SPACE / COSMOLOGY
Magnifying deep space through the “carousel lens”
A newly discovered cluster-scale strong gravitational lens, with a rare alignment of seven background lensed galaxies, provides a unique opportunity to study cosmology.
In a rare and extraordinary discovery, researchers have identified a unique configuration of galaxies that form the most exquisitely aligned gravitational lens found to date. The Carousel Lens is a massive cluster-scale gravitational lens system that will enable researchers to delve deeper into the mysteries of the cosmos, including dark matter and dark energy.
“This is an amazingly lucky ‘galactic line-up’ – a chance alignment of multiple galaxies across a line-of-sight spanning most of the observable universe,” said David Schlegel, a co-author of the study and a senior scientist in Berkeley Lab’s Physics Division. "Finding one such alignment is a needle in the haystack. Finding all of these is like eight needles precisely lined up inside that haystack."
The Carousel Lens is an alignment consisting of one foreground galaxy cluster (the ‘lens’) and seven background galaxies spanning immense cosmic distances and seen through the gravitationally distorted space-time around the lens. In the dramatic image below:
- The lensing cluster, located 5 billion light years away from Earth, is shown by its four brightest and most massive galaxies (indicated by La, Lb, Lc, and Ld), and these constitute the foreground of the image.
- Seven unique galaxies (numbered 1 through 7), appear through the lens. These are located far beyond, at distances from 7.6 to 12 billion light years away from Earth, approaching the limit of the observable universe.
- Each galaxy’s repeated appearances (indicated by each number’s letter index, e.g., a through d) show differences in shape that are curved and stretched into multiple “fun house mirror” iterations caused by the warped space-time around the lens.
- Of particular interest is the discovery of an Einstein Cross – the largest known to date – shown in galaxy number 4’s multiple appearances (indicated by 4a, 4b, 4c, and 4d). This rare configuration of multiple images around the center of the lens is an indication of the symmetrical distribution of the lens’ mass (dominated by invisible dark matter) and plays a key role in the lens-modeling process.
Light traveling from far-distant space can be magnified and curved as it passes through the gravitationally distorted space-time of nearer galaxies or clusters of galaxies. In rare instances, a configuration of objects aligns nearly perfectly to form a strong gravitational lens. Using an abundance of new data from the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, recent observations from NASA’s Hubble Space Telescope, and the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC), the research team built on their earlier studies (in May 2020 and Feb 2021) to identify likely strong lens candidates, laying the groundwork for the current discovery.
“Our team has been searching for strong lenses and modeling the most valuable systems,” explains Xiaosheng Huang, a study co-author and member of Berkeley Lab’s Supernova Cosmology Project, and a professor of physics and astronomy at the University of San Francisco. “The Carousel Lens is an incredible alignment of seven galaxies in five groupings that line up nearly perfectly behind the foreground cluster lens. As they appear through the lens, the multiple images of each of the background galaxies form approximately concentric circular patterns around the foreground lens, as in a carousel. It’s an unprecedented discovery, and the computational model generated shows a highly promising prospect for measuring the properties of the cosmos, including those of dark matter and dark energy.”
The study also involved several Berkeley Lab student researchers, including the lead author, William Sheu, an undergraduate student intern with DESI at the beginning of this study, now a PhD student at UCLA and a DESI collaborator.
The Carousel Lens will enable researchers to study dark energy and dark matter in entirely new ways based on the strength of the observational data and its computational model.
“This is an extremely unusual alignment, which by itself will provide a testbed for cosmological studies,” observes Nathalie Palanque-Delabrouille, director of Berkeley Lab’s Physics Division. “It also shows how the imaging done for DESI can be leveraged for other scientific applications,” such as investigating the mysteries of dark matter and the accelerating expansion of the universe, which is driven by dark energy.
Learn more:
- The Carousel Lens: A Well-modeled Strong Lens with Multiple Sources Spectroscopically Confirmed by VLT/MUSE – August 19, 2024 / William Sheu et al / The Astrophysical Journal
- View the Carousel Lens in the DESI Legacy Survey Viewer.
Hubble Space Telescope image of the Carousel Lens, taken in two 10-minute exposures, one using an optical filter and another using an infrared filter. The “L” indicators near the center (La, Lb, Lc, and Ld) show the most massive galaxies in the lensing cluster, located 5 billion light years away. Seven unique galaxies (numbered 1 through 7) – located an additional 2.6 to 7 billion light years beyond the lens – appear in multiple, distorted “fun-house mirror” iterations (indicated by each number’s letter index, e.g., a through d), as seen through the lens.
Credit
William Sheu (UCLA) using Hubble Space Telescope data.
Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to delivering solutions for humankind through research in clean energy, a healthy planet, and discovery science. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 16 Nobel Prizes. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science.
DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
Journal
The Astrophysical Journal
Article Publication Date
20-Sep-2024
NJIT researchers awarded NSF grant to develop AI-powered solar eruption forecasting system
New Jersey Institute of Technology
NJIT researchers have been awarded a $593,864 National Science Foundation grant to develop a new AI system for more quickly and accurately predicting when explosive space weather events on the Sun will strike, from solar flares to coronal mass ejections (CMEs).
The three-year project, led by Yan Xu at NJIT's Institute for Space Weather Sciences (ISWS) and Jason Wang at the university’s Ying Wu College of Computing, will develop AI-powered space weather forecasting capabilities that could offer solar researchers a new window into the complex magnetic processes in regions of the Sun's atmosphere that trigger such eruptions, and to this point, have rarely been observed.
According to the researchers, the new AI-powered forecasting system — called SolarDM — could boost early-warning detection of these eruptive events on Earth by days, while offering vital insights to the space weather science community as activity on our nearest star ramps up over the course of the current 11-year solar cycle, which began in 2019.
“Major solar eruptions are powered by magnetic processes taking place in the solar corona, where we’ve lacked critical data due to poor observation conditions and insufficient instruments,” said Xu, the project’s principal investigator and research professor at NJIT’s Center for Solar-Terrestrial Research. “Observations of the atmospheric layer underneath are crucial to study 3D magnetic fields. SolarDM’s data insights potentially give us a way to map the magnetic landscape of this region, allowing us to better predict these powerful eruptions."
Solar physicists have long studied the structure and evolution of magnetic fields in the corona (the Sun’s upper atmosphere). The breaking and reconnecting of these field lines are known to power explosive events capable of disrupting technologies on Earth, such as satellite operations.
However, challenges persist in observing the magnetic field conditions in the second layer of the Sun’s atmosphere, the chromosphere, a rarely visible region positioned above the lowest layer of the star’s atmosphere, the photosphere.
To address this, the NJIT team is leveraging advanced artificial intelligence to generate synthetic vector magnetograms — computer-generated images of magnetic field dynamics in both the photosphere and chromosphere — providing critical data that could shed light on the precursors to solar eruptions.
The SolarDM AI system will be trained using simulations of the Sun's magnetic field and observational data from NSF's Synoptic Optical Long-term Investigations of the Sun (SOLIS) — one of the world’s most advanced solar telescopes for long-term monitoring of the Sun, currently stationed at NJIT’s Big Bear Solar Observatory. In addition, data from NASA’s missions will be used to augment the training set.
“Due to the differences between the instruments on board the ground-based and space-borne observatories, it is extremely challenging to obtain high-quality alignments of the data needed for training and testing the AI system,” explained Wang. “The forecast horizon of state-of-the-art solar eruption forecasting systems is 24 hours. If successful, with SolarDM’s generated vector magnetograms, it is expected that the new AI system can extend the forecast horizon from 24 hours to three days.”
Ultimately, Xu and Wang say the AI modeling system will use the data not only to predict when and where eruptions are likely to occur across millions of miles of the solar atmosphere, but it will also explain why it has arrived at those conclusions.
“Insights into why the AI model is making its forecasts could significantly enhance our understanding of the underlying physics that are behind these powerful events,” noted Xu.
The NJIT project, "AI-Driven Generation of Vector Magnetograms in the Chromosphere and Photosphere with Application to Explainable Solar Eruption Predictions," will run from Sept. 15, 2024, to Aug. 31, 2027, as part of a broader wave of funding by NSF's Collaborations in Artificial Intelligence and Geosciences (CAIG) program.
The 25 NSF-CAIG projects — each integrating AI approaches with aspects of geoscience research — aim to enhance our understanding of complex Earth systems, improve natural hazard forecasting, and inform decision-making in the face of climate change, while driving the development of innovative AI techniques and expanding educational opportunities.
For more information, visit here.
NASA develops process to create very accurate eclipse maps
NASA/Goddard Space Flight Center
New NASA research reveals a process to generate extremely accurate eclipse maps, which plot the predicted path of the Moon’s shadow as it crosses the face of Earth. Traditionally, eclipse calculations assume that all observers are at sea level on Earth and that the Moon is a smooth sphere that is perfectly symmetrical around its center of mass. As such, these calculations do not take into account different elevations on Earth or the Moon’s cratered, uneven surface.
For slightly more accurate maps, people can employ elevation tables and plots of the lunar limb — the edge of the visible surface of the Moon as seen from Earth. However, now eclipse calculations have gained even greater accuracy by incorporating lunar topography data from NASA’s LRO (Lunar Reconnaissance Orbiter) observations.
Using LRO elevation maps, NASA visualizer Ernie Wright at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, created a continuously varying lunar limb profile as the Moon’s shadow passes over the Earth. The mountains and valleys along the edge of the Moon’s disk affect the timing and duration of totality by several seconds. Wright also used several NASA data sets to provide an elevation map of Earth so that eclipse observer locations were depicted at their true altitude.
The resulting visualizations show something never seen before: the true, time-varying shape of the Moon’s shadow, with the effects of both an accurate lunar limb and the Earth’s terrain.
“Beginning with the 2017 total solar eclipse, we’ve been publishing maps and movies of eclipses that show the true shape of the Moon’s central shadow — the umbra,” said Wright.
“And people ask, why does it look like a potato instead of a smooth oval? The short answer is that the Moon isn’t a perfectly smooth sphere.”
The mountains and valleys around the edge of the Moon change the shape of the shadow. The valleys are also responsible for Baily’s beads and the diamond ring, the last bits of the Sun visible just before and the first just after totality.
Wright is lead author of a paper published Sept. 19 in The Astronomical Journal that reveals for the first time exactly how the Moon’s terrain creates the umbra shape. The valleys on the edge of the Moon act like pinholes projecting images of the Sun onto the Earth’s surface.
The umbra is the small hole in the middle of these projected Sun images, the place where none of the Sun images reach.
The edges of the umbra are made up of small arcs from the edges of the projected Sun images.
This is just one of several surprising results that have emerged from the new eclipse mapping method described in the paper. Unlike the traditional method invented 200 years ago, the new way renders eclipse maps one pixel at a time, the same way 3D animation software creates images. It’s also similar to the way other complex phenomena, like weather, are modeled in the computer by breaking the problem into millions of tiny pieces, something computers are really good at, and something that was inconceivable 200 years ago.
For more about eclipses, refer to:
https://science.nasa.gov/eclipses
Journal
The Astronomical Journal
Method of Research
Computational simulation/modeling
Subject of Research
Not applicable
Article Title
A Raster-oriented Method for Creating Eclipse Maps
Article Publication Date
19-Sep-2024
Viewed from behind the Moon, the Sun images projected by lunar valleys on the Moon’s edge fall on the Earth’s surface in a flower-like pattern with a hole in the middle, forming the umbra shape.
Credit
NASA SVS/Ernie Wright
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