“I didn’t sign up to try to measure a new record or anything”, said Matt Carnicle, a volunteer for the NASA-sponsored Community Collaborative Rain, Hail, and Snow Network, or CoCoRaHS, project. Carnicle measured a whopping 29.06 inches of rainfall on June 18th, 2026, breaking an all-time 24-hour record for the state of Louisiana of 22.00 inches. “I’m just a regular guy who likes to track the weather, and I report what I get in my gauge whether it’s zero, two hundredths, or whatever is in there when I read it.” 

CoCoRaHS  (pronounced KO-ko-rozz) is a network of volunteer weather observers of all ages working together to measure and map rain, hail, and snow by measuring precipitation in their backyards. Together, these thousands of daily precipitation reports – openly available on the project website – are used by scientists and citizens for a wide variety of purposes, to include improving weather forecasting, informing water and land management, driving atmospheric models, and triggering flash flood and severe weather warnings. 

Matt joined through a storm-spotter class where he learned how CoCoRaHS is part of a NASA hail research project focused on Gulf States in the Southeast United States. CoCoRaHS reports (and photos) of hail are used for researching the “melt rate” between when the satellite estimates the stone sizes in the clouds and what volunteers measure on the ground. Matt took it a step further and purchased a standardized rain gauge in order to participate with CoCoRaHS by measuring rainfall.

Matt’s June 18, 2026 rain measurement shatters Louisiana’s 1962 state record of 22.00 inches of rain in 24-hours (Hawaii holds the national record with 49.69 inches in 24-hours). Even more remarkably, the 29.06 inches he measured fell in less than 12 hours! According to Louisiana State Climatologist Jay Grymes, who validated Matt’s measurement along with National Weather Service representatives, an event of this magnitude in this area is expected to happen less than once in a thousand years. A National Oceanic and Atmospheric Administration (NOAA) committee will convene in the coming months to verify and document the new record.

You can join Matt and other CoCoRaHS volunteers and submit official rainfall reports to the National Weather Service. They’re also on the lookout for hail in the southeast, where CoCoRaHS and NASA are doing research on how hail melts as it moves from the clouds to the ground. The only requirement for participation is that volunteers use the correct manual gauge, which is precise to the nearest 1/100th of an inch and is approved by the National Weather Service (measurements from automated rain gauges are not accepted). Sign up here, and you might measure the next record precipitation event: https://science.nasa.gov/citizen-science/community-collaborative-rain-hail-and-snow-network/

Some stars have planets. Others are orbited by brown dwarfs, balls of gas too massive to be planets, but too low-mass to be stars. Astronomers love these brown dwarf-star pairs because being paired with a star helps reveal a brown dwarf’s age. Ages of astronomical objects are often hard to measure, but essential for understanding how they form. 

Now, you can join NASA’s new Backyard Worlds: Binaries project and help astronomers discover these rare and interesting pairs. As a volunteer, you’ll inspect images from NASA’s Wide-field Infrared Survey Explorer (WISE) space telescope. Brown dwarfs may appear as small dots moving across a field of otherwise static stars. 

“We need your help to gain critical insights into these enigmatic cosmic objects,” said project lead Aaron Meisner.

Brown dwarfs are common but mysterious because they are so faint. There’s one for every three or four stars in our corner of the Milky Way galaxy. They are important laboratories for understanding giant planets like Jupiter.

Join the Backyard Worlds: Binaries project today and help astronomers understand where and when brown dwarfs form! You can also try one of our other brown dwarf-related projects: Backyard Worlds: Cool Neighbors! Anyone with a laptop or cell phone can participate. Participation does not require citizenship in any particular country.

The post Search for Hidden Cosmic Companions in Sun’s Backyard appeared first on NASA Science.

Some stars have planets. Others are orbited by brown dwarfs, balls of gas too massive to be planets, but too low-mass to be stars. Astronomers love these brown dwarf-star pairs because being paired with a star helps reveal a brown dwarf’s age. Ages of astronomical objects are often hard to measure, but essential for understanding how they form. 

Now, you can join NASA’s new Backyard Worlds: Binaries project and help astronomers discover these rare and interesting pairs. As a volunteer, you’ll inspect images from NASA’s Wide-field Infrared Survey Explorer (WISE) space telescope. Brown dwarfs may appear as small dots moving across a field of otherwise static stars. 

“We need your help to gain critical insights into these enigmatic cosmic objects,” said project lead Aaron Meisner.

Brown dwarfs are common but mysterious because they are so faint. There’s one for every three or four stars in our corner of the Milky Way galaxy. They are important laboratories for understanding giant planets like Jupiter.

Join the Backyard Worlds: Binaries project today and help astronomers understand where and when brown dwarfs form! You can also try one of our other brown dwarf-related projects: Backyard Worlds: Cool Neighbors! Anyone with a laptop or cell phone can participate. Participation does not require citizenship in any particular country.

The post Backyard Worlds: Binaries appeared first on NASA Science.

Motivated by a desire to support community members financially during the coronavirus pandemic, researchers employed 30 local citizen scientists in the Hubble Image Similarity Project. This project quantified the similarities between astronomical images, providing a way to test the results of image-search algorithms.

Seeking Similarities

Say you have an image of a star-forming region, featuring eye-catching gas clouds, dense and dusty knots, and newborn stars. How would you go about finding other images that resemble yours?

You might start your search with an astronomical image database, using filters for object type or instrument to sift through thousands and thousands of options. But even filtering out everything but star-forming regions might yield vastly different results, given the widely varying shapes, colors, and sizes of these regions.

Or maybe you’ll feed your image into a neural network that has been trained to spot similar images. The results may seem promising, but how can you tell whether the algorithm has found the images that are the most similar? Would another algorithm do better?

The Hubble Image Similarity Project

Astronomical image collections rarely contain information about similarities between images in their metadata, and while neural networks appear to excel at gathering similar images, the results of these models are generally unverified. The Hubble Image Similarity Project, led by Richard White (Space Telescope Science Institute) and Josh Peek (Space Telescope Science Institute and Johns Hopkins University), addressed these issues with a team of citizen scientists who generated similarity information for astronomical images, providing a quantitative means to test the results of neural networks.

White and Peek began by amassing a sample of images from the Hubble Legacy Archive. This sample included many different object types, such as galaxies, planetary nebulae, star-forming regions, and star clusters. After trimming and binning the images, converting them to 8-bit grayscale, filtering out low-contrast images, and eliminating satellite trails, image artifacts, and repeated observations of the same patch of sky, 2,098 images of 666 objects remained.

Citizen Scientists, Assemble

White and Peek recruited 30 members of the community within walking distance of the Space Telescope Science Institute to identify similar astronomical images, and the reviewers were paid for their work. In the three phases of the project, reviewers considered test images one at a time and 1) selected all similar images from a set of 15 comparison images, 2) selected the most similar image from a narrowed-down set of 6 comparison images, and finally 3) selected the most similar image from a set of 3 comparison images.

The citizen science team ultimately compared 5.4 million pairs of images, and White and Peek used these comparisons to produce an image similarity matrix. The matrix describes the metaphorical “distance” between the images, with the most similar images being the smallest distance apart.

Similar images resemble one another in terms of structure, texture, and other factors that White and Peek say are “difficult even to describe in words” — for example, the diffuse glow of a galaxy interrupted by a bright star with diffraction spikes, or a nebula speckled with stars and dense dusty clumps. The similarity data from this study are available online and can be used to test the performance of image-search algorithms. In future work, the authors plan to carry out a similar project using images of the Martian landscape.

Citation

“The Hubble Image Similarity Project,” Richard L. White and J. E. G. Peek 2025 AJ 169 306. doi:10.3847/1538-3881/adcb43

The post Citizen Science Born in the Pandemic: The Hubble Image Similarity Project appeared first on AAS Nova.