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  • A nova eruption.
    Imaging novae up close: a new look at how stellar eruptions unfold

    When a nova erupts, it’s often described as a single dramatic blast—but new observations suggest the real story is far more complex. In a recent Nature Astronomy paper, an international team used the CHARA Array’s cutting-edge infrared interferometer to capture the most detailed early images of two nova explosions to date. Their results reveal that nova ejecta can emerge in multiple distinct outflows, sometimes colliding to produce high-energy gamma rays, while other systems eject most of their material only weeks after the initial eruption. These findings provide rare, direct evidence that nova eruptions unfold through layered, time-dependent processes rather than a single instantaneous event.

    Kirill Sokolovskii.
    Former Illinois postdoctoral researcher, Kirill Sokolovskii.

    The study includes contributions from Kirill Sokolovskii, a former postdoctoral researcher at the University of Illinois Urbana-Champaign, now at Texas Tech University, who coordinated multiwavelength observations that helped interpret CHARA’s unprecedented images.

    Below, Kirill shares additional insight into the discovery and what it means for the future of Nova Research.

    From your perspective, what is the most novel or important takeaway from these observations, especially for readers who may not work on novae?

    The main takeaway from this work is that a nova event is more complex than a single explosion. Nova ejecta has structure, and the ejection process takes time - it's not a on-off event.

    What made it possible to image these multiple outflows now, when this hadn’t been done before?

    Images of a nova eruption that resolve multiple outflows became possible thanks to recent technological advances in infrared interferometry, good coordination among the observers, and luck. The recently upgraded sensitive Michigan InfraRed Combiner-eXeter instrument combines light from six 1-m telescopes of the CHARA array, providing baselines up to 330m. However, even with the upgraded sensitivity, the array can image only bright near-infrared sources (H band magnitude ~< 8).

    Only a few Galactic novae reach that brightness, and a tight coordination between astronomers studying novae and operating the array was necessary to perform the interferometric observations once a nova appeared that was sufficiently bright in the region of the sky reachable to CHARA. The observations had to be organized on short notice before the expanding nova ejecta became too large and dim to be imaged.

    Can you briefly describe your specific role in the study and how you contributed to the observations or analysis?

    I was coordinating multi-wavelength observations of the two novae with facilities including NuSTAR, Swift (X-ray), VLA, and VLBA (radio). The CHARA array was an unconventional addition to these facilities, delivering the most exceptional results. My role in preparing the CHARA paper involved interpreting the CHARA results within a multi-wavelength context.

    Was there a moment during the research when the results surprised you or exceeded expectations?

    I was really surprised to see the polar outflow emanating from the central bright region in V1674 Her. While this structure was expected from VLA imaging and spectral line profile modelling in other novae, this is the first time such a structure has been imaged clearly.

    What do you see as the most exciting implication of this work for future nova studies?

    Interferometric imaging opens up a new channel of information on novae. First, we may learn about the structure of the nova outflow in the first days of the eruption. Second, we can measure distances to novae by comparing the rate of nova envelope expansion in the plane of the sky (as imaged via interferometry) with the expansion velocity derived from optical spectroscopy via the Doppler effect.  Many novae do not have accurate distance measurements associated with them, as in quiescence, they are too faint and too distant to have a reliable Gaia parallax measurement.

     

  • Illinois Physics Professor Nicolás Yunes
    Unlocking the secrets of strong gravity: A new era in black hole research

    The Simons Foundation has awarded $8 million to launch the Simons Collaboration on Black Holes and Strong Gravity, bringing together 12 leading scientists from across the globe, including Illinois Physics professor and Astronomy affiliate professor Nicolás Yunes, who will direct the effort. This multidisciplinary network of physicists, mathematicians, and data scientists will tackle some of the universe’s deepest mysteries by developing the theoretical tools to interpret rapidly advancing gravitational wave data.

    With new upgrades set to increase detectors' sensitivity, like LIGO and Virgo dramatically, researchers are entering the “precision era” of gravitational wave science. The collaboration will use advanced models, simulations, and AI-driven analysis to uncover the hidden signatures of black hole mergers and strong gravity, potentially shedding light on dark matter, the matter–antimatter imbalance, and even physics beyond Einstein’s theory of relativity.

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  • Headshot
    Unlocking the cosmos with AI

    It began with a problem of scale. 

    Modern telescopes like the Vera C. Rubin Observatory, set to begin operations in October 2025, are poised to revolutionize astronomy by capturing unprecedented volumes of data—millions of time-variable astrophysical events across the sky each night. But with this scale comes a new challenge: How do you process, classify, and interpret this firehose of information when human analysis alone can’t keep up with the hundreds of new events being reported by Rubin every second? 

    That’s the frontier being navigated by Gautham Narayan, professor of astronomy at the University of Illinois and deputy director for astrophysics research at the NSF-Simons AI Institute for the Sky (SkAI). His group has spent over a decade pioneering artificial intelligence in astrophysics, developing early classification and inference algorithms, and helping lay the groundwork for a supercharged field by SkAI. “My group has been doing AI and astrophysics for about the last decade,” Narayan explains. “SkAI is a way to take the things I've been doing locally here at Illinois, and really make it a much larger effort that is multi-institute.” 

    Founded to harness the parallel revolutions in AI and astronomy, the SkAI Institute is working toward what Narayan calls “foundation models for astronomy.” These are AI models inspired by systems like ChatGPT but tailored to the complexities of “multi-modal” astrophysical data—images, spectra, and time series. If applied at scale, these models could be transformative. “If you could do classification, forecasting, inference, and analysis now at an industrial scale for billions of objects in a year,” he says, “that has the potential to change everything we know about astrophysics.” 

    A powerful example of this potential came with GW170817—the first observed merger of two neutron stars. Astronomers detected the event across multiple wavelengths and combined data from observatories worldwide to form a multidimensional view of the explosion and the origins of heavy elements. “What was valuable was seeing how combining data from all of these telescopes could give you such a multidimensional view,” Narayan recalls. “The obvious question is, well, what if you could do that for 100,000 objects?” 

    That’s where AI steps in—not just as a tool for managing data at scale but as an engine for discovery. 

    A mission at the intersection

    SkAI’s mission is to marry astronomy and AI in a scientifically rigorous and fundamentally interpretable way. “We care about our model’s predictive power,” Narayan emphasizes, “and making sure our models are interpretable, that a human can look at what an AI algorithm has done and say, ‘Yes, this makes physical sense.’” 

    The Institute’s reach is broad and collaborative, with partner institutions including Northwestern, the University of Illinois, the University of Chicago, the Adler Planetarium, Fermilab, Argonne National Lab, and community colleges like Parkland in Champaign. Cultural institutions such as the Spurlock and Krannert Art museums on campus are also involved, broadening the impact beyond traditional scientific spaces. 

    Education and outreach are key pillars of SkAI’s vision. Students at Illinois whose work aligns with the Institute’s priorities can receive funding, and the same opportunities extend to postdoctoral fellows, including through a unique preceptorship program. “We also have opportunities for postdocs,” Narayan notes, “including a preceptor postdoctoral fellow who has to be engaged in education and outreach work in the community colleges.” 

    Rethinking astronomy education and infrastructure

    Looking ahead, Narayan envisions a research environment where scientists interact with powerful AI tools not by writing specialized code but through intuitive, natural-language queries. “You could ask it: ‘Can you find me 100 low-redshift spiral galaxies in Rubin images with space-based Euclid images that also show stellar streams?’” he says. “Something that would have taken a grad student a year, you could potentially do in seconds.” 

    This shift could significantly reshape how educators teach astronomy. “I think in the long term, we are going to have to revise our astronomy curriculum to embed AI into almost every class we offer,” Narayan predicts. 

    The ripple effects extend well beyond university classrooms. Through efforts like SCiMMA (Scalable Cyberinfrastructure for Multi-Messenger Astrophysics), Narayan and his collaborators are bridging siloed observatories nationwide, building a national infrastructure where telescopes and AI systems work in concert. “SCiMMA’s mission is to connect all of these things,” he explains, “so that ultimately you can build this AI-enabled future that I’ve been trying to describe.” 

    What’s next?

    Narayan is building a foundation model for time series data, especially for explosive events like supernovae and kilonovae. The goal is to move beyond simply classifying these events to analyzing them at scale—studying their physical properties, distributions, host environments, and behaviors as a population. “That’s a hard task because nobody’s done it systematically yet,” he says. “It’s hard to assemble the data and develop methods that combine data from different facilities with traditional non-AI techniques. But I want to get to the point where we can essentially ask these AI models for a large sample and analyze them all at once.” 

    In parallel, he continues work on projects like the Rubin Observatory, the Young Supernova Experiment, and SCiMMA—all feeding into SkAI’s broader mission of building an AI-powered ecosystem for astronomy. “I think you’ll see a revolutionary change in how we deal with data, not just doing what we do now at a larger scale, but asking entirely new questions that we couldn’t have asked before,” he adds.