Editor’s Note: Astrobites is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the partnership between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original can be viewed at astrobites.org.

Title: JWST Discovery of Warm Dust in the Circumgalactic Medium of the Makani Galaxy
Authors: Sylvain Veilleux et al.
First Author’s Institution: University of Maryland
Status: Published in ApJ

The Windy Galaxy

Billions of light-years away lies Makani, a record-breaking galaxy discovered in 2019 by researchers at the Keck Observatory atop Maunakea, Hawaiʻi. The galaxy shows evidence of a large merger event, where two galaxies collided to form a larger galaxy. This event triggered starburst activity, or several waves of intense star formation, that changed the makeup of not just Makani, but the region around it.

There are several processes that can create galactic winds. An active supermassive black hole at the center of the galaxy can slingshot particles out of the galaxy at close to the speed of light, or supernovae leftover from previous starburst activity can help to expel material from the galaxy. Makani, whose name in Hawaiian just so happens to mean “wind,” has both factors contributing to its galactic winds: massive amounts of star formation and a very active black hole in its center.

This contributes to some of the strongest galactic winds discovered to date, stretching approximately 100 kiloparsecs (330,000 light-years) beyond the galaxy, roughly ten times the extent of a typical galactic wind. These galactic winds remove gas from the galaxy and help to enrich the circumgalactic medium around the galaxy. There is still much to discover about how galactic winds change the makeup of such galaxies, and what exactly they take with them on their way out.

Earth’s Favorite Carcinogen: Polycyclic Aromatic Hydrocarbons

Polycyclic aromatic hydrocarbons (PAHs, pronounced P-A-Hs or “paws”) are small flat dust grains scattered into every corner of the universe, from the atmosphere of Earth in the form of exhaust and wildfire smoke to the circumgalactic medium (the mostly empty space between galaxies). These PAHs contribute to regulating the temperature of the interstellar medium, but astronomers have long wondered how they migrate across galaxies.

Even though they are smaller than a human hair, PAHs are still easily observable even in very distant galaxies.  These dust grains emit infrared light, and if there are enough PAHs in a region, they are bright enough to be observed with modern telescopes, such as JWST. By combining multiple observations across multiple filters, astronomers can calculate ratios that reveal information about the PAHs’ size, temperature, and charge, allowing reserachers to further discern properties about the regions in which these PAHs reside.

With redshifted galaxies like Makani, the light from PAHs is stretched beyond its usual wavelength, but fortunately for today’s authors, the light was stretched just enough to land known PAH features well within JWST filters, as seen in Figure 1. The authors were then able to use this information to track the distribution of these PAHs across Makani.

Figure 1: Makani’s spectrum overlaid on top of the JWST filters. The peaks in the spectrum are PAH features that neatly overlap with JWST’s observing bands. [Veilleux et al. 2025]

PAHs Gone Extragalactic

Using JWST, the authors scrutinized Makani for any signs of PAHs. They found that not only did PAHs exist within the galaxy, these molecules were found up to 30 kpc (~100,000 light-years) outside the galaxy. Makani’s strong galactic winds were ejecting the dust grains from the galaxy over an estimated timescale of a billion years.

Previously, astronomers had not expected to find high quantities of dust grains outside the protection of a galaxy. The journey across a galaxy is long and hot, with temperatures in the millions of degrees. A dust grain would likely not survive the trip, as it would be destroyed by high-energy photons. This suggests that the PAHs were somehow shielded from the hotter parts of the interstellar medium and circumgalactic medium in large enough volumes to make it beyond the edge of Makani.

This answered questions about how galaxies enrich the circumgalactic medium, but it also added several more. Since today’s authors were limited by the sensitivity and range of their instruments, they were unable to confirm if there were PAHs along Makani’s entire wind structure, or just the first 35 kpc (114,000 light-years) of it. They also would like to turn their sights towards the intergalactic medium to see if PAHs could make it even farther beyond the reach of their host galaxy.

Original astrobite edited by Chloe Klare.

About the author, Natalie Price:

As a first-year master’s student at Wesleyan University, I study how stellar winds interact with the local interstellar medium. Outside of the observatory, you can find me dancing, with my nose in a book, or running at non-relativistic speeds.

The post Hitchhiking PAHs Caught Catching a Ride on Galactic Winds appeared first on AAS Nova.

Editor’s Note: Astrobites is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the partnership between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original can be viewed at astrobites.org.

Title: The JWST EXCELS Survey: Insights into the Nature of Quenching at Cosmic Noon
Authors: Maya Skarbinski et al.
First Author’s Institution: Johns Hopkins University
Status: Published in ApJ

How to Quench a Galaxy

Look at an image of the sky taken with a sufficiently sensitive telescope, and you’ll quickly notice that galaxies tend to cluster into two main types: blue spiral galaxies, which have flat disk shapes with a central bulge, and red elliptical galaxies, which look like spherical or elliptical balls of red stars. Blue spiral galaxies can form tens to hundreds of stars every year, while elliptical galaxies have completely stopped forming stars, meaning that some process has to transform star-forming spirals into non-star-forming, or “quiescent,” elliptical galaxies. To form the current population of massive elliptical galaxies, this process had to be common about 2–3 billion years after the Big Bang at “cosmic noon,” the period when star formation in the universe peaked. The processes that “quench” star formation in massive galaxies are still being studied, and one of the best ways to study them is to find galaxies that recently quenched and look for clues about the processes that quenched them.

Post-starburst galaxies are galaxies that have rapidly quenched after a short burst of star formation. Because these galaxies quenched so quickly and so recently, it’s often possible to find signs of whatever quenched them, like the signatures of past galaxy mergers, feedback from supermassive black hole accretion, or the shutoff of gas flowing into the galaxy. Rapid quenching is pretty uncommon now, but it was a much more common way for galaxies to quench at cosmic noon. Understanding post-starburst galaxies at cosmic noon is therefore critical for understanding the formation of massive elliptical galaxies in the local universe.

The Quest for Quenched Galaxies

Today’s article uses data from the JWST Early eXtragalactic Continuum and Emission Line Survey (EXCELS) to identify post-starburst galaxies at cosmic noon and try to determine why they quenched. EXCELS is a spectroscopic survey, so the authors get a spectrum for every galaxy. The spectrum encodes information about the galaxy’s stellar population, including the mass of stars in the galaxy, the number of stars forming every year, and the history of star formation throughout the galaxy’s life.

The authors use a technique called principal component analysis to further divide the sample into young and old post-starbursts. Principal component analysis is a machine-learning technique that learns the most important features of a data set. This technique is used for “dimension reduction,” or reducing the number of data points needed to learn something about the object. A typical spectrum has hundreds or even thousands of data points, which means performing data analysis on a spectrum can be very computationally expensive. Principal component analysis takes these thousands of data points and learns broad patterns that correlate with each other. These patterns are called “supercolors” in the context of spectral data, and they encode things like the overall shape and color of the spectrum as well as the spectral shape around key features (see Figure 1 for a visualization). Since the overall shape, color, and emission/absorption line features of a spectrum come from the galaxy’s stellar population, this method can be used to identify galaxies with lots of star formation a billion years ago but very little star formation today — in other words, post-starburst galaxies.

Figure 1: An example of principal component analysis for sample star-forming (SF), quiescent (Qu), and post-starburst (PSB) galaxy spectra (left-hand side). Principal component analysis simplifies a many-dimensional data set (for example, spectra) into fewer dimensions. In this case, Super-Color 1 measures the overall color of the spectrum (shown on the left-hand side as the slope of the spectra, marked with red lines), while Super-Color 2 measures the shape of the spectrum around 4,000 angstroms (orange box). The authors use principal component analysis to identify post-starburst galaxies for further analysis (right-hand side). [Adapted from Skarbinski et al. 2026]

The authors apply principal component analysis to the galaxies in their sample and find that 11 of the galaxies in their sample are classified as post-starburst, 9 are quiescent, and 4 still have some star formation. To further analyze the stellar populations of the post-starbursts in their sample, the authors use a program called Bagpipes to fit the galaxies’ spectra. Bagpipes is a spectral energy distribution fitting software, which means that it takes the observed spectrum of a real galaxy and tries to match it to a library of different stellar spectra. By measuring the relative contribution of different kinds of stars (which all have different lifetimes), Bagpipes can compute the likely history of star formation in the galaxy (e.g., when the star formation rate peaked) as well as the present-day properties of the galaxy (things like the mass in stars versus dust and the current star formation rate). The authors use the galaxies’ star formation histories to try to find clues as to how they quenched.

How Quickly Do Post-Starbursts Quench?

First, the authors measure something called a “quenching timescale,” which they define as the length of time between when the galaxy’s star formation rate peaked and when it fell low enough that the galaxy was quenched. The quenching timescale depends on which process shut down star formation in the galaxy — feedback from black hole accretion or star formation should cause fast quenching, while galaxies that are starved of gas from the intergalactic medium should quench more slowly. The authors find that 15 of their galaxies quenched in under 500 million years, 6 took between 500 million and 1 billion years, and 3 took longer than 1 billion years to quench. The galaxies that had the highest peak star formation rates quenched the fastest, suggesting that feedback from star formation could have played a role in quenching these galaxies.

Figure 2: Possible evolutionary tracks in supercolor for two example galaxies as they quench. The galaxy in the top panel quenches quickly and has post-starburst supercolors for about a billion years, while the galaxy in the bottom panel quenches without ever going through the post-starburst phase. The authors use these tracks to determine how important the post-starburst phase is for quenching massive galaxies. [Skarbinski et al. 2026]

Next, the authors measure how important the post-starburst phase is to form massive quiescent galaxies. Not all galaxies that quench go through a post-starburst phase; some objects, especially those that quench slowly, will transition directly from star forming to quiescent. The authors use the star formation histories from their spectral energy distribution fits to predict how the galaxies’ supercolors changed after their star formation peaked (Figure 2) and find that six of the nine quiescent galaxies went through a post-starburst phase in the past, while the other three did not. For the objects that went through a post-starburst phase, the median time spent as a post-starburst was around 600 million years.The authors can use this measured “visibility timescale” to constrain whether the post-starburst phase is important for forming massive quiescent galaxies. If the fraction of post-starburst galaxies in a sample is high, that can be for two reasons: either a larger fraction of galaxies will eventually go through a post-starburst phase, or the post-starburst phase is very long, making it easy to find post-starburst galaxies. Using the measured timescale of 600 million years and combining with results from another article, the authors find that 40% of quiescent galaxies likely went through a post-starburst phase; for the more massive end of the sample, this fraction increases to around 73% due to the shorter visibility timescale. This suggests that the post-starburst phase is very important for forming the kind of massive quiescent galaxies we see in the local universe.

While the post-starburst phase is important, the different quenching timescales present across the sample suggest that multiple pathways existed to quench galaxies at cosmic noon, similar to what has been found in less-distant galaxies and at cosmic noon in other samples. This is also supported by the fact that four of the five galaxies with sufficient data show evidence of an actively accreting supermassive black hole that may help shut down star formation in many (but perhaps not all) massive galaxies. While the precise processes that quench massive galaxies are still uncertain, one thing is clear: JWST EXCELS at solving the mystery!

Original astrobite edited by Anavi Uppal.

About the author, Margaret Verrico:

I am a fourth-year graduate student at the University of Illinois Urbana-Champaign. I study the connection between supermassive black hole transients and their host galaxies. I am also an avid knitter and reader, and I am passionate about opening up STEM opportunities for people of all backgrounds.

The post EXCELlent Work, Detectives! Solving the Murder of Star Formation in Galaxies with JWST appeared first on AAS Nova.

Editor’s Note: Astrobites is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the partnership between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original can be viewed at astrobites.org.

Title: Stars Born in the Wind: M82’s Outflow and Halo Star Formation
Authors: Vaishnav V. Rao et al.
First Author’s Institution: University of Michigan
Status: Published in ApJ

Outflows and Star Formation

Some galaxies, known as starburst galaxies, form stars at exceptionally high rates. High star formation rates mean, you guessed it, lots of new stars! The most massive stars live comparatively short lives and can die in a brilliant cosmic explosion known as a supernova. So, when you have a starburst galaxy, you get lots of young stars, a fraction of which produce supernovae. While these stellar explosions occur on relatively small scales, they can collectively drive galactic-scale expulsions of gas and dust known as outflows.

Outflows expel gas laden with metals out of the galaxy, playing a pivotal role in the evolution of galaxies. They can also drive additional star formation in the areas surrounding the galaxy, which is exactly what today’s authors are interested in. Messier 82 (M82), a quintessential local starburst galaxy, is the focus of today’s article (see Figure 1). M82’s proximity makes its spectacular outflows a prime testing ground for studying the impact of outflows on a galaxy and its surroundings.

Figure 1: An image of the local starburst galaxy M82, taken by the Subaru Telescope. The Hubble Space Telescope field encompassing the Southern Arcs is highlighted as a green box. [Rao et al. 2025]

The Southern Arcs of M82

The main focus of today’s article are arc-like groups of stars called the Southern Arcs that are located near M82’s southern outflow. Using photometry from the Hubble Space Telescope, today’s authors derive star formation histories for the Southern Arcs, with the goal of understanding the impact that M82’s outflows have had on star formation in its halo. Figure 1 shows the Southern Arcs region of M82 highlighted in green alongside an image of M82.

Star Formation Histories

If you’ve ever taken an astronomy class, you’re probably familiar with the Hertzsprung–Russell (HR) diagram. HR diagrams are one of the most powerful tools available to astronomers, as they encode a ton of information regarding populations of stars and their formation. In practice, astronomers can construct HR diagrams using resolved stellar populations. That is, if you can resolve individual stars in a galaxy, you can construct a color–magnitude diagram, which is essentially the HR diagram you may be familiar with, but uses observed properties as a proxy for temperature (color) and luminosity (magnitude).

Today’s authors use the color–magnitude diagrams of the Southern Arc to derive star formation histories for the region. Star formation histories describe the star formation rate as a function of time, providing an insight into when and how stellar populations formed. To derive the Southern Arc star formation histories, the authors use the MATCH color–magnitude diagram fitting code, which determines the combination of stellar populations that reproduce the observed color–magnitude diagram, accounting for observational biases along the way. Figure 2 shows the star formation histories obtained using three different stellar evolution models. The authors find that about 85% of the stellar mass in the Southern Arc field formed sometime between 70 and 150 million years ago before star formation slowed down. About 30 million years ago, star formation picked up again, producing the rest of the stellar mass in the Southern Arc field.

Figure 2: The star formation history (left) and cumulative star formation history (right) of the Southern Arc region. Different stellar evolutionary models are marked in different colors. The star formation history shows the star formation rate as a function of time, while the cumulative star formation history shows the buildup of the stellar mass as a fraction of the total mass observed now. [Rao et al. 2025]

So, What’s the Deal with the Southern Arcs?

The authors explore two mechanisms that could explain the star formation histories. In the first scenario, M82’s outflows trigger star formation when impacting the cooler circumgalactic gas. When the outflow shocks collide with the cooler gas, it causes it to collapse and form stars. In the second scenario, star formation is occurring within the outflows themselves. Figure 3 is a schematic of the two proposed mechanisms.

Figure 3: A schematic outlining the two possible mechanisms for the formation of the Southern Arcs. The left panel shows the scenario in which shocks produced by the outflow collide with gas in the circumgalactic medium, triggering star formation. The right panel shows the scenario in which star formation is triggered within the outflow itself. [Rao et al. 2025]

Original astrobite edited by Jessie Thwaites.

About the author, Drew Lapeer:

Drew is a first-year PhD student at the University of Massachusetts Amherst. They are broadly interested in the evolution of galaxies, with a focus on the impact of cosmic feedback on the galactic ecosystem. In their free time, they enjoy reading, rock climbing, hiking, and baking!

The post Outflows, Shocks, and Star Formation in Messier 82 appeared first on AAS Nova.