Dark matter and dark energy: the mysterious ingredients in our universe
Science is an ongoing flirtation with the unknown.
Marcelo Gleiser is a professor of natural philosophy, physics, and astronomy at Dartmouth College. He is a Fellow of the American Physical Society, a recipient of the Presidential Faculty Fellows Award from the White House and NSF, and was awarded the 2019 Templeton Prize. Gleiser has authored five books and is the co-founder of 13.8, where he writes about science and culture with physicist Adam Frank.
- The history of modern cosmology is one of the great triumphs of the human imagination.
- Still, mysteries abound, particularly the nature of dark matter and dark energy.
- Science moves forward by embracing the unknown as a challenge; taking the wrong turn is part of the way forward.
"Where did everything come from?" is perhaps the most fascinating question we can ask — so much so, that it's much older than science itself, given that most religions have also wondered about our origins. That science joined in during the 20th century as a powerful new voice in this conversation is nothing short of extraordinary. How amazing is it that a mammalian species on a small planet could develop the intellectual and technological tools to say something concrete about the history of the universe itself? And how far can we go telling this story?
Dark matter and dark energy are vivid reminders that science is an ongoing flirtation with the unknown.
We know that the universe has a history that started some 13.8 billion years ago — hence, the name of our column, 13.8 — and that it has been expanding and cooling ever since. How do we know this? There are several ways: (1) Galaxies are receding from one another with speeds proportional to their distance, carried by the expansion of space itself; (2) A bath of microwave photons (i.e., the particles that make up light and all other forms of electromagnetic radiation) permeates the whole universe, serving as fossils from the time when the first hydrogen atoms formed, some 400,000 years after the Big Bang — as predicted by theory; and (3) Between a second and three minutes after the Big Bang, the first light atomic nuclei were formed by a process called "primordial nucleosynthesis" in quantities also predicted by theory and verified by observations.
The missing ingredients in the universe
All the above is solid science. But it's not enough. We want to go further back in time to explain some of the finer details of the cosmic expansion, before and beyond the formation of light nuclei and the microwave background. So we add two more components to the cosmic recipe, both suggested by observational evidence but still shrouded in mystery: dark matter and dark energy.
If we think of the material composition of the universe as a cake recipe, we find ourselves currently in the odd situation of knowing that we have three main ingredients — regular matter, dark matter, and dark energy — and how much of each we need, but we don't really know what the two most abundant are. We do know a lot about them, but certainly not enough. And that's the agony and the (potential) ecstasy of scientific research, the power of speculation to open new ways of thinking about nature or sinking us into further confusion.
A dark mystery
Credit: NASA, ESA, M. J. Jee and H. Ford et al. (Johns Hopkins Univ.)
Dark matter was first speculated to exist in the 1930s by the Swiss-American astronomer Fritz Zwicky as he noticed that galaxies in clusters moved faster than they should if the matter in the cluster was only the matter that shined (and, hence, was visible to our telescopes). Things evolved faster after American astronomer Vera Rubin and her collaborators noticed in the late 1970s that stars in galaxies rotated faster than they should if the matter within them was, again, only the matter that shined. An intense search for dark matter — so-named because we can't see it — has been ongoing for the past four decades or so, still with negative results. The puzzling thing is that we see its effects quite clearly as we look to objects in space. Having mass (and thus gravitational pull), it affects the stuff we can see. But efforts to collect particles of dark matter have been unsuccessful so far, a somewhat stressful tension between astronomical observations and fundamental theory.
Dark energy was discovered in 1998 and is even more mysterious and elusive. We know it's not made of particles or smaller chunks of material stuff as dark matter probably is; it seems to be an ethereal substance that permeates the whole cosmos with the bizarre property of making space stretch out faster than expected. We can't think of it as a localized thing but rather as a spread-out thing, like air in the atmosphere (sort of).
Efforts to collect particles of dark matter have been unsuccessful so far, a somewhat stressful tension between astronomical observations and fundamental theory.
Dark energy candidates are all quite weird. One candidate consists of quantum fluctuations of energy in empty space that materialize as particles that pop in and out of existence, the energy of the vacuum itself. Or it could be a mysterious property of space itself, something Einstein invented to save his 1917 failed model of a static universe, today called the "cosmological constant." Most probably, if this is dark energy, it is only an approximation for something much more complex and subtle that only looks constant to us now. Or perhaps dark energy is some unknown kind of substance modeled as a diaphanous field that pervades all of space, affectionally called "quintessence" by cosmologists, echoing the substance Aristotle proposed to make up celestial objects and fill up the heavens.
Like footprints in the snow
Whatever they are, dark matter and dark energy have the potential to revolutionize our understanding of the universe. Like subtle tracks of a fox on a vast snowfield, we know they are out there in some form due to the way they impress their presence on what we can see in the world. If we didn't know a fox existed, we would infer an animal made those tracks. We would then try to imagine what kind of animal it was that left tracks such as these using the evidence at hand.
Likewise, we see the tracks of dark matter and dark energy imprinted in the universe, and we are trying to determine what mysterious things they could be. Dark matter and dark energy are vivid reminders that science is an ongoing flirtation with the unknown. Even if our current speculations turn out to lead us in the wrong direction, we need to take risks to advance our understanding of the world.
If computers can beat us at chess, maybe they could beat us at math, too.
- Most everyone fears that they will be replaced by robots or AI someday.
- A field like mathematics, which is governed solely by rules that computers thrive on, seems to be ripe for a robot revolution.
- AI may not replace mathematicians but will instead help us ask better questions.
The following is an excerpt adapted from the book Shape. It is reprinted with permission of the author.
Will machines replace us? Since the origin of artificial intelligence (AI), people have worried that computers eventually (or even imminently!) will surpass the human cognitive capacity in every respect.
Artificial intelligence pioneer Oliver Selfridge, in a television interview from the early 1960s, said, "I am convinced that machines can and will think in our lifetime" — though with the proviso, "I don't think my daughter will ever marry a computer." (Apparently, there is no technical advance so abstract that people can't feel sexual anxiety about it.)
Let's make the relevant question more personal: will machines replace me? I'm a mathematician; my profession is often seen from the outside as a very complicated but ultimately purely mechanical game played with fixed rules, like checkers, chess, or Go. These are activities in which machines have already demonstrated superhuman ability.
Some people imagine a world where computers give us all the answers. I dream bigger. I want them to ask good questions.
But for me, math is different: it is a creative pursuit that calls on our intuition as much as our ability to compute. (To be fair, chess players probably feel the same way.) Henri Poincaré, the mathematician who re-envisioned the whole subject of geometry at the beginning of the 20th century, insisted it would be hopeless
"to attempt to replace the mathematician's free initiative by a mechanical process of any kind. In order to obtain a result having any real value, it is not enough to grind out calculations, or to have a machine for putting things in order: it is not order only, but unexpected order, that has a value. A machine can take hold of the bare fact, but the soul of the fact will always escape it."
But machines can make deep changes in mathematical practice without shouldering humans aside. Peter Scholze, winner of a 2018 Fields Medal (sometimes called the "Nobel Prize of math") is deeply involved in an ambitious program at the frontiers of algebra and geometry called "condensed mathematics" — and no, there is no chance that I'm going to try to explain what that is in this space.
Meet AI, your new research assistant
What I am going to tell you is the result of what Scholze called the "Liquid Tensor Experiment." A community called Lean, started by Leonardo de Moura of Microsoft Research and now open-source and worldwide, has the ambitious goal of developing a computer language with the expressive capacity to capture the entirety of contemporary mathematics. A proposed proof of a new theorem, formalized by translation into this language, could be checked for correctness automatically, rather than staking its reputation on fallible human referees.
Scholze asked last December whether the ideas of condensed mathematics could be formalized in this way. He also wanted to know whether it could express the ideas of a particularly knotty proof that was crucial to the project — a proof that he was pretty sure was right.
When I first heard about Lean, I thought it would probably work well for some easy problems and theorems. I underestimated it. So did Scholze. In a May 2021 blog post, he writes, "[T]he Experiment has verified the entire part of the argument that I was unsure about. I find it absolutely insane that interactive proof assistants are now at the level that within a very reasonable time span they can formally verify difficult original research."
And the contribution of the machine wasn't just to certify that Scholze was right to think his proof was sound; he reports that the work of putting the proof in a form that a machine could read improved his own human understanding of the argument!
The Liquid Tensor Experiment points to a future where machines, rather than replacing human mathematicians, become our indispensable partners. Whether or not they can take hold of the soul of the fact, they can extend our grasp as we reach for the soul.
Slicing up a knotty problem
That can take the form of "proof assistance," as it did for Scholze, or it can go deeper. In 2018, Lisa Piccirillo, then a PhD student at the University of Texas, solved a long-standing geometry problem about a shape called the Conway knot. She proved the knot was "non-slice" — this is a fact about what the knot looks like from the perspective of four-dimensional beings. (Did you get that? Probably not, but it doesn't matter.) The point is this was a famously difficult problem.
A few years before Piccirillo's breakthrough, a topologist named Mark Hughes at Brigham Young had tried to get a neural network to make good guesses about which knots were slice. He gave it a long list of knots where the answer was known, just as an image-processing neural net would be given a long list of pictures of cats and pictures of non-cats.
Hughes's neural net learned to assign a number to every knot; if the knot were slice, the number was supposed to be 0, while if the knot were non-slice, the net was supposed to return a whole number bigger than 0. In fact, the neural net predicted a value very close to 1 — that is, it predicted the knot was non-slice — for every one of the knots Hughes tested, except for one. That was the Conway knot.
For the Conway knot, Hughes's neural net returned a number very close to 1/2, its way of saying that it was deeply unsure whether to answer 0 or 1. This is fascinating! The neural net correctly identified the knot that posed a really hard and mathematically rich problem (in this case, reproducing an intuition that topologists already had).
Some people imagine a world where computers give us all the answers. I dream bigger. I want them to ask good questions.
Dr. Jordan Ellenberg is a professor of mathematics at the University of Wisconsin and a number theorist whose popular articles about mathematics have appeared in the New York Times, the Wall Street Journal, Wired, and Slate. His most recent book is Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else.
Laughing gas may be far more effective for some than antidepressants.
- Standard antidepressant medications don't work for many people who need them.
- With ketamine showing potential as an antidepressant, researchers investigate another anesthetic: nitrous oxide, commonly called "laughing gas."
- Researchers observe that just a light mixture of nitrous oxide for an hour alleviates depression symptoms for two weeks.
The usual antidepressants don't work for everyone. That's what makes a new study of the antidepressant properties of nitrous oxide so intriguing. It looks like just a single low dose of what your dentist may call "laughing gas" can help alleviate symptoms of depression for weeks afterward.
The study, from researchers at University of Chicago and Washington University-St. Louis, is published in the journal Science Translational Medicine.
Resistance to anti-depression medications
Nitrous oxide: two atoms of nitrogen, one of oxygenCredit: Big Think
According to the senior author of the study, Charles Conway, "A significant percentage — we think around 15 percent — of people who suffer from depression don't respond to standard antidepressant treatment."
"These 'treatment-resistant depression' patients," Conway says, "often suffer for years, even decades, with life-debilitating depression. We don't really know why standard treatments don't work for them, though we suspect that they may have different brain network disruptions than non-resistant depressed patients. Identifying novel treatments, such as nitrous oxide, that target alternative pathways is critical to treating these individuals."
"There is a huge unmet need," says lead author Peter Nagele. "There are millions of depressed patients who don't have good treatment options, especially those who are dealing with suicidality."
If ketamine can help, can nitrous oxide?
Credit: sudok1 / Adobe Stock
The researchers wondered if some of the anti-depression properties seen in ketamine might also apply to nitrous oxide. Nagele explains, "Like nitrous oxide, ketamine is an anesthetic, and there has been promising work using ketamine at a sub-anesthetic dose for treating depression."
The researchers conducted a one-hour session — they describe it as a "proof-of-principle" trial — in which 20 individuals with depression were administered an air mixture with 50 percent nitrous oxide. Twenty-four hours later, the researchers found a significant reduction in the participants' symptoms of depression versus a control group.
However, the individuals also suffered the unpleasant side effects that laughing gas often causes in dental patients: headache, nausea, and vomiting.
Smaller dose, longer effect
Credit: sudok1 / Adobe Stock
"We wondered if our past concentration of 50 percent had been too high," recalls Nagele. "Maybe by lowering the dose, we could find the 'Goldilocks spot' that would maximize clinical benefit and minimize negative side effects."
In a new trial, 20 people with depression were given a lighter nitrous oxide mix, just 25 percent, and the individuals tested reported a 75 percent reduction in side effects compared to the a control group given an air/oxygen placebo. This time, the researchers also tracked the effect of nitrous oxide on symptoms of depression for a far longer period, two weeks instead of just 24 hours.
"The reduction in side effects was unexpected and quite drastic," reports Nagele, "but even more excitingly, the effects after a single administration lasted for a whole two weeks. This has never been shown before. It's a very cool finding."
Nagele also notes that, despite its popular renown as laughing gas, even a light 25 percent mix of nitrous actually causes people to nod off. "They're not getting high or euphoric; they get sedated."
Delivering help to people with depression
Nagele cautions, "These have just been pilot studies. But we need acceptance by the larger medical community for this to become a treatment that's actually available to patients in the real world. Most psychiatrists are not familiar with nitrous oxide or how to administer it, so we'll have to show the community how to deliver this treatment safely and effectively. I think there will be a lot of interest in getting this into clinical practice."
After all, Nagele adds, "If we develop effective, rapid treatments that can really help someone navigate their suicidal thinking and come out on the other side — that's a very gratifying line of research."
How one startup plans to use "death rays" for good instead of evil.
- A new advance in concentrated solar power makes temperatures of 2700° F possible from nothing but sunlight.
- The heat produced can be used to produce electricity, make clean fuels, or power industrial processes.
- Founder Bill Gross sees these plants as part of a grand design to wean the world off oil.
The need for clean, consistent, renewable energy sources has never been more pressing. Rising energy prices threaten to kick-start inflation and slow economic growth. Control of the supply of fossil fuels has caused wars before and may well cause them again. Burning fossil fuels continues to create greenhouse gas emissions, making solving the problem of climate change difficult.
While low-carbon and renewable sources of power are being used more than ever before, none of them are perfect. Solar and wind power are very clean and increasingly inexpensive but have an energy storage problem. The batteries required to store that energy require rare earth metals, which are messy to extract and increasingly in demand. Hydro power is great but can have negative impacts on the river ecosystem. Nuclear is still a tough sell.
If we're going to solve our energy problems, we either need to find a new way to produce a lot of energy or fix the problems with the power sources we have. A renewable energy technology company backed by Bill Gates and founded by serial entrepreneur Bill Gross called Heliogen has a new approach to an existing model that may just accomplish the latter with a giant, extremely precise magnifying glass and some really hot rocks.
Concentrated solar power
The Crescent Dunes Solar Energy Project near Las Vegas, Nevada. This project, while not associated with Heliogen is a typical example of concentrated solar power. DANIEL SLIM/AFP via Getty Images
In Lancaster, California, a mid-sized city in the Mojave Desert, Heliogen has built a miniature version of their planned solar refinery. While concentrated solar power is nothing new — it has been operating commercially since the 1960s and is said to have been used by Archimedes to build a heat ray to burn the Roman fleet — this plant improves on the concept with stunning results.
Essentially a lot of mirrors arranged in a circle reflecting sunlight at an elevated target, concentrated solar power uses the energy in the sun's light to heat that target, which could be water, molten salt, or even something solid, to very high temperatures. (When this heat is used for something other than producing electricity, it is called concentrated solar thermal energy.)
Heliogen's current test refinery has 400 mirrors, known as heliostats, though it is only a tenth the size of what the company is proposing. Even with this reduced number of mirrors, the refinery has produced eye-popping results. Its operation has produced temperatures as high as 1500° C (2732° F). For comparison, most existing, full-sized concentrated solar power plants are able to produce temperatures in the 400° to 500° C range.
Heliogen's advance is made possible by state of the art software. Using AI and a series of cameras, the heliostats are kept on target as much as possible (currently to a twentieth of a degree) through micro-adjustments to their position throughout the day. By keeping the mirrors on target, the greatest amount of sunlight possible is focused on the target, creating more heat than was previously possible.
Concentrated solar power isn't just for electricity
It's important to remember that this is technically a solar thermal system. Unlike solar panels, this project does not use the photovoltaic effect to turn sunlight directly into electricity. This project is about generating heat. This heat can then be used to produce electricity — and the high temperatures involved mean it can do so very efficiently — but it has applications beyond that as well.
Many industries use intense heat in their manufacturing processes, like smelting or cement making, and they often burn fuels to create those high temperatures. Heliogen's refinery is able to produce similar temperatures without burning fuels and could provide the heat for these industries in the future. Additionally, the heat produced is high enough to make hydrogen fuel via electrolysis.
As Gross explained to CNN, "If you can make hydrogen that's green, that's a game-changer. Long term, we want to be the green hydrogen company."
If not used immediately, the heat energy can also be stored in plain old rocks, which can stay hot for days or even up to a week in a properly insulated storage unit. Their energy can then be called upon when needed or possibly even shipped to a location in need of heat. Compared to the difficulties of storing electricity produced from solar, this is child's play.
How can concentrated solar be applied at scale?
Gross hopes to improve the process by reaching the same results with increasingly smaller heliostats. His are already smaller than usual, which would allow them to be mass produced more cheaply than they are today. The hope is that this, along with other refinements to the system, would help lower the cost of energy produced by concentrated solar until it is cheaper than fossil fuel energy.
Currently, energy from concentrated solar power is more expensive than burning fossil fuels but only slightly. Also, compared to large arrays of solar panels, solar refineries are more expensive to build and operate. But costs are expected to decrease, in part because they are much better at energy storage than traditional solar, as discussed earlier. Furthermore, large scale concentrated solar power operations already exist in Spain, the Middle East, and the Southwestern U.S.
Concentrated solar power could radically change manufacturing
Gross's grand vision is to build many refineries all over the world using their heat to power industrial processes. The electricity produced by other refineries would create vast quantities of cheap "HelioFuels," starting with hydrogen. Since hydrogen fuel cells are extremely efficient and can run everything from submarines to laptops, this would be a huge step toward cleaning up the energy supply.
Similar ideas exist and have been used elsewhere to cleanly produce jet fuel, another industrial process that normally requires burning fossil fuels in order to create high temperatures.
The reduction in carbon emissions due to widespread use of concentrated solar could be substantial. Concrete manufacturing alone is responsible for 8 to 10 percent of all global emissions. Nearly 40 percent of those emissions are caused by burning the fossil fuels needed to create heat for the manufacturing process. Quick mental math suggests that if concentrated solar power replaced fossil fuel burning for heat in concrete production alone, global carbon emissions would fall by as much as four percent. For comparison, that is roughly equal to the share of carbon emissions created by France, Italy, the United Kingdom, and Brazil combined.
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