The Sloan Digital Sky Survey (SDSS) fundamentally changed the way modern astronomy research is done. This “grand and bold thing” is not frozen in a museum exhibit, however: instead, the SDSS telescopes and team have continued to iterate. The collaboration recently described the fifth generation of this groundbreaking survey in The Astronomical Journal.

Roots in History

Astronomy is now without question a “big data” science. While a lucky few researchers still occasionally travel to distant mountaintops to sojourn with the stars, most astronomers who can describe their work as “observational” rarely ever leave their laptops. Petabytes of historical data, including images, spectra, enormous simulation archives, and more, are available the instant one’s fingers touch a keyboard. As a result, the speed of discovery has increased exponentially, and the barriers to cutting-edge research have dramatically shrunk.

While no paradigm shift can be pinned to an exact date, astronomy’s turn towards the data-rich era arguably began in 1998 when SDSS first began collecting data. This was among the first of the “industrial” surveys that observed large swaths of the sky with electronic detectors and used automated pipelines for bulk data processing. In the years since, larger robotic telescopes have begun to mimic the SDSS strategy of “mowing the sky.” The latest of these successors, which itself may usher in another paradigm shift in terms of data volume and availability, is the soon-to-begin Legacy Survey of Space and Time at the Vera C. Rubin Observatory.

While SDSS’s twin 2.5-meter telescopes (one in New Mexico and one in Chile, allowing the survey to observe the entire sky) are dwarfed by Rubin’s 8-meter mirror, there is still thrilling science to be done with these moderate-sized observatories. This is especially true after a series of upgrades to the spectrographs and thanks to a deep expertise in not only survey-style science, but also in project management and survey planning. In 2021, in the middle of COVID-19 delays and distancing requirements, the team began upgrades to their existing facilities and construction of a brand new telescope to kick off the latest survey, SDSS-V.

The Next Generation

In a wide-ranging publication that references not only astronomy research articles but also Plato, the Bhagavad Gita, an Egyptian tomb, and Tolstoy, the large collaboration describes that this latest survey is divided into three parts: the Milky Way Mapper, the Black Hole Mapper, and the Local Volume Mapper. The preexisting telescopes, which are outfitted with hundreds of tiny robots that can swivel fiber optic cables into place to collect spectra of up to 500 objects at a time, will handle the first two of these “mappers.” A brand-new facility in Chile that’s specifically designed to map the structure of nearby gas will handle the last mapper. Instead of aiming the fibers at a known star or galaxy, this telescope will instead collect a spectrum for every point across an area the size of the full Moon with every exposure.

By the end of the survey, SDSS-V will have collected optical and near-infrared spectra for roughly 6 million objects spread across the whole sky, as well as time-series spectra from about a million of these sources. This dataset, which is unprecedented in both its spatial and temporal coverage, will allow researchers to probe questions ranging from how quasars evolve over time to how commonly young stars have brown-dwarf companions. Long since SDSS initiated the era of industrial survey astronomy, this latest publication shows how the survey continues to lead the way.

Citation

“Sloan Digital Sky Survey. V. Pioneering Panoptic Spectroscopy,” Juna A. Kollmeier et al 2026 AJ 171 52. doi:10.3847/1538-3881/ae0576

The post A New View of the Spectroscopic Universe appeared first on AAS Nova.

Spacecraft scattered throughout the solar system keep tabs on the solar wind, but the measurements are dotted with data gaps. How can scientists prevent these gaps from biasing their studies of the solar wind?

Sampling the Solar Wind

The solar wind is a tenuous, fast-moving, turbulent plasma that constantly streams out from the Sun. Studying the solar wind is important for understanding how potentially damaging space weather events like coronal mass ejections travel through the solar system. Solar wind studies also have far-reaching implications, since solar-wind-like plasmas exist throughout the universe, such as in the sparse medium between galaxies in a cluster.

Luckily, there are many spacecraft that sample the solar wind from various locations within our solar system. Unluckily, many of these spacecraft do not continuously monitor the solar wind. For example, when a spacecraft orbiting a planet dips into the planet’s atmosphere, this detour creates an hours-long gap in solar wind monitoring.

The Impact of Data Gaps

How do these data gaps affect studies of the solar wind? In a recent research article, Daniel Wrench and Tulasi Parashar (Victoria University of Wellington) approached this question in terms of how gaps affect the structure function: a mathematical description of how the solar wind fluctuates over different length or time scales.

Wrench and Parashar introduced random artificial gaps into data from the Parker Solar Probe, which monitors the solar wind continuously, and compared the structure functions calculated from the continuous and interrupted data sets. They used the difference between the true structure function — calculated from the continuous data set — and the structure functions from the artificially gapped data sets to develop a correction factor.

The team then tested their correction factor using data from the Wind spacecraft, which continuously monitors the solar wind from the L₁ Lagrange point. The team calculated the structure function from 1) the continuous data set, 2) the artificially gapped data, 3) the gapped data with linear interpolation used to fill the gaps, and 4) the gapped data with the correction factor applied. This test showed that the correction factor performs better than other commonly used gap-handling methods, such as linear interpolation, which can systematically underestimate the structure function. Using the correction factor, the error in the structure function remained below 50% even when 95% of the data were missing.

Correcting Voyager 1

Finally, the authors applied their validated method to data from Voyager 1. Data from the two Voyager spacecraft are extremely scientifically valuable — no other spacecraft have ventured as far from the Sun and can measure the solar wind at such large distances. The data are also extremely sparse; one segment used in this work was missing 85% of the data. This means that an effective method of correcting these data has potentially huge scientific significance.

Wrench and Parashar applied their correction factor to the Voyager 1 data, showing that this method handily removes artifacts while avoiding the underestimation introduced by linear interpolation. While the authors saved the interpretation of the newly corrected Voyager 1 data for future research, this study makes it clear that even large data gaps needn’t hinder studies of the solar wind.

Citation

“Debiasing Structure Function Estimates from Sparse Time Series of the Solar Wind: A Data-Driven Approach,” Daniel Wrench and Tulasi N. Parashar 2025 ApJ 987 28. doi:10.3847/1538-4357/addc6a

The post Minding the Gaps in Solar Wind Data appeared first on AAS Nova.

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.

Paleontologists have identified a new species of the axolotl genus Ambystoma from several fossilized specimens found deep in the rocky outcrops of the Mexican state of Hidalgo.

The post New Species of Fossil Axolotl Unearthed in Mexico appeared first on Sci.News: Breaking Science News.