This isn't your grandfather's stargazing: The amount of data we have on our universe is doubling every year thanks to big telescopes and better light detectors.
Think of all the data humans have collected over the long history of astronomy, from the cuneiform tablets of ancient Babylon to images---like the one above---taken by the Hubble Space Telescope. If we could express all of that data as a number of bits, our fundamental unit of information, that number would be, well, astronomical. But that's not all: in the next year that number is going to double, and the year after that it will double again, and so on and so on.
There are two reasons that astronomy is experiencing this accelerating explosion of data. First, we are getting very good at building telescopes that can image enormous portions of the sky. Second, the sensitivity of our detectors is subject to the exponential force of Moore's Law. That means that these enormous images are increasingly dense with pixels, and they're growing fast---the Large Synoptic Survey Telescope, scheduled to become operational in 2015, has a three-billion-pixel digital camera. So far, our data storage capabilities have kept pace with the massive output of these electronic stargazers. The real struggle has been figuring out how to search and synthesize that output.
Alberto Conti is the Innovation Scientist for the James Webb Space Telescope, the successor to the Hubble Space Telescope that is due to launch in 2018. Before transitioning to the Webb, Conti was the Archive Scientist at the Space Telescope Science Institute (STScI), the organization that operates the Hubble. For almost ten years, he has been trying to make telescope data accessible to astronomers and to the public at large. What follows is my conversation with Conti about the future of, and the relationship between, big telescopes and big data.
Last year I was researching the Hubble Deep Field (pictured below) and I interviewed
Bob Williams, the former head of STScI who originally conceived of and
executed the deep field image. He told me that the deep field, in
addition to its extraordinary scientific value, had changed the way that
data is distributed in astronomy. Can you explain how?
It's interesting, one of the very first papers I wrote as a graduate
student in astronomy was on the Hubble Deep Field. I was a graduate
student in 1995 when it came out, and of course there was this "wow"
factor---the fact that this was one of the deepest images ever taken,
the fact that you have thousands of galaxies in this tiny patch of
sky---you would take out your calculator and try to calculate how many
galaxies there are in the universe and you would come up with a hundred
billion, and it was mind-boggling. It still is.
it also changed the data regime. Before the Hubble Deep Field, data
(raw images) would be deposited in some archive and you would just tell
astronomers to "go get the images." Astronomers would then have to
download the images and run software on them in order to find all of the
objects using certain parameters, and then they'd have to assess the
quality of the data, for instance whether an object that was thought to
be a star was actually a star. So you had to do a lot of analysis before
you could really get into your research.
decided that this data was so overwhelmingly powerful, in terms of what
it was telling us about the universe, that it was worth it for the
community to be able to get their hands on the data immediately. And so
the original deep field team processed the data, found the objects in
it, and then catalogued each of them, so that every object in the deep
field had a description in terms of size, distance, color, brightness
and so forth. And that catalogue was available to researchers from the
very start---it started a whole new model, where the archive does all
I can tell you firsthand how
incredible it was at the time, because as a graduate student studying
quasars, I was able to identify all of the quasars within the data in
just a few minutes. What Bob did, which I thought was brilliant, was
enable us to do the science much quicker. If you take a look at what's
happening with these massive archives now, it's being done in the exact
same way; people realized that you aren't going to be able to download
and process a terabyte of images yourself. It's a huge waste of time.
The other thing Bob did was he released the data to the world almost
immediately; I remember it took forever to download, not because the
data set was especially large, but because there were so many people
accessing the archive at the same time. That was one of astronomy's
first open source exercises, in the sense that we use that term today.
Has data always been an issue for astronomy? Did Galileo ever run out of log books? I remember reading about William Herschel's sister Caroline, an accomplished astronomer in her own right, spending these long, cold nights underneath their wooden telescope, listening for her brother, who would scream these numbers for her to write down in a notebook. How have data challenges changed since then?
Conti: That's a good question. Astronomy has changed quite a bit since Galileo and Herschel. Galileo, for instance, had plenty of paper on which to record his observations, but he was limited in his capacity for observation and so was Herschel to some extent. Today we don't have those same observational limits.
There are two issues driving the current data challenges facing astronomy. First, we are in a vastly different data regime in astronomy than we were even ten or fifteen years ago. Over the past 25 to 30 years, we have been able to build telescopes that are 30 times larger than what we used to be able to build, and at the same time our detectors are 3,000 times more powerful in terms of pixels. The explosion in sensitivity you see in these detectors is a product of Moore's Law---they can collect up to a hundred times more data than was possible even just a few years ago. This exponential increase means that the collective data of astronomy doubles every year or so, and that can be very tough to capture and analyze.
You spent part of your career working with GALEX, the Galaxy Evolution Explorer. How did that experience change the way you saw data and astronomy?
Conti: GALEX was a big deal because it was one of the first whole sky ultraviolet missions. I want to stress "whole sky" here, because taking measurements of ultraviolet sources all over the sky is a lot more data-intensive than zooming in on a single source. Whole sky ultraviolet measurements had been done before, but never at the depth and resolution made possible by GALEX. This had tremendous implications for data archives at the time. When I started working on GALEX nine years ago, the amount of data it produced was gigantic compared with anything that we had in-house at the Space Telescope Science Institute, and that includes the Hubble Space Telescope, which of course doesn't take whole sky images.
What we were able to do was create a catalog of objects that were detected in these whole sky images, and the number was quite large---GALEX had detected something close to three hundred million ultraviolet sources in the sky. That forced the archive to completely revisit the way it allowed users to access these very large catalogs. There were large databases in astronomy ten years ago, but databases that would allow you to search large collections of objects were not common. GALEX helped to pave the way with this new searchable archive. I can remember when we first introduced the data, we had people all over the world trying to download all of the data, because they thought that was the only way they could access it. They were thinking that to use the data you had to have it locally, which was the old way of thinking. The big leap was that we created an interface that allowed you to get to your data, to a level where you're one step away from analysis, and we were able to do that without you having to download it. We did it by creating interfaces that allowed you to mine all three hundred million sources of ultraviolet light in just a few seconds. You could ask the interface to show you all of the objects that had a particular color, or all of the sources from a certain position in the sky, and then you could download only what you needed. That was a big shift in how astronomers do research.
How much data are we talking about?
Conti: Well, GALEX as a whole produced 20 terabytes of data, and that's actually not that large today---in fact it's tiny compared to the instruments that are coming, which are going to make these interfaces even more important. We have telescopes coming that are going to produce petabytes (a thousand terabytes) of data. Already, it's difficult to download a terabyte; a petabyte would be, not impossible, but certainly an enormous waste of bandwidth and time. It's like me telling you to download part of the Internet and search it yourself, instead of just using Google.
Would something like the exoplanet-hunting Kepler Space Telescope have been possible with the data mining and data storage capacities of twenty years ago?
Conti: Well, Kepler is an extraordinary mission for many reasons. Technologically, it would not have been possible even just a few years ago. Kepler measures the light of 170,000 stars very precisely at regular intervals looking for these dips in light that indicate a planet is present. The area that they sample is not very large---it's a small patch of sky---but they're sampling all of those stars every thirty minutes. So that's already a huge breakthrough, and it creates a lot of data, but it's still not as much as a whole sky mission like GALEX.
What's different about Kepler, from a data perspective, is that it's opening up the time domain. With a mission like GALEX, we collect data and store it in the database, but it's relatively static. It sits there and it doesn't really change, unless we get a new dump of data that helps us refine it, and that may only happen once a year. With Kepler you have these very short intervals for data collection, where you have new images every thirty minutes. That really opens up the time domain. We're working hard to figure out how to efficiently analyze time domain data. And of course the results are spectacular: a few years ago we had less than twenty exoplanets, and now we have thousands.
Is there a new generation of telescopes coming that will make use of these time domain techniques?
Conti: Oh yes. With Kepler we've developed this ability to make close observations of objects in the sky over time, but if you add millions or even billions of objects, then you get into the new regime of telescopes like the Large Synoptic Survey Telescope (LSST) which we expect to come online at the end of this decade. These telescopes are going to take images of the whole sky every three days or so; with that kind of data you can actually make movies of the whole sky. You can point to a place in the sky and say "there was nothing there the other day, but today there's a supernova." You couple that kind of big data, whole sky data, with the time domain and you're talking about collecting terabytes every night. And we don't have to wait that long; ALMA, the Atacama Large Millimeter Array is going to have its first data release very soon and its raw data is something like forty terabytes a day. Then in 2025, we're going to have the Square Kilometre Array (SKA), the most sensitive radio instrument ever built, and we expect it will produce more data than we have on the entire Internet now---and that's in a single year. This is all being driven by the effect that Moore's Law has on these detectors; these systematic advances let us keep packing in more and more pixels.
In my view, we've reached the point where storage is no longer the issue. You can buy disk, you can buy storage, and I think that at some point we may even have a cloud for astronomy that can host a lot of this data. The problem is how long it's going to take me to get a search answer out of these massive data sets. How long will I have to wait for it?
Has citizen science played a meaningful role in helping astronomy tackle all of this data?
Conti: I think so. I'm part of a group that has done a lot of work on citizen science, especially with the folks over at Galaxy Zoo and CosmoQuest on an in-house project called Hubble Zoo. The original Galaxy Zoo was a galaxy classification project, where volunteers could log on to the server and help to classify galaxies by shape. Galaxy shapes give you a lot of information about their formation history; for instance, round galaxies are much more likely to have cannibalized other galaxies in a merger, and on average they're a little older. Spiral galaxies are structures that need time to evolve; generally, they're a little younger than round galaxies. And so when you have thousands of ordinary, non-scientists classifying these galaxies you can get some great statistics in a short period of time. You can get the percentage of round galaxies, elliptical galaxies, spiral galaxies, irregular galaxies and so forth; you can get some really interesting information back. What's great about citizen science is that you can feed images to citizens that have only been fed through machines---no human eyes have ever looked at them.
There's another citizen science project that I'm trying to get started in order to to make use of all the old GALEX data. With GALEX we took these whole sky images in ultraviolet, and we did it at certain intervals, so there is a time domain at work, even if it's not as rapid as the Kepler. But as I said before, we have over three hundred million sources of UV light in these images. There was a professor who had a graduate student looking at this data at different intervals with the naked eye, and they were able to find four hundred stars that seemed to be pulsating over time. When I saw the data, I said "this is interesting, but it should be an algorithm." So we made an algorithm to detect these pulsating stars, and we ran it inside the entire database of 300 million sources, and we found 2.1 million pulsating star candidates. And of course, this is just the first pass at this; who knows how many of those candidates will convert. But it's an illustrative case---the idea is to feed these kinds of projects to the next generation of citizen scientists, and to have them to do what that graduate student did, and then in some cases they'll be able to find something remarkable, something that otherwise might never have been found.
Can we talk about image-processing? What percentage of Hubble images
are given the kind of treatment that you see with really iconic shots
like the Sombrero Galaxy or the Pillars of Creation?
It depends. There's an image coming out for the 22nd anniversary (of
the Hubble) here in a few days, and as you'll be able to see, it's a
very beautiful image. I'm a little biased in the sense that I tend to
think that every image from the Hubble is iconic, but they aren't all
treated equally. There's a group of people here in the office of public
outreach at STScI that think a lot about how images are released. But if
you go back to the Hubble Deep Field, or even earlier, you can see that
the imaging team really does put a lot of care into every Hubble image.
And that's not because each one of those images is iconic; rather it's
because we have this instrument that is so unbelievable and each piece
of data it produces is precious, and so a lot of work goes into
And now, with the Hubble
Legacy Archive, people can produce their own Hubble images, with new
colors, and they can do it on the fly.
Like Instagram filters?
Kind of, yeah. As you know, all data in astronomy is monochrome
data---it's black and white---and then the processing team combines it
into layers of red, green and blue, and so forth. Zolt Levay, the head
of the imaging team, takes these colored layers and combines them and
tries to make them as accurate as possible in terms of how they would
look to the human eye, or to a slightly more sensitive eye. This program
lets you take three monochrome images, which you can then make any
color you like, and it let's you make them into a single beautiful
image. There's actually a contest being held by the office of public outreach to see who can upload the most beautiful new image.
Einstein’s gravitational waves rest on a genuinely radical idea.
After decades of anticipation, we have directly detected gravitational waves—ripples in spacetime traveling at the speed of light through the universe. Scientists at LIGO (the Laser Interferometic Gravitational-wave Observatory) have announced that they have measured waves coming from the inspiral of two massive black holes, providing a spectacular confirmation of Albert Einstein’s general theory of relativity, whose hundredth anniversary was celebrated just last year.
Finding gravitational waves indicates that Einstein was (once again) right, and opens a new window onto energetic events occurring around the universe. But there’s a deeper lesson, as well: a reminder of the central importance of locality, an idea that underlies much of modern physics.
Today’s empires are born on the web, and exert tremendous power in the material world.
Mark Zuckerberg hasn’t had the best week.
First, Facebook’s Free Basics platform was effectively banned in India. Then, a high-profile member of Facebook’s board of directors, the venture capitalist Marc Andreessen, sounded off about the decision to his nearly half-a-million Twitter followers with a stunning comment.
“Anti-colonialism has been economically catastrophic for the Indian people for decades,” Andreessen wrote. “Why stop now?”
After that, the Internet went nuts.
Andreessen deleted his tweet, apologized, and underscored that he is “100 percent opposed to colonialism” and “100 percent in favor of independence and freedom.” Zuckerberg, Facebook’s CEO, followed up with his own Facebook post to say Andreessen’s comment was “deeply upsetting” to him, and not representative of the way he thinks “at all.”
Most people know how to help someone with a cut or a scrape. But what about a panic attack?
Here’s a thought experiment: You’re walking down the street with a friend when your companion falls and gashes her leg on the concrete. It’s bleeding; she’s in pain. It’s clear she’s going to need stitches. What do you do?
This one isn’t exactly a head-scratcher. You'd probably attempt to offer some sort of first-aid assistance until the bleeding stopped, or until she could get to medical help. Maybe you happen to have a Band-Aid on you, or a tissue to help her clean the wound, or a water bottle she can use to rinse it off. Maybe you pick her up and help her hobble towards transportation, or take her where she needs to go.
Here’s a harder one: What if, instead of an injured leg, that same friend has a panic attack?
Ben Stiller’s follow-up to his own comedy classic is a downright bummer, no matter how many celebrity cameos it tries to cram in.
You don’t need to go to the theater to get the full experience of Zoolander 2. Simply get your hands on a copy of the original, watch it, and then yell a bunch of unfunny topical lines every time somebody tells a joke. That’s how it feels to watch Ben Stiller’s sequel to his 2001 spoof of the fashion industry: Zoolander 2 takes pains to reference every successful gag you remember from the original, and then embellish them in painful—often offensive, almost always outdated—fashion. It’s a film that has no real reason to exist, and it spends its entire running time reaffirming that fact.
The original Zoolander, to be fair, had no business being as funny as it was—it made fun of an industry that already seems to exist in a constant state of self-parody, and much of its humor relied on simple malapropisms and sight gags. But it was hilarious anyway as a candid snapshot of the fizzling-out of ’90s culture. Like almost any zeitgeist comedy, it belonged to a particular moment—and boy, should it have stayed there. With Zoolander 2, Stiller (who directed, co-wrote, and stars) tries to recapture the magic of 2001 by referencing its past glories with increasing desperation, perhaps to avoid the fact that he has nothing new to say about the fashion industry or celebrity culture 15 years laters.
By mining electronic medical records, scientists show the lasting legacy of prehistoric sex on modern humans’ health.
Modern humans originated in Africa, and started spreading around the world about 60,000 years ago. As they entered Asia and Europe, they encountered other groups of ancient humans that had already settled in these regions, such as Neanderthals. And sometimes, when these groups met, they had sex.
We know about these prehistoric liaisons because they left permanent marks on our genome. Even though Neanderthals are now extinct, every living person outside of Africa can trace between 1 and 5 percent of our DNA back to them. (I am 2.6 percent Neanderthal, if you were wondering, which pales in comparison to my colleague James Fallows at 5 percent.)
This lasting legacy was revealed in 2010 when the complete Neanderthal genome was published. Since then, researchers have been trying to figure out what, if anything, the Neanderthal sequences are doing in our own genome. Are they just passive hitchhikers, or did they bestow important adaptations on early humans? And are they affecting the health of modern ones?
The bureau successfully played the long game in both cases.
The story of law enforcement in the Oregon standoff is one of patience.
On the most obvious level, that was reflected in the 41 days that armed militia members occupied the Malheur National Wildlife Refuge near Burns. It took 25 days before the FBI and state police moved to arrest several leaders of the occupation and to barricade the refuge. It took another 15 days before the last of the final occupiers walked out, Thursday morning Oregon time.
Each of those cases involved patience as well: Officers massed on Highway 395 didn’t shoot LaVoy Finicum when he tried to ram past a barricade, nearly striking an FBI agent, though when he reached for a gun in his pocket they finally fired. Meanwhile, despite increasingly hysterical behavior from David Fry, the final occupier, officers waited him out until he emerged peacefully.
The number of American teens who excel at advanced math has surged. Why?
On a sultry evening last July, a tall, soft-spoken 17-year-old named David Stoner and nearly 600 other math whizzes from all over the world sat huddled in small groups around wicker bistro tables, talking in low voices and obsessively refreshing the browsers on their laptops. The air in the cavernous lobby of the Lotus Hotel Pang Suan Kaew in Chiang Mai, Thailand, was humid, recalls Stoner, whose light South Carolina accent warms his carefully chosen words. The tension in the room made it seem especially heavy, like the atmosphere at a high-stakes poker tournament.
Stoner and five teammates were representing the United States in the 56th International Mathematical Olympiad. They figured they’d done pretty well over the two days of competition. God knows, they’d trained hard. Stoner, like his teammates, had endured a grueling regime for more than a year—practicing tricky problems over breakfast before school and taking on more problems late into the evening after he completed the homework for his college-level math classes. Sometimes, he sketched out proofs on the large dry-erase board his dad had installed in his bedroom. Most nights, he put himself to sleep reading books like New Problems in Euclidean Geometry and An Introduction to Diophantine Equations.
Jim Gilmore joins Chris Christie and Carly Fiorina, and leaves the race after a poor showing in New Hampshire.
Jim Gilmore’s candidacy this year was improbable—but even more improbable was the minor cult of personality that developed around it.
The former Virginia governor never had a chance. Not, like, in the sense of Lindsey Graham, a candidate with national standing but no path to the presidency. More in the George Pataki sense: a guy who had no real business in race, but was running anyway. Except that Gilmore made Pataki look like a juggernaut. Also, Pataki saw the writing on the wall and had the sense to drop out in late December. Gilmore soldiered on, and ended up as the last of the truly longshots to leave.
The result was that Gilmore turned into a sort of folk hero. Not for voters, mind you—he managed only 12 votes in Iowa and 125 in New Hampshire, and his campaign was funded largely by loans from himself. Because of his low support in the polls, Gilmore only made the cut for the very first kid’s-table debate in August, and then again for the undercard in late January. Other than that, he was shut out completely.
Two hundred fifty years of slavery. Ninety years of Jim Crow. Sixty years of separate but equal. Thirty-five years of racist housing policy. Until we reckon with our compounding moral debts, America will never be whole.
And if thy brother, a Hebrew man, or a Hebrew woman, be sold unto thee, and serve thee six years; then in the seventh year thou shalt let him go free from thee. And when thou sendest him out free from thee, thou shalt not let him go away empty: thou shalt furnish him liberally out of thy flock, and out of thy floor, and out of thy winepress: of that wherewith the LORD thy God hath blessed thee thou shalt give unto him. And thou shalt remember that thou wast a bondman in the land of Egypt, and the LORD thy God redeemed thee: therefore I command thee this thing today.
— Deuteronomy 15: 12–15
Besides the crime which consists in violating the law, and varying from the right rule of reason, whereby a man so far becomes degenerate, and declares himself to quit the principles of human nature, and to be a noxious creature, there is commonly injury done to some person or other, and some other man receives damage by his transgression: in which case he who hath received any damage, has, besides the right of punishment common to him with other men, a particular right to seek reparation.
When four American women were murdered during El Salvador’s dirty war, a young U.S. official and his unlikely partner risked their lives to solve the case.
On December 1, 1980, two American Catholic churchwomen—an Ursuline nun and a lay missionary—sat down to dinner with Robert White, the U.S. ambassador to El Salvador. They worked in rural areas ministering to El Salvador’s desperately impoverished peasants, and White admired their commitment and courage. The talk turned to the government’s brutal tactics for fighting the country’s left-wing guerrillas, in a dirty war waged by death squads that dumped bodies in the streets and an army that massacred civilians. The women were alarmed by the incoming Reagan administration’s plans for a closer relationship with the military-led government. Because of a curfew, the women spent the night at the ambassador’s residence. The next day, after breakfast with the ambassador’s wife, they drove to San Salvador’s international airport to pick up two colleagues who were flying back from a conference in Nicaragua. Within hours, all four women would be dead.