New panoramic view of the universe showcases over 3 billion stars

The impressive image has been captured by the Dark Energy Camera.
Loukia Papadopoulos

A new galactic panorama has been captured by the Dark Energy Camera (DECam) instrument on the Víctor M. Blanco 4-meter Telescope at Cerro Tololo Inter-American Observatory (CTIO), a Program of NSF's NOIRLab, according to a press release by Harvard published on Wednesday.

It shows over 3 billion stars bristling among the wispy bands of dust in our home galaxy: the Milky Way. The data used to create this image originates from the second release of the Dark Energy Camera Plane Survey (DECaPS2).

A high density of stars

"One of the main reasons for the success of DECaPS2 is that we simply pointed at a region with an extraordinarily high density of stars and were careful about identifying sources that appear nearly on top of each other," said Andrew Saydjari, a graduate student at Harvard University.

"Doing so allowed us to produce the largest catalog ever from a single camera, in terms of the number of objects observed," added Saydjari, who is also a researcher at the Center for Astrophysics, Harvard & Smithsonian, and lead author of the new paper. 

DECaPS's first data was released in 2017. Its second release now covers 6.5 percent of the night sky and spans a staggering 130-degrees in length. 

"When combined with images from Pan-STARRS 1, DECaPS2 completes a 360-degree panoramic view of the Milky Way's disk and additionally reaches much fainter stars," said Edward Schlafly, a researcher at the AURA-managed Space Telescope Science Institute and a co-author of the paper. 

"With this new survey, we can map the three-dimensional structure of the Milky Way's stars and dust in unprecedented detail."

3.32 billion objects identified

The DECaPS2 survey identified 3.32 billion objects from over 21,400 individual exposures producing more than ten terabytes of data. This allowed astronomers to delve into the galactic plane to gain a better understanding of our Milky Way. 

They made use of near-infrared wavelength observations to peer past much of the light-absorbing dust and get a clearer image of all celestial bodies. An innovative data-processing approach was also put into effect which allowed them to better predict the background behind each star, ensuring that the final catalog of processed data is more accurate.

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"Since my work on the Sloan Digital Sky Survey two decades ago, I have been looking for a way to make better measurements on top of complex backgrounds," said Douglas Finkbeiner, a professor at the Center for Astrophysics, co-author of the paper, and principal investigator behind the project. 

"This work has achieved that and more!"

"This is quite a technical feat. Imagine a group photo of over three billion people and every single individual is recognizable!" said Debra Fischer, division director of Astronomical Sciences at NSF. 

"Astronomers will be poring over this detailed portrait of more than three billion stars in the Milky Way for decades to come. This is a fantastic example of what partnerships across federal agencies can achieve."

The new study was first published in The Astrophysical Journal Supplement.

Study abstract:

Deep optical and near-infrared imaging of the entire Galactic plane is essential for understanding our Galaxy's stars, gas, and dust. The second data release of the Dark Energy Camera (DECam) Plane Survey extends the five-band optical and near-infrared survey of the southern Galactic plane to cover 6.5% of the sky, ∣b∣ ≤ 10°, and 6° > ℓ > −124°, complementary to coverage by Pan-STARRS1. Typical single-exposure effective depths, including crowding effects and other complications, are 23.5, 22.6, 22.1, 21.6, and 20.8 mag in g, r, i, z, and Y bands, respectively, with around 1" seeing. The survey comprises 3.32 billion objects built from 34 billion detections in 21,400 exposures, totaling 260 hr open shutter time on the DECam at Cerro Tololo. The data reduction pipeline features several improvements, including the addition of synthetic source injection tests to validate photometric solutions across the entire survey footprint. A convenient functional form for the detection bias in the faint limit was derived and leveraged to characterize the photometric pipeline performance. A new postprocessing technique was applied to every detection to debias and improve uncertainty estimates of the flux in the presence of structured backgrounds, specifically targeting nebulosity.