The research, based on more than 30,000 molecular clouds, provides the most systematic view yet of widespread vertical corrugations in the Milky Way’s outer carbon-monoxide, or CO, disk. Molecular clouds are dense concentrations of cold gas where stars are born, and astronomers often use carbon monoxide as an indirect tracer of molecular hydrogen, the dominant constituent of these clouds. Because the clouds are distributed throughout the Galactic disk, their positions can reveal subtle changes in the structure of the Milky Way on scales of thousands of light-years.

Detecting these ripples is challenging because the corrugations are superimposed on the much larger Galactic warp. The warp causes the outer disk to rise and fall over broad distances, potentially masking smaller vertical displacements. To isolate the corrugations, the researchers first constructed and subtracted a global model of the CO warp. The remaining vertical offsets, measured relative to the expected warped disk, revealed coherent patterns that could not be explained as random scatter among individual clouds.

The residual structure resembles a series of giant waves running through the outer molecular disk. The measured vertical amplitudes are approximately 100 to 200 parsecs, meaning that the molecular gas can be displaced hundreds of light-years above or below the underlying warped mid-plane. Although these amplitudes are modest compared with the overall size of the Galaxy, they are enormous on the scale of star-forming clouds and are large enough to alter the vertical environment in which new stars emerge.

The study also identifies radial wavelengths between roughly 3.9 and 7.9 kiloparsecs. A wavelength describes the distance between repeating peaks and troughs in a wave, so these values indicate that the corrugations are not small, local disturbances. Instead, they form broad structures spanning several thousand light-years. Their coherence across such distances points to large-scale dynamical processes affecting the Galactic disk as a whole.

One particularly striking feature is an azimuthal corrugation mode detected near a Galactocentric radius of about 12.7 kiloparsecs. Rather than varying mainly with distance from the Galactic centre, this pattern changes around the disk’s circumference, with an angular wavelength of 52.6 degrees. At that radius, the angular spacing corresponds to approximately 11.6 kiloparsecs. The mode appears to extend across nearly 40 kiloparsecs, making it one of the clearest indications that vertical gas motions are organized over a substantial fraction of the outer Milky Way.

The origin of the corrugations remains an open question, but their scale and coherence provide important clues. Such structures could be connected to gravitational interactions with satellite galaxies, disturbances caused by the Milky Way’s past encounters, or the long-term response of the disk to asymmetries in its surrounding dark-matter halo. Spiral structure, gas dynamics and bending waves within the disk may also contribute. The new measurements do not identify a single cause, but they offer a set of observational constraints that future models of Galactic evolution will have to reproduce.

The discovery is especially significant because the molecular gas records the current architecture of the star-forming disk. Earlier observations of young stars had already suggested that large-scale vertical corrugations exist in the Milky Way, but gas clouds provide an independent and dynamically important tracer. Since molecular clouds are short-lived compared with the age of the Galaxy, their arrangement may preserve information about ongoing or relatively recent perturbations. At the same time, their concentration in star-forming regions allows astronomers to investigate whether vertical waves influence where and how new stars are produced.

The findings establish that corrugations are not isolated curiosities but a widespread feature of the outer Galactic molecular disk. By revealing vertical amplitudes of 100–200 parsecs, radial wavelengths of 3.9–7.9 kiloparsecs and a coherent azimuthal mode spanning tens of thousands of light-years, the study transforms the Milky Way from a two-dimensional map into a dynamic, rippling structure. As surveys improve and simulations become more realistic, these immense gas waves may become a powerful record of the Galaxy’s encounters, internal evolution and continuing transformation.

Subject of Research: Widespread vertical corrugations in the Milky Way’s outer molecular gas disk and their relationship to the Galactic warp.

Article Title: Widespread corrugations superimposed on the Galactic warp in the outer CO disk

Article References: Sun, Y., Zhang, S., Yang, J. et al. “Widespread corrugations superimposed on the Galactic warp in the outer CO disk.” Nature Astronomy (2026). https://doi.org/10.1038/s41550-026-02938-y

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41550-026-02938-y

Keywords: Milky Way, Galactic warp, molecular clouds, carbon monoxide, CO disk, vertical corrugations, galactic dynamics, star formation, spiral galaxies, interstellar medium

As part of their collaboration with NASA, CAII will play a pivotal role in developing an open-source deep learning framework aimed at processing images captured by the Euclid spacecraft. This undertaking is spearheaded by Principal Investigator Xin Liu, who emphasizes the critical nature of this research. A primary hurdle in the analysis of data generated by Euclid is the phenomenon of blended galaxies. Overlapping images can obscure distinct sources of galactic information, making it challenging to derive accurate astrophysical measurements. The blending of galaxies is particularly problematic in areas such as photometry, redshift estimation, and galaxy morphology, as it introduces biases that can lead to significant inaccuracies in scientific interpretations.

Deep learning, specifically through a tool known as Detection, Instance Segmentation and Classification with Deep Learning (DeepDISC), is revolutionizing the approach to identifying celestial objects. This innovative tool harnesses machine learning techniques to enable a more accurate detection of stars and galaxies. Liu and his team will leverage DeepDISC within the framework of the Euclid mission to not only enhance the accuracy of galaxy identification but also to quantify the uncertainties associated with their predictions. This dual approach promises to significantly improve the reliability of data analysis, ensuring that researchers can draw trusted conclusions from their findings.

Liu articulates the importance of addressing blended sources in Euclid’s data analysis, citing that overcoming this challenge is essential for the integrity of the mission’s scientific outputs. The implications of this research extend beyond Euclid itself; the techniques developed can be adapted for other ambitious space exploration projects, most notably the Vera C. Rubin Observatory. With its anticipated first light occurring later this year, the observatory stands to benefit from advancements in deblending images both from ground-based telescopes and those deployed in space.

The research team is bolstered by a collaborative spirit, with critical contributions from co-principal investigators such as Vlad Kindratenko, Director of CAII, along with Astronomy Professor Yue Shen and Computer Science Professor Yuxiong Wang. Their distinct expertise in computational infrastructure, data analysis methodologies, and machine learning act as a strong foundation for this interdisciplinary project. Each member brings a wealth of knowledge that enhances the scientific rigor and innovative spirit of the mission.

Wang emphasizes that the advancements in computer vision and artificial intelligence are leading to foundational models that redefine our understanding of visual information through natural images. The current phase in AI research represents a thrilling opportunity to adapt these powerful models toward unraveling the mysteries of the universe. In an age where AI is becoming increasingly capable, its integration into fields like astrophysics showcases the potential for transformative discoveries and enhances our collective understanding of fundamental cosmic principles.

The CAII’s initiative reflects broader trends in interdisciplinary collaboration, showcasing how artificial intelligence can intersect with scientific inquiry in profound ways. As AI methodologies evolve and improve, their application to space exploration promises unprecedented advancements in our quest for knowledge. The focus on deep learning and its ability to sort through complex datasets will be vital in maximizing the scientific return of missions like Euclid, ensuring that the wealth of information collected translates into significant scientific insights.

Moreover, the interdisciplinary nature of the project heralds a new era of scientific research where collaboration across different fields is not just beneficial but necessary. The integration of knowledge from AI, computer science, and astrophysics underscores the importance of blending disciplines to address the multifaceted challenges presented by complex datasets in space exploration.

As the Euclid mission prepares to launch, the work being done by CAII serves as a beacon of innovation, particularly in the realm of AI applications in astrophysics. The developments achieved through deep learning frameworks have the potential to redefine how scientists engage with astronomical data, paving the way for future missions and research endeavors in understanding the dark universe.

In conclusion, the collaboration between CAII and NASA represents an exciting juncture in the intersection of artificial intelligence and astrophysics. Through substantial investment, innovative methodologies, and a commitment to scientific excellence, this initiative will not only enhance the accuracy of data analysis in the Euclid mission but also contribute to the broader dialogue about dark matter and dark energy, ultimately aiming to unlock new dimensions of understanding within the universe’s vast expanse.

Subject of Research: Analysis of dark matter and dark energy using AI in the Euclid mission
Article Title: CAII Harnesses AI for the Euclid Mission to Explore the Dark Universe
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Keywords

AI, dark matter, dark energy, Euclid mission, deep learning, NCSA, CAII, astrophysics, Vera C. Rubin Observatory, machine learning, blended galaxies, data analysis.