi su zaključili da je
02:09
that the only thing small and heavy enough to cause this motion
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jedina stvar koja je dovoljno mala i teška da prouzroči takvo kretanje
02:12
is a supermassive black hole --
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supermasivna crna rupa –
02:14
an object so dense that it sucks up anything that ventures too close --
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objekt toliko gust da usiše sve u svojoj blizini –
02:18
even light.
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čak i svjetlost.
02:20
But what happens if we were to zoom in even further?
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No što se događa ako se još više približimo?
02:23
Is it possible to see something that, by definition, is impossible to see?
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Je li moguće vidjeti nešto što je, u pravilu, nemoguće vidjeti?
02:28
Well, it turns out that if we were to zoom in at radio wavelengths,
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Pa, čini se da ako uvećamo sliku s pomoću radiovalova,
02:31
we'd expect to see a ring of light
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trebali bismo vidjeti svjetlosni prsten
02:33
caused by the gravitational lensing of hot plasma
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koji nastaje zbog utjecaja gravitacijskih leća na vruću plazmu
02:36
zipping around the black hole.
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što brzo kruži oko crne rupe.
02:37
In other words,
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Drugim riječima,
02:39
the black hole casts a shadow on this backdrop of bright material,
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crna rupa baca sjenu na tu svjetlosnu pozadinu
02:42
carving out a sphere of darkness.
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oblikujući kuglu tame.
02:44
This bright ring reveals the black hole's event horizon,
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Taj svijetli prsten otkriva obzor događaja crne rupe,
02:47
where the gravitational pull becomes so great
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gdje je gravitacijska sila toliko snažna
da joj čak ni svjetlost ne može pobjeći.
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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Einsteinove jednadžbe predviđaju veličinu i oblik tog prstena,
02:54
so taking a picture of it wouldn't only be really cool,
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tako da njegova fotografija ne bi samo bila fora,
02:57
it would also help to verify that these equations hold
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nego bi nam pomogla i da provjerimo vrijede li te jednadžbe
03:00
in the extreme conditions around the black hole.
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u ekstremnim uvjetima oko crne rupe.
03:02
However, this black hole is so far away from us,
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Međutim, ta crna rupa toliko je udaljena od nas
03:05
that from Earth, this ring appears incredibly small --
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da se sa Zemlje taj prsten doima nevjerojatno malim –
03:08
the same size to us as an orange on the surface of the moon.
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tako bi nam nekako izgledala naranča na Mjesecu.
03:12
That makes taking a picture of it extremely difficult.
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Zbog toga ga je vrlo teško fotografirati.
03:16
Why is that?
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Zašto je tomu tako?
03:18
Well, it all comes down to a simple equation.
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Pa, sve se svodi na jednostavnu jednadžbu.
03:21
Due to a phenomenon called diffraction,
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Zbog pojave poznate kao difrakcija,
03:24
there are fundamental limits
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postoje temeljna ograničenja
03:25
to the smallest objects that we can possibly see.
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koja određuju koliki su najmanji vidljivi predmeti.
03:28
This governing equation says that in order to see smaller and smaller,
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Prema toj glavnoj jednadžbi, da bismo vidjeli sve manje i manje stvari,
03:32
we need to make our telescope bigger and bigger.
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trebaju nam sve veći i veći teleskopi.
03:35
But even with the most powerful optical telescopes here on Earth,
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No čak ni najsnažniji optički teleskopi na Zemlji
03:38
we can't even get close to the resolution necessary
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nemaju rezoluciju koja nam je potrebna
03:40
to image on the surface of the moon.
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kako bismo fotografirali objekt na površini Mjeseca.
03:42
In fact, here I show one of the highest resolution images ever taken
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Ustvari, ovo je jedna od fotografija s dosad najvećom rezolucijom
na kojoj je Mjesec, fotografiran sa Zemlje.
03:46
of the moon from Earth.
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03:47
It contains roughly 13,000 pixels,
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Sadržava oko 13 000 piksela,
03:50
and yet each pixel would contain over 1.5 million oranges.
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a u svaki od njih „stane” preko 1,5 milijuna naranči.
03:55
So how big of a telescope do we need
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Koliki bi dakle trebao biti teleskop
03:57
in order to see an orange on the surface of the moon
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kako bismo vidjeli naranču na površini Mjeseca,
04:00
and, by extension, our black hole?
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a tako i našu crnu rupu?
04:02
Well, it turns out that by crunching the numbers,
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Pa, jednostavan matematički izračun pokazuje
04:04
you can easily calculate that we would need a telescope
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da nam je potreban teleskop
veličine cijele Zemlje.
04:07
the size of the entire Earth.
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04:08
(Laughter)
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(Smijeh)
04:09
If we could build this Earth-sized telescope,
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Kad bismo mogli izgraditi teleskop veličine Zemlje,
uspjeli bismo tek nazreti taj karakterističan svjetlosni prsten
04:12
we could just start to make out that distinctive ring of light
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04:14
indicative of the black hole's event horizon.
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koji označava obzor događaja crne rupe.
Iako se na toj slici ne bi vidjeli svi detalji koji su vidljivi
04:17
Although this picture wouldn't contain all the detail we see
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na računalno-grafičkom prikazu,
04:20
in computer graphic renderings,
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04:21
it would allow us to safely get our first glimpse
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svakako bi nam dala prvi uvid
04:23
of the immediate environment around a black hole.
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u neposredno okruženje crne rupe.
04:26
However, as you can imagine,
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Međutim, kao što možete i zamisliti,
04:28
building a single-dish telescope the size of the Earth is impossible.
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nemoguće je izgraditi jedinstveni teleskop veličine Zemlje.
04:31
But in the famous words of Mick Jagger,
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No, slavnim riječima Micka Jaggera,
04:33
"You can't always get what you want,
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„ne možeš uvijek dobiti ono što želiš,
04:35
but if you try sometimes, you just might find
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no pokušaš li ponekad, možda shvatiš
04:37
you get what you need."
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da si dobio ono što ti treba.”
04:38
And by connecting telescopes from around the world,
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Povezivanjem teleskopa diljem svijeta,
04:41
an international collaboration called the Event Horizon Telescope
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međunarodni projekt Event Horizon Telescope
04:44
is creating a computational telescope the size of the Earth,
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stvara računalni teleskop veličine Zemlje,
koji će moći zabilježiti strukturu
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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razmjera obzora događaja crne rupe.
04:51
This network of telescopes is scheduled to take its very first picture
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Ta mreža teleskopa trebala bi prvi put fotografirati
crnu rupu sljedeće godine.
04:55
of a black hole next year.
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04:57
Each telescope in the worldwide network works together.
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Svi teleskopi u toj svjetskoj mreži rade zajedno.
05:00
Linked through the precise timing of atomic clocks,
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Povezani preciznim mjerenjem vremena s pomoću atomskih satova,
05:03
teams of researchers at each of the sites freeze light
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timovi istraživača na svakoj lokaciji zamrzavaju svjetlost
05:05
by collecting thousands of terabytes of data.
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prikupljajući tisuće terabajta podataka.
05:08
This data is then processed in a lab right here in Massachusetts.
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Ti se podaci potom obrađuju u laboratoriju ovdje u Massachusettsu.
05:13
So how does this even work?
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Kako to zapravo funkcionira?
05:15
Remember if we want to see the black hole in the center of our galaxy,
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Sjećate li se da, ako želimo vidjeti crnu rupu u središtu naše galaktike,
trebamo izgraditi nemoguće velik teleskop veličine Zemlje?
05:19
we need to build this impossibly large Earth-sized telescope?
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05:22
For just a second, let's pretend we could build
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Zamislimo na trenutak da je moguće izgraditi
05:24
a telescope the size of the Earth.
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teleskop veličine Zemlje.
05:26
This would be a little bit like turning the Earth
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To bi pomalo nalikovalo pretvaranju Zemlje
05:28
into a giant spinning disco ball.
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u divovsku disko-kuglu koja se vrti.
05:30
Each individual mirror would collect light
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Svako pojedino zrcalo skupljalo bi svjetlost,
05:32
that we could then combine together to make a picture.
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koju bismo zatim spojili u jednu sliku.
05:35
However, now let's say we remove most of those mirrors
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Međutim, recimo da uklonimo većinu tih zrcala
05:38
so only a few remained.
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i da ih ostane tek nekolicina.
05:40
We could still try to combine this information together,
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I dalje bismo mogli kombinirati podatke koje su prikupili,
05:43
but now there are a lot of holes.
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no u tom bi slučaju ostalo puno praznina.
05:45
These remaining mirrors represent the locations where we have telescopes.
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Ta preostala zrcala predstavljaju lokacije na koje smo postavili teleskope.
05:49
This is an incredibly small number of measurements to make a picture from.
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To nam daje nevjerojatno malen broj podataka za izradu slike.
05:53
But although we only collect light at a few telescope locations,
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No iako skupljamo svjetlost na samo nekoliko lokacija s teleskopima,
05:57
as the Earth rotates, we get to see other new measurements.
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budući da se Zemlja okreće, dobivamo i druge, nove podatke.
06:00
In other words, as the disco ball spins, those mirrors change locations
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Drugim riječima, kako se disko-kugla vrti, ta zrcala mijenjaju lokacije
06:04
and we get to observe different parts of the image.
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i možemo vidjeti različite dijelove slike.
06:07
The imaging algorithms we develop fill in the missing gaps of the disco ball
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Algoritmi za stvaranje slike koje smo razvili popunjavaju praznine na disko-kugli
06:11
in order to reconstruct the underlying black hole image.
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kako bismo rekonstruirali osnovnu sliku crne rupe.
06:14
If we had telescopes located everywhere on the globe --
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Kad bismo rasporedili teleskope po cijelom planetu,
06:17
in other words, the entire disco ball --
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drugim riječima, po cijeloj disko-kugli –
06:19
this would be trivial.
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bio bi to čas posla.
06:20
However, we only see a few samples, and for that reason,
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Međutim, vidimo svega nekoliko uzoraka i zbog toga
06:23
there are an infinite number of possible images
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postoji beskonačan broj mogućih slika
06:26
that are perfectly consistent with our telescope measurements.
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koje su posve u skladu s podacima naših teleskopa.
06:29
However, not all images are created equal.
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Međutim, nisu sve slike jednake.
06:32
Some of those images look more like what we think of as images than others.
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Neke od njih više nalikuju onome što obično smatramo slikama od drugih.
06:37
And so, my role in helping to take the first image of a black hole
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Moja je uloga u stvaranju prve fotografije crne rupe
06:40
is to design algorithms that find the most reasonable image
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dizajnirati algoritme koji će pronaći najsmisleniju sliku
06:43
that also fits the telescope measurements.
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koja je u skladu s podacima teleskopa.
06:46
Just as a forensic sketch artist uses limited descriptions
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Baš kao što se forenzički crtač koristi manjkavim opisima
06:50
to piece together a picture using their knowledge of face structure,
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kako bi, uz pomoć svojeg poznavanja strukture lica, sastavio sliku,
06:54
the imaging algorithms I develop use our limited telescope data
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algoritmi za dobivanje slike koje sam razvila
koriste se manjkavim podacima naših teleskopa
06:57
to guide us to a picture that also looks like stuff in our universe.
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kako bi nas doveli do slike koja izgleda kao nešto iz našeg svemira.
07:01
Using these algorithms, we're able to piece together pictures
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S pomoću tih algoritama možemo sastaviti slike
07:05
from this sparse, noisy data.
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od tih malobrojnih, nejasnih podataka.
07:07
So here I show a sample reconstruction done using simulated data,
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Ovo je primjer rekonstrukcije napravljene s pomoću simuliranih podataka
07:12
when we pretend to point our telescopes
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kad zamislimo da smo usmjerili teleskope
07:14
to the black hole in the center of our galaxy.
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prema crnoj rupi u središtu naše galaktike.
07:16
Although this is just a simulation, reconstruction such as this give us hope
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Premda je to samo simulacija,
rekonstrukcije kao što je ova pružaju nam nadu
07:21
that we'll soon be able to reliably take the first image of a black hole
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da ćemo uskoro moći dobiti prvu pouzdanu sliku crne rupe
07:24
and from it, determine the size of its ring.
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i s pomoću te slike odrediti veličinu njezina prstena.
07:28
Although I'd love to go on about all the details of this algorithm,
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Iako bih rado još razglabala o tom algoritmu,
07:31
luckily for you, I don't have the time.
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srećom po vas, nemam vremena.
07:33
But I'd still like to give you a brief idea
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No ipak bih vam htjela ukratko dočarati
07:35
of how we define what our universe looks like,
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kako određujemo izgled našeg svemira
07:37
and how we use this to reconstruct and verify our results.
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i kako uz pomoć toga rekonstruiramo i provjeravamo svoje rezultate.
07:42
Since there are an infinite number of possible images
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Budući da postoji beskonačno mnogo mogućih slika
07:44
that perfectly explain our telescope measurements,
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koje su potpuno u skladu s podacima naših teleskopa,
07:47
we have to choose between them in some way.
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moramo ih nekako filtrirati.
07:49
We do this by ranking the images
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To činimo rangiranjem slika
07:51
based upon how likely they are to be the black hole image,
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na temelju toga koliko je vjerojatno da su upravo one slike crne rupe
07:54
and then choosing the one that's most likely.
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i zatim biramo onu za koju je ta vjerojatnost najveća.
07:57
So what do I mean by this exactly?
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Što točno želim reći?
07:59
Let's say we were trying to make a model
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Recimo da pokušavamo napraviti model
08:01
that told us how likely an image were to appear on Facebook.
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koji bi nam rekao kolika je vjerojatnost da se neka slika pojavi na Facebooku.
08:05
We'd probably want the model to say
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Vjerojatno bismo htjeli da nam model kaže
08:06
it's pretty unlikely that someone would post this noise image on the left,
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kako je malo vjerojatno da netko objavi sliku sa šumovima lijevo,
08:10
and pretty likely that someone would post a selfie
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a vrlo vjerojatno da netko objavi selfie
08:12
like this one on the right.
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poput ovoga desno.
08:14
The image in the middle is blurry,
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Slika u sredini je mutna,
08:15
so even though it's more likely we'd see it on Facebook
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pa iako bismo je prije mogli vidjeti na Facebooku
08:18
compared to the noise image,
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nego sliku sa šumovima,
08:19
it's probably less likely we'd see it compared to the selfie.
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vjerojatno su manje šanse da ćemo vidjeti nju nego onaj selfie.
08:22
But when it comes to images from the black hole,
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Međutim, kad je riječ o slikama crne rupe,
pred nama je prava zagonetka: nikad je dosad nismo vidjeli.
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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Koja je vjerojatna slika crne rupe u tom slučaju
08:31
and what should we assume about the structure of black holes?
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i što da pretpostavimo o strukturi crnih rupa?
08:33
We could try to use images from simulations we've done,
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Mogli bismo pokušati iskoristiti slike iz simulacija koje smo napravili,
08:36
like the image of the black hole from "Interstellar,"
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poput slike crne rupe iz filma „Interstellar”,
no učinimo li to, mogli bi nastati ozbiljni problemi.
08:39
but if we did this, it could cause some serious problems.
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08:42
What would happen if Einstein's theories didn't hold?
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Što ako bi se pokazalo da Einsteinove teorije ne drže vodu?
08:45
We'd still want to reconstruct an accurate picture of what was going on.
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I dalje bismo željeli točno rekonstruirati što se događa.
08:49
If we bake Einstein's equations too much into our algorithms,
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Ako svoje algoritme previše baziramo na Einsteinovim teorijama,
08:52
we'll just end up seeing what we expect to see.
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na kraju ćemo vidjeti samo ono što i očekujemo da ćemo vidjeti.
08:55
In other words, we want to leave the option open
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Drugim riječima, želimo ostaviti otvorenom mogućnost
da se u središtu naše galaktike zapravo nalazi divovski slon.
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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(Smijeh)
09:02
Different types of images have very distinct features.
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Različite vrste slika imaju vrlo različite osobine.
09:05
We can easily tell the difference between black hole simulation images
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Možemo lako razlikovati simulacijske slike crne rupe
09:08
and images we take every day here on Earth.
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od onih koje svakodnevno snimamo ovdje na Zemlji.
09:10
We need a way to tell our algorithms what images look like
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Treba nam način na koji bismo algoritmima rekli kako slike izgledaju,
09:14
without imposing one type of image's features too much.
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a da istovremeno previše ne namećemo osobine jedne vrste slika.
09:17
One way we can try to get around this
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Jedan od načina na koji bismo to mogli riješiti
09:19
is by imposing the features of different kinds of images
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jest da ubacimo osobine različitih vrsta slika
09:22
and seeing how the type of image we assume affects our reconstructions.
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i vidimo kako pretpostavljena vrsta slike utječe na naše rekonstrukcije.
09:27
If all images' types produce a very similar-looking image,
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Ako sve vrste slika dovode do vrlo slične slike,
09:31
then we can start to become more confident
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možemo biti sigurniji
09:33
that the image assumptions we're making are not biasing this picture that much.
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da naše pretpostavke ne idu previše u prilog jednoj slici.
09:37
This is a little bit like giving the same description
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To je donekle kao da date isti opis
09:40
to three different sketch artists from all around the world.
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trima različitim crtačima diljem svijeta.
09:43
If they all produce a very similar-looking face,
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Nacrtaju li svi vrlo slična lica,
09:46
then we can start to become confident
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možemo biti sigurni
09:48
that they're not imposing their own cultural biases on the drawings.
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da njihovi crteži nisu previše uvjetovani njihovom kulturom.
09:51
One way we can try to impose different image features
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Jedan od načina na koji možemo pokušati nametnuti različite osobine slike
09:55
is by using pieces of existing images.
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jest korištenjem dijelova postojećih slika.
09:58
So we take a large collection of images,
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Veliku zbirku slika
10:00
and we break them down into their little image patches.
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rastavimo na male komadiće.
10:03
We then can treat each image patch a little bit like pieces of a puzzle.
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Svaki od tih komadića pomalo nalikuje dijelu slagalice.
10:07
And we use commonly seen puzzle pieces to piece together an image
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Često viđenim dijelovima slagalice koristimo se kako bismo složili sliku
10:11
that also fits our telescope measurements.
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koja je u skladu s podacima naših teleskopa.
Različite vrste slika imaju vrlo karakteristične skupove dijelova slagalice.
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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A što se događa kad s istim podacima,
10:21
but we use different sets of puzzle pieces to reconstruct the image?
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ali s različitim skupovima dijelova slagalice pokušamo rekonstruirati sliku?
10:25
Let's first start with black hole image simulation puzzle pieces.
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Započnimo s dijelovima slagalice za simulaciju slike crne rupe.
10:30
OK, this looks reasonable.
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U redu, ovo izgleda vjerojatno.
10:32
This looks like what we expect a black hole to look like.
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Izgleda onako kako očekujemo da crna rupa izgleda.
10:34
But did we just get it
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No jesmo li dobili takvu sliku
10:36
because we just fed it little pieces of black hole simulation images?
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jer smo uključili djeliće slika simulacije crne rupe?
10:39
Let's try another set of puzzle pieces
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Pokušajmo s drugim skupom dijelova slagalice,
10:41
from astronomical, non-black hole objects.
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koji potječe od astronomskih objekata koji nisu povezani s crnom rupom.
10:44
OK, we get a similar-looking image.
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U redu, dobivamo vrlo sličnu sliku.
Što je s dijelovima koji potječu od svakodnevnih slika,
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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kao što su one koje snimate vlastitim fotoaparatom?
10:53
Great, we see the same image.
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Odlično, dobivamo istu sliku.
10:55
When we get the same image from all different sets of puzzle pieces,
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Kad dobijemo istu sliku od svih raznih skupova dijelova slagalice,
10:58
then we can start to become more confident
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možemo biti sigurniji
11:00
that the image assumptions we're making
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da naše pretpostavke o slici
11:02
aren't biasing the final image we get too much.
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ne utječu pretjerano na konačan rezultat.
11:05
Another thing we can do is take the same set of puzzle pieces,
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Usto, možemo i isti skup dijelova slagalice,
11:09
such as the ones derived from everyday images,
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kao što su oni koji potječu od svakodnevnih slika,
11:11
and use them to reconstruct many different kinds of source images.
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upotrijebiti za rekonstrukciju raznih izvornih slika.
11:15
So in our simulations,
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Dakle, u našim simulacijama
11:16
we pretend a black hole looks like astronomical non-black hole objects,
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pretvaramo se da crna rupa izgleda kao astronomski objekti koji nisu crna rupa
11:20
as well as everyday images like the elephant in the center of our galaxy.
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te kao svakodnevne slike, kao što je slon u središtu naše galaktike.
11:24
When the results of our algorithms on the bottom look very similar
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Kad rezultati naših algoritama na dnu izgledaju vrlo slično
11:27
to the simulation's truth image on top,
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pravoj slici simulacije na vrhu,
11:29
then we can start to become more confident in our algorithms.
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možemo se početi pouzdavati u te algoritme.
11:32
And I really want to emphasize here
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Ono što stvarno želim naglasiti jest
11:34
that all of these pictures were created
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da su sve ove slike nastale
11:36
by piecing together little pieces of everyday photographs,
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slaganjem djelića svakodnevnih fotografija,
11:39
like you'd take with your own personal camera.
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kao što su one koje biste fotografirali vlastitim fotoaparatom.
11:41
So an image of a black hole we've never seen before
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Dakle, sliku crne rupe koju nikad nismo vidjeli
11:45
may eventually be created by piecing together pictures we see all the time
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naposljetku ćemo možda dobiti od slika koje stalno viđamo –
11:49
of people, buildings, trees, cats and dogs.
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slika ljudi, zgrada, stabala, mačaka i pasa.
11:51
Imaging ideas like this will make it possible for us
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Takve ideje o dobivanju slika omogućit će nam
11:54
to take our very first pictures of a black hole,
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da prvi put fotografiramo crnu rupu
11:57
and hopefully, verify those famous theories
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i, nadajmo se, potvrditi slavne teorije
11:59
on which scientists rely on a daily basis.
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na koje se znanstvenici svakodnevno oslanjaju.
12:02
But of course, getting imaging ideas like this working
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No, naravno, takve se ideje
12:04
would never have been possible without the amazing team of researchers
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ne bi mogle ostvariti bez nevjerojatnog tima istraživača
12:08
that I have the privilege to work with.
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s kojima imam čast raditi.
I dalje me zadivljuje
12:10
It still amazes me
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to što bi, iako sam počela rad na ovom projektu bez predznanja o astrofizici,
12:11
that although I began this project with no background in astrophysics,
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12:14
what we have achieved through this unique collaboration
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ono što smo postigli tom jedinstvenom suradnjom
moglo rezultirati prvim slikama crne rupe.
12:17
could result in the very first images of a black hole.
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12:20
But big projects like the Event Horizon Telescope
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No veliki projekti kao što je Event Horizon Telescope
12:22
are successful due to all the interdisciplinary expertise
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uspješni su zahvaljujući interdisciplinarnoj stručnosti
12:25
different people bring to the table.
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različitih sudionika.
12:27
We're a melting pot of astronomers,
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Mi smo raznolika skupina astronoma,
12:29
physicists, mathematicians and engineers.
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fizičara, matematičara i inženjera.
12:31
This is what will make it soon possible
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To je ono što će uskoro omogućiti
12:34
to achieve something once thought impossible.
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da postignemo nešto što se nekoć smatralo nemogućim.
12:36
I'd like to encourage all of you to go out
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Htjela bih vas sve ohrabriti da odavde iziđete
12:39
and help push the boundaries of science,
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spremni pomicati granice znanosti,
12:41
even if it may at first seem as mysterious to you as a black hole.
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i onda kada se suočite s nečim tako misterioznim kao što je crna rupa.
12:45
Thank you.
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Hvala vam.
12:46
(Applause)
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(Pljesak)
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