Howdy Star Gazers and welcome to this episode
of Star Trails. My name is Drew and I'll be your
guide to the night sky for the week of November
the 9th to the 15th. This week we're going to
kick things off with yet another exercise related
to a subject that I find endlessly fascinating.
We're going to take another look at the Drake
Equation, a tool used to estimate how many intelligent
species we might have in our home galaxy. And
we're going to use that equation to simulate
more than a million variations of the Milky Way,
employing some quantum physics along the way
to kickstart our experiment. I think you're going
to be blown away by the implications, because
I know I was. Later, we'll check in on the night
sky and I'll tell you how I captured an image
of last week's full beaver moon using some of
my favorite astronomy smartphone apps. Whether
you're tuning in from the backyard, the balcony,
or just your imagination, I'm glad you're here.
So grab a comfortable spot and let's see what
the universe holds for us this week. A couple
weeks ago, we explored the Fermi paradox, the
idea that in a universe so vast, we should be
hearing or seeing indications of intelligent
life. Yet, here we are in the cosmic void, seemingly
all by ourselves. For the past 50 years or more,
programs like the Search for Extraterrestrial
Intelligence, or SETI, have churned away, theorizing
and scanning the skies with radio telescopes
in search of something, anything, to tell us
that we're not alone. And for two decades, volunteers
even donated their CPU cycles to analyzing radio
data via the SETI At Home Project. pioneering
distributed computing methodologies in the process.
And so far we've found nothing. But surely something
must be out there. So in this episode, we'll
turn to the world of statistics and data analysis,
and I promise it's not going to be as boring
as it sounds. We're going to use a quantum computer
to tap into the inherent randomness of the universe
itself. And we're going to use that information
to run the Drake Equation more than a million
times. We're going to play dice with the universe
and explore the probability of extraterrestrial
life. To start, let's get a refresher on what
the Drake Equation is. I've sort of had a lifelong
fascination with it since I cracked open Carl
Sagan's Cosmos as a preteen and I first encountered
it. The idea that an equation could make a prediction
about the density of alien life sparked profound
wonder in me at that age. We talked about it
in some detail back in episode 85, the silence
between the stars. Today we're going to dig in
a little deeper. You may recall from that earlier
episode that the Drake equation was an attempt
by scientist Frank Drake to mathematically make
sense of the Fermi paradox. In 1961, Drake hosted
a small meeting at the Green Bank Observatory
in West Virginia, the first gathering devoted
to the search for extraterrestrial intelligence.
He scribbled a simple formula on a blackboard
to structure the discussion. It's really just
a series of numbers that we multiply together.
Here's the equation. And if you were counting,
that's just seven variables. It's almost unnervingly
simple on the surface. Seven factors. Seven cosmic
knobs, if you will. and it breaks down like this.
R is the average rate of star formation in our
galaxy. Fp is the fraction of those stars with
planets. Ne is the average number of planets
that can potentially support life. F1 is the
fraction of planets that actually develop life.
Fi is the fraction of planets that develop intelligent
life. FC is the fraction of civilizations that
release signs of their existence into space.
And L is the length of time such civilizations
release those signals of their existence. Just
multiply them all together and you get N, the
number of intelligent civilizations in the Milky
Way right now. But this is where the simplicity
collapses, because we don't know any of those
variables with any confidence. We only have one
biological data point, that's us. We have one
technological species data point, that's us again.
We have one lifetime data point, also us. And
since we don't know where our own story ends
yet, even that data point is incomplete. So when
people plug numbers into Drake and they solve
for in that neat answer is an illusion Drake
isn't an equation you simply solve it forces
you to confront the fact that everything important
in that multiplication is unknown So what do
you do with an equation whose inputs are unknown
you treat those inputs probabilistically You
don't just pick single values, you define plausible
ranges based on astrophysics, exoplanet surveys,
and reasonable scientific priors. And then you
sample from those ranges again and again and
again. You generate not one answer, but a landscape
of possible answers, a distribution. This is
how modern SETI researchers think about Drake,
not as an answer machine, but as a probability
engine. So in that spirit, we're about to run
some code to Big Bang more than a million versions
of the Milky Way and see what those universes
tell us. We are in effect gambling with the universe's
uncertainty, and we're going to roll nature's
dice using some quantum mechanics. Einstein famously
said, God does not play dice with the universe.
He wasn't a fan of quantum randomness and he
believed the laws governing the universe were
fixed. But we're going to play dice with the
universe anyway. My apologies to Albert. Here's
the plan. We're going to make use of the general
programming language of Python to produce more
than a million outcomes of the Drake equation.
The program will randomly select a number for
each variable from a predefined range of conservative,
educated estimates for each. Think of it as generating
a million versions of the Milky Way. The predefined
ranges for each variable are the glue that makes
this work. We're going to use some plausible
data drawn from everything we know about the
universe and everything we don't. These aren't
my ranges, by the way. These are some widely
accepted best -guess envelopes based on papers,
expert intuition, SETI tradition, astrobiology,
and more. Once the program runs, we'll analyze
the resulting data. We're looking for two extremes,
the highest number of estimated intelligent civilizations
compared to the lowest number, and we'll be on
the lookout for a zero result. meaning an iteration
produced no intelligent civilizations. This approach
is called a Monte Carlo simulation. To add some
philosophical flair, I specifically wanted to
seed my sim with a 128 -bit random number produced
by a quantum computer. It sounds like science
fiction, but it's not. For this experiment, I
used IBM's quantum computing platform via their
Qiskit framework. It's a real quantum computer
that we can rent time on. It isn't a simulation.
It's actual superconducting qubits chilling near
absolute zero. Why 128 bits? Well, this is a
cryptography grade random number. It represents
2 to the 128th power in terms of possible outcomes.
That's more possibilities than there are atoms
in a typical galaxy. We wrote a simple quantum
random number generator, a QRNG in Python, with
some help from an AI, because this realm is way
beyond my skill set. This code essentially generates
128 quantum coin flips. And if you think creating
a quantum random number generator sounds like
overkill for what's usually a simple randomized
function in almost every programming language,
you're not wrong. But we're using quantum physics
to generate this number because that's about
as close as we can get to asking the universe
itself for randomness. The quantum computer is
literally driven by the uncertainty built into
the fabric of reality. Quantum computers don't
use bits, the ones and zeros used in classical
computers. They use qubits, which are similar
to traditional bits, but they can exist in a
state of superposition, meaning they could either
be a zero or a one. In fact, they have no state
until they're measured, meaning they hold multiple
possibilities until we force an outcome. You
may have heard this called the observer effect
when it comes to quantum mechanics. You may have
also heard of the thought experiment known as
Schrodinger's cat, a feline in a box that exists
in both an alive and dead state, until we open
the box and look at it. That's quantum mechanics
at its most basic, and that's also why it's so
weird. These are behaviors at the smallest level
of existence, individual particles. Our Python
script constructed a tiny quantum circuit that
creates qubits in superposition and collapses
them when they're measured. And we harvested
the randomness from that collapse to generate
our seed. Essentially, we fire qubits through
a series of quantum logic gates 128 times. The
gates force the qubits into a one or zero outcome.
Once we have 128 ones and zeros, we convert that
binary string into base 10 decimal, and that
extremely random number becomes the seed. Strangely,
operating a quantum computer reminds me of the
old mainframe systems of the 60s and 70s. To
use one, you submit a job to it. Your job waits
in a queue until the machine can run your code,
then it returns a result. IBM's Qiskit framework
lets you both simulate a quantum computer and
send jobs to their real one via a public application
programming interface, or API in coding parlance.
So before I started burning qubits on the real
hardware, I ran a local sim to make sure the
code worked as intended. When I was satisfied
with the output, I submitted the job to IBM and
waited a minute or two for the result to come
back. And there it was, a QRNG seed, the randomness
that sets our experiment into motion. And let
me tell you, that was a lot of work to get a
random number. But the beauty of this method
would reveal itself later in a moment of realization
that left me in a state of both shock and awe.
The rest of our calculations would take place
in a very simple Python script here on my workstation.
And by the way, I'll make all of my code available
if you want to try and run it. Check the show
notes for details. In classical computing, a
seed initializes the random number generator
in your programming language of choice. Once
you have a seed, every random number after that
traces back to that original seed. We're starting
with a quantum seed, but we're turning that chaos
into classical determinism. On the first run,
I set up my script to generate 250 ,000 solved
versions of the Drake equation. It ran in seconds.
Think of each version as one possible cosmic
outcome. Basically, we simulated a quarter million
pocket universes, each with its own set of Drake
parameters. Some outcomes produced thousands
of intelligent species, some produced hundreds.
The maximum seemed to land somewhere just under
a quarter million intelligent species in the
galaxy. That's pretty insane. Most runs produced
galaxies with at least 700 species. So I ran
the Monte Carlo sim again, this time with a half
million outcomes, then a million, and then two
million. Strangely, the numbers didn't vary by
much, but here's the part that hit me like a
dinosaur -killing asteroid. In each run, seeded
by that actual quantum hardware, zero universes
came up empty. Zero. In other words, all the
universes we simulated produced intelligent life,
based on the conservative range of values we
plugged into the Drake equation. Let me state
that another way. A real quantum mechanical process,
not a software algorithm, but a literal collapse
event inside superconducting hardware gave us
a seed that led to a distribution of data that
tells us we're probably not alone in the universe.
I don't think I breathed for about a full minute
as this realization washed over me, and even
now I get chills thinking about it. Now this
doesn't mean we've proven anything. It doesn't
mean aliens are guaranteed. It doesn't mean the
equation reveals the truth. But it does mean
that when you explore the space of plausible
Milky Ways with honest scientific uncertainty,
there's almost no chance that we're alone. And
just for fun, I tried to get absurd. I tried
to simulate a billion universes. That's when
my workstation threw up the white flag. The CPU,
all 24 cores of it, shot up to its maximum 5
GHz clock speed. The RAM usage hit the ceiling
at 64 gigs and the cooling fans spun up like
jet engines. It completely choked and the funny
thing is it didn't really matter. We already
saw everything we needed to see back at a quarter
million samples. And it was completely stable
by one million. The shape of the uncertainty
reveals itself quickly. And throwing more universes
at it doesn't make reality any more certain.
You just generate heat and thermodynamic suffering
on the CPU. This Monte Carlo exercise, this million
universe experiment, it isn't meant to be proof,
it simply offers perspective. We don't get a
single number from Drake. We get a distribution
of possibility. And inside that distribution,
the middle ground, the typical universe, contains
neighbors, sometimes a few, sometimes many. And
we didn't bias that outcome with wishful thinking.
We seeded that simulation with randomness drawn
from quantum measurement, the most fundamental
unpredictability that nature gives us. People
think the Drake equation is about estimating
aliens. I think it's actually about estimating
ourselves. It forces us to confront how narrow
our experience is and how broad the universe
might be. The Fermi paradox wonders, where is
everybody? Drake tells us there might be many
somebodies, but probability and distance and
lifetimes might keep us apart. Scientists working
on Drake today are pushing into better estimations,
especially around habitable planets and biosignatures.
The James Webb Space Telescope and future missions
may eventually constrain the F1 parameter, life
emergence, far more than we ever could before.
Exoplanet atmospheric chemistry may eventually
give us actual statistics, not just speculative
boundaries. And the biggest uncertainty, L, the
lifetime of communicative civilizations, may
be the most existential. Because its true value
might say more about our future than our past.
And maybe that's the deepest irony here. The
Drake Equation may eventually tell us more about
what kind of species we're capable of becoming
than it tells us about the species that already
exist somewhere else. With that, let's step out
of the multiverse and drop back into the single
universe we inhabit, the one overhead tonight,
and talk about what's happening in the real night
sky this week. That's coming up after the break.
Stay with us. Welcome back. Before we get into
this week's sky, I wanted to give another quick
observation report. As you all recall, the full
moon was last week and it was a supermoon. Sometimes
I enjoy photographing a full moon. My favorite
shots show the moon in relation to the urban
landscape. I love how the moon looks gigantic
near the horizon, an effect we call the moon
illusion. It's not bigger, it just seems bigger
when it's adjacent to objects that we already
understand the scale of. So last Tuesday was
a gorgeous clear evening, and I didn't have any
after work commitments So I drove out to a location
I've shot from in the past a highway overpass
just outside the city that faces due east Using
a 200 millimeter lens on my full -frame camera
I managed to capture a massive moon just as it
rose over the skyline I only had a few minutes
to get the shot as once the moon rises higher
and the ambient light dips, it's hard to balance
the moon's brightness with the city's darkness.
Twilight, with some light in the sky, works the
best. If you'd like to see the photo, check the
show notes for a link. And just know that capturing
a shot like this isn't luck, it's planning. There
are three apps I lean on when I want to line
up the moon precisely with the skyline. Stellarium,
photo pills, and the photographer's ephemeris.
Stellarium gives me the astronomy first view.
I can preview the night sky exactly as it will
appear from my location, jump forward in time,
and verify altitude and azimuth. That tells me
when and where the moon will be above the horizon.
Photo pills takes that same information and grounds
it into the language photographers think in.
exact rise time, exact bearing, and whether the
moon's trajectory will intersect a specific place
on Earth, if I stand right here. This app uses
augmented reality to help you place the moon
or sun in your scene. I can check focal lengths,
distances, compression, and get confidence that
the scale will look right. And the photographer's
ephemeris helps lock the terrestrial geometry
in. That's the piece that tells me where to physically
stand, which road, which hilltop, and even which
building the moon will appear behind. It shows
the rise line across the map, and I can drag
my shooting location until the moon's path intersects
the exact building I want. Together, those three
apps turn an event that looks lucky into something
you can intentionally design, days or even weeks
ahead of time. The shot begins before you ever
set the tripod down. I'll include links in the
show notes to these resources if you're interested.
This week the moon slides from a waning gibbous
toward the last quarter phase, which happens
on Wednesday, the 12th, and the very early hours
of the morning. After that, the moon shrinks
into a thinner crescent each night, which means
a darker sky window is opening up as we head
toward the weekend. Saturn still rules the early
evening sky over in the southeast. The rings
are almost edge -on now, so in a telescope they're
a fine thin line. Jupiter steals the second half
of the night. It rises later in the evening and
is high before dawn. The moon makes a close pass
with Jupiter Monday night into Tuesday morning,
so if you're out late or awake before sunrise
on the 11th, take a look for that pairing. Uranus
is also in the evening sky and headed toward
opposition later this month. If you have binoculars
and a dark sky, spend a little time trying to
pick it out. It'll look like a tiny blue -green
star. Neptune is in the evenings too, but you'll
need a telescope. Venus is now extremely low
in the morning twilight. If you have a clear
flat eastern horizon, you might catch it in the
dawn light later in the week. Especially if you
pair it with the delicate crescent moon Friday
or Saturday morning. Mars and Mercury, meanwhile,
are essentially lost in the sun's glare. We also
have some meteor action this week. The northern
taurids peak on Tuesday night into Wednesday,
the 11th into the 12th. This is not a high -rate
shower, but the taurids are known for bright,
slow fireballs. Moonlight will wash out the dimmer
ones, but if you're patient, you might still
spot a good one. And the Leonids are right behind
them. Their main peak will arrive next week under
much better moon conditions. The deep sky season
continues to get better as the moon wanes. The
Andromeda galaxy is high after dark. So are the
Pleiades and the Hyades, perfect binocular targets.
You know, at the end of all this, numbers are
only part of why we look up. The equations, the
instruments, the simulations, they help us measure
possibility. But wonder lives before measurement.
Wonder is older than science. Wonder is the first
tool we ever had. When we look at the night sky,
we aren't just observing the universe. We're
also observing ourselves. The cosmos isn't just
a place we study. It's a mirror that we hold
up to our own short, small moment in time. So
yes, we count stars, we model probability, we
write code, we build telescopes, but beneath
all that we're trying to remember that being
alive is strange and extraordinary, and the very
act of wondering is itself part of what makes
us human. We look up not just because the sky
is beautiful, but because it reminds us that
there's always more to know, and that curiosity
is a kind of hope. If you found this episode
interesting, please share it with a friend who
might enjoy it. The easiest way to do that is
by sending folks to our website, StarTrails .Show.
And if you want to support us, use the link on
the site to buy me a coffee. It really helps.
Be sure to follow Star Trails on Blue Sky and
YouTube. Links are in the show notes. Until we
meet again beneath the stars, clear skies, everyone.