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Astronomers Discover Milky Way–Sized Galaxy That's 99.99% Dark Matter

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At left, a wide view of the Dragonfly 44 galaxy, and at right, a close-up of the same image, revealing its large, elongated shape and halo of spherical clusters of stars around its core, similar to the halo that surrounds the Milky Way. Image credit: Pieter van Dokkum, Roberto Abraham, Gemini Observatory/AURA

 
An unusual galaxy, made up almost entirely of exotic “dark matter," has left astronomers and physicists scratching their heads. The galaxy, known as Dragonfly 44, is located some 300 million light-years from our own Milky Way and is about the same size as our galaxy—but a mere 100th of 1 percent of it is made up of ordinary matter. The rest—99.99 percent—is dark matter.

Dragonfly 44 actually has about as much dark matter as our galaxy, but it has far fewer stars. As a result, the dark matter almost completely dominates. “It’s kind of a dark twin of the Milky Way,” lead researcher Pieter van Dokkum of Yale University tells mental_floss.

The findings were published today in Astrophysical Journal Letters [PDF].

First proposed in the 1930s, dark matter is a mysterious form of matter believed to account for more than one-quarter of the mass and energy in the universe. (A larger proportion—more than two-thirds—is the even-more-mysterious dark energy; a mere 5 percent of the universe is made of ordinary, visible matter.) Dark matter doesn’t interact with ordinary matter—it can’t be seen with optical or radio telescopes—but its presence can be deduced through the gravitational tug that it exerts.

The fact that dark matter dominates over regular matter is not by itself a surprise: In most galaxies, van Dokkum explains, there’s about 50 times as much dark matter as ordinary matter. But in Dragonfly 44, that ratio is even more extreme, thanks to the lack of stars.

The only other galaxies known to be this heavily skewed toward dark matter are the small dwarf galaxies that orbit the Milky Way. But Dragonfly 44 isn’t like those galaxies—rather, it’s just as large and massive as the Milky Way itself. How it ended up so dark matter–heavy, and with so few stars, is a mystery. “We thought we understood these [more massive] galaxies quite well,” says van Dokkum. “They usually have a relatively small amount of dark matter, in proportion to the number of stars that they have. This galaxy turns that on its head.”

Because of the paucity of stars, Dragonfly 44 is extremely faint. It’s one member of a new class of diffuse, dim galaxies discovered recently using the Dragonfly telescope array, an innovative imaging system that uses ultra-“fast” commercial telephoto lenses (the kind that sports photographers use) to find dim objects in the night sky. The brainchild of van Dokkum and University of Toronto astronomer Roberto Abraham, Dragonfly was tailor-made to detect objects with “low surface brightness”: While the light from stars is concentrated in specific points in the sky, galaxies are dim and their light is spread out—and these peculiar galaxies are even dimmer, and thus even harder to see. “These objects had always been missed, but with the Dragonfy telescope, we found them,” van Dokkum says.

Later, he and his colleagues aimed Hawaii’s Keck telescope at Dragonfly 44 for a closer look (because the galaxy is so dim, this required collecting data over six nights). They were able to measure the speeds of some of the galaxy’s stars, from which the total mass of the galaxy can be calculated. From the brightness and the mass, they determined how much mass is “missing”—that is, they inferred how much extra mass must be present in the form of dark matter, so as to keep the galaxy from flying apart. Observations with the Gemini North telescope, also in Hawaii, revealed a halo of spherical clusters of stars surrounding the galaxy’s core—similar to the halo known to surround our own Milky Way. “Ultimately, we may learn about the connection between dark matter and these mysterious star clusters,” van Dokkum says.

Meanwhile, the biggest mystery of all remains the identity of the dark matter itself. Physicists’ best guess is that it’s made up of some kind of primordial particle, perhaps created at the time of the big bang—but numerous attempts to detect such particles directly (including the most recent effort) have come up empty. And Dragonfly 44, being so far away, isn’t likely to help much—but in principle, other dark matter–dominated galaxies could still be awaiting detection, much closer to home. “If we found a galaxy like this, that’s close to us—that might be the ideal place to look, to make a direct detection of the dark matter particle,” van Dokkum says.

David Spergel, a Princeton University astrophysicist who was not involved in the current research, tells mental_floss that these low surface–brightness galaxies “are useful ‘laboratories’ both for studying the properties of dark matter and understanding galaxy formation.”

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iStock // Ekaterina Minaeva
technology
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Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Stephen Missal
crime
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New Evidence Emerges in Norway’s Most Famous Unsolved Murder Case
May 22, 2017
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A 2016 sketch by a forensic artist of the Isdal Woman
Stephen Missal

For almost 50 years, Norwegian investigators have been baffled by the case of the “Isdal Woman,” whose burned corpse was found in a valley outside the city of Bergen in 1970. Most of her face and hair had been burned off and the labels in her clothes had been removed. The police investigation eventually led to a pair of suitcases stuffed with wigs and the discovery that the woman had stayed at numerous hotels around Norway under different aliases. Still, the police eventually ruled it a suicide.

Almost five decades later, the Norwegian public broadcaster NRK has launched a new investigation into the case, working with police to help track down her identity. And it is already yielding results. The BBC reports that forensic analysis of the woman’s teeth show that she was from a region along the French-German border.

In 1970, hikers discovered the Isdal Woman’s body, burned and lying on a remote slope surrounded by an umbrella, melted plastic bottles, what may have been a passport cover, and more. Her clothes and possessions were scraped clean of any kind of identifying marks or labels. Later, the police found that she left two suitcases at the Bergen train station, containing sunglasses with her fingerprints on the lenses, a hairbrush, a prescription bottle of eczema cream, several wigs, and glasses with clear lenses. Again, all labels and other identifying marks had been removed, even from the prescription cream. A notepad found inside was filled with handwritten letters that looked like a code. A shopping bag led police to a shoe store, where, finally, an employee remembered selling rubber boots just like the ones found on the woman’s body.

Eventually, the police discovered that she had stayed in different hotels all over the country under different names, which would have required passports under several different aliases. This strongly suggests that she was a spy. Though she was both burned alive and had a stomach full of undigested sleeping pills, the police eventually ruled the death a suicide, unable to track down any evidence that they could tie to her murder.

But some of the forensic data that can help solve her case still exists. The Isdal Woman’s jaw was preserved in a forensic archive, allowing researchers from the University of Canberra in Australia to use isotopic analysis to figure out where she came from, based on the chemical traces left on her teeth while she was growing up. It’s the first time this technique has been used in a Norwegian criminal investigation.

The isotopic analysis was so effective that the researchers can tell that she probably grew up in eastern or central Europe, then moved west toward France during her adolescence, possibly just before or during World War II. Previous studies of her handwriting have indicated that she learned to write in France or in another French-speaking country.

Narrowing down the woman’s origins to such a specific region could help find someone who knew her, or reports of missing women who matched her description. The case is still a long way from solved, but the search is now much narrower than it had been in the mystery's long history.

[h/t BBC]

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