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.
He lives near San Francisco, makes more than $50,000 per year, and is voting for the billionaire to fight against political correctness.
For several days, I’ve been corresponding with a 22-year-old Donald Trump supporter. He is white, has a bachelor’s degree, and earns $50,000 to $60,000 per year.
He lives near San Francisco.
“I recently became engaged to my Asian fiancée who is making roughly 3 times what I make, and I am completely supportive of her and proud she is doing so well,” he wrote. “We’ve both benefitted a lot from globalization. We are young, urban, and have a happy future planned. We seem molded to be perfect young Hillary supporters,” he observed, “but we're not. In 2016, we're both going for Trump.”
At first, we discussed Bill Clinton.
Last week, I wrote an article asking why Trump supporters aren’t bothered that their candidate called Clinton a shameful abuser of women who may well be a rapist. After all, Trump used to insist that Clinton was a victim of unfair treatment during his sex scandals. Either Trump spent years defending a man that he believed to be a sexual predator, even welcoming him as a guest at his wedding, or Trump is now cynically exploiting a rape allegation that he believes to be false.
Finally, an explanation for Bitchy Resting Face Nation
Here’s something that has always puzzled me, growing up in the U.S. as a child of Russian parents. Whenever I or my friends were having our photos taken, we were told to say “cheese” and smile. But if my parents also happened to be in the photo, they were stone-faced. So were my Russian relatives, in their vacation photos. My parents’ high-school graduation pictures show them frolicking about in bellbottoms with their young classmates, looking absolutely crestfallen.
It’s not just photos: Russian women do not have to worry about being instructed by random men to “smile.” It is Bitchy Resting Face Nation, seemingly forever responding “um, I guess?” to any question the universe might pose.
This does not mean we are all unhappy! Quite the opposite: The virile ruler, the vodka, the endless mounds of sour cream—they are pleasing to some. It’s just that grinning without cause is not a skill Russians possess or feel compelled to cultivate. There’s even a Russian proverb that translates, roughly, to “laughing for no reason is a sign of stupidity.”
A rock structure, built deep underground, is one of the earliest hominin constructions ever found.
In February 1990, thanks to a 15-year-old boy named Bruno Kowalsczewski, footsteps echoed through the chambers of Bruniquel Cave for the first time in tens of thousands of years.
The cave sits in France’s scenic Aveyron Valley, but its entrance had long been sealed by an ancient rockslide. Kowalsczewski’s father had detected faint wisps of air emerging from the scree, and the boy spent three years clearing away the rubble. He eventually dug out a tight, thirty-meter-long passage that the thinnest members of the local caving club could squeeze through. They found themselves in a large, roomy corridor. There were animal bones and signs of bear activity, but nothing recent. The floor was pockmarked with pools of water. The walls were punctuated by stalactites (the ones that hang down) and stalagmites (the ones that stick up).
In the 1990s, A.J. Benza learned first hand how the real-estate developer got his name––and his net worth––in all the New York City papers.
Earlier this month, I heard A.J. Benza, the host of the celebrity-scandal show “Case Closed with A.J. Benza,” tell the podcast host Adam Carolla about his younger days as a gossip reporter in New York City. He hung out with celebrities until the wee hours of the morning, reported out sensational rumors, and constantly traded favors in order to get juicy tidbits for columns at Newsday and the New York Daily News. Most trades involved information he wanted about a particular person at a particular moment––and he would then owe his source a favor in the future.
“Donald Trump was the biggest guy in the world with that,” he said. “Trump spent every morning on the phone with me, with Page 6––he loved to get his name in the paper. As a result, he would drop dimes on other people in every industry he knew dirt on. You put the story in the paper, and then, three days later, you say, ‘Donald Trump was at a Knicks game with this supermodel.’ And he’s happy. That’s all it took.”
A conversation about how Game of Thrones’s latest twist fits in with George R.R. Martin’s typically cliché-busting portrayal of disability
In 2014, a few media outlets ran stories diagnosing Game of Thrones’s Hodor as having expressive aphasia, a neurological condition restricting speech. Some aphasia experts pushed back, saying that while Hodor has often been described as “simple-minded” or “slow of wits,” aphasia only affects linguistic communication—not intelligence.
A real-time chronicle of Donald Trump’s unpresidential statements.
People will look back on this era in our history. Here’s a running chronicle from James Fallows on the ways in which Trump has been unpresidential in an unprecedented way. (If you’d like to flag examples to include, please let us know.)
Our peshmerga are the best fighting force against ISIS in Iraq. But we cannot force Sunni and Shia Arabs to live together in peace.
This week marked the start of offensives ultimately aimed at retaking two of ISIS’s last major urban strongholds—Raqqa, the group’s de facto capital in Syria, and Fallujah, the first major Iraqi city to fall to ISIS some two years ago. The final prize, Mosul, seems to remain out of reach for the foreseeable future, despite indications a year ago that a battle to retake the city could come any day. An Iraqi army offensive launched in late March stalled quickly.
Mosul is Iraq’s second-largest city. ISIS wrested it from Iraqi government control in 2014 in its first major show of strength, and it is where Islamic State leader Abu Bakr al-Baghdadi declared a “caliphate” and demanded the allegiance of the world’s Muslims. Taking it back will be essential to winning the war against ISIS. But as fighters opposed to ISIS try to advance elsewhere on the battlefield, little is being done to promote the reconciliation between Shia and Sunni Arabs that Iraq really needs—both to construct a force capable of beating ISIS, in Mosul and beyond, and to create the political conditions to prevent its return.
As I learned when I met her, the late author believed that true arrogance lay in denying one's own specialness—and denying the specialness of others.
“You may now kiss my cheek,” said Maya Angelou. Her deep voice hung in the air, filling the large dining room inside of her Harlem home.
Stunned, I sat there for a minute. I had never been asked at the end of an interview to kiss someone else’s cheek.
It was October 2008 and I had flown to New York after haggling for months for an interview for an in-flight magazine cover story. Prior to the interview, a set of “communication courtesy” instructions for meeting Angelou were emailed to me, much like a list I imagine boarding schools send out to students for review before making an appearance.
Greeting & Introductions
Dr. Angelou will greet you by your last name. She will use your title and your last name in all communications. Dr. Angelou may ask you the origin of your name. You should greet her as Dr. or Mrs. Angelou. Please address her staff as Mr., Ms., or Mrs. - using their last name.
Dr. Angelou would like to receive an agenda prior to the meeting.
Dr. Angelou will often pause prior to speaking or when completing her thought.
Please hold your thought until she is finishing speaking.
Dr. Angelou speaks five different languages. She will enjoy speaking French, Spanish, Hebrew, Italian, or Fanti with you.
During formal business, meetings Dr. Angelou ask the men to wear a jacket and tie and women in appropriate business attire.
Dr. Angelou requires warm rooms. You may choose to remove your jacket or loosen your tie if you find the room too warm.
Dr. Angelou would like for participants in the same meeting to arrive together on time.
Dr. Angelou will sit in the chair at the end of the table to have access to her staff and phones.
Dr. Angelou is highly allergic to seafood. Please do not eat any seafood prior to meeting with her.
For centuries, philosophers and theologians have almost unanimously held that civilization as we know it depends on a widespread belief in free will—and that losing this belief could be calamitous. Our codes of ethics, for example, assume that we can freely choose between right and wrong. In the Christian tradition, this is known as “moral liberty”—the capacity to discern and pursue the good, instead of merely being compelled by appetites and desires. The great Enlightenment philosopher Immanuel Kant reaffirmed this link between freedom and goodness. If we are not free to choose, he argued, then it would make no sense to say we ought to choose the path of righteousness.
Today, the assumption of free will runs through every aspect of American politics, from welfare provision to criminal law. It permeates the popular culture and underpins the American dream—the belief that anyone can make something of themselves no matter what their start in life. As Barack Obama wrote in The Audacity of Hope, American “values are rooted in a basic optimism about life and a faith in free will.”