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.
In the name of emotional well-being, college students are increasingly demanding protection from words and ideas they don’t like. Here’s why that’s disastrous for education—and mental health.
Something strange is happening at America’s colleges and universities. A movement is arising, undirected and driven largely by students, to scrub campuses clean of words, ideas, and subjects that might cause discomfort or give offense. Last December, Jeannie Suk wrote in an online article for The New Yorker about law students asking her fellow professors at Harvard not to teach rape law—or, in one case, even use the word violate (as in “that violates the law”) lest it cause students distress. In February, Laura Kipnis, a professor at Northwestern University, wrote an essay in The Chronicle of Higher Education describing a new campus politics of sexual paranoia—and was then subjected to a long investigation after students who were offended by the article and by a tweet she’d sent filed Title IX complaints against her. In June, a professor protecting himself with a pseudonym wrote an essay for Vox describing how gingerly he now has to teach. “I’m a Liberal Professor, and My Liberal Students Terrify Me,” the headline said. A number of popular comedians, including Chris Rock, have stopped performing on college campuses (see Caitlin Flanagan’s article in this month’s issue). Jerry Seinfeld and Bill Maher have publicly condemned the oversensitivity of college students, saying too many of them can’t take a joke.
Why haven’t more challengers entered the race to defeat the Iraq War hawk, Patriot Act supporter, and close friend of big finance?
As Hillary Clinton loses ground to Bernie Sanders in Iowa, where her lead shrinks by the day, it’s worth noticing that she has never made particular sense as the Democratic Party’s nominee. She may be more electable than her social-democratic rival from Vermont, but plenty of Democrats are better positioned to represent the center-left coalition. Why have they let the former secretary of state keep them out of the race? If Clinton makes it to the general election, I understand why most Democrats will support her. She shares their views on issues as varied as preserving Obamacare, abortion rights, extending legal status to undocumented workers, strengthening labor unions, and imposing a carbon tax to slow climate change.
The NBC show isn’t casting its net wide enough when it comes to finding new players.
Since the departure of many of its biggest stars two years ago, Saturday Night Live has mostly avoided major cast changes. Yesterday, NBC announced the show would add only one new cast member for its 41st season—the near-unknown stand-up comic Jon Rudnitsky. SNL is, of course, a sketch-comedy show, but it keeps hiring mostly white stand-ups who have a markedly different skill set, with limited results. As critics and viewers keep calling out for greater diversity on the show, it’s hard to imagine the series’s reasoning in sticking to old habits.
As is unfortunately typical today, controversy has already arisen over some tasteless old jokes from Rudnitsky’s Twitter and Vine feeds, similar to the furore that greeted Trevor Noah’s hiring at The Daily Show this summer. But Rudnitsky was apparently hired on the back of his stand-up performances, not his Internet presence, similar to the other young stand-ups the show has hired in recent years: Pete Davidson, Brooks Wheelan (since fired), and Michael Che. It’s a peculiar route to the show, because SNL is 90 percent sketch acting, and unless you’re hosting Weekend Update (like Che), you’re not going to do a lot of stand-up material. So why hire Rudnitsky?
Though it wasn’t pretty, Minaj was really teaching a lesson in civility.
Nicki Minaj didn’t, in the end, say much to Miley Cyrus at all. If you only read the comments that lit up the Internet at last night’s MTV Video Music Awards, you might think she was kidding, or got cut off, when she “called out” the former Disney star who was hosting: “And now, back to this bitch that had a lot to say about me the other day in the press. Miley, what’s good?”
To summarize: When Minaj’s “Anaconda” won the award for Best Hip-Hop Video, she took to the stage in a slow shuffle, shook her booty with presenter Rebel Wilson, and then gave an acceptance speech in which she switched vocal personas as amusingly as she does in her best raps—street-preacher-like when telling women “don’t you be out here depending on these little snotty-nosed boys”; sweetness and light when thanking her fans and pastor. Then a wave of nausea seemed to come over her, and she turned her gaze toward Cyrus. To me, the look on her face, not the words that she said, was the news of the night:
Many educators are introducing meditation into the classroom as a means of improving kids’ attention and emotional regulation.
A five-minute walk from the rickety, raised track that carries the 5 train through the Bronx, the English teacher Argos Gonzalez balanced a rounded metal bowl on an outstretched palm. His class—a mix of black and Hispanic students in their late teens, most of whom live in one of the poorest districts in New York City—by now were used to the sight of this unusual object: a Tibetan meditation bell.
“Today we’re going to talk about mindfulness of emotion,” Gonzalez said with a hint of a Venezuelan accent. “You guys remember what mindfulness is?” Met with quiet stares, Gonzalez gestured to one of the posters pasted at the back of the classroom, where the students a few weeks earlier had brainstormed terms describing the meaning of “mindfulness.” There were some tentative mumblings: “being focused,” “being aware of our surroundings.”
Beijing’s top five scapegoats, from journalists to hedge funds to the U.S. federal reserve
China’s stock markets continue to stumble, despite the massive stimulus that the government has unleashed to prop them up. The Shanghai benchmark index fell by 1.23 percent Tuesday, after closing down slightly Monday. The index has fallen by nearly 40 percent from its mid-June peak.
In some ways, the slide isn’t surprising—after all, Chinese stocks were trading at extremely rich valuations before they started to fall, even as signs emerged that China’s economy was slowing.
After calling his intellectual opponents treasonous, and allegedly exaggerating his credentials, a controversial law professor resigns from the United States Military Academy.
On Monday, West Point law professor William C. Bradford resigned after The Guardianreported that he had allegedly inflated his academic credentials. Bradford made headlines last week, when the editors of the National Security Law Journaldenounced a controversial article by him in their own summer issue:
As the incoming Editorial Board, we want to address concerns regarding Mr. Bradford’s contention that some scholars in legal academia could be considered as constituting a fifth column in the war against terror; his interpretation is that those scholars could be targeted as unlawful combatants. The substance of Mr. Bradford’s article cannot fairly be considered apart from the egregious breach of professional decorum that it exhibits. We cannot “unpublish” it, of course, but we can and do acknowledge that the article was not presentable for publication when we published it, and that we therefore repudiate it with sincere apologies to our readers.
Every time you shrug, you don’t need to Google, then copy, then paste.
Updated, 2:20 p.m.
All hail ¯\_(ツ)_/¯.
In its 11 strokes, the symbol encapsulates what it’s like to be an individual on the Internet. With raised arms and a half-turned smile, it exudes the melancholia, the malaise, the acceptance, and (finally) the embrace of knowing that something’s wrong on the Internet and you can’t do anything about it.
As Kyle Chayka writes in a new history of the symbol at The Awl, the meaning of the “the shruggie” is always two, if not three- or four-, fold. ¯\_(ツ)_/¯ represents nihilism, “bemused resignation,” and “a Zen-like tool to accept the chaos of universe.” It is Sisyphus in unicode. I use it at least 10 times a day.
For a long time, however, I used it with some difficulty. Unlike better-known emoticons like :) or ;), ¯\_(ツ)_/¯ borrows characters from the Japanese syllabary called katakana. That makes it a kaomoji, a Japanese emoticon; it also makes it, on Western alphabetical keyboards at least, very hard to type. But then I found a solution, and it saves me having to google “smiley sideways shrug” every time I want to quickly rail at the world’s inherent lack of meaning.
If the Fourteenth Amendment means that the children of undocumented immigrants are not citizens, as Donald Trump suggests, then they are also not subject to American laws.
Imagine the moon rising majestically over the Tonto National Forest, highlighting the stark desert scenery along the Superstition Freeway just west of Morristown, Arizona. The sheriff of Maricopa County sips coffee from his thermos and checks that his radar gun is on the ready. A lot of lawmen wouldn’t have bothered to send officers out at night on such a lonely stretch of road, much less taken the night shift themselves. But America’s Toughest Sheriff sets a good example for his deputies. As long as he’s the sheriff, at least, the rule of law—and the original intent of the Constitution—will be enforced by the working end of a nightstick.
Suddenly a car rockets by, going 100 miles an hour by the gun. Siren ululating, the sheriff heads west after the speeder. The blue Corolla smoothly pulls over to the shoulder. The sheriff sees the driver’s side window roll down. Cautiously he approaches.
When cobbling together a livable income, many of America’s poorest people rely on the stipends they receive for donating plasma.
There is no money to be made selling blood anymore. It can, however, pay off to sell plasma, a component in blood that is used in a number of treatments for serious illnesses. It is legal to “donate” plasma up to two times a week, for which a bank will pay around $30 each time. Selling plasma is so common among America’s extremely poor that it can be thought of as their lifeblood.
But no one could reasonably think of a twice-weekly plasma donation as a job. It’s a survival strategy, one of many operating well outside the low-wage job market.
In Johnson City, Tennessee, we met a 21-year-old who donates plasma as often as 10 times a month—as frequently as the law allows. (The terms of our research prevent us from revealing her identity.) She is able to donate only when her husband has time to keep an eye on their two young daughters. When we met him in February, he could do that pretty frequently because he’d been out of work since the beginning of December, when McDonald’s reduced his hours to zero in response to slow foot traffic. Six months ago, walking his wife to the plasma clinic and back, kids in tow, was the most important job he had.