Space and astronomy news
The Gum Nebula is an emission nebula almost 1400 light-years away. It’s home to an object known as “God’s Hand” among the faithful. The rest of us call it CG 4.
Many objects in space take on fascinating, ethereal shapes straight out of someone’s psychedelic fantasy. CG4 is definitely ethereal and extraordinary, but it’s also a little more prosaic. It looks like a hand extending into space.
Continue reading “A Nebula that Extends its Hand into Space”
Nature often defies our simple explanations. Take comets and asteroids, for example. Comets are icy and have tails; asteroids are rocky and don’t have tails. But it might not be quite so simple, according to new research.
Continue reading “Citizen Scientists Find Fifteen “Active Asteroids””
Stars more massive than the Sun blow themselves to pieces at the end of their life. Usually leaving behind either a black hole, neutron star or pulsar they also scatter heavy elements across their host galaxy. One such star went supernova nearly 11,000 years ago creating the Vela Supernova Remnant. The resultant expanding cloud of debris covers almost 100 light years and would be twenty times the diameter of the full Moon. Astronomers have recently imaged the remnant with a 570 megapixel Dark Energy Camera (DECam) creating a stunning 1.3 gigapixel image.
Continue reading “This is a 1.3 Gigapixel Image of a Supernova Remnant”
There are some astronomical images that capture rapturous beauty, with their brilliant colors and interplay of shadow and light. A beautiful image can be enough to stir the soul, but in astronomy they often also have a story to tell. An example of this can be seen in a recent image released by NSF’s NOIRLab.
Continue reading “There's So Much Going on in This Star-Forming Nebula”
The first written record of a supernova comes from Chinese astrologers in the year 185. Those records say a ‘guest star’ lit up the sky for about eight months. We now know that it was a supernova.
All that remains is a ring of debris named RCW 86, and astronomers working with the DECam (Dark Energy Camera) used it to examine the debris ring and the aftermath of the supernova.
Continue reading “The Dark Energy Camera Captures the Remains of an Ancient Supernova”
In August 2013, the Dark Energy Survey (DES) began its six-year mission to map thousands of galaxies, supernovae, and patterns in the cosmic structure. This international collaborative effort is dedicated to investigating the mysterious phenomenon known as Dark Energy. This theoretical force counter-acts gravity and accounts for 70% of the Universe’s energy-mass density. Their primary instrument in this mission is the 570-megapixel Dark Energy Camera (DECam), mounted on the Victor M. Blanco 5-meter (16.4 ft) telescope at the Cerro Tlelolo Inter-American Observatory in Chile.
Between 2013 and 2019, the DECam took over one million exposures of the southern night sky and photographed around 2.5 billion astronomical objects – including galaxies, galaxy clusters, stars, comets, asteroids, dwarf planets, and supernovae. For our viewing pleasure, the Dark Energy Survey recently released fifteen spectacular images taken by the DECam during the six-year campaign. These images showcase the capabilities of the DECam, the types of objects it observed, and the sheer beauty of the Universe!
Continue reading “The Dark Energy Camera has Captured a Million Images, an Eighth of the Entire sky. Here are Some of its Best Pictures so far”
If you thought dark matter was difficult to study, studying dark energy is even more challenging. Dark energy is perhaps the most subtle phenomenon in the universe. It drives the evolution of the cosmos, but its effects are only seen on intergalactic scales. So to study dark energy in detail, you need a great deal of observations of wide areas of the sky.
Continue reading “On its Hunt for Dark Energy, a Telescope Stopped to Look at the Lobster Nebula”
Since the early 20th century, scientists and physicists have been burdened with explaining how and why the Universe appears to be expanding at an accelerating rate. For decades, the most widely accepted explanation is that the cosmos is permeated by a mysterious force known as “dark energy”. In addition to being responsible for cosmic acceleration, this energy is also thought to comprise 68.3% of the universe’s non-visible mass.
Much like dark matter, the existence of this invisible force is based on observable phenomena and because it happens to fit with our current models of cosmology, and not direct evidence. Instead, scientists must rely on indirect observations, watching how fast cosmic objects (specifically Type Ia supernovae) recede from us as the universe expands.
This process would be extremely tedious for scientists – like those who work for the Dark Energy Survey (DES) – were it not for the new algorithms developed collaboratively by researchers at Lawrence Berkeley National Laboratory and UC Berkeley.
“Our algorithm can classify a detection of a supernova candidate in about 0.01 seconds, whereas an experienced human scanner can take several seconds,” said Danny Goldstein, a UC Berkeley graduate student who developed the code to automate the process of supernova discovery on DES images.
Currently in its second season, the DES takes nightly pictures of the Southern Sky with DECam – a 570-megapixel camera that is mounted on the Victor M. Blanco telescope at Cerro Tololo Interamerican Observatory (CTIO) in the Chilean Andes. Every night, the camera generates between 100 Gigabytes (GB) and 1 Terabyte (TB) of imaging data, which is sent to the National Center for Supercomputing Applications (NCSA) and DOE’s Fermilab in Illinois for initial processing and archiving.
Object recognition programs developed at the National Energy Research Scientific Computing Center (NERSC) and implemented at NCSA then comb through the images in search of possible detections of Type Ia supernovae. These powerful explosions occur in binary star systems where one star is a white dwarf, which accretes material from a companion star until it reaches a critical mass and explodes in a Type Ia supernova.
“These explosions are remarkable because they can be used as cosmic distance indicators to within 3-10 percent accuracy,” says Goldstein.
Distance is important because the further away an object is located in space, the further back in time it is. By tracking Type Ia supernovae at different distances, researchers can measure cosmic expansion throughout the universe’s history. This allows them to put constraints on how fast the universe is expanding and maybe even provide other clues about the nature of dark energy.
“Scientifically, it’s a really exciting time because several groups around the world are trying to precisely measure Type Ia supernovae in order to constrain and understand the dark energy that is driving the accelerated expansion of the universe,” says Goldstein, who is also a student researcher in Berkeley Lab’s Computational Cosmology Center (C3).
The DES begins its search for Type Ia explosions by uncovering changes in the night sky, which is where the image subtraction pipeline developed and implemented by researchers in the DES supernova working group comes in. The pipeline subtracts images that contain known cosmic objects from new images that are exposed nightly at CTIO.
Each night, the pipeline produces between 10,000 and a few hundred thousand detections of supernova candidates that need to be validated.
“Historically, trained astronomers would sit at the computer for hours, look at these dots, and offer opinions about whether they had the characteristics of a supernova, or whether they were caused by spurious effects that masquerade as supernovae in the data. This process seems straightforward until you realize that the number of candidates that need to be classified each night is prohibitively large and only one in a few hundred is a real supernova of any type,” says Goldstein. “This process is extremely tedious and time-intensive. It also puts a lot of pressure on the supernova working group to process and scan data fast, which is hard work.”
To simplify the task of vetting candidates, Goldstein developed a code that uses the machine learning technique “Random Forest” to vet detections of supernova candidates automatically and in real-time to optimize them for the DES. The technique employs an ensemble of decision trees to automatically ask the types of questions that astronomers would typically consider when classifying supernova candidates.
At the end of the process, each detection of a candidate is given a score based on the fraction of decision trees that considered it to have the characteristics of a detection of a supernova. The closer the classification score is to one, the stronger the candidate. Goldstein notes that in preliminary tests, the classification pipeline achieved 96 percent overall accuracy.
“When you do subtraction alone you get far too many ‘false-positives’ — instrumental or software artifacts that show up as potential supernova candidates — for humans to sift through,” says Rollin Thomas, of Berkeley Lab’s C3, who was Goldstein’s collaborator.
He notes that with the classifier, researchers can quickly and accurately strain out the artifacts from supernova candidates. “This means that instead of having 20 scientists from the supernova working group continually sift through thousands of candidates every night, you can just appoint one person to look at maybe few hundred strong candidates,” says Thomas. “This significantly speeds up our workflow and allows us to identify supernovae in real-time, which is crucial for conducting follow up observations.”
“Using about 60 cores on a supercomputer we can classify 200,000 detections in about 20 minutes, including time for database interaction and feature extraction.” says Goldstein.
Goldstein and Thomas note that the next step in this work is to add a second-level of machine learning to the pipeline to improve the classification accuracy. This extra layer would take into account how the object was classified in previous observations as it determines the probability that the candidate is “real.” The researchers and their colleagues are currently working on different approaches to achieve this capability.
Further Reading: Berkley Lab