Astronomers Trace Fast Radio Burst to Extreme Cosmic Neighborhood

On Christmas Eve 2016, Andrew Seymour, an astronomer at the Arecibo Observatory in Puerto Rico, kissed his 4-year-old daughter, Cora Lee, goodnight, telling her he was off to track Santa. He walked to the well-worn telescope, occasionally passing revelers riding horses through the empty streets—a common sight in Arecibo during the holidays. Sometimes a lonely firework would light up in the distance. Close to midnight, he nodded to a guard and entered the nearly empty complex.

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Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

The radio dish was on a break from its regular schedule, so Seymour decided to test out new hardware that he and his colleagues had been working on. Soon after he began recording his observations, an extremely powerful radio source, 3 billion light-years away, decided to say hello. Seymour didn’t find Santa that Christmas, but rather an unexpected twist in the tale of one of the most mysterious objects in the cosmos.

The object that Seymour caught that night was the only known repeating fast radio burst (FRB), an ultra-brief flash of energy that flickers on and off at uneven intervals. Astronomers had been debating what might be causing mysterious repeater, officially called FRB 121102 and unofficially the “Spitler burst,” after the astronomer who discovered it.

In the weeks following that Christmas detection, Arecibo registered 15 more bursts from this one source. These flashes were the highest frequency FRBs ever captured at the time, a measurement made possible by the hardware Seymour and his team had just installed. Based on the new information, the scientists have concluded in a study released this week in the journal Nature that whatever object is creating the bursts, it must be in a very odd and extreme cosmic neighborhood, something akin to the environment surrounding a black hole with a mass of more than 10,000 suns.

The new work helps to strengthen the theory that at least some FRBs might be produced by magnetars—highly magnetized, rotating neutron stars, which are the extremely dense remains of massive stars that have gone supernova, said Shami Chatterjee, an astrophysicist at Cornell University. In the case of the repeater, it could be a neutron star “that lives in the environment of a massive black hole,” he said. Or it might also be like nothing we’ve seen before—a different kind of magnetar ensconced in a very intense, magnetically dense birth nebula, unlike any known to exist in our galaxy—“quite extraordinary circumstances,” he said.

Too Extreme to Find

It wasn’t obvious at first that the repeating burst had to live in such an extreme environment. In October, 10 months after Seymour detected that initial burst at Arecibo, Jason Hessels, an astronomer at the University of Amsterdam, and his student Daniele Michilli were staring at the data on Michilli’s laptop screen. They had been trying to determine whether a magnetic field near the source might have twisted its radio waves, an effect known as Faraday rotation. There appeared to be nothing to see.

But then Hessels had an idea: “I wondered whether maybe we had missed this effect simply because it was very extreme.” They had been looking for just a little bit of a twist. What if they were to search for something exceptional? He asked Michilli to crank up the search parameters, “to try crazy numbers,” as Michilli put it. The student expanded the search by a factor of five—a rather “naive thing to do,” Chatterjee said, because such a high value would be completely unprecedented.

When Michilli’s laptop displayed the new data plot, Hessels immediately realized that the radio waves had gone through a hugely powerful magnetic field. “I was shocked to see how extreme the Faraday rotation effect is in this case,” he said. It was like nothing else ever seen in pulsars and magnetars. “I’m also embarrassed because we were sitting on the critical data for months” before attempting such an analysis, he added.

Jason Hessels led the team that identified the Faraday rotation coming from the burst.

Courtesy of Jason Hessels

The discovery sent ripples across the community. “I was shocked by the email announcing the result,” said Vicky Kaspi, an astrophysicist at McGill University. “I had to read it multiple times.”

Final confirmation came from a team searching for aliens. The Breakthrough Listen initiative ordinarily uses radio telescopes such as the Green Bank Telescope in West Virginia to scan the skies for signs of extraterrestrial life. Yet “since it’s not obvious in which direction they should point the telescope to search for E.T., they decided to spend some time looking at the repeating FRB, which clearly paid off,” said the astronomer Laura Spitler, namesake of the Spitler burst.

The Green Bank Telescope not only confirmed the Arecibo findings, it also observed several additional bursts from the repeater at even higher frequencies. These bursts also showed the same mad, highly twisted Faraday rotation.

What Powers Them

The extreme Faraday rotation is a signal that “the repeating FRB is in a very special, extreme environment,” Kaspi said. It takes a lot of energy to produce and maintain such highly magnetized conditions. In one hypothesis outlined by the researchers, the energy comes from a nebula around the neutron star itself. In another, it comes from a massive black hole.

In the nebula hypothesis, flares from a newly born neutron star create a nebula of hot electrons and strong magnetic fields. These magnetic fields twist the radio waves coming out of the neutron star. In the black hole model, a neutron star has its radio waves twisted by the enormous magnetic field generated by a nearby massive black hole.

Researchers haven’t come to an agreement about what’s going on here. Kaspi leans toward the black hole model, but Brian Metzger, an astrophysicist at Columbia University, feels that it’s somewhat contrived. “In our galaxy, only one out of dozens of magnetars resides so close to the central black hole. What makes such black hole-hugging magnetars so special that they would preferentially produce fast radio bursts? Did we just get really lucky with the first well-localized FRB?”

And the debate may get muddier before it gets cleared up. Chatterjee said theorists are certain to soon jump on the paper and start producing a multitude of new models and possibilities.

Burst Machines

The Spitler repeater is still the only FRB source that has been nailed down to a particular galaxy. No one knows quite where the other bursts are coming from. To say with any certainty that some—or all—of these energetic radio flashes come from highly magnetized environments, researchers need more data. And data are coming in. The Australian Square Kilometer Array Pathfinder (ASKAP), which is not yet officially complete, has already netted more FRBs than any other telescope in the world. With a tally of about 10 FRBs last year alone, it has proven to be “a remarkable FRB-finding machine,” said Matthew Bailes, an astrophysicist at Swinburne University of Technology—although none of them repeat.

Soon another telescope with a highly unusual design, called CHIME, will come online in Canada, and should spot many more FRBs—maybe 10 times more than ASKAP. Other next-generation telescopes, like the Square Kilometer Array (SKA), with dishes in South Africa and Australia, will surely contribute as well. As we register more of these flashes, chances are that some of them will repeat. Once scientists can sift through such data, the Faraday rotation effect may help them understand whether all FRBs are powered by a similar mechanism—or not.

Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

Clean power Is a Bright place Amid a Dark Tech Cloud

The mood around tech is dark nowadays. Internet sites are a definite cesspool of harassment and lies. On-demand organizations are producing a bleak economy of gig labor. AI learns to be racist. Can there be anyplace in which the tech news is radiant with conventional optimism? In which good cheer abounds?

Why, yes, there is certainly: clean energy. It’s, in place, the newest Silicon Valley—filled with giddy, breathtaking ingenuity and flat-out very good news.

This might appear astonishing given the climate-change denialism in Washington. But consider, first, residential solar technology. The cost of panels has plummeted in the past ten years and is projected to drop another 30 percent by 2022. Why? Clever engineering breakthroughs, like the use of diamond wire to cut silicon wafers into ever-skinnier slabs, creating higher yields with less natural material.

Manufacturing expenses are down. According to US government projections, the fastest-growing career regarding the next a decade will likely to be solar voltaic installer. And you understand who switched to solar powered energy last year, because it ended up being therefore cheap? The Kentucky Coal Museum.

Related Tales

Tech could have served up Nazis in social media channels, but, hey, it is additionally creating microgrids—a locavore equivalent for the solar set. One of these simple efforts is Brooklyn-based LO3 Energy, a business that produces a paperback-sized unit and pc software that lets owners of solar-equipped domiciles sell power to their neighbors—verifying the transactions using the blockchain, on top of that. LO3 is testing its system in 60 domiciles on its Brooklyn grid and hundreds more in the areas.

“Buy power and you’re buying from your own community,” LO3 founder Lawrence Or­sini tells me. Their chipsets also can connect with smart appliances, so you might save cash by allowing his system period down your devices as soon as the system is low on energy. The business uses internet logic—smart devices that communicate with one another more than a foolish network—to optimize energy consumption on fly, making local clean energy ever more viable.

But wait, does not blockchain number-crunching usage so much electricity it creates wasteful heat? It will. So Orsini invented DareHenry, a rack filled with six GPUs; although it processes mathematics, phase-­changing goo absorbs the outbound temperature and uses it to warm a house. Blockchain cogeneration, individuals! DareHenry is 4 feet of gorgeous, Victorian­esque steampunk aluminum—so lovely you’d want anyone to showcase to guests.

Solar and blockchain are just the end of clean technology. Within few years, we’ll probably start to see the first home fuel-cell systems, which convert propane to electricity. Such systems are “about 80 per cent efficient,” marvels Garry Golden, a futurist who has studied clean energy. (He’s additionally on LO3’s grid, along with the rest of his block.)

The point is, clean energy has a utopian character that reminds me personally associated with beginning of computers. The pioneers of the 1970s had been crazy hackers, hell-bent on making devices inexpensive sufficient the masses. Everybody thought these people were peanuts, or little potatoes—yet they revolutionized interaction. When I look at Orsini’s ­blockchain-based energy-trading routers, we start to see the Altair. And you can find oodles more inventors like him.

Mind you, early Silicon Valley had one thing crucial that clean energy now doesn’t: massive authorities help. The armed forces purchased a great deal of microchips, helping measure up computing. Trump’s musical organization of weather deniers aren’t probably be buyers of very first resort for clean energy, but states may do a lot. Ca currently has, for instance, by producing quotas for renewables. Therefore even though you can’t pay for this stuff yourself, you ought to pressure state and neighborhood officials to crank up their solar technology usage. It’ll give us all a boost of much-needed cheer.

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The Math Behind Gerrymandering and Wasted Votes

Imagine fighting a war on 10 battlefields. You and your opponent each have 200 soldiers, and your aim is to win as many battles as possible. How would you deploy your troops? If you spread them out evenly, sending 20 to each battlefield, your opponent could concentrate their own troops and easily win a majority of the fights. You could try to overwhelm several locations yourself, but there’s no guarantee you’ll win, and you’ll leave the remaining battlefields poorly defended. Devising a winning strategy isn’t easy, but as long as neither side knows the other’s plan in advance, it’s a fair fight.

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Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

Now imagine your opponent has the power to deploy your troops as well as their own. Even if you get more troops, you can’t win.

In the war of politics, this power to deploy forces comes from gerrymandering, the age-old practice of manipulating voting districts for partisan gain. By determining who votes where, politicians can tilt the odds in their favor and defeat their opponents before the battle even begins.

In 1986, the Supreme Court ruled extreme partisan gerrymanders unconstitutional. But without a reliable test for identifying unfair district maps, the court has yet to throw any out. Now, as the nation’s highest court hears arguments for and against a legal challenge to Wisconsin’s state assembly district map, mathematicians are on the front lines in the fight for electoral fairness.

Simple math can help scheming politicians draw up districts that give their party outsize influence, but mathematics can also help identify and remedy these situations. This past summer the Metric Geometry and Gerrymandering Group, led by the mathematician Moon Duchin, convened at Tufts University, in part to discuss new mathematical tools for analyzing and addressing gerrymandering. The “efficiency gap” is a simple idea at the heart of some of the tools being considered by the Supreme Court. Let’s explore this concept and some of its ramifications.

Start by imagining a state with 200 voters, of whom 100 are loyal to party A and 100 to party B. Let’s suppose the state needs to elect four representatives and so must create four districts of equal electoral size.

Imagine that you have the power to assign voters to any district you wish. If you favor party A, you might distribute the 100 A voters and 100 B voters into the four districts like this:

With districts constructed in this way, party A wins three of the four elections. Of course, if you prefer party B, you might distribute the voters this way:

Here, the results are reversed, and party B wins three of the four elections.

Notice that in both scenarios the same number of voters with the same preferences are voting in the same number of elections. Changing only the distribution of voters among the districts dramatically alters the results. The ability to determine voting districts confers a lot of power, and attending to some simple math is all that’s needed to create an electoral edge.

What if, instead of creating an advantage for one party over the other, you wished to use your power to create fair districts? First, you’d need to determine what “fair” means, and that can be tricky, as winners and losers often have different perspectives on fairness. But if we start with some assumptions about what “fair” means, we can try to quantify the fairness of different voter distributions. We may argue about those assumptions and their implications, but by adopting a mathematical model we can attempt to compare different scenarios. The efficiency gap is one approach to quantifying the fairness of a voter distribution.

To understand the efficiency gap, we can begin with the observation that, in a series of related elections, not all votes have the same impact. Some votes might make a big difference, and some votes might be considered “wasted.” The disparity in wasted votes is the efficiency gap: It measures how equally, or unequally, wasted votes are distributed among the competing parties.

So what counts as a wasted vote? Consider California’s role in presidential elections. Since 1992, California has always backed the Democratic nominee for president. Therefore, California Republicans know they are almost certainly backing a losing candidate. In some sense their vote is wasted: If they were allowed to vote in a toss-up state like Florida, their vote might make more of a difference. From a Republican perspective, that would be a more efficient use of their vote.

As it turns out, Democratic voters in California can make a similar argument about their vote being wasted. Since the Democratic candidate will likely win California in a landslide, many of their votes, in a sense, are wasted, too: Whether the candidate wins California with 51 percent of the vote or 67 percent of the vote, the outcome is the same. Those extra winning votes are meaningless.

Thus, in the context of the efficiency gap, there are two kinds of wasted votes: those for a losing candidate and those for a winning candidate that go beyond what is necessary for victory (for simplicity, we take the threshold for victory to be 50 percent, even though this could technically result in a tie; an actual tie is beyond unlikely with hundreds of thousands of voters in each congressional district). In a multi-district election, each party will likely have wasted votes of each kind. The efficiency gap is the difference in the totals of the wasted votes for each party, expressed as a percentage of total votes cast. (We subtract the smaller number from the larger when possible, to ensure a nonnegative efficiency gap. We could also take the absolute value of the difference.)

Let’s return to our four-district scenarios and examine their efficiency gaps. Our first distribution looked like this.

In this scenario, 75 of B’s votes are wasted: 60 in losing causes and 15 more than the 25 needed to win district 4. Only 25 of party A’s votes are wasted: 5 extra votes in each victory and 10 losing votes. The raw difference in wasted votes is 75 − 25 = 50, so the efficiency gap here is 50/200 = 25 percent. We say the 25 percent efficiency gap here favors party A, as party B had the larger number of wasted votes. In the second scenario, where the numbers are reversed, the 25 percent efficiency gap now favors party B.

Can the efficiency gap give us a sense of the fairness of a distribution? Well, if you had the power to create voting districts and you wanted to engineer victories for your party, your strategy would be to minimize the wasted votes for your party and maximize the wasted votes for your opponent. To this end, a technique colorfully known as packing and cracking is employed: Opposition votes are packed into a small number of conceded districts, and the remaining block of votes is cracked and spread out thinly over the rest of the districts to minimize their impact. This practice naturally creates large efficiency gaps, so we might expect fairer distributions to have smaller ones.

Let’s take a deeper look at efficiency gaps by imagining our 200-voter state now divided into 10 equal districts. Consider the following voter distribution, in which party A wins 9 of the 10 districts.

On the surface, this doesn’t seem like a fair distribution of voters. What does the efficiency gap say?

In this scenario, almost all of party B’s votes are wasted: nine losing votes in each of nine districts, plus nine excess votes in one victory, for a total of 90 wasted votes. Party A’s voters are much more efficient: only 10 total votes are wasted. There is a difference of 90 − 10 = 80 wasted votes and an efficiency gap of 80/200 = 40 percent, favoring party A.

Compare that with the following distribution, where party A wins 7 of the 10 districts.

Here, the wasted vote tally is 70 for party B and 30 for party A, producing an efficiency gap of 40/200 = 20 percent. A seemingly fairer distribution results in a smaller efficiency gap.

As a final exercise, consider this even split of district elections.

The symmetry alone suggests the answer, and the calculations confirm it: 50 wasted votes for each party means a 0 percent efficiency gap. Notice here that a 0 percent efficiency gap corresponds to an independent notion of fairness: Namely, with voters across the state evenly split between both parties, it seems reasonable that each party would win half of the elections.

These elementary examples demonstrate the utility of the efficiency gap as a measure of electoral fairness. It’s easy to understand and compute, it’s transparent, and its interpretations are consistent with other notions of fairness. It’s a simple idea, but one that is being used in a variety of complex ways to study gerrymandering. For example, mathematicians are now using simulations to consider millions of theoretical electoral maps for a given state and then examining the distribution of all possible efficiency gaps. Not only does this create a context for evaluating the fairness of a current map against other possibilities, it can also potentially be used to suggest fairer alternatives.

Though voters are not actually assigned to districts in the way we have imagined in our examples, the practice of gerrymandering achieves similar results. By strategically redrawing district boundaries, gerrymanderers can engineer voting distributions to create an uneven electoral playing field. These unfair fights affect how we are governed and help majority-party incumbents coast to re-election term after term. The case before the Supreme Court involves just one of many potentially unfair maps. Objective mathematical tools like the efficiency gap may be the only way to root out gerrymandering and keep our political battlefields in balance.

Download the “Doing the Political Math” PDF worksheet to practice these concepts or to share with students.

Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.