Multiple telescopes, including Chandra, observed the Milky Way’s giant black hole simultaneously with the Event Horizon Telescope (EHT). This combined effort gave insight into what is happening farther out than the field-of-view of the EHT.

The main panel of this graphic contains X-ray data from Chandra (blue) depicting hot gas that was blown away from massive stars near the black hole. Two images of infrared light at different wavelengths from NASA’s Hubble Space Telescope show stars (orange) and cool gas (purple). These images are seven light years across at the distance of Sgr A*. A pull-out shows the new EHT image, which is only about 1.8 x 10-5 light years across (0.000018 light years, or about 10 light minutes). (Credit: X-ray: NASA/CXC/SAO; IR: NASA/HST/STScI. Inset: Radio (EHT Collaboration))

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As the Event Horizon Telescope collected data for its remarkable new image of the Milky Way’s supermassive black hole, a legion of other telescopes including three NASA X-ray observatories in space was also watching.

Astronomers are using these observations to learn more about how the black hole in the center of the Milky Way galaxy — known as Sagittarius A * (Sgr A* for short) — interacts with, and feeds off, its environment some 27,000 light years from Earth.

Read the full article at: www.nasa.gov

NASA’s James Webb Space Telescope is aligned across all four of its science instruments, as seen in a previous engineering image showing the observatory’s full field of view. Now, we take a closer look at that same image, focusing on Webb’s coldest instrument: the Mid-Infrared Instrument, or MIRI. The MIRI test image (at 7.7 microns) shows part of the Large Magellanic Cloud. This small satellite galaxy of the Milky Way provided a dense star field to test Webb’s performance.

 

Here, a close-up of the MIRI image is compared to a past image of the same target taken with NASA’s Spitzer Space Telescope’s Infrared Array Camera (at 8.0 microns). The retired Spitzer telescope was one of NASA’s Great Observatories and the first to provide high-resolution images of the near- and mid-infrared universe. Webb, with its significantly larger primary mirror and improved detectors, will allow us to see the infrared sky with improved clarity, enabling even more discoveries.

For example, Webb’s MIRI image shows the interstellar gas in unprecedented detail. Here, you can see the emission from “polycyclic aromatic hydrocarbons,” or molecules of carbon and hydrogen that play an important role in the thermal balance and chemistry of interstellar gas. When Webb is ready to begin science observations, studies such as these with MIRI will help give astronomers new insights into the birth of stars and proto-planetary systems.

 

In the meantime, the Webb team has begun the process of setting up and testing Webb’s instruments to begin science observations this summer. Today at 11 a.m., Webb experts will preview these next two months of instrument preparations in a teleconference for media. Listen to the audio stream live at nasa.gov/live.

Read the full article at: blogs.nasa.gov

Visualizing your data can be the key to success in projects because it can reveal hidden insights in the data, and improve understanding. The best way to convince people is by letting them see and interact with their data. Despite many visualization packages being available in Python, it is not always straightforward to make beautiful stand-alone and interactive charts that can also work outside your own machine. The key advantage of D3 is that it works with web standards so you don’t need any other technology than a browser to make use of D3. Importantly, interactive charts can help to not just tell the reader something but let the reader see, engage, and ask questions.

 

This blog outlines how you can build your own stand-alone, interactive force-directed D3 network using Python. Note that the steps are similar to any other D3 chart. If you need a readily working version, the d3graph library is for you!

Read the full article at: towardsdatascience.com

Do you want to do machine learning using Python, but you’re having trouble getting started? In this post, you will complete your first machine learning project using Python.

 

In this step-by-step tutorial you will:

  1. Download and install Python SciPy and get the most useful package for machine learning in Python.
  2. Load a dataset and understand it’s structure using statistical summaries and data visualization.
  3. Create 6 machine learning models, pick the best and build confidence that the accuracy is reliable.

 

If you are a machine learning beginner and looking to finally get started using Python, this tutorial was designed for you.

Read the full article at: machinelearningmastery.com

Curated by a team of US-based Eastern European designers, The New Exhibition connects artists caught in the crossfire of war directly with art directors and agencies in the West.

When a team of designers at design company Collins began researching portfolios for an online directory of Ukrainian creatives, they noticed something striking. “The style of many illustrators has completely shifted since Russian tanks rolled across the border into Ukraine,” says the team behind The New Exhibition, launched by Collins. It is but one of many creative revelations that have come out of the research process behind the new project, The New Exhibition – an ongoing online resource that features artists from across the creative world.

Read the full article at: www.itsnicethat.com

A new study shows that it is possible to use the genetic sequences of a person’s antibodies to predict what pathogens those antibodies will target. Reported in the journal Immunity, the new approach successfully differentiates between antibodies against influenza and those attacking SARS-CoV-2, the virus that causes COVID-19.

  

Read the full article at: news.illinois.edu

Astronomers have identified a rapidly growing black hole in the early universe that is considered a crucial “missing link” between young star-forming galaxies and the first supermassive black holes. They used data from NASA’s Hubble Space Telescope to make this discovery. Until now, the monster, nicknamed GNz7q, had been lurking unnoticed in one of the best-studied areas of the night sky, the Great Observatories Origins Deep Survey-North (GOODS-North) field.

 

 

Read the full article at: www.nasa.gov