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An illustration of an M-dwarf star covered in starspots

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

Title: Transient Corotating Clumps Around Adolescent Low-Mass Stars from Four Years of TESS
Authors: Luke G. Bouma et al.
First Author’s Institution:
California Institute of Technology
Status: Published in AJ

The flow of time gently pushes us all downstream, making the present past and the future present. So much of science, and even life, is trying to understand how time will change what we experience and observe. What remains the same? Is this something stable that we can rely on? What changes? Is this moment precious? Just a fleeting incident? And how can we tell the difference? These are hard questions, no matter the subject. Today’s authors are unfazed by the challenge and seek to understand what can be said about a mysterious group of M-dwarf stars that seem to be stable in their changing!

Complex Periodic Variables

The subjects of today’s study are complex periodic variables, a new name for objects previously called complex rotators and, before that, scallop-shell objects. While the name has changed, the type of object the name describes hasn’t. Complex periodic variables are all young (less than 200 million years old), rapidly spinning (rotation periods of less than two days), small M-dwarf stars. Only about 150 of them are known, and each has been associated with groups of other young stars, ranging from 2 to 200 million years of age.

What makes complex periodic variables stand out from the crowd in their young groups are their light curves: the measure of their brightness over time. Young stars tend to have big, dark starspots on their surfaces that rotate into and out of view. Starspots normally produce a smooth repeating pattern in the light curve, varying at the spin rate of the star. However, complex periodic variables’ light curves show sudden, sharp repeating features rather than smooth ones. This can’t be caused by starspots alone, but like starspots, these repeating features are stable for weeks and seem to change with the star’s rotation. These sharp features are thought to be caused by either clumps of material orbiting at the right distance to be phased with the star and blocking starlight, or material in prominences formed off the surface of the star due to its magnetic field (Figure 1).

diagram illustrating the clumps of material in orbit or trapped in the star's magnetic field

Figure 1: Two possible physical causes for the complex light curve shapes. The spotted star is seen with clumps of material orbiting around it (left), or with material trapped in the star’s magnetic field as prominences off the surface (right). [Adapted from Bouma et al. 2024]

What Remains the Same? What Changes?

To get a better handle on how stable these complex variations are, and to potentially find some more, the authors searched the two-minute-cadence data from the Transiting Exoplanet Survey Satellite (TESS) for objects with complex variability and found 50 of them. Many of the objects were previously known, but some were new, and the 50 objects served as a great data set for looking at the complex shapes of the light curves. Of the 50, a subset had observations at least two years apart. The authors show the difference in the repeating light curve shape for 27 of the objects in Figure 2.

light curves of 27 complex periodic variables as seen by TESS

Figure 2: Twenty-seven complex periodic variables that have TESS observations separated by at least two years. For each object there are two panels, showing first the earlier and then the later observations. The x axis is the rotational phase, or when the star is in the repeating pattern, and the y axis is the percent change in brightness. Tag yourself, I’m TIC 264767454. [Bouma et al. 2024]

The authors found, for the most part, that the objects’ light curves were still showing complex shapes in the later observations, but the shapes were different between years. Furthermore, some stars (TIC 201898222, TIC 404144841) actually do lose all complexity between observation windows. So while the complexity is stable over the course of weeks or months, it looks like things do change over the course of years.

To examine that further, the authors did a deep dive on a particular star: LP 12-502. They broke up the light curve patterns even further; now, instead of looking at month-long TESS observation sectors, they look at a specific number of cycle repetitions for the object (Figure 3). This finer subdivision revealed that even after every 10–20 cycles of rotation there were subtle, and sometimes dramatic, changes in the shape of the light curve. Furthermore, they were able to point to noticeable flares occurring before some of the changes in the light curve, potentially pointing to a link between flares and changing light curve shape.

light curves of the complex rotator LP 12-502

Figure 3: The light curves of the complex rotator LP 12-502 broken up over particular cycles of rotation (given in the title of each panel). Gaps in coverage are caused by TESS’s observation windows. The patterns of complexity shift and change subtly even over these short time frames. [Bouma et al. 2024]

The River of Time

river plot for the star LP 12-502

Figure 4: A river plot for the star LP 12-502, showing the change in brightness as a function of cycle on the y axis and rotational phase on the x axis. The light curve has been sliced into cycles of rotation and then converted into strips of color stacked on top of each other. [Adapted from Bouma et al. 2024]

The authors concluded that the complex variability of the complex periodic variables changes on multiple time scales, and even more analysis needs to be done. To really drive the point home, they made a series of river plots, such as those shown in Figure 4. Instead of a light curve, the change in brightness in each cycle is laid out as a strip of color, and then the strips from each cycle are stitched together on top of one another. It creates a river of time, each cycle a slice of the river seen from above. This view shows the variation as crests or waves. From our “bird’s-eye view,” we can see the texture of the “water” or brightness changes.

I find these plots peaceful; I want to be sat in an inner tube, floating along their surface. Tough questions of change become easier when considered in comfort. The ripples of brightness ferry me down the lazy-flowing river of time, the gentle motion meandering my thoughts around the potential cause of these mysterious objects.

Original astrobite edited by Ivey Davis.

About the author, Mark Popinchalk:

I’m a postdoc at the American Museum of Natural History. I study the age of stars by measuring how quickly they rotate. I enjoy ultimate frisbee, baking bread, and all kinds of games. My favorite color is sky-blue-pink.

Hubble image of the galaxy Messier 87

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

Title: Distinguishing Active Galactic Nuclei Feedback Models with the Thermal Sunyaev–Zel’dovich Effect
Authors: Skylar Grayson et al.
First Author’s Institution:
Arizona State University
Status: Published in ApJ

Let’s imagine the evolution of the universe as a regular day on Earth. If the Big Bang was when the clock struck midnight, then the cosmic “dawn” is when the first stars and galaxies started to light up our universe. After the cosmic dawn ended, galaxies continued to form stars and grew rapidly throughout the morning. Around cosmic “noon,” most galaxies in our universe were huge and even hosted massive black holes at their centers. However, as the afternoon rolled by, the universe seems to have gotten pretty lethargic in forming stars and expanding galaxies. Looks like the universe is fond of taking an afternoon siesta!

What caused the rapidly forming galaxies in the universe to suddenly slow down and stop star formation? Why didn’t they continue growing to bigger and bigger sizes? Some astronomers believe this cosmic downsizing is due to feedback from active black holes (also known as active galactic nuclei). This feedback typically occurs when the black hole spews out fast winds called outflows that contain gas and dust and expel it to large distances, sometimes far beyond the galaxy itself. Such outflows can clear the galaxy of any star-forming gas and effectively shut down star formation.

There is still much mystery around exactly how the active galactic nucleus feedback affects star formation in the galaxy. Nevertheless, various feedback models are often incorporated into simulations of galaxy formation to understand how the universe evolved. This leads to contrasting theories of galaxy evolution, so comparing the simulation feedback models to actual observations of feedback effects is essential to improve our understanding of active galactic nucleus feedback.

It is hard to directly observe an active galactic nucleus outflow, as they can be as fast as thousands of kilometers per second and cannot be captured instantaneously. Instead, we can look for indirect evidence. Our universe is filled with a microwave background believed to be a remnant of the primordial universe. If we have some high-energy electrons from gas heated by a powerful activity, such as a black hole ejecting material, they would possibly interact with the low-energy photons from the cosmic microwave background and give the low-energy photons a slight boost. This effect, known as the thermal Sunyaev–Zeldovich effect, is a good clue of a possible active galactic nucleus feedback episode.

The authors of today’s research article set out to do look for this indirect evidence of active galactic nucleus feedback. They compare observations of galaxies with signals from the thermal Sunyaev–Zeldovich effect and signals produced by galaxy simulations with and without active galactic nucleus feedback. This can help them determine if the simulations correctly model feedback and if this feedback accurately explains the observed cosmic downsizing.

Comparing Simulations vs. Observations

The authors generate maps of the thermal Sunyaev–Zeldovich effect signal from SIMBA, which is a simulation that explores the co-evolution of galaxies and black holes. They generate one set of maps with galaxies that have active galactic nucleus feedback and another set with no feedback. They highlight a sub-sample of galaxies from the former as quiescent or galaxies that have shut down and have no ongoing star formation. There are plenty of such galaxies in the universe, and they are likely there because active galactic nucleus feedback completely removed all the gas in the galaxy and thus permanently halted star formation.

The thermal Sunyaev–Zeldovich effect can be detected in observations by looking for distortions in the cosmic microwave background from data collected by radio telescopes. (The photons of the cosmic microwave background have low energies and thus need telescopes sensitive to long wavelengths of light to study them.) The authors take radio data of a similar sample of galaxies with active galactic nuclei (including quiescent galaxies) and without. They do this at two different redshifts, z = 1 and z = 0.5. The authors matched the simulated data with suitable observational effects so they could be compared realistically.

The Compton y-parameter is then used to compare the observed signal to the simulated signal. The Compton y-parameter is defined as the number of scatterings multiplied by the energy gained per scatter instance and is a commonly used term to characterize scattering in a system. As seen in Figure 1, at z = 1, the observed data match well with the quiescent active galactic nucleus model and the active galactic nucleus model in general. The data do not line up well with the no active galactic nucleus (i.e., no feedback) model, indicating that the feedback incorporated by the simulation is successful at replicating observed properties at these redshifts.

A plot of the Compton y-parameter versus radius for observed and simulated signals

Figure 1: A comparison of observed and simulated thermal Sunyaev–Zeldovich signals at a redshift of z = 1. The observed data (black points) agree with the active galactic nucleus feedback model. [Grayson et al. 2023]

At z = 0.5, the authors compare temperature fluctuations, which also depend on the Compton y-parameter. In doing this, they find that the observed properties match the model without active galactic nuclei rather than the model with them (Figure 2). However, this could also indicate that the simulations are not correctly modeling the active galactic nucleus feedback. The simulations likely inject too much power into their feedback models at later redshifts, which can harm our understanding of the galaxy properties that they reproduce.

Plot comparing the observed and simulated thermal Sunyaev–Zel'dovich signals at a redshift of z = 0.5

Figure 2: A comparison of observed and simulated thermal Sunyaev–Zeldovich signals at a redshift of z = 0.5.  The observed data (black points) do not agree with the active galactic nucleus feedback model, but this is likely because the feedback is modeled incorrectly in the simulations. [Grayson et al. 2023]

This work highlights the importance of looking for signatures to compare the observations and simulations to improve our understanding of how galaxies evolved, specifically after cosmic noon. The benefits of this are two-fold: to see if the observations agree with our understanding of how galaxies are supposed to evolve by incorporating the physics in the feedback models, and to help us understand if the simulations are modeling the physics correctly and if any modifications are required in our knowledge of the feedback processes.

Original astrobite edited by Jack Lubin.

About the author, Archana Aravindan:

I am a PhD candidate at the University of California, Riverside, where I study black hole activity in small galaxies. When I am not looking through some incredible telescopes, you can usually find me reading, thinking about policy, or learning a cool language!

Hubble image of the galaxy NGC 5728

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

Title: Deconvolution of JWST/MIRI Images: Applications to an AGN Model and GATOS Observations of NGC 5728
Authors: M. T. Leist et al.
First Author’s Institution:
The University of Texas at San Antonio
Status: Published in AJ

Optical Instrumentation 101

When it comes to optical instrumentation in astronomy, one of the fundamental principles is the Rayleigh criterion for angular resolution: how far apart on the sky do two sources need to be for you to distinguish them from each other? For single-piece circular apertures — i.e., most apertures smaller than about 5 meters — the angular scale is famously defined as 1.22 λ/D, where λ is the wavelength you’re observing at and D is the diameter of your aperture. This equation comes from taking the Fourier transform (a translation from spatial information to spatial frequency information) of a circular aperture and describes how the aperture “responds” to light coming in. This response is more commonly referred to as the point spread function, in part because it describes how a point source (like most stars) looks on your detector. More specifically, what your detector sees is the convolution of the point source with the point spread function. An example of this is shown in Figure 1.

illustration of the process of convolution

Figure 1: From left to right: 1) A point spread function for a circular aperture made from the AiryDisk2DKernel class, a part of astropy’s convolution module; 2) a fake field of stars acting as “true sources” made using the random module in numpy and the CircularAperture class in photutils; and 3) the convolution between the point spread function and the field of stars to make a model image of how a telescope might see the star field, done using the convolve function of astropy’s convolution module. This is a very simplified version of the model image building that the authors do, and you can play around with the parameters yourself here! [Ivey Davis]

However, many large telescopes these days — including JWST — are composed of many hexagonal pieces rather than one singular aperture. This changes the shape of the point spread function significantly. Compare, for instance, the point spread function in Figure 1 to the JWST point spread functions in Figure 2; there are quite strong, repeating hexagonal features in JWST’s point spread functions, as well as spike features originating from the support structures of additional mirrors. This poses a problem: how do we distinguish sources from the point spread function effects? What happens to faint sources that lie in the brightest regions of the artifacts? And what happens to extended sources (like galaxies and nebulae) when convolved with such a point spread function? The authors of today’s article work to remove the effects of JWST’s point spread function by deconvolving the image, specifically in the context of active galactic nuclei, actively accreting regions at the center of some galaxies that have a variety of both extended features and point-source components.

illustration of JWST's point spread functions

Figure 2: Modeled JWST point spread functions of five different filters ranging from 5.6 microns (leftmost panel) to 21 microns (rightmost panel) made with the WebbPSF package. [Leist et al. 2024]

This Work

To figure out how effectively they can deconvolve a JWST image of an active galactic nucleus, the authors start by building a model image of what they expect the active galactic nucleus to look like to JWST’s mid-infrared imaging instrument, MIRI. This way, they’ll know what the deconvolved image is supposed to look like and can thus better evaluate the performance of the deconvolution methods.

Their active galactic nucleus model has four components: 1) The central region of the active galactic nucleus (panel 1 in Figure 3); 2) long, collimated outflows of dust originating from the poles of the central region, called polar outflows (panel 2 in Figure 3); 3) two cones of ionization, called ionization bicones, that are commonly seen in certain types of active galactic nuclei (panel 3 in Figure 3); and 4) the host galaxy of the active galactic nucleus, taken here to be the galaxy NGC 5728 (panel 4 in Figure 3). While the host galaxy and the ionization bicones can be resolved and are thus extended features, the polar outflows can only be resolved along one axis, and the central region appears only as a point source. This makes the model a really rigorous example for the effects of both the point spread function and the deconvolution processes.

four components of the active galactic nucleus model

Figure 3: The four components of the active galactic nucleus model used as a “real source” for the model JWST observation. [Leist et al. 2024]

After building their model to act as a “true” image, the authors then use it and the JWST point spread function to make fake MIRI images for five filters ranging from 5.6 microns to 21 microns (1 micron = 10-6 meter). It is this dataset that they use to test five deconvolution methods. Although each method is different, they all work by deconvolving over many iterations until either the size of the point spread function stops significantly shrinking between iterations or until the difference in flux of a source is different enough from the original image.

Of the five deconvolution methods used, the method that improved the point spread function the most, and did so at all wavelengths, was a method called Kraken deconvolution. Not only did it improve the point spread function the most, but it also did it in the fewest number of iterations — 22 iterations at most, while all other methods required between 29 and 105 iterations. Despite the quality of Kraken’s performance, it was not able to make the polar outflow component detectable. Regardless, the method showed true improvement in image quality and so was used to deconvolve a real JWST observation of NGC 5728 that was collected as a part of the Galactic Activity, Torus, and Outflow Survey (GATOS).

The original JWST image of NGC 5728 and the results of running the Kraken deconvolution method are shown in Figure 4. The Kraken method deconvolved the JWST image both in a similar number of iterations as well as to a similar quality as it had for the mock observation, meaning the results are consistent with expectations! The results also reveal extended structure to the south east of the central region, as shown in Figure 4. Although this feature had been observed in 2019 with other instruments, it would be undetectable in JWST images without the deconvolution technique.

two views of the spiral galaxy NGC 5728 showing the result of the deconvolution method

Figure 4: The JWST observation of NGC 5728 as observed by the MIRI instrument (left) and the result of deconvolving the image using the Kraken method (right). Although the outflow is indistinguishable in the original JWST image, it is readily observed to the bottom left of the central region in the deconvolved image. [Leist et al. 2024]

Overall, the authors demonstrate the power — and necessity — of deconvolution for doing science with JWST data. It’ll be interesting to see how the deconvolution methods’ performance holds up at shorter wavelengths, as well as to find other fun features that might have already been missed in previous observations!

Original astrobite edited by Isabella Trierweiler.

About the author, Ivey Davis:

I’m a fourth-year astrophysics grad student working on the radio and optical instrumentation and science for studying magnetic activity on stars. When I’m not crying over radio-frequency interference, I’m usually baking, knitting, harassing my cat, or playing the banjo!

X-rays, dark matter and galaxies in cluster Abell 2744

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

Title: Deep Chandra Observations of Abell 2495: A Possible Sloshing-Regulated Feedback Cycle in a Triple-Offset Galaxy Cluster
Authors: Luca Rosignoli et al.
First Author’s Institution:
University of Bologna and National Institute for Astrophysics, Italy
Status: Published in ApJ

Galaxy clusters are the biggest gravitationally bound objects in the universe. They have three main components: galaxies, their surrounding dark matter halos, and hot gas between the galaxies known as the intracluster medium. It’s the interaction between these components that makes galaxy clusters so interesting, and also (we think) significantly affects galaxy formation and evolution as a whole. As an example of this interaction, many galaxy clusters have a supermassive black hole inside their central galaxy (known as the brightest cluster galaxy) that’s acting as an active galactic nucleus — it’s being fed so much material by the galaxy and the cluster that it can’t contain it all, and it’s spitting material and energy back out into the cluster.

Thanks to gravity, each of these three components in a galaxy cluster should be centered on the same point in space — the dark matter forms a kind of gravitational “pit” that both the intracluster medium and the galaxies themselves can’t help but be pulled into. Other forms of energy (like heat keeping the intracluster medium puffed out, or leftover kinetic energy keeping the galaxies orbiting) prevent the intracluster medium and galaxies from collapsing entirely into the center of the pit, but they’re still definitely centered on where the pit is deepest. It’s very interesting, therefore, when the mass centers in galaxy clusters aren’t fully lined up. In today’s article, the authors explore one of these cases.

A Misaligned Galaxy Cluster

The cluster explored in this study, called Abell 2495, has been known about for a long time — it was discovered in 1998 through an X-ray search for clusters using the ROSAT satellite. Since then, a wide variety of data have been collected about this cluster at many different wavelengths that trace different components of the cluster. The data that are most relevant here are radio data from the Very Large Array at a frequency of 5 gigahertz (tracing activity from the active galactic nucleus inside the central brightest cluster galaxy) and optical images (showing the positions of the galaxies in the cluster) from the Hubble Space Telescope.

In addition to the ROSAT observations used to discover the galaxy cluster, there were also Chandra X-ray Observatory data of Abell 2495. However, the observations from both telescopes had very low sensitivity, making it difficult to distinguish any features in the X-ray-emitting gas. The authors present six new deep Chandra observations in this article, vastly improving the sensitivity of the X-ray observations (which trace the hot gas of the intracluster medium). Figure 1 combines several of these different types of observations, plotting them together as contours. X’s mark the various centers of the galaxy cluster components.

observations of the galaxy cluster Abell 2495

Figure 1: Multiwavelength observations of the galaxy cluster Abell 2495. The greyscale (and black contours) shows the X-ray emission from the hot gas in the galaxy cluster, the red contours show hydrogen gas (Hα), and the green contours show radio emission coming from the supermassive black hole inside the cluster’s central galaxy. The X’s show the center of the cluster determined in different ways: the black X is the center of the X-ray emission, the red X is the center of the Hα emission, the yellow X is the center of mass of the cluster (determined using the positions of the individual cluster galaxies), and the green X is the center of the brightest cluster galaxy. [Rosignoli et al. 2024]

A Gassy History

In order to understand why the galaxy cluster isn’t centered properly, we need to look back at the history of its movements. Luckily, there’s an easy way to do that — the X-ray light, which traces the intracluster medium, contains signatures of any strange happenings in the cluster’s recent history. This is because the intracluster medium is normally smooth and symmetric, but it takes a while to settle after disturbances. The authors’ new Chandra observations are thus perfect for finding out what’s going on in Abell 2495.

Some of the more interesting features the authors found in the X-ray observations are shown in Figure 2. The four cavities (the dark regions in Figure 2a) are indicators that the active galactic nucleus inside Abell 2495’s brightest cluster galaxy had periods in the past where it was extra active, emitting enough energy to blast holes in the intracluster medium. What’s particularly interesting is how some of these cavities line up with the current position of the emission from that active galactic nucleus (shown with the blue contours). The authors also found a significant density jump on one side of the intracluster medium (the light region in Figure 2b), suggesting that some force has piled up the gas on one side of the cluster in a dense cold front.

depictions of the cavities and cold front seen by the authors in this work

Figure 2: Interesting features in the X-ray observations of Abell 2495. In the left panel, the four cavities discovered by the authors are circled in green. These are areas of the otherwise smooth intracluster medium where very little material is present. In the right panel, a fitted profile of the intracluster medium has been removed, showing a large asymmetry with a significant jump — a “cold front.” [Adapted from Rosignoli et al. 2024]

temperature map of the intracluster medium of Abell 2495

Figure 3: A binned temperature map of the intracluster medium inside Abell 2495, measured using the X-ray emission. The regions outlined in black are cold enough to condense significantly in a reasonable timescale. [Adapted from Rosignoli et al. 2024]

Finally, the authors bin up their X-ray image in 2D space so they can use the spectra of the X-rays to calculate the temperature of the intracluster medium gas in each bin. Figure 3 shows the resulting temperature map. The regions of the map outlined in black have gas temperatures below a key threshold that means it can likely condense enough to fuel the active galactic nucleus in the center of the cluster. This also means that the cluster is cool-core (see an astrobite about this here). Interestingly, there’s also an extended tail of cold intracluster medium spiraling away from the center of the cluster, in a position that almost lines up with the cold front discussed above.

The Cosmic Bathtub

The authors of this article believe that all of this evidence points to one very interesting phenomenon: the galaxy cluster is sloshing. This is a phenomenon that’s appeared widely in simulations and has also been observed several times. It happens when some gravitational disturbance, typically a small “sub-cluster” of galaxies, passes by the cluster. This disturbance pulls both the dark matter and the intracluster medium in its direction. The dark matter can pass right through anything in its way, but the intracluster medium of the sub-cluster collides with the other intracluster medium of the other cluster, creating cold fronts, and so the two components get separated. This results in offsets between the centers, just like the ones observed in Abell 2495! As the disturbance passes, the dark matter and intracluster medium fall back towards the center of the cluster, setting up an oscillation just like water sloshing in a bathtub.

The Scientific Relevance of Baths

The reason why sloshing is so fascinating is because it could be regulating active galactic nucleus feedback. Normally, galaxy clusters act out the feedback cycle described in the introduction — the intracluster medium cools and collapses onto the active galactic nucleus, which in turn gets extra active and heats the intracluster medium back up again. In this case, however, the authors believe that the sloshing in this galaxy cluster is driving the whole feedback cycle. There is some quantitative evidence for this — the time the cluster takes to slosh is fairly consistent with the time between the formation of the different cavities in the intracluster medium, and the size of the X-ray cavities is consistent with the amount of energy that would be involved. If this is the case, sloshing could drive the whole life cycle of the cluster.

Sloshing Towards the Future

The authors have done a lot of analysis to come up with these findings, but there are some limitations in the data that mean significant uncertainties remain. Better X-ray data could uncover more details about the X-ray cavities, helping to narrow down when they were formed and how much energy was required to create them. A clearer picture of the cold front would allow the intracluster medium in the cluster center to be examined in more detail. It would also be helpful to get a larger sample of clusters with active galactic nucleus feedback potentially regulated by sloshing to understand if this is a common process, or if Abell 2495 is unique. Either way, this is a fascinating addition to our picture of one of the most extreme places in the universe — the center of a galaxy cluster.

Original astrobite edited by Lina Kimmig.

About the author, Delaney Dunne:

I’m a PhD student at Caltech, where I study how galaxies form and evolve by mapping their molecular gas! I do this using COMAP, a radio-frequency line intensity mapping experiment based in California’s Owens Valley.

photograph of the Small Magellanic Cloud

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

Title: A Galactic Eclipse: The Small Magellanic Cloud is Forming Stars in Two, Superimposed Systems
Authors: Claire E. Murray et al.
First Author’s Institution: Space Telescope Science Institute
Status: Published in ApJ

The Small Magellanic Cloud is one of the most-studied galaxies in our universe due to its proximity (just 200,000 light-years away), and because its structure and elements are so distinctly different from our own Milky Way galaxy. For example, it has a lower metallicity and is therefore an excellent laboratory for understanding the physics of the interstellar medium at lower-metallicity conditions. The interstellar medium is the gas and dust between stars within a galaxy (as opposed to the circumgalactic medium, which is the gas and dust between galaxies).

Despite its potential utility as a nearby parallel probe of galaxy evolution, the Small Magellanic Cloud’s structure is still relatively unknown. For example, stars with different ages appear to be distributed through the galaxy differently. The oldest stellar populations appear to be distributed spherically without rotating, whereas younger stars are rotating. Furthermore, the structure of the Small Magellanic Cloud’s interstellar medium indicates that it has potentially been severely disrupted by recent interactions with the Large Magellanic Cloud. Modeling the evolution and interaction history of the Small Magellanic Cloud from its conflicting observational constraints will help inform our understanding of its future.

The authors combined observations of neutral hydrogen (HI) gas emission and radial velocity (velocities along our line of sight) measurements to model the Small Magellanic Cloud’s evolutionary history. Because the interstellar medium of the galaxy is dominated HI emission (hydrogen gas that has not been ionized by any astrophysical source like a star), observing this atomic gas allows us to trace the bulk properties of the interstellar medium. As shown in Figure 1, the radial velocities derived from the emission of the HI gas show two distinct structures moving away from us — one at a higher speed and one at a lower speed.

radial velocity map of the Small Magellanic Cloud

Figure 1: The radial-velocity map derived from HI emission of the Small Magellanic Cloud shows two distinct structures moving away from us, one at high radial velocity (~170 km/s) in red on the left, and one at low velocity in blue on the right (~130 km/s). The magenta cross represents the center of the Small Magellanic Cloud. [Adapted from Murray et al. 2024]

Although the HI emission can provide a window into the velocity structure of the Small Magellanic Cloud, it cannot probe the relative distances of sources. The authors instead use a map of extinction from dust to look at the relative spatial order of stars along the line of sight (i.e., in “front” or “behind” the dust). A star at a location with high extinction is behind more dust than a star at a location with low extinction. If you also assume the dust and stars are located in similar locations, then you can trace the actual locations of the stars. For example, a star behind a lot of dust is probably farther away from us (“behind”) than a star in front of the dust (“front”). To estimate the extinction towards each source, the authors use the Rayleigh Jeans color excess method, which assumes the amount of dust based on an observed color. A star with a redder color is assumed to suffer from higher extinction.

maps showing the locations of the stars in the front and behind structures

Figure 2: The stars in the “front” structure and stars in the “behind” structure, derived from the extinction and radial velocities of these stars. [Murray et al. 2024]

Figure 2 shows the final inferred maps of the “front” and “behind” structures of the Small Magellanic Cloud. Using metallicities derived from the APOGEE survey, the authors also determined that generally the stars in the front component are higher metallicity than the stars in the behind component.

In conclusion, these results indicate the Small Magellanic Cloud is clearly composed of two distinct star-forming systems. One possible explanation for these results is that these two systems are actually remnants of different galaxies, potentially indicated by the fact that they have different metallicities. Alternatively, the authors propose that the “behind” structure is actually tidal debris from an interaction with the Large Magellanic Cloud. Ultimately, further observational constraints, such as direct distance measurements to the interstellar medium components and simulations of the Small Magellanic Cloud’s evolution, will help paint a more detailed picture of its history and future.

Original astrobite edited by Jessie Thwaites.

About the author, Abby Lee:

I am a graduate student at UChicago, where I study cosmic distance scales and the Hubble tension. Outside of astronomy, I like to play soccer, run, and learn about fashion design!

Illustration of a rogue planet floating through space

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

Title: Tilted Circumbinary Planetary Systems as Efficient Progenitors of Free-Floating Planets
Authors: Cheng Chen et al.
First Author’s Institution: University of Leeds
Status: Published in ApJL

All planets orbit stars.

Okay, that was a lie. But a hundred years ago, it seemed super true — we only knew of the planets around our own Sun.

Then, later, it still seemed decently true. Astronomers started finding planets around other stars in the 1990s (which the opening statement supported!). At this point, though, the field had at least begun to consider that there could be a population of “free-floating planets”: planet-sized bodies wandering through space, unaffiliated with any particular star.

Because our methods of finding planets, at that point, relied on the dimming or shifting of a host star’s light, these rogues seemed unknowable (if they existed at all). So perhaps the statement could be modified to read “all planets that we can detect orbit stars,” which would still maybe hold a little truth.

As our telescopes (and our techniques) have improved, though, it has become very clear that the statement is… irredeemably untrue. Since the turn of the century, largely through a more direct observational technique called microlensing, we’ve gradually uncovered a rich population of free-floating planets dancing in the vast cosmic dark. (Just in the past few months, JWST revolutionized yet another sub-field with its discovery of free-floating pairs of planets, but that’s a topic for another ’bite.)

Young, Wild, and Free: The Instability of Newborn Planetary Systems

But where do these unbound beauts come from? There are a few proposed mechanisms, but one of the most likely methods involves planets forming around stars (like normal!) but subsequently being violently ejected from their bound orbits.

The period immediately following the formation of the planets is often suspected to be pretty messy. In the solar system, we think there was a major instability right after planets formed, in which our eight major planets interacted strongly with each other before settling into roughly the orbits they have today.

In fact, simulations of such an instability suggest that there could have originally been a fifth giant planet in the outer solar system, formed alongside Jupiter, Saturn, Uranus, and Neptune. During the period of unrest (often called a “late instability” due to its occurrence after planet formation was complete), this planet would have been “kicked” off of its original orbit — and ejected from the solar system altogether — through interactions with the other giants. Any such planet would, at this point, be a free-floater.

Today’s article talks about these sorts of “planet–planet scattering” ejections, but it doesn’t talk about the solar system (nor any particular observed planetary system). Instead, in a common theorist tactic, the authors consider a whole class of planetary systems: those existing around two stars rather than one.

Two Stars Are Better than One (for Planetary Ejections)

Just as planets can orbit stars, stars can orbit each other. And if their orbit is small enough, protoplanetary disks — and, thereafter, planets — can form around the pair of them. One of the most famous (fictional) examples of such a system is Star Wars’ Tatooine, which enjoys an iconic double sunset courtesy of the binary stars around which it orbits. A diagram of planets orbiting a pair of stars is shown in Figure 1.

diagram depicting the setup described in this work

Figure 1: In the setup described in this work, two planets (green) orbit around a binary pair of stars (yellow). The planets’ orbits are separated by a dimensionless distance parameter Δ — the distance between the orbits in terms of their mutual sphere of influence. The orbits start off aligned with each other (though this can change over the course of a simulation!), but they aren’t necessarily aligned with the orbit of the stellar binary. The authors test the stability of systems spanning a range of different inclinations (ip) and separations (Δ). [Mark Dodici]

Ejections in planetary systems around single stars, though possible, probably don’t happen often enough to explain how common free-floating planets are. In dynamics, things can get a lot more complicated when you add just one extra body (see, e.g., the n-body problem!), so the authors of today’s article wonder if adding another star will provide enough complication to eject more planets.

(This isn’t an unreasonable addition; binary pairs — and even binaries close enough to allow for planet formation — make up a significant fraction of all stars, so they’re definitely worth modeling.)

Specifically, the authors simulate planetary systems where two planets, separated by some distance, trace out orbits that are inclined relative to the orbit of the central binary stars. (See Figure 2 for examples of different orbital inclinations.) They test systems with a range of initial inclinations, for a range of planet separations, for a select few choices of unequal planet masses. Through these simulations, they cover a broader range of parameter space — i.e., the ranges of values that a real-life system could have for its properties — than previous work.

Examples of planetary systems with a few different inclinations

Figure 2: Examples of planetary systems with a few different inclinations, from prograde to retrograde. From this work, the orbits that most commonly lead to ejections are neither prograde, nor polar, nor retrograde — that is, systems with ip ≈ 45° see ejections most often. [Mark Dodici]

For all planet masses, the systems most likely to yield ejections involve close-together planets with orbits that are neither well-aligned (“prograde” or “retrograde”) nor perpendicular to the binary (“polar”). (Results are presented in Figure 3.) Systems are much more likely to yield ejections when they have at least one planet more massive than Neptune, though the less-massive planet, if there is one, is always the one ejected. Eccentric binary stars — those orbiting each other on oblong, elliptical paths rather than perfect circles — also boost the ejection rate of the planets around them.

Plots showing which parameters resulted in an ejection and which parameters resulted in a stable system

Figure 3: For a given system, the authors reported whether each set of parameters was stable (blue) or resulted in an ejection (light red). On the left, we see a range of systems with different inclinations and planet separations around circular binaries. On the right, we see the same range of inclinations and separations around eccentric binaries, with e = 0.8. All systems in these plots have a Jupiter-mass outer planet with an Earth-mass planet orbiting interior to it. Dashed, colorful lines on these plots point out a few mean motion resonances (see text), which are known to be unstable in tilted planetary systems. In the article, the authors show similar plots for different combinations of planet masses; these generally show similar trends in ejection vs. stability for various inclinations and separations. [Adapted from Chen et al. 2024]

There are some fun planetary dynamics underlying these results! From the basic law of gravity, we know that more-massive, closer planets have more of an impact on others in their own system; it makes sense, then, that more-massive, closer planets cause more ejections. Oddly inclined orbits (i.e., not prograde, polar, or retrograde) often lead to long-term oscillations in inclination; if the two planets take on different inclinations over the course of their lifetime, von Zeipel-Kozai-Lidov cycles can sometimes increase their eccentricity until one of them becomes unbound. And although ejections were most common among close-together planet pairs, they aren’t impossible with a bigger gap; instability can occur near mean motion resonances, where the periods of the two planets are integer multiples of each other (e.g., at the 2:1 resonance, the inner planet orbits twice as quickly as the outer).

A Few More Free-Floaters

That all said, this isn’t an article about planetary dynamics — it’s about free-floating planets. These results show that a decent variety of planets can be ejected from planetary systems around binary stars, yielding a decent total occurrence rate for free-floaters from this mechanism. This is a decent win for planet–planet scattering as a potential source of unbound planets!

As in any theory article, there’s work to be done to constrain exactly how often we expect these Tatooine-like systems to eject planets, as well as the range of masses we would expect these ejected planets to have. And in reality, the total population of free-floating planets almost certainly comes from some combination of several effects, each contributing their own subpopulations. But with recent and upcoming observations from Subaru Hyper Suprime-Cam, TESS, Roman, and JWST, there will certainly be no lack of data to which models like these can be compared.

Original astrobite edited by Sahil Hedge.

About the author, Mark Dodici:

Mark is a PhD student in astronomy and astrophysics at the University of Toronto. His space-based interests include planetary systems, from their births to their varied deaths, as well as the dynamics of just about anything else. His Earth-based interests include coffee, photography, and a little bit of singing now and again. You can follow him on X (@MarkDodici) or on BlueSky (

Artist's impression of a protoplanetary disk

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

Title: Disentangling CO Chemistry in a Protoplanetary Disk Using Explanatory Machine Learning Techniques
Authors: Amina Diop et al.
First Author’s Institution: University of Virginia
Status: Published in ApJ

As the birthplaces of planets, protoplanetary disks hold many useful clues for how planets form and evolve. One of the pieces of information we can gather from disks is composition — to date, more than 35 different molecules have been detected in disks (see this bite for a summary of how these molecules are detected). Ideally, we want to be able to connect the abundances of different molecules to disk properties, such as total mass. This is more easily said than done, however, as a lot of different factors can alter the observed abundances of different molecular species. For example, one of the molecules we arguably have the best chemical understanding of is CO (carbon monoxide), which has been studied in a number of protoplanetary disks. Nonetheless, measured CO abundances tend to be lower than expected, indicating that effects such as local variations in temperature and dust distributions throughout the disk may have a significant impact on the observed abundances.

In order to try to understand the connection between local disk environments and molecular abundances, studies typically simulate a disk many times over, running through a huge range of different disk properties. As you might imagine, this approach is slow and takes a lot of computational resources. Today’s authors are trying a new method, using machine learning to more quickly find connections between disk properties and abundances. The goal is to use machine learning to search large parameter spaces and understand which physical disk properties are most important to creating and destroying CO in disks.

CO abundances relative to hydrogen throughout the disk

Figure 1: The initial CO abundances relative to hydrogen throughout the disk. You can think of r and z as x and y coordinates in the disk, where the star would be at (0,0). [Diop et al. 2024]

Making a Disk Model

The authors start with a disk around a T Tauri star (a type of young variable star that could be a typical host to disks). They assign a corresponding temperature and X-ray luminosity to the star, and they assume the disk is a mix of gas and dust, with both large and small dust grains. After assuming some initial compositions for the disk, they then apply a complex chemical code, including several thousand (!) different reactions to the modeled disk to calculate the CO abundances over 3 million years. Figure 1 shows the initial distribution of CO in one of these models.

Bring on the Machine Learning!

The authors consider each CO abundance calculated in the disk as a separate sample (so each point in Figure 1 is a sample) and check how each point’s CO value compares to local disk properties at that point, such as temperature, ultraviolet flux, and gas density. Figure 2 shows one example, with CO abundances plotted for specific temperatures and gas densities throughout the disk.

CO to H abundance ratio as a function of gas density and temperature

Figure 2: The disk from Figure 1, a million years later. The points now show the CO abundance ratio for the corresponding temperature and gas density at each point in the disk. [Adapted from Diop et al. 2024]

To test the relationship between CO and the selected disk parameters, the authors assume their data follow a polynomial regression, where the CO abundance is a function of the different disk parameters, each multiplied by some coefficient. They then use a machine-learning algorithm to solve for the coefficients that best recreate their modeled disk. The resulting coefficients indicate which parameters are most important to determining the resulting CO abundances.

The Top Contributors

After running their model and machine-learning algorithms, the authors found that the log of gas density is the most important factor in determining CO abundance. They note that this makes sense because the abundance of CO is typically related to the abundance of hydrogen (H2) gas in a disk. Interestingly, they also find that the log of gas density squared decreases with increasing CO. This is because at higher densities there is a competing effect of depleting CO as it freezes into ice in dense, cold regions of the disk.

Plot of correlation coefficients

Figure 3: The coefficients indicating positive or negative correlation of the parameters listed on the left with CO abundance. Tgas and ngas correspond to the gas temperature and density, while FUV is the ultraviolet flux from the star, ζcr describes the cosmic ray rate, and ζxr is the X-ray rate. [Diop et al. 2024]

All together, 10 factors are considered as disk parameters, and Figure 3 shows the different coefficients for each, where positive coefficients indicate positive correlations with CO abundance and negative coefficients indicate negative correlations. The authors find that the negative correlations (aside from the strong effects of gas density) are stronger than the positive ones, indicating that disks may tend to destroy CO overall. They also find that other aspects such as the initial carbon-to-oxygen ratios and rates of cosmic rays can also influence the relative strengths of the different factors.

Know Thy Disk

This study provides a great proof of concept for the ways in which machine learning can be used to identify complex relationships in datasets — a particularly useful tool for studying the large number of entwined reactions involved in chemistry. It also brings up the important issue that molecular abundances can be influenced by many different factors, so we should consider disks as holistically as possible and always think about how multiple parameters may interact for a particular disk!

Original astrobite edited by Lucas Brown.

About the author, Isabella Trierweiler:

I’m a fifth-year grad student at UCLA. I’m interested in planet formation, and I study the compositions of exoplanets using polluted white dwarfs. In my free time I like knitting, playing train games, and growing various fruit trees.

poster advertising the Rainbow Village at AAS 243 and the logos of the four supporting organizations

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

Written and edited by Rainbow Village partners Arianna Long, Nicole Cabrera Salazar, Ashley Walker and Junellie Gonzalez Quiles.

Are you a person of color or an ally to people of color in astronomy and related fields? Have you wanted to connect with other people of color at the American Astronomical Society (AAS) meetings but found it difficult to do so? Have you wanted a space throughout the entire meeting to connect with other folks and receive support and resources for your career? If so, you are not alone!

This January, we are launching The Rainbow Village at #AAS243, an initiative born out of the need to provide a permanent space throughout the AAS meeting where people of color can gather, support each other, and obtain access to organizations that are directly serving people of color in astronomy.

The Rainbow Village in Context

photograph of Fred Hampton

Fred Hampton speaking at a rally in Chicago in 1969. [Wikipedia]

After recognizing the overlap in class struggles among different racial groups, Fred Hampton, the leader of the Chicago Black Panther Party, began to form alliances with organizations like the Latinx group, the Young Lords Organization, and working class white people of the Young Patriots. This alliance was named the Rainbow Coalition and it targeted the structural inequalities that Chicagoans faced in the system through community building and collective action.

Following this historical precedent, Dra. Nicole Cabrera Salazar, an astronomer and STEM equity advocate at Movement Consulting, came up with the new initiative of the Rainbow Village at AAS. The Rainbow Village was created with the similar intention of bringing people of color from different demographics to gather, meet, and support each other. It will be a place to form meaningful connections and obtain direct access to resources about how to navigate the field of astronomy as a person of color.

The Rainbow Village is a collaboration of four organizations who have partnered to provide this space at AAS:

  • AAS Committee on the Status of Minorities in Astronomy (CSMA): A committee of the AAS dedicated to enhancing the participation of underrepresented minorities in astronomy at all levels of experience.
  • #BlackinAstro: A community and grassroots organization formed to celebrate and amplify Black scientists and engineers within the space community.
  • VanguardSTEM: An online platform and empowered community devoted to connecting and uplifting emerging and established women of color, girls of color, and non-binary people of color in STEM.
  • League of Underrepresented Minoritized Astronomers (LUMA): A peer mentoring community for Black, Indigenous, and Latinx women of color in their graduate degrees or beyond in their careers in astronomy, physics, and related fields.
Logos of the organizations that have partnered to create the Rainbow Village

Logos of all four organizations that have partnered to create the Rainbow Village at AAS.

Each organization will provide resources for people of color to succeed in their careers while highlighting their meaningful contributions to creating a safe and inclusive place in the field of astronomy.

How can I participate in the Rainbow Village at AAS?

The Rainbow Village is a furnished booth and will be located next to the AAS Pavilion at the New Orleans Convention Center Exhibit Hall. The space is designed for folks to relax between sessions, connect with each other, share and celebrate AAS presentations, and grow together through exploratory salon-style discussions. We will have a variety of scheduled and ongoing programming, including:

  • A Community Calendar: Giving a talk? Presenting your first poster? We will have a digital and physical calendar of events that showcase our community members. Tell us about yours so we can cheer you on as your astro siblings, cousins, and fam!
  • Community Discussions: Join us for daily salon-style discussions on topics that are important to our community (e.g., radical mentorship, activism, climate justice). These will be group discussions led by our own community members. Stay tuned to learn more about the topic schedule!
  • Walls of Support: How are you navigating self-care while conferencing? What words of kindness would you give to yourself the first time you attended an AAS meeting? You can share these and other tips/affirmations in written form on the Rainbow Village walls.

I am not a person of color, but I would love to get involved. How can I help?

We will be advertising AAS meeting talks, posters, and events led by people of color at the Rainbow Village and on social media with the hashtags #AAS243 and #RainbowVillage. We would love for folks to signal boost and make sure to attend those events. If you will be present in person at the January 2024 meeting and would like to volunteer, please fill out this form.

Please also stop by our booth in the Exhibit Hall to listen to our community discussions and offer resources for people of color. We would love to have you!

Starting in December, Astrobites has released a series of articles highlighting the representatives of the Rainbow Village and their respective organizations. Please stay tuned to learn more about these organizations and get updates on the programming available for all throughout the AAS meeting in New Orleans this January 2024!

poster advertising the Rainbow Village at AAS 243

Artwork by Arianna Long.

Original Astrobite edited by Sahil Hedge.

illustration of the structure of the Milky Way

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

Title: SPLUS J142445.34-254247.1: An r-process-enhanced, Actinide-Boost, Extremely Metal-Poor Star Observed with GHOST
Authors: Vinicius M. Placco et al.
First Author’s Institution: NSF’s National Optical-Infrared Astronomy Research Laboratory (NOIRLab)
Status: Published in ApJ

Have you ever looked at the periodic table and wondered where all the elements come from? Nearly all of the hydrogen, helium, and some of the lithium present in the universe formed in the first three minutes after the Big Bang. Beyond that, most of the elements (those that are heavier than helium are called metals by astronomers) were created in nuclear reactions that take place in the heart of stars. Up through iron, the formation processes happened systematically, where heavier elements were formed from the fusion of lighter elements. Beyond iron, about half of the elements are created by a slow and gradual process of capturing neutrons (called the s process, with s for slow).

Elements beyond iron are created in extreme events, such as stellar explosions (see Figure 1 for an overview). Such cataclysmic events cause neutrons to rapidly bombard atomic nuclei, forming heavier elements. This rapid capture of neutrons is called the r process (with r for rapid). There is a constant exchange of elements between the stars and the gas surrounding them. This eventually leads to the formation of more stars that are enriched in metals, setting up different generations of stars.

periodic table of the elements indicating the origins of each element

Figure 1: The astronomer’s periodic table indicates the elements’ astrophysical origin. [NASA’s Goddard Space Flight Center]

Studying the s and r processes can give us valuable insight into the nature and conditions prevalent in the universe when stars form. The s-process elements are believed to originate from low-mass stars as they evolve throughout their lifetime. The r-process elements come from dramatic events, such as supernovae or neutron star mergers. The mechanism through which a star’s r-process elements were created is determined mainly by modeling, as direct observational evidence of such events is rare.

If we obtain high-resolution spectroscopy of a star and map the abundances of all its elements, it would paint a picture of the events that led to its formation. In today’s article, the authors try to understand how stars were formed in the halo of the Milky Way by recreating the formation scenario based on the fingerprints in the high-resolution spectrum obtained from a Milky Way halo star called SPLUS J142445.34–254247.1, or SPLUS J1424−2542 for short.

Recreating the Formation Scenario

The star’s chemical abundances are determined from absorption features in its spectrum. If a particular element is present, it will absorb the starlight passing through the cold gas at a characteristic wavelength, and the extent of absorption can be used to determine how much of the element is present in the star. Looking at the elemental abundances of SPLUS J1424−2542, the star showed signs of being poor in iron (atomic number Z = 26) but being enhanced in elements with atomic numbers 26 < Z < 38 compared to the standard values measured from the Sun. This indicates the star formed from a gas cloud polluted by two distinct populations of stars in a multi-enriched process.

The abundances of the heavier elements indicate that the primary process involved in the formation is the r process. This is not uncommon for old stars in the galactic halo. The s-process elements are formed from the death of low-mass stars, which would not have occurred when such old stars were formed in the halo. Most elements in halo stars are created through the r process, even those typically formed by the s process. However, the authors found that certain elements, such as strontium, do not agree with the predicted values from either the r process or s process (Figure 2). Other elements, such as barium, are overproduced, indicating contributions from both s and r processes. The team also found an overabundance of thorium, which could indicate a possible contribution from a separate r-process event. Thus, this star is metal poor with enhanced heavy elements produced by r-process events.

observed elemental abundances compared to expected values from the r process and the s process

Figure 2: Observed abundances (red circles) with the expected values from the r process (blue line) and s process (yellow line). Most points lie on the blue line, indicating a more significant contribution from the r process. [Placco et al. 2023]

Modeling the formation scenarios predicts that the lighter elements (Z ≤ 30) were likely produced by a metal-free star with a mass in the 11.3–13.4-solar-mass range that exploded with low energies, a characteristic property of older Population III stars. The heavier elements (Z ≥ 38) were likely formed from the merger of two neutron stars with masses of 1.66 and 1.27 solar masses, indicating that at least two progenitor populations enriched the star.

Mysterious Circumstances Prevail

The authors derived the star’s kinematic properties (such as orbital dynamics, velocities, etc.), which are displayed in Figure 3. They found that the proposed formation scenario and the derived kinematics do not connect the star with any known structure in the Milky Way. This highlights that a distinct star formation mechanism may occur in the galactic halo. We must continue studying similar stars with high-resolution spectroscopy to help us understand the formation of old stars in the Milky Way halo.

comparison of the properties of the star studied in this article to those of stars in known Milky Way substructures

Figure 3: The yellow star indicates the star studied in this article. The upper panel shows it does not fall into the expected Milky Way streams. The bottom panel shows that it has distinct properties from other stars in a similar position on the upper panel. [Placco et al. 2023]

Original astrobite edited by Roel Lefever.

About the author, Archana Aravindan:

I am a PhD candidate at the University of California, Riverside, where I study black hole activity in small galaxies. When I am not looking through some incredible telescopes, you can usually find me reading, thinking about policy, or learning a cool language!

illustration of planets around an M-dwarf star

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

Title: A Comparison of the Composition of Planets in Single-planet and Multiplanet Systems Orbiting M dwarfs
Authors: Romy Rodríguez Martínez et al.
First Author’s Institution: The Ohio State University
Status: Published in AJ

The most common type of star in the universe is the M dwarf, making up ~70% of all stars. Because M dwarfs are so common, and because we know exoplanets are common, it is only natural that we have found many exoplanets orbiting M-dwarf stars. In fact, Astrobites covered seminal articles on the occurrence rate of planets around M dwarfs and their compositions; read them here and here. But with all these planets, we can take our knowledge one step further and begin to ask deeper questions. For example, in M-dwarf planetary systems, how do single-planet systems (only child) and multi-planet systems (siblings) compare?

A new study seeks to answer this question, focusing on three key parameters: planet bulk density, planet core mass fraction, and host-star metallicity. In particular, the authors wish to investigate whether siblings and only-child systems are two outcomes of the same formation process, or if they truly form differently. In other words, are they from the same population, or are they two distinct populations of planets?

Bulk Density

First, bulk density, or the average density of the planet as a whole. We know that Earth is made up of many different materials (rocks, water, gases, etc.) and each one of these has its own density. But for exoplanets, we cannot explore the details of different materials and so instead we measure bulk density by simply taking the total mass of the planet and dividing by the total volume (assuming the planet is a sphere). The authors compute bulk density for a sample of planets around M dwarfs, computing this quantity for both the single-planet systems and all the planets in multi-planet systems.

Next they apply a statistical test (the Kolmogorov–Smirnov test) to determine if the two sets of planets are truly distinct populations or are consistent with one population (see Figure 1 top panel). The result overwhelmingly shows that these are two different populations. However, the authors caution that this result may be biased. Many of the single-planet systems in the sample are giant planets, which are naturally lower density than smaller planets are because they have higher gas fractions. Removing the gas giants from the sample and re-running the test, the authors find that actually the siblings and only-child planets are consistent with coming from the same population (see Figure 1 bottom panel).

plots of cumulative distribution function vs planet density

Figure 1: Top: The results of the statistical test when including all planets to determine if the two populations are distinct. The gap between the single and multis suggests they are indeed two populations. Bottom: The same as the top panel but for the sample that excludes giant planets. Here the finding of two populations is less statistically significant. [Adapted from Martínez et al. 2023]

Core Mass

Next, planet core mass. The mass of a planet is generally meant to include everything that makes up the planet. However, the core mass is just that, the mass of the core of the planet alone. Core masses are valuable pieces of information because it is thought that the size of the core, which is the first to form, can determine how big the planet eventually grows to be. Bigger cores are better at gravitationally attracting material, including gas, to grow the planet. While we cannot directly measure the core mass of a planet, we can use models that are tuned to Earth’s parameters to estimate planet core mass based on a few things we can measure, like mass and radius. Now taking only the planets that are likely to be rocky and again splitting by single versus multi-planet systems, the authors find that planets in single-planet systems have, on average, larger core masses than those in multi-planet systems. They further test if core mass correlates with orbital period but find no correlation.


Lastly, the authors explore the host star, particularly its metallicity, or the percentage of the star’s composition that is made up of “metals” (astronomers define “metal” as anything heavier than helium!). Host star metallicity is thought to correlate with the kinds of planets and number of planets in the system, the thinking being that since planets form out of the same disk of material as the host star, if there are more heavy materials in that disk (which would appear as higher metallicity in the host star) then there is more opportunity to make more and bigger planets. The authors here find that host stars of single planets are more metal rich than those hosts of multi-planet systems. This is counterintuitive, but the authors hypothesize this could be because more metal-rich stars might produce more and bigger planets, which may gravitationally interact in the early days of the system and fling out all but one planet. On the other hand, metal-poor stars cannot build big planets and instead build small planets that are dynamically “quiet.”

In all, the authors find that single- and multi-planet M-dwarf systems are likely two distinct populations. This could have large implications for how we understand the formation and evolution of planetary systems.

Original astrobite edited by Mark Popinchalk.

About the author, Jack Lubin:

Jack received his PhD in astrophysics from UC Irvine and is now a postdoc at UCLA. His research focuses on exoplanet detection and characterization, primarily using the radial-velocity method. He enjoys communicating science and encourages everyone to be an observer of the world around them.

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