[MUSIC PLAYING]
ERIC: Welcome Joel Primack.
[APPLAUSE]
JOEL PRIMACK: Thanks, Eric.
It's a pleasure to be here.
I'm going to talk
about, basically,
an introduction to modern
cosmology and the simulation
game.
And I'm going to mention
that the ideas we used
to have about how
galaxies form and evolve
were basically wrong.
When we actually got to see
what the galaxies look like,
starting with the installation
of a new camera that
gave us infrared capability on
Hubble Space Telescope in 2009,
we actually saw what galaxies
look like when they start,
and we were all shocked.
But our simulations look
a lot like these images
that we're now
getting from Hubble,
so we're beginning
to figure it out.
And one of the ways
that we're hoping
to improve our understanding is
using deep learning technology.
So to start with--
just to get the
terminology understood--
we live in the solar system.
Earth is eight minutes--
eight light minutes
from the sun.
Jupiter is 40 light minutes.
Saturn's 80 light minutes.
The entire solar system is
less than a light day across.
The Milky Way, our home galaxy,
is 100,000 light years across--
the visible part
of the Milky Way,
and we live about a quarter of--
well, about half of the way
out, about 25,000 light years
away from the center.
Our galaxy is part of
what we call the local,
or the Virgo, SuperCluster, the
nearest 10,000 or so galaxies.
There's a cluster called
the Virgo Cluster, which
is the densest group of galaxies
in a 100 million light years
around.
But this is a fairly small
part of the universe.
On the really large scale
of billions of light years,
we've surveyed the
universe and we
discovered that the universe
has a lot of structure.
So the other thing we've learned
is that the visible universe
is just a tiny fraction
of the universe that's
really out there.
What we see represents
about half of 1%.
So most of the universe, as
far as the energy and matter
content, is dark
energy, about 70%.
About 25% is a
mysterious stuff that we
call cold dark matter, a
theory that, as Eric said,
I helped to create and develop.
We don't know what it is,
but we know how it behaves.
"Cold" means that
it was moving very
sluggishly in the
early universe.
And, of course, that
term "sluggish"--
which my collaborator
George Blumenthal, now
a chancellor of UC Santa
Cruz, and I always use--
is a call out to our mascot at
Santa Cruz, the banana slug.
All the atoms are about 5%.
The invisible atoms are
about 4 and 1/2 percent.
The visible stuff, the stars
and glowing gas clouds,
is about half a percent
of cosmic density.
And the stuff we're
made of, all other atoms
besides hydrogen and helium,
is about a hundredth of 1%.
So we and Earth are
extremely rare parts
of the universe that
are not typical at all.
So we call this the Double
Dark Theory or, technically,
Lambda CDM.
Lambda-- Einstein's
representation for dark
energy--
and cold dark matter.
So dark matter and dark energy.
Imagine the entire universe
is an ocean of dark energy.
On that ocean sail
billions of ghostly ships
made of dark matter.
But we don't see the ocean
and we don't see the ships,
we just see the lights on
the tops of the tallest
mast of the biggest ships.
Those are the galaxies.
Now, how do we know
that this is true?
And the answer is,
the theory made
all kinds of predictions
that were subsequently
confirmed in fantastic detail.
For example, the green curve
is the predicted distribution
of fluctuations in temperature
in the cosmic background
radiation.
This is the map of
the whole sky as seen
by the Planck satellite, our
latest instrument for this.
And every one of these
little red points
is an independent measurement.
And the agreement is
absolutely spectacular.
And this is the temperature
polarization data,
the polarization
polarization data.
And also, for the large scale
distribution of the galaxies
it's a similar story.
As we look out in space
we're looking back in time,
because we see light
that left longer ago when
we look farther out.
So we're seeing those galaxies
as they were longer and longer
ago.
Nearby galaxies, like
our own Milky Way,
we see on the scale
of 100,000 years ago,
nearby stars a few years ago.
As you look further
out this first sphere
represents the period
when the earth and the sun
formed, about 4 and
1/2 billion years ago.
And the next sphere
represents when
big galaxies like the Milky
Way took, more or less,
their present shape.
So these galaxies look
like nearby galaxies.
They're not very different.
But if you look beyond--
that's about 10
billion years ago.
If you look beyond
that, the galaxies
look very different indeed.
These are all images from
Hubble Space Telescope.
And one of the
things you'll notice
is that they're not very round.
And that's turned out to be
the common understanding.
And beyond the visible galaxies
is this cosmic dark ages,
where galaxies haven't yet
formed enough stars for us
to see them.
We'll start to see a
little further into this
when James Webb Space Telescope
gets launched next year.
Then the cosmic
background radiation,
this colorful sphere,
and The Big Bang itself.
Now, simulations allow
theorists to make
metaphorical or real movies
of how the universe evolves.
The astronomers basically
just give us snapshots.
We don't get to see how galaxies
evolve because they evolve
on a time scale of hundreds
of thousands of years
to hundreds of millions
or even billions of years.
Cosmological dark
matter simulations
show large-scale structure
and the properties
of the lumps of dark
matter that we call halos,
and then we can try to
put galaxies into those.
If you look at a galaxy,
the part that you see
is just a tiny little
fraction of the full galaxy
that's actually there.
So this is a dark matter halo
of a galaxy like the Milky Way,
with these big lumps
hosting satellites
and the smaller lumps invisible.
But maybe having effects
that we can see, for example,
on gravitational lensing.
How does this fit into
the large scale structure?
More or less like this.
So this is a slice from
one of our big simulations
that we ran next door
at NASA Ames Research
Center on the biggest
NASA computer, which
is called Pleiades.
This one took about
two months using
about 14,000 cores on Pleiades.
And we've run a bunch of
these big simulations.
So let's zoom in on a little
piece of this big simulation.
It's only about a thousandth
of the size of the simulation
itself.
So this is what the distribution
of dark matter halos
looks like.
What you see is a rich
cluster, a cluster
with thousands of
galaxies, would
be hosted by this big
lump of dark matter.
The white areas are regions
with a lot of dark matter.
And these big clumps form
where these filaments cross.
The dark matter is
spread out along
these long, linear structures
that we call filaments,
and galaxies really are
distributed primarily
in these long,
linear structures,
just as the theory predicts.
Now, how did this massive
cluster halo form?
We keep track when we
run the big simulations
of every single particle,
many billion particles,
and every dark matter halo.
And all of the
things you see here,
the thousands of
halos-- each of which
will be hosting galaxies
or maybe several galaxies--
are going to end up in that one
structure, the big central one.
Notice that they're
not spherical.
They're elongated.
The earlier they form and
the more massive they are,
the more elongated they are.
As these halos accrete
they change their shape,
and what we're
doing is playing you
the video based on storying
about 200 time steps
as the big simulation ran.
So that's the dark matter halo.
Of course, all of
that substructure
continues to exist inside
that halo and the galaxies.
So we try to
understand in detail
how stars form in galaxies.
A couple years ago--
last year, I guess,
is when they finally came out--
we published this
paper suggesting
that maybe the way the dark
matter halos accrete matter
is what's really controlling
star formation in at least many
of the dark matter halos,
hosting star-forming galaxies.
We called this Stellar Halo
Accretion Rate Co-evolution,
or SHARC for short.
There's a kind of
model that theorists
make that we call bathtub
models of galaxy formation
and, of course, I put
the SHARC in the bathtub.
So we came up with
that clever name
so people would remember it.
And I need to tell you,
to explain this story, two
basic facts about galaxies.
And this will also,
I think, give you
an idea of some regularities
that we're learning
about how galaxies work.
So one is, the stellar
mass to halo mass ratio
as a function of halo mass.
The Milky Way is about a 10
to the 12 solar mass object.
So Milky Way is
right around here.
And that means that about 2% or
3% of the mass of the Milky Way
is stars.
You might have
thought it was more.
But that's as much
as you ever get.
Lower-mass galaxies and
higher-mass galaxies
have an even smaller fraction
of their mass in stars.
And also, it's pretty much
independent of redshift.
These are values of
different redshifts.
Redshift corresponds
to time back
in the history of the universe.
But it's a strong
function of the mass.
Now, the other thing
is that galaxies
form stars in a
rate that's largely
governed by the stellar mass
of the galaxy and the redshift.
So this is what it is
for nearby galaxies,
and more distant galaxies.
This is the stellar mass of the
galaxy and the star formation
rate, number of stars
formed per year,
stellar mass formed per year.
And what you see is that, at
least at the lower-mass end,
we're pretty much proportional
to this plot, which
is the star formation rate just
growing linearly with the mass.
So the rate at which galaxies
form stars is largely
regulated by the stellar
mass of the galaxy,
and it's a little
bit higher at high
redshift than at low redshift.
About an order of magnitude
higher at redshift 2 and 1/2.
So two basic facts
about how galaxies work.
And what we noticed when we
looked at these big simulations
is that the dispersion-- so
we notice that the accretion
rate grows like this.
So it grows by about
this order of magnitude
as you go out in redshift,
and the dispersion
is independent of mass and
independent of redshift.
And the dispersion in the star
formation rate of galaxies
is known to be about 0.3 dex.
In other words, a
factor of 2 up or down.
Most of the stars
in the universe
form at rates that are really
controlled by the stellar mass
of the galaxy.
And there's a simple derivation
that shows that the rate
at which stars form, as long
as there isn't much change
in the that plot that I showed
you a minute ago-- this plot,
which is stellar mass
versus halo mass--
as long as there isn't much
change with redshift, then
it's going to be
governed by this term.
So the star formation
rate, the MstarDT,
is DMstar, the
Mhalo, the MhaloDT.
That's the accretion rate.
And that allowed us to predict
what the star formation
rate should be across more
than an order of magnitude
in stellar mass, and back to
redshift four, and even higher.
So back over most of the
history of the universe.
And it actually works
remarkably well.
Now I want to move
on to understanding--
so that's something about
how the large-scale structure
of the universe relates
to star formation
in individual galaxies.
This is an astronaut installing
this amazing instrument
in Hubble Space Telescope.
They took out an old instrument
that doesn't work anymore,
or that wasn't working so
well, Wide Field Camera 2,
and they replaced it
with Wide Field Camera 3.
Which, for the
first time, gave us
these huge Hubble images,
sharp from edge to edge,
in the infrared.
And before Wide Field
Camera 3 was installed
we only had images like
this of forming galaxies.
All we were seeing
was ultraviolet light,
which is emitted by the stars
that have just recently formed.
You've got a few very
massive stars which put our
a lot of their light
in the ultraviolet,
and then that gets redshifted
so that Hubble could see it
in visible wavelengths.
But to see the full
stellar populations
you have to see the infrared.
And that's what we now could do.
These are the same galaxies.
And you can see that, with
the infrared capability,
we're seeing a lot more.
So that's the basis of
my saying that now we
can see what galaxies look
like when they were forming.
Back to redshifts 2.3 for red
light and 3 for blue light,
that's about 10 to
12 billion years ago.
So we can see the
galaxies-- of course,
the universe starts at 13.7
to 13.8 billion years ago,
so the first couple
of billion years
we're going to have to
depend on James Webb Space
Telescope, which goes up
next year, to illuminate.
So the CANDELS program was
the program that succeeded--
there was a big
competition and we at Santa
Cruz were part of
the winning team--
My colleague, David Kou, is an
astronomer, I'm a theorist--
who are part of
the CANDELS team.
And that's Cosmic Assembly
Near-infrared Deep
Extragalactic Legacy Survey.
I think David first came
out with the CANDELS acronym
and then came up with a--
[LAUGHTER]
Anyway.
So we're focusing on
this period when stars
mostly formed in the universe,
the first 5 billion years
or so.
That's redshift 2, about 3
billion years after the Big
Bang.
Redshift 3, less than 2 billion
years after the Big Bang.
That's the peak
of star formation.
That's when the
galaxies really formed.
So to understand
galaxy formation
we do what we call
hydrodynamics simulations where
we follow the gas and
not just the dark matter.
And the gas turns into stars.
Some of the gas ends up as
supermassive black holes
at the centers of the galaxies.
And, of course,
there's a huge amount
of energy output by the stars.
Sometimes they explode.
More energy output.
The supermassive black
holes-- as they accrete,
a lot more energy output.
And we're trying to follow how
all this stuff is happening.
There's basically
two approaches.
Low-resolution
approaches, kiloparsec.
That's about 3,000 light years.
Small compared to
the size of a galaxy,
but very large
compared to the regions
where stars actually form.
And then much higher-resolution.
Tens of parsecs.
So that's 100 elements
compared to these.
And that's 100
cubed because it's
100 in each of the
three dimensions.
So these are much more
expensive simulations.
These simulations cost
100,000 to a million CPU hours
per galaxy.
These simulations cost 10
or 20 million CPU hours,
and you get tens of
thousands of galaxies.
But at much lower resolution.
And we're actually
using both of these
to compare to the observations.
I tend to emphasize
these because we
try to be as self-consistent as
possible in these simulations.
So these are examples
from our simulations.
That's a face-on image.
You're seeing the gas.
And there's the stars.
This is what it would
look like to a telescope.
This is the same
galaxy seen edge-on.
So you see it's really a disk.
And there it is, edge-on.
And this is that same
galaxy, edge-on and face-on.
And these are two
different observed galaxies
at redshift 2, about
10 billion years ago,
and you're supposed
to notice that they
look a lot like the
simulated galaxies.
Notice that they
are very clumpy.
We've run the simulation
several times.
We run, typically,
35 simulations
at a time, each one
costing between 100,000
and a million CPU
hours per simulation.
I get tens of
millions of CPU hours
from Google and-- from
NASA Ames and other places.
So this is some simulations that
we don't like so much anymore
because it turns out that the
galaxies really are elongated
and they don't have as many
stars as these simulations did
at the same redshift.
These are a better match
to the observations.
This is what these would look
like if you could see them
close up.
This is what Hubble
would see, and these
are the infrared images.
So Hubble has seen three
different phenomena.
We have interpreted
the Hubble images
to see these three
different phenomena.
So one is that galaxies
start down here.
Star formation rate is
increasing downward,
and the compactness
of the galaxy
is increasing to the right.
So they start not very compact.
Some of them become very
compact, very, very dense, very
rapidly-moving stars and gas.
And then they also
have-- a large fraction
of these galaxies have
active galactic nuclei
quasars, supermassive black
holes accreting like crazy.
And after only a few
hundred million years,
which is a short time for
galaxies, they quench.
That is, they stop
forming stars.
So the star formation
rate goes way down--
which is up in this diagram--
And then they grow a
little bit in size.
A lot of other galaxies are
what we call the "slow track."
We called this the fast track
in this paper published in 2013.
There have been a number
of follow-up papers.
And those are
galaxies that never
entered this compact
rapidly-star-forming part
of the diagram.
They just slowly grow
in size, but fade out.
In our simulations we
have some slow track,
we have some fast track.
These are organized the
same way as the picture.
We can follow in the
individual simulations--
this is the inner part of
the galaxy, the very center
part of the galaxy,
and we notice
that the center
part of the galaxy
is mostly dark matter
until what we call
a "compaction event" occurs.
A lot of gas flows
into the center--
there's the gas flowing
into the center--
and it becomes stars.
And so the rapid conversion of
gas into stars depletes the gas
and then also it
drives some gas out,
all these energetic processes
in the center including
the supermassive black hole.
And unfortunately
we couldn't include
the supermassive black
holes in these simulations
so we haven't figured out enough
about how they really work.
So gas flows in and
then it flows out.
And in the inner
region of the galaxy
this is what's happening.
In the whole galaxy--
that's pretty much
the whole galaxy.
That's out to
30,000 light years,
so bigger than the
size of the Milky Way.
Further out than we are, anyway.
So you see that the dark
matter still dominates.
The stars grow, although they're
not growing in the center.
The stellar mass is
constant in the center.
But in the larger galaxy,
the stars continue to grow.
So how does this work
when we actually look
at our individual simulations?
So this is going to show you how
a simulation evolves, and we're
going to track the dark
matter, the stars in red
and the gas in blue.
And this is in the
inner five kiloparsecs,
so that's about this big.
And this is the
inner kiloparsec.
That's about just that
very central region.
So this is stars
and gas, face-on.
Gas and stars, edge-on.
Here it goes.
So you see that
there's a compaction
event about to occur.
A bunch of gas just
flowed into the center,
and the center is now
becoming much rounder.
It was elongated before.
And now you're seeing
this nice spiral
structure and a nice disk.
Now, the observer's,
when they looked at--
this CANDELS program-- when
we looked at the distribution
of the access ratio--
so on the sky you can
see an image that's
got a long axis and
a short axis and you
can take the ratio, short
axis over long axis.
And if you plot the
histogram it looks like this.
This is for galaxies between
redshift 1 and 1/2 and 2,
so that's going back--
redshift 2 is about 10 or 11
billion years ago.
Redshift 1 and 1/2 is a couple
of billion years closer.
So this is very much the period
when galaxies are forming.
And what you see is
that the distribution
for these lower-mass
galaxies and also
intermediate-mass
galaxies is an inverted
V. It peaks at around 0.3.
So axis ratio 0.3.
So basically long axis,
more than three times
longer than the short axis.
And there are very
few up here near 1.
Now, a spheroid is going to
look like an axis ratio of 1
no matter how you look at it.
You're going to catch
galaxies in every orientation.
A disk is going to look like
axis ratio close to 1 face-on.
It'll be a 0.2 or
something, whatever
the thickness of the
disk is, edge-on,
and everything in between
more or less equally-likely.
We're just not catching
very many near 1.
So that means that
a majority have
to be these elongated objects.
And very few are
spheroidal and not
a very large fraction are disky.
They're mostly these
elongated objects.
So we used to think that
galaxies are a mixture of disks
and spheroids, like
nearby galaxies,
sometimes with a
spheroid in the middle.
But actually when they start
galaxies look like pickles.
And our simulations are showing
exactly that same phenomenon.
There's the dark matter.
There's the stars aligned
with the dark matter.
This is from the paper
that we published on this.
So the paper that
really established
that this is what the
images looked like
was led by a CANDELS
astronomer, published in 2014.
And our theory paper explaining
it, the first of our papers,
came out in 2015.
So there's the stars and
dark matter highly-aligned.
This is looking down the long
axis, so now they look round.
And again, the dark matter
and stars are very aligned,
and they're elongated.
On the left-hand
side of this diagram
the stellar mass is less
than the dark matter mass
in the center of the galaxy.
On the right-hand
side the stellar mass
is the dominant part,
the more star-dominated.
More dark-matter-dominated
on this side.
And when it's star-dominated
you see that the galaxies
are becoming round.
These are round,
these are elongated.
So that's just like
what the images showed.
And we published a much
more-detailed paper led
by a post-doc, [? Michaeli ?]
[? Tomasetti. ?]
And what we found was
that, essentially,
all galaxies go through
this elongated phase.
So the ones we saw out to
redshift 2 and 1/2 or 3
with Hubble were just galaxies
that ended up a little smaller
than the Milky Way.
The Milky Way would have
gone through that stage,
we think, at redshifts
between 3 and 4.
And that's what James
Webb Space Telescope
is going to let us see.
So we're predicting that the
vast majority of galaxies
that James Webb lets us see
out at the higher redshift
are going to be these
elongated things.
We don't know if that's true.
We'll find out in
about a couple years.
So I'm now near
the end of the talk
and I'm going to talk a
little bit about the way we're
using deep learning.
So Sander Dielman, who was
then a graduate student
at the University
of Guelph in Belgium
and is now at Google
DeepMind in London--
he used a deep learning
code to predict
how GalaxyZoo, a
citizen science project,
classified nearby galaxy images.
And he was able to predict
the GalaxyZoo classification
with 99 percent accuracy.
And he won a Kaggle competition
in the machine learning
community in 2014.
Marc Muertas-Company
used Dielman's code
to classify CANDELS galaxies.
So these are our galaxies
out in the period
when galaxies are forming.
About 8,000 of them
had been classified
by a team of about
65 astronomers,
and that was used as a
training set in order
to get enough to be
a useful training set
and not over-fit the data.
He had to rotate them and also
change the colors a little bit
to get about 60,000.
And so Marc Muertas-Company
also had to do something similar
with the CANDELS galaxies.
And he's published
a big catalog.
So only one of the fields--
CANDELS's five different
fields, only one
of the different regions of
the sky that CANDELS used had
the galaxies classified.
And so once the machine was
able to classify the galaxies as
well as any astronomer,
Marc was able to classify
all of the galaxies,
and then he was
able to study how
the galaxies have
changed in their appearance
since redshift 3.
So that paper was
published last year.
With support from Google, Marc
Muertas-Company has visited
UCSC last summer,
briefly last spring,
and will again be
back this summer,
and his grad student
Fernando Caro--
Fernando, would you
raise your hand?
So he's been visiting
us since March
and will be through
mid-august this summer.
And we're working
together to try
to understand galaxy formation
better using deep learning.
And the people whose names are
in blue here are here today.
And then we have two other
deep learning projects
that I'll tell you about.
Trying to understand better
the galaxy environment out
at fairly large redshifts,
so the more distant galaxies,
where we mostly only have very
rough estimates of how far
away the galaxies are.
And also looking
for the big clouds
of neutral hydrogen in Sloan
Digital Sky Survey spectra.
So this is just a short summary
of what Sander Dielman did.
It's based on his sort
of blog entry here.
"My solution for the
GalaxyZoo challenge."
So this was a flowchart
of how GalaxyZoo works.
People are given an
image and they're
asked to classify it into
disky or more roundy.
And then, if they answer this
then they go down this part.
If they answer this way
they go down this part.
If you count all the
different boxes here
there's a total of 37.
So they get 37 possible choices,
although it's a decision tree
so they only go down one side.
And the game in the
Kaggle competition
was to predict all 37 as
accurately as possible.
So they were given--
I forget-- 100,000
images or something.
And they could use some
of them as a training set
and then some of them
as a confirmation set.
And what Sander Dielman did was
to construct a deep learning
network.
He actually constructed
16 different ones,
training them
slightly differently,
and then he polled them
to get the best estimate.
And he ended up actually
getting 99% right.
So it was really quite amazing.
And, although he's a
machine learning guy,
he published a nice
paper along with Willett,
who's the leader of
this GalaxyZoo project,
in "Monthly Notices of the
Royal Astronomical Society," one
of the two leading
astronomy journals.
So it was basically the same
sort of deep learning code
that Marc Muertas-Company used
to classify the CANDELS galaxy
images.
So, "we mimic human
perception with deep learning
using convolutional
neural networks trained
to reproduce the CANDELS
visual morphological
classification based
on the 65 classifiers,
and it is then released to
the astronomical community."
So this is the sort
of standard game.
Once we figured out how
to do all these things,
then it becomes the
property of all astronomers,
very quickly released.
CANDELS, as a matter of
policy and also NASA rules,
releases everything
very, very quickly.
So all astronomers
are able to use it.
So this is a little bit of
detail on how this worked.
And then Marc published this
second paper, "Mass assembly
of morphological transformations
since redshift 3 from CANDELS."
We quantified the evolution of
star forming quiescent galaxies
as a function of morphology.
Our main results are at redshift
2, 10 billion years ago.
80% of the stellar mass density
of star forming galaxies
is in irregular systems,
not disks and not spheroid.
These elongated
things are things
that are hard to classify.
However, by redshift
about 0.5, that's
about 4 billion years
ago, irregular objects
only dominated solar
masses below 10
to the ninth solar masses.
Much smaller than
the mass of the Milky
Way, which is about 6 or
7 times 10 to the tenth.
Quenching.
We confirm the galaxy
is reaching a seller
mass of about 10 to
the 10.8, so a little
over the mass of the
Milky Way, tend to quench.
Also, quenching implies
the presence of a bulge.
The abundance of massive red
disks-- in other words galaxies
without a bulge,
red means quenched--
is negligible at all redshifts.
So this is the kind
of thing you can
say when you have access to
these classifications of all
of these galaxies.
So that's how the machine
learning made it possible
for us to summarize
evolution of galaxies
in this sort of grand way.
Now, what we're
trying to do is--
what we want to do
is, ultimately--
I mean, this is our big goal.
We have these
high-resolution simulations
where we know exactly what's
happening to the galaxies.
We know if a merger event
or maybe a flyby event--
another galaxy comes past
the galaxy, interacts
with it gravitationally
but doesn't merge with it--
or some other event that
leads to a big inflow of gas
in the galaxy.
We know those things are
happening in the simulations,
so what we're going to do
is tell the deep learning
code what's happening in
the simulations and show it
images, realistic images that
we make from the simulations.
And we're making images at
20 different orientations,
most of them random,
at many, many times
steps as these galaxies evolve.
So the idea is, we'll
gives the deep learning
code all that
information and then
see if it can be
trained to recover
that sort of information
from realistic images.
And, of course,
if it works we'll
apply it to the real images.
And we've already
made 100,000 images
that we think are typical
of what James Webb Space
Telescope is going to be showing
us, according to our theory.
So as soon as that
data comes in we'll
be all set to analyze
it if this all works.
So this was a test
to see if we were
able to get something right.
So Marc Muertas-Company trained
three deep learning codes
to recognize a bulge in a
galaxy, a bulge in a disk
to see if we can get
a better fit that way,
and just a pure disk
without a bulge.
And so we looked, as
the Galaxy evolved--
so each of these is
showing the evolution
of a galaxy across time
from higher redshift
to lower redshift, and so this
is showing where a bulge is--
the code says, yeah, that
galaxy's probably got a bulge.
And that corresponded
to a compaction event
where the gas flowed in, a lot
of stars formed in the center.
So that's redshift
about 3.7 or something.
And that's consistent with this.
And here bulge with
disk came down--
so more bulge, less disk--
and at that same
redshift, pure disk.
Hardly at all.
And then near redshift 1,
about 7 billion years ago,
there's this big jump
in [INAUDIBLE] stars.
That means that another galaxy
merged with this galaxy.
And so that merger
event corresponds
to this big increase in the
bulge plus disk fraction.
And anyway, by looking at
this we're concluding--
this was also done
for 35 simulations.
And we concluded that
the deep learning
code is sort of working.
It's not nearly as good
as we hope to end up with,
but this is a step
along the way.
Another example.
So we start with the
zoom-in simulations.
We looked to see if
there's a merger,
and we looked in a
very trivial way, just
to see if there's a big jump
in the dark matter content.
And so we used that.
We use the images together
with merger or no merger
to train the deep learning code.
So this was an example
of one simulation.
Merger here, no merger here,
merger here, no merger there.
And this is what the code did,
looking at 20 images at each
of many different time steps.
So it got this merger.
It thought there
was some chance--
this is 50%.
It thought there was some
chance of a merger here,
when there wasn't.
It picked up this merger, and it
correctly says no merger here.
And this is applied
to the simulations.
And this is 100% merger,
this is the number,
and this is basically
saying that it's
crossing over at
about 50% probability
of getting the mergers right.
So it's not great, but it's sort
of beginning to pick things up.
Now, this is a completely
different project
that we're working on.
So this is from a paper that--
it's a pre-print
that I'm a co-author
of along with Nicholas
Tejos, who is now
a professor in Chile, and
Aldo Rodriguez-Puebla,
my former post-doc who's now
a professor back in Mexico
City at UNAM, the
national University.
And what we did is we took
a bunch of real redshifts
from our big simulation, we
divided them into about 20%
that we basically used
exactly the same redshift.
So this is RA, the angle
on the sky across--
it's like longitude, and
it's a thin latitude slice,
a thin declination slice.
And so here you see there's
a void, a cosmic void,
a cosmic wall or a filament.
And with only 20% you don't
see them quite so clearly,
but all the numbers
are exactly the same.
For the photo-z's,
what we did is
we greatly degraded
the resolution.
If all you have is how
bright the galaxy is
at a bunch of
different wavelengths,
you can estimate
what its redshift is,
but it's pretty inaccurate.
So this is the
photometric redshift
versus the true redshift,
and you see this big spread.
Equality would be this line.
And this is what came out after
we applied an algorithm that we
call "sort."
And so what we did is we
used the nearby spectroscopic
redshifts to guess what the
correct photometric redshifts
were, and you can see
we did a lot better.
This is true.
This is how well we did,
taking these and improving
them using the small fraction
of spectroscopic redshifts.
This is basically the
count of pairs of galaxies.
And we basically are getting
it right at 4 megaparsecs
with this and with
the photo-z's,
the imprecise redshifts.
We don't even get it
right at 40 megaparsecs.
So this is one particular
scheme that we cooked up.
And we show that it
works pretty well,
but it's certainly not optimal.
And what we're hoping is
that, with deep learning
we can come up with schemes
that will be much more optimal.
Now, we're building a fantastic
telescope in the southern
hemisphere-- it's
going to be in Chile--
called the Large Synoptic
Survey Telescope, LSST.
LSST is going to take a
movie of the southern sky.
Every two nights it will
cover the entire southern sky.
We discover about 3,000
supernovas per year
with all the telescopes
in the world.
LSST will discover 10,000
supernovae per night.
It's 15 terabytes of
data per night, which
has to be analyzed on the fly.
It's sent up to
three supercomputer
centers in the United States.
If one of them is
down another one
has to be able to
step in because you
can't allow this kind of
data to become too old.
So that's going to be
several billion galaxies
for which we're going to have
good photometric redshifts.
The fraction of galaxies--
we're going to
have a few million
spectroscopic redshifts.
So being able to use
photometric redshift
data in a more efficient
way is going to be critical.
And that's why this is a
very important project.
And then the last
project is finding
damped Lyman alpha systems,
DLAs, in the Sloan Digital Sky
Survey data.
And these are the big
lumps of neutral hydrogen
in the universe, the
amount of hydrogen
in the disk of a
star-forming galaxy.
About 2 times 10 to the 20
atoms of neutral hydrogen
per square centimeter
along the line of sight.
And you see these not
because they emit--
they don't emit very
strongly at all--
but because they absorb light,
especially the N equals 1 to N
equals 2 transition, the
Lyman alpha transition
in neutral hydrogen.
So a quasar gives
us lots of light,
and so we look in the
spectra of quasars.
And about 100,000 quasar
spectra were analyzed by hand,
by many astronomers.
And then the most
recent project,
run out of Lawrence
Berkeley Laboratory,
called BOS, provided another
270,000 of these spectra.
And so Shawfeng Dong, who's
here in the audience--
raise your hand Shaw--
and David Park in Santa Cruz,
and Professor X. Prochaska,
and Zheng Cai, have
created a deep learning
code that analyzes the
spectra and finds these gaps,
these places where the
light from the quasar
has been strongly absorbed.
And those are these damp
Lyman alpha systems.
And so this is basically linear
data-- it's not an image--
and they analyze it
in a sliding window.
And they've caught a number of
these double damp Lyman alpha
systems.
So this is going
to greatly expand
the data that's available.
And they're just
finishing their paper,
and they're going to
make all of this data
publicly available to the
world-wide astronomy community.
So this is a huge
expansion in the amount
of data that's available.
And so that's the third
use of deep learning.
So those are the topics
I wanted to talk about,
and I think I've allowed
plenty of time for questions.
ERIC: OK.
Let's thank Joel for a
very interesting talk.
[APPLAUSE]
JOEL PRIMACK: Of course I'm
glad to answer questions
about any of this.
ERIC: I've got a mic here
I can bring around in case
anyone wants to ask a question.
AUDIENCE: So, I know this isn't
the focus of your research,
but in these elongated
galaxies, what's
the motion of the stars
like in those galaxies?
JOEL PRIMACK: It's largely
along the long axis.
There's hardly any
rotation at all.
The dark matter in
these long filaments--
remember I showed you the
picture of the filaments--
the dark matter is supported
largely by anisotropic velocity
dispersion.
In other words, there's a
lot more random velocity
in this direction than in the
two perpendicular directions.
So the stars are
doing the same thing.
They're moving randomly
in the long axis.
And also suppose that a
merger of two galaxies
occurs-- this fairly frequently
happens-- along the filament.
The stars are what we
call non-collisional.
Stars are so small compared
to the distance between them
that they essentially never hit.
So they don't
actually scatter off
each other very
much, unlike the gas
molecules in this room which,
of course, have a pressure.
So that's why the stars also,
like the dark matter particles,
can support this anisotropic
velocity dispersion.
So that was actually a very
thoughtful question, because--
so, we predict that these
things are not rotating.
And with spectra we're
going to be able to tell.
Hubble Space Telescope has only
limited spectral capability,
but James Webb
Space Telescope is
going to have superb
special capability.
It's going to be able
to get multiple spectra
in the same direction
at the same time.
So we're making
detailed predictions.
Meanwhile, our same code
makes detailed predictions
for what the spectra will
look like on the ground.
Of course, on the
ground the seeing
is much worse because
of the atmosphere.
But with adaptive optics using
laser guide stars, for example,
you can improve the seeing.
And so there's a limited amount
of high-resolution spectral
data from the ground.
But because of the atmosphere
being so noisy in the infrared
and not transmitting
all of the infrared,
James Webb is going to
be a complete revelation.
So we can't wait
to see what we're
going to learn from James Webb.
The first proposals for
use of James Webb time
are already being requested.
So the time is going to
be allocated very soon.
So from the point of view
of the astronomy community,
James Webb is almost here.
AUDIENCE: You talk
about stars and gas
and that a very small
percentage of the universe
is stars and gas and
atoms and whatnot.
What exactly is dark
matter and dark energy?
Is it subatomic particles or--
I'm just not that
familiar with the theory.
JOEL PRIMACK: We don't know.
[LAUGHTER]
AUDIENCE: Ah.
OK.
JOEL PRIMACK: So I was the
one who first proposed--
along with the
late Heinz Pagels--
that the favorite theory of
theorists in particle physics
to go beyond our
current understanding
would also be the explanation
of the dark matter.
So the standard picture
of particle physics
is called the "standard model."
And back when I was a graduate
student and my first post-doc--
my post-doc at Harvard--
I actually wrote some
of the basic papers
on what we call the "standard
model of particle physics."
It's the theory of the
electroweak interaction.
Now we know the weak
interaction is closely
associated with
electromagnetism,
and also the strong
interaction, which
is a similar kind of theory,
non abelian gague theory.
And the trouble
with that theory is,
although it beautifully
describes all the results
at particle
accelerators, there's
no room in it for dark matter.
And, moreover, we have no idea
where the theory came from.
I mean, we can write it
down, but why does it
have the particular
structure that it has?
We don't understand.
So we're looking
for a bigger theory
that this theory will just be
a part of, the standard model.
And the best candidate
is supersymmetry.
And supersymmetry is the
basis for string theory.
It's actually
superstring theory.
So if you heard of that,
that involves supersymmetry.
So in supersymmetry
all of the particles,
all the fundamental particles--
the quarks, the leptons,
the force particles, the
photon and its partners,
the W and Z bosons,
and the gluons--
all of those particles
have partner particles
whose properties,
except for their masses,
are totally predicted
by the theory.
Their interactions
are totally predicted.
The masses arise from what
we call "symmetry breaking,"
and we don't know
how that works.
So the masses
can't be predicted.
But we know that the
masses must be much higher
than the masses of all
the ordinary particles,
because we haven't made
them yet at accelerators.
So that's supersymmetry.
Now, in most versions
of supersymmetry
the supersymmetric particles
have a different quantum
number, called "R-parity,"
"negative R-parity."
All the ordinary particles,
positive R-parity.
And parity is conserved.
So the lightest R-parity
odd particle must be stable,
and so it should
be the dark matter.
That was what we
pointed out 1982.
And that led to the
cold dark matter theory.
So that's a plausible story.
It hasn't yet been
ruled out, but we also
haven't found the particle.
If it exists we should
start making these particles
at the Large Hadron
Collider in Geneva.
And they've been turning
up the intensity,
and they may, in fact,
be able to increase
the energy a little bit.
We're on a big run and
much higher intensity.
Maybe we'll actually
start to see something.
My colleagues at
Santa Cruz were part
of the team that built the inner
tracker for the Atlas detector
at the Large Hadron
Collider, but they
don't tell us anything.
[CHUCKLES]
They're over in Geneva, and
they're working on the thing.
But when they discovered
the Higgs boson
we learned about it just
like everybody else did.
We didn't learn
about it from them.
So they may even
have some results,
but if so they haven't told us.
We're also looking for these
particles deep underground.
If you go deep underground
to shield from cosmic rays--
the most sensitive
detector until recently
was the LUX detector,
Large Underground Xenon,
in the Homestake gold
mine in South Dakota.
My wife and I--
we've written popular
books on this stuff.
Those images from the
beginning of my slides
were from our books.
And we were the speakers
at the South Dakota place.
In fact, we gave a National
Public Radio interview deep
in the mine.
That was a couple of years ago.
And they announced--
when they introduced us,
the Department of
Energy people announced
that they had made a commitment
to drastically expand the LUX
detector.
So LUX was 300 kilograms
of liquid xenon gas,
and they're expanding it
to 8,600 kilograms, 8.6
metric tons.
Which will be an increase in the
sensitivity of a factor of 100.
So if the dark
matter particle is
what we suggested--
which is often
called a WIMP,
weakly-interacting massive
particle-- they
should detect it.
If they don't they'll
rule out the theory.
Meanwhile, a 1,000
kilogram detector
is going into operation
in Italy this year.
So it'll be steps along the way.
If these particles
are the dark matter,
they should annihilate
sometimes in gamma rays.
There's a signal of
gamma rays coming
from the center
of the Milky Way,
but it's not clear
if that's due--
it's distributed
just the way you'd
expect it to be if it's coming
from dark matter annihilation.
The dark matter
particles annihilate.
Two of them have R-parity
even, so they can annihilate
into ordinary matter.
But there are other
possible explanations.
So the assumption is
that the dark matter
is some kind of particle.
Sorry for all that introduction,
but partly because I'm
responsible for part of it.
I sort of live this
stuff all the time.
Of course, I'm hoping
that it turns out
to be what I predicted.
But, you know, we don't know.
As far as the dark energy,
it's even more mysterious.
Einstein, in 1916, applied his
theory of general relativity--
which he'd finished
at the end of 1915--
to the universe.
And he discovered
the universe has
to be expanding or contracting.
It can't be static.
And he asked his
astronomy colleagues,
so is the universe is
expanding or contracting?
And they basically
said, we don't know.
They didn't even know there were
galaxies outside the Milky Way.
That was controversial.
It wasn't until Hubble, in
1924, showed that the Andromeda
Galaxy is far
outside the Milky Way
that we knew that galaxies
are an external phenomenon.
And then in 1929
Hubble discovered
that the more distant the
galaxy the faster it's
moving away from us, so the
universe is actually expanding.
In 1916 nobody
knew these things.
But Einstein was motivated
to try to come up with a way
to make his theory static.
So he realized you could
change the equations
by adding an extra
term that he called
the cosmological constant,
represented by lambda,
the Greek letter, and that
would be a repulsion of space
by space that he
thought could counteract
the attraction of mass and
could make a static universe.
It turns out it doesn't work.
But when we discovered that
the universe is actually
accelerating the expansion--
using type 1a supernovae, the
Nobel Prize a few years ago--
that basically resurrected
the cosmological constant,
or some other form
of dark energy.
We realized that we can add
this to Einstein's equations.
It's perfectly consistent.
Is it some fundamental
new aspect of nature?
Is it a consequence
of something else?
There are ways that
you could construct it
so it's a consequences
of something else.
We don't know.
So one of the biggest
projects in astronomy today
is the dark energy survey and
the dark energy spectroscopic
instrument.
And these things are led partly
by people here in this area.
My former graduate
student, Risa Wechsler,
is the spokesperson for the
Dark Energy Spectroscopic
Instrument, DESI, at Stanford.
So this is big astronomy.
And the Large Synoptic
Survey Telescope
that I mentioned that's going
to go into operation in Chile--
the southern sky
every two nights--
that's largely funded by
the Department of Energy.
Mike Turner, an
astronomer at Chicago,
coined this term "dark energy,"
and it was a brilliant term
because the Department of
Energy has taken on funding.
[LAUGHTER]
Unless left the Trump
administration kills it.
Because they want to,
basically, drastically curtail
science and the
Department of Energy.
So, I don't know.
AUDIENCE: They should
rename it "fake energy."
[LAUGHTER]
AUDIENCE: [INAUDIBLE]
AUDIENCE: So what's the
evidence that black holes--
you're for or against
that black holes
are, in fact, dark matter,
primordial or otherwise?
JOEL PRIMACK:
Primordial black holes
could form in certain
versions of cosmic inflation
theory, sort of the early
stages of the Big Bang,
but it's very, very tricky.
The danger is, you
make too many and then
it wrecks the whole universe.
They have to be--
they can't be too small or
we'd see them exploding.
It turns out black
holes explode.
And so they have to be in
a very certain mass range.
They can't be exploding.
We would have seen that.
And if they're as
massive even as earths
we would have seen the
effects because they
would have amplified the
light from supernovae
and they would have given us a
tail of extra-bright supernovae
that we don't see.
So there's strong constraints
on the mass of any dark matter
candidate that's a
massive sort of object.
MCHOs is one of the names
people call these things,
Massive Compact Halo Objects.
One of my former
graduate students
introduced that terminology.
But that's been
largely ruled out.
So it's very unlikely, I
think, that the dark matter
is black holes.
Also-- well, I
can go on on that.
So there's two kinds of black
holes we are quite sure exist,
the kind that form at the ends
of the life of massive stars,
and the kind that we see
at the centers of all
the massive galaxies, which we
call supermassive black holes.
There's no question they exist.
And, of course, the best
proof that there really
are these massive black holes
that are in the stellar mass
range was this discovery a
couple of years ago by LIGO--
Laser Interferometry
Gravity-wave Observatory--
of two merging
30-solar-mass black holes.
And the pattern of gravity
waves was exactly what
Einstein's theory predicted.
And we had previously been
testing Einstein's theory
at scales in the solar system.
The velocity of the sun
is 30 kilometers a second.
The speed of light is 300,000.
When those two
black holes merged,
they were going
around each other
at essentially the
speed of light.
So we were testing general
relativity at the limit,
and it was bang on.
Incidentally, two 30-mass
black holes merged
and we got a
58-solar-mass black hole.
2 solar masses-- two times
the mass of the sun--
was gravity waves.
And now they're
detecting a lot more.
There's going to be a public
talk by Gabriella Gonzalez,
the spokesperson for LIGO, in
Santa Cruz this Thursday night,
June 1st.
And she's a wonderful
speaker, incidentally.
You guys should
invite her if you
want to have a wonderful talk.
And they're now
souping up LIGO so they
can detect about a
third the intensity
that they detected before.
That will let them look
three times farther out--
and that's three times
in every direction.
So that's about 27
times the signal rate.
We've been getting
about one detection
a month when they
have the thing on,
and that's going to
turn it into one a day.
So we're going to learn a
lot more about black holes.
But I don't see
any evidence at all
that black holes
are the dark matter,
and we're going to learn
much more about black holes
both on the stellar
mass scale and then also
if we can put a
detectors in space--
LISA was the name.
The Europeans want
to go ahead with it.
If the US will join we
could do it much faster.
And so that'll let
us detect merging
black holes of the
masses that we find
in the centers of galaxies.
And that will give us a
much better census on those.
Anyway, the whole
black whole story
is very much a hot research
topic, especially with LIGO.
AUDIENCE: I was wondering
where the dark matter is,
or how it's spread
across the Milky Way.
Because you said there is a
massive halo of dark matter.
How is it that we
don't see the effects
of this dark matter
or dark energy
on things like the solar system?
The [INAUDIBLE] that
we see probably,
would they match
the mass of the sun?
So does that mean
that either there
is no dark matter
in the solar system
or does it mean that part of
the sun's mass is dark matter
or dark energy or
something else?
JOEL PRIMACK:
Wonderful questions.
Those are all subjects
of many articles
in the scientific literature.
So the basic story is that
the dark matter is not
the dominant component
of mass in the inner part
of the solar system.
If you remember those
pictures I showed,
in the early stages
of galaxy formation
the dark matter is dominant
all the way to the center.
Then gas flows in,
turns into stars,
and the dark matter is only
dominant on large scales.
So on the scale of
the solar system,
ordinary matter is by far
the main source of mass.
The solar system is enormously
more-concentrated mass
than you find just
spread out-- if you
look at the mass on large
scales, even in the Milky Way.
Little planetary systems like
ours are very, very dense
compared to the average.
So the dark matter would
make very little difference
on the scale of
the solar system.
We've worked that out.
So the amount of dark
matter is about a third
of a hydrogen atom
per cubic centimeter.
That's about the mass of
dark matter in the Milky Way
at our distance from the
center of the Milky Way.
So that's less than
a very high vacuum.
The solar system has a
lot more stuff than that.
But as you go out to
the edges of galaxies
it becomes the
dominant form of mass.
So very little effect
on the solar system,
but it's possible that the
sun and the earth and Jupiter
and so forth will have
accreted a certain amount
of dark matter.
If so, that dark matter
would end up in the center.
It would occasionally
collide with atoms
and lose energy and
end up in the center,
and the dark matter would
started annihilating,
if it's the kind
that I predicted,
and there should
be neutrinos coming
from the center of the sun.
And we've looked for them
and we haven't seen them yet,
but we haven't looked
with very high precision.
So this is one of the
ways that people are
looking to detect dark matter.
It's very lively.
And so all of those
issues that you raised,
the questions you raised, are
exactly the kinds of things
that we've been thinking
about very hard for decades.
ERIC: Let's thank Joel again.
[APPLAUSE]
