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A photo of the huge elliptical galaxy M87 [left] is compared to its three-dimensional shape as gleaned from meticulous observations made with the Hubble and Keck telescopes [right]. Because the galaxy is too far away for astronomers to employ stereoscopic vision, they instead followed the motion of stars around the center of M87, like bees around a hive. This created a three-dimensional view of how stars are distributed within the galaxy.  CREDIT ILLUSTRATION: NASA, ESA, Joseph Olmsted (STScI), Frank Summers (STScI) SCIENCE: Chung-Pei Ma (UC Berkeley)
A photo of the huge elliptical galaxy M87 [left] is compared to its three-dimensional shape as gleaned from meticulous observations made with the Hubble and Keck telescopes [right]. Because the galaxy is too far away for astronomers to employ stereoscopic vision, they instead followed the motion of stars around the center of M87, like bees around a hive. This created a three-dimensional view of how stars are distributed within the galaxy. CREDIT ILLUSTRATION: NASA, ESA, Joseph Olmsted (STScI), Frank Summers (STScI) SCIENCE: Chung-Pei Ma (UC Berkeley)
Tyler O'Neal, Staff Editor ACADEMIA April 13, 2023, 11:00 am

UC Berkeley measures the 3D shape of one of the biggest, closest elliptical galaxies to us

Though we live in a vast three-dimensional universe, celestial objects seen through a telescope look flat because everything is so far away. Now for the first time, astronomers have measured the three-dimensional shape of one of the biggest and closest elliptical galaxies to us, M87. This galaxy turns out to be "triaxial," or potato-shaped. This stereo vision was made possible by combining the power of NASA's Hubble Space Telescope and the ground-based W. M. Keck Observatory in Maunakea, Hawaii.

In most cases, astronomers must use their intuition to figure out the true shapes of deep-space objects. For example, the whole class of huge galaxies called "ellipticals" look like blobs in pictures. Determining the true shape of giant elliptical galaxies will help astronomers understand better how large galaxies and their central large black holes form.

Scientists made the 3D plot by measuring the motions of stars that swarm around the galaxy's supermassive central black hole. The stellar motion was used to provide new insights into the shape of the galaxy and its rotation, and it also yielded a new measurement of the black hole's mass. Tracking the stellar speeds and position allowed researchers to build a three-dimensional view of the galaxy.

Astronomers at the University of California, Berkeley, were able to determine the mass of the black hole at the galaxy's core to high precision, estimating it at 5.4 billion times the mass of the Sun. Hubble observations in 1995 first measured the M87 black hole as being 2.4 billion solar masses, which astronomers deduced by clocking the speed of the gas swirling around the black hole. When the Event Horizon Telescope, an international collaboration of ground-based telescopes, released the first-ever image of the same black hole in 2019, the size of its pitch-black event horizon allowed researchers to calculate a mass of 6.5 billion solar masses using Einstein's theory of general relativity.

The stereo model of M87 and the more precise mass of the central black hole could help astrophysicists learn the black hole's spin rate. "Now that we know the direction of the net rotation of stars in M87 and have an updated mass of the black hole, we can combine this information with data from the Event Horizon Telescope to constrain the spin," said Chung-Pei Ma, a UC Berkeley lead investigator on the research.

Over ten times the mass of the Milky Way, M87 probably grew from the merger of many other galaxies. That's likely the reason M87's central black hole is so large – it assimilated the central black holes of one or more galaxies it swallowed.

Ma, together with UC Berkeley graduate student Emily Liepold and Jonelle Walsh at Texas A&M University was able to determine the 3D shape of M87 thanks to a new precision instrument mounted on the Keck II Telescope. They pointed Keck at 62 adjacent locations of the galaxy, mapping out the spectra of stars over a region about 70,000 light-years across. This region spans the central 3,000 light-years where gravity is largely dominated by the supermassive black hole. Though the telescope cannot resolve individual stars because of M87's great distance, the spectra can reveal the range of velocities to calculate the mass of the object they're orbiting.

"It's sort of like looking at a swarm of 100 billion bees," said Ma. "Though we are looking at them from a distance and can't discern individual bees, we are getting very detailed information about their collective velocities." 

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The researchers took the data between 2020 and 2022, as well as earlier star brightness measurements of M87 from Hubble, and compared them to supercomputer model predictions of how stars move around the center of the triaxial-shaped galaxy. The best fit to this data allowed them to calculate the black hole's mass. "Knowing the 3D shape of the 'swarming bees' enabled us to obtain a more robust dynamical measurement of the mass of the central black hole that is governing the bees' orbiting velocities," said Ma.

In the 1920s, astronomer Edwin Hubble first classified galaxies according to their shapes. Flat disk spiral galaxies could be viewed from various projection angles of the sky: face-on, oblique, or edge-on. But the "blobby-looking" galaxies were more problematic to characterize. Hubble came up with the term elliptical. They could only be sorted out by how great the ellipticity was. They didn't have any apparent dust or gas inside of them to better distinguish between them. Now, a century later astronomers have a stereoscopic look at a prototypical elliptical galaxy.

The Hubble Space Telescope is a project of international cooperation between NASA and ESA. NASA's Goddard Space Flight Center in Greenbelt, Maryland, manages the telescope. The Space Telescope Science Institute (STScI) in Baltimore, Maryland, conducts Hubble and Webb science operations. STScI is operated for NASA by the Association of Universities for Research in Astronomy, in Washington, D.C.

Model of M87

Tyler O'Neal, Staff Editor ACADEMIA April 13, 2023, 10:30 am
This animation begins with a Hubble Space Telescope photo of the huge elliptical Galaxy M87. It then fades to a supercomputer model of M87. A grid is overlayed to trace out its three-dimensional shape, made more evident by rotating the model and grid. This was gleaned from meticulous observations made with the Hubble and Keck telescopes. Because the galaxy is too far away for astronomers to employ...

Read more https://www.supercomputingonline.com/gallery/category/news/model-of-m87

The machine learning model can effectively predict a patient’s risk for a sleep disorder using demographic and lifestyle data, physical exam results and laboratory values.  CREDIT Hernan Sanchez, Unsplash, CC0 (https://creativecommons.org/publicdomain/zero/1.0/)
The machine learning model can effectively predict a patient’s risk for a sleep disorder using demographic and lifestyle data, physical exam results and laboratory values. CREDIT Hernan Sanchez, Unsplash, CC0 (https://creativecommons.org/publicdomain/zero/1.0/)

VCU, Northwestern med schools use XGBoost to predict sleep disorders from patient records

Tyler O'Neal, Staff Editor ACADEMIA April 12, 2023, 2:00 pm

Depression, age, and weight were three factors that the artificial intelligence model identified as predictive of an insomnia diagnosis

A machine learning model can effectively predict a patient’s risk for a sleep disorder using demographic and lifestyle data, physical exam results, and laboratory values, according to a new study published this week in the open-access journal PLOS ONE by Samuel Y. Huang of Virginia Commonwealth University School of Medicine, and Alexander A. Huang of Northwestern Feinberg University School of Medicine, US.

The prevalence of diagnosed sleep disorders among American patients has significantly increased over the past decade. This trend is important to better understand and reverse since sleep disorders are a significant risk factor for diabetes, heart disease, obesity, and depression.

In the new work, the researchers used the machine learning model XGBoost to analyze publicly available data on 7,929 patients in the US who completed the National Health and Nutrition Examination Survey. The data contained 684 variables for each patient, including demographic, dietary, exercise, and mental health questionnaire responses, as well as laboratory and physical exam information.

Overall, 2,302 patients in the study had a physician diagnosis of a sleep disorder. XGBoost could predict the risk of sleep disorder diagnosis with a strong accuracy (AUROC=0.87, sensitivity=0.74, specificity=0.77), using 64 of the total variables included in the full dataset. The greatest predictors for a sleep disorder, based on the machine learning model, were depression, weight, age, and waist circumference.

The authors conclude that machine learning methods may be effective first steps in screening patients for sleep disorder risk without relying on physician judgment or bias. 

Samuel Y. Huang adds: “What sets this study on the risk factors for insomnia apart from others is seeing not only that depressive symptoms, age, caffeine use, history of congestive heart failure, chest pain, coronary artery disease, liver disease, and 57 other variables are associated with insomnia, but also visualizing the contribution of each in a very predictive model.”

  1. China demos photonic filter that separates signals from noise to support future 6G wireless communication
  2. S&T prof Huang wins over $14M in funding to develop fiber optic sensors for harsh, extreme conditions

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