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Messier 82

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: Quantifying Baryonic Feedback on Warm-Hot Circumgalactic Medium in CAMELS Simulations
Authors: Isabel Medlock et al.
First Author’s Institution: Yale University
Status: Published in ApJ

Simulating the Universe

Galaxies are complex ecosystems with a wide variety of physical processes impacting how they form and evolve. Studying these processes  is often quite difficult, in part because we can’t actually travel to galaxies and poke and prod at them to see what’s going on. So we have to turn to another handy tool in the astrophysicist’s toolkit: simulations. Cosmological hydrodynamic simulations can model how giant swaths of the universe have evolved since the universe’s early years, and take into account a wide range of processes ranging from dark matter structure formation to star formation to galaxy mergers. But because these simulations are so large and complex, there is a limit to how small of a scale can be resolved, and a lot of the activity falls into what we call “subgrid physics”: phenomena that occur on scales below a simulation’s resolution. Instead of modeling activity from foundational behavior, we have to approximate what impact the subgrid physics would have on the scales we can resolve. For example, instead of directly modeling how a molecular cloud collapses, we would say if a volume of gas reaches some critical density it automatically forms some amount of stars with some initial mass function. (If that sounds a bit hand-wavy, that’s because it is, but we gotta work with what we’ve got, and right now we don’t have the computing power to model all the scales of astrophysics at once.)

Some of the most important subgrid processes are stellar and black hole feedback. Stellar feedback refers to how star-related events like star formation and supernovae deposit energy into the interstellar medium, which can impact the galactic environment significantly, slowing down star formation and even ejecting material from the galaxy entirely. Black hole feedback (sometimes called active galactic nucleus or AGN feedback) comes from activity around the supermassive black holes found in the centers of practically every large galaxy. As material accretes around the black hole, the material can heat up or even be ejected at high velocities, causing large amounts of energy to be expelled into and around the host galaxy. Between stars and black holes, these feedback processes have a large impact, and without their inclusion in simulations the results look nothing like the universe as we observe it.

A Flock of Camels

There are many cosmological simulations widely used today, but comparing their differing feedback implementations can be difficult as other parameters (i.e., initial conditions, cosmological parameters, resolution and simulation volume) are varied. The simulations used in this work come from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, which provides a suite of model runs that change feedback parameters but keep everything else constant. CAMELS pulls feedback implementations from several simulations, but this work looks specifically at comparing the simulations SIMBA and IllustrisTNG. For each of these simulations, CAMELS identifies four parameters that drive feedback (two each for stellar and black hole processes) and provides a range of runs that span this parameter space. These parameters describe things like the mass loading and speed of winds, accretion rates, and energy and momentum flux.

SIMBA and IllustrisTNG are broadly similar in their feedback treatments, but they have a few key differences. To model stellar feedback, both codes heat and move around gas particles, but the means in which they calculate temperature gains and velocities are different. Both simulations have two modes of AGN feedback: one associated with low accretion rates around the black hole, and the other associated with high accretion rates. For IllustrisTNG, when accretion rates are high the feedback is all thermal, heating nearby gas particles. At lower accretion rates, the feedback is more kinetic, where particles are ejected with energies set by the CAMELS black hole feedback parameters. In SIMBA, both AGN modes have some form of kinetic feedback, with high accretion rates corresponding to lower velocities.

This work compares how the different feedback treatments in CAMELS-SIMBA and CAMELS-IllustrisTNG impact black hole growth, feedback energies, and gas distribution in and around galaxies. The authors do this by comparing two different values: fCGM and the closure radius. The quantity fCGM provides a ratio of the mass found in the circumgalactic medium (the region surrounding a galaxy) relative to the total mass found in the halo. The closure radius takes a different approach, by looking at the physical scale at which the ratio between the mass of baryons (gas, stars, and black holes) and all the mass (including dark matter) is the same as the ratio for the universe as a whole. The measurement provides a way of determining the scale at which matter is associated with a given galaxy.

Some Feedback on the Feedback

Relationship between the halo mass and closure radius

Figure 1: Relationship between the halo mass and closure radius in different simulations run with varying stellar feedback strengths. Here, we can see the ability of SIMBA (purple) to disperse gas to a larger radius than IllustrisTNG (green). [Adapted from Medlock et al. 2025]

Overall, this work found that the variations in feedback processes not only impacted the gas distribution and galaxy properties, but also that the links between stellar and AGN feedback are important and vary between different simulations. In general, the feedback implemented by the IllustrisTNG code had a higher energy than that from SIMBA. However, SIMBA had a greater impact on the baryon distribution, with larger closure radii than the IllustrisTNG runs, as shown in Figure 1. When examining links between stellar and black hole feedback, the authors found that improving the efficiencies of stellar feedback weakened AGN feedback in IllustrisTNG but slightly strengthened it in SIMBA. Figure 2 shows how varying one of the stellar feedback parameters changed the energy of an AGN feedback mode. Finally, the authors looked at changes over time; earlier in the universe, AGN feedback was rarer than stellar activity, and the complexities of their interplay did not become important until redshifts of z < 2.

Plot showing the relationship between the halo mass and energy

Figure 2: Relationship between the halo mass and energy coming from the thermal AGN mode in IllustrisTNG (left) and SIMBA (right). The different colors represent different strengths of stellar feedback, with purple being the lowest and orange the highest. Here we see how changing the stellar feedback parameters impacts the black hole feedback, with stronger stellar feedback corresponding to lower AGN energies in IllustrisTNG, but slightly higher energies in SIMBA. [Adapted from Medlock et al. 2025]

Overall, these results indicate that one cannot simply treat stellar and black hole feedback independently (insert Boromir meme here). These processes are related in complex ways that are currently not fully understood. The fact that different simulations have different interplay (sometimes with directly opposite results!) points to a need for further constraining of these subgrid models going forward.

Original astrobite edited by Lucie Rowland.

About the author, Skylar Grayson:

Skylar Grayson is an Astrophysics PhD Candidate and NSF Graduate Research Fellow at Arizona State University. Her primary research focuses on active galactic nucleus feedback processes in cosmological simulations. She also works in astronomy education research, studying online learners in both undergraduate and free-choice environments. In her free time, Skylar keeps herself busy doing science communication on social media, playing drums and guitar, and crocheting!

Illustration of the seven known planets in the TRAPPIST-1 system

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 Detectability of CH4/CO2/CO and N2O Biosignatures Through Reflection Spectroscopy of Terrestrial Exoplanets
Authors: Armen Tokadjian, Renyu Hu, and Mario Damiano
First Author’s Institution: NASA’s Jet Propulsion Laboratory
Status: Published in AJ

Are we alone in the universe? This is one of the longest-standing questions ever asked by humankind. Though extraterrestrial life is discussed as a philosophical thought exercise, the NASA Astrophysics flagship mission of the 2040s, the Habitable Worlds Observatory (HWO), will get us closer than ever to the answer. This mission aims to detect signs of life on exoplanets. When active, the HWO will observe the atmospheres of Earth-like worlds to see if there are any particular molecules that could reveal past or present life. On Earth today, oxygen gas (O2) is a large component of our atmosphere and is considered a biosignature, since it is primarily produced as a byproduct of photosynthesis by living organisms. However, Earth’s atmosphere looked very different billions of years ago, when life had just begun to form. The authors of today’s article put forth theoretical models of how habitable exoplanets might appear to HWO using the molecules present in the Earth’s atmosphere from earlier eons, and test if HWO will be able to detect these molecules in simulated observations.

It’s Not Just a “Phase,” Mom, It’s an Eon

We all look different from the pictures from our youth, and Earth is no exception. And since life has been flourishing on Earth for billions of years, we can expect that any potentially life-harboring exoplanet may look like Earth from a different stage of its life. The authors of today’s article take two snapshots of Earth from two different phases, or eons, of its youth — but instead of revealing awkward braces and self-cut bangs, they are looking for the different molecules present in its early atmosphere. The first signs of life — microbes such as bacteria and archaea — emerged about 4 billion years ago during the Archean Eon. At this time, Earth’s atmospheric makeup was mostly nitrogen (N), carbon dioxide (CO2), and methane (CH4). Of these, CH4 is particularly interesting to astrobiologists because the primary source of CH4 during the Archean Eon was methanogenesis, a process that produces methane as a byproduct during microbial respiration, making CH4 a strong biosignature. However, abiotic processes like volcanism can produce CH4 as well. Therefore, the co-presence of CO has been suggested to distinguish the production mechanism of CH4, as volcanic activity typically produces more CO than CH4. A detection of CH4 in an atmosphere with a high CO to CH4 ratio should be considered a false positive of the CH4 biosignature as it is likely to have been produced abiotically.

In the following eon, the Proterozoic (2.5 billion to 541 million years ago), the atmospheric oxygen level began to rise, and the first eukaryotic organisms evolved. Microbes using the nitrogen cycle became the predominant producers of dinitrogen oxide (N2O), a compelling biosignature. Even more enticing is that N2O has few abiotic sources, unlike CH4. The authors of today’s article produced atmospheric models based on the biosignatures present in both the Archean and Proterozoic eons, and from those models tried to infer the presence of CH4/CO2/CO and N2O, respectively.

Earth as a Model

For both cases, the authors generated spectra using radiative transfer models, inputting parameters such as the cloud coverage, surface albedo (i.e., the fraction of light that a surface reflects), and the amount of the molecules of interest (specifically the number of a specific gas molecule divided by total number of molecules in a given volume). To model the Archean Earth atmosphere, the authors fix the amount of CH4 to be the upper limit of CH4 that could have been present in the Archean atmosphere, and they consider three different amounts of CO, corresponding to CO/CH4 ratios of 1, 5, and 10, respectively.

To model the Proterozoic Earth atmosphere, the authors consider two examples with different amounts of N2O. The first model corresponds to the upper limit of N2O for a Proterozoic Earth-like planet around a G-type star, and the second model corresponds to the same kind of planet, but around a K-type star instead. The K-type host star allows for much higher amounts of N2O in the planet’s atmosphere. This is because G-type stars are hotter and therefore have a higher ultraviolet flux, which causes the N2O in the atmosphere to photodissociate. These parameters are used in their radiative transfer model, which will produce a synthetic reflected light spectrum — i.e., the spectrum of light that is reflected off a cool exoplanet, which resembles what the HWO aims to observe.

Show Me the Biosignatures

To see if the authors can detect these molecules from these synthetic spectra, they employ an atmospheric retrieval code. In short, the synthetic reflection spectra generated by their radiative transfer model is sampled many, many times to see which one best fits the parameters of interest (most importantly, the presence of potential biosignatures). The planet-to-star flux ratio as a function of wavelength for the forward model with CO/CH4 = 10 along with the retrieval result for the Archean Earth atmosphere is shown in Figure 1. The authors find that they are able to detect CH4 and CO2 in the atmosphere, but they fail to constrain the CO abundance for any of the three cases. This is illustrated in Figure 1, where the CO features are relatively weak or located within a stronger CO2 feature. Because of this, it will be challenging to know if a potential detection is a false positive for the CH4/CO2 biosignature pair with similar observations.

Plot of the Planet-to-star flux ratio versus wavelength for Archean Earth-like planets

Figure 1: Planet-to-star flux ratio versus wavelength for Archean Earth-like planets with CO/CH4 = 10. The model is shown by the red circles and the retrieval result is overlaid. Each molecule of interest is labeled in a separate color to show the individual molecular contributions. While CH4 and CO2 are well constrained by the retrieval, CO is not. [Tokadjian et al. 2024]

On the other hand, the authors report that they are able to robustly detect N2O for the Proterozoic Earth-like planet around a K-type star (i.e., the model with much more N2O present), though not for the G-type star. The planet-to-star flux ratio plot for the atmosphere of a Proterozoic Earth around a K-type star is shown in Figure 2. This result warrants optimism, as few abiotic sources can replicate that level of N2O production in a planet’s atmosphere, making it top a biosignature candidate for the HWO.

Plot of the Planet-to-star flux ratio versus wavelength for Proterozoic Earth-like planets

Figure 2: Same as Figure 1, but for the Proterozoic Earth-like planet around a K-type star. N2O is well constrained by the retrieval result. [Tokadjian et al. 2024]

This study is just one of many that astrobiologists will need by the time HWO launches in order to better understand its detection capabilities. It is important to look for Earth analogs at each stage of development, and not just analogs to the stage of Earth we all know and love now. Furthermore, as many abiotic processes can produce false positive biosignatures, we must be cautious in making any strong claims about potential detections of life. Nevertheless, this marks an exciting new era in astronomy, where we will be able to better understand the atmospheres of planets like our own and potentially see if life exists on other worlds.

Original astrobite edited by Kylee Carden.

About the author, Tori Bonidie:

I am a 4th-year PhD candidate studying exoplanets at the University of Pittsburgh. Prior to this, I earned my BA in astrophysics at Franklin and Marshall College, where I worked on pulsar detection as a member of NANOGrav. In my free time you can find me cooking, napping with my cat, or reading STEMinist romcoms!

illustration of a hot Jupiter exoplanet orbiting close to its host 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 astrobites.org.

Title: The Roasting Marshmallows Program with IGRINS on Gemini South. II. WASP-121 b has Superstellar C/O and Refractory-to-Volatile Ratios
Authors: Peter C. B. Smith et al.
First Author’s Institution: Arizona State University
Status: Published in AJ

Our solar system has a very nice, ordered structure to it. Close to the Sun are the rocky, terrestrial planets, and then once you cross the asteroid belt you find the big, surface-less gas giants. But over the past 30 years, astronomers have learned that this is not a universal setup. A majority of the exoplanets we have discovered to date fall in the gas giant category, but not all of these gas giant planets lie at the outer edges of their systems. In fact, we’ve found so many gas giants that are close to their host star that they’re given their own name: hot Jupiters. These are large, gaseous planets that orbit closer to their star than Mercury does to the Sun, and how they came to be there is a bit of a mystery.

Divvying Up the Protoplanetary Disk

Planets form out of a swirling ring of gas and dust called a protoplanetary disk. But this disk isn’t the same at all radii. As you move away from the star and the temperature drops, certain compounds change from gas to solid phase, which can have a big impact on the types of planets that form there. One way of dividing up the disk is via “snow lines.” Interior to a snow line, volatile materials (like water) are in gas form, but once you cross this line it is cold enough for them to freeze, forming small solid particles that provide seeds for planet growth. To highlight the role this could play in the types of planets that form, it’s thought that the snow line for water for our solar system was around 3 au, which places it right in between Mars and Jupiter. With the presence of solid particles, it’s easier for planetary cores to grow quickly and thus reach a point where they can hold on to the large gas envelopes that make a planet a gas giant. Another line that’s grown in interest recently is the soot line, which similarly marks the transition into gas specifically for carbon molecules. The properties of planets, including their potential for habitability, can depend greatly on where they formed relative to these lines.

So this is great and all, but when we go and observe exoplanets, we aren’t able to see a nice highlight reel of their formation history. We don’t know where they formed, and we can’t tell how they might have migrated through the disk. But if we can measure their compositions, we can start to get some clues as to where they might have formed and how they came to be where they are.

WASP-121b: An “Ultra-Hot” Planet

That’s where today’s article comes in. This work looks at an exoplanet with the beautifully poetic name of WASP-121b, which is a bit bigger than Jupiter but orbits so close to its host star that it’s actually classified as an “ultra-hot Jupiter.” (Side note: the title of the article is “The Roasting Marshmallows Program” because the planets studied in the program are all hot and puffy.) Ultra-hot Jupiters are, as you may have guessed, hotter than hot Jupiters, and thus they have fewer clouds in their atmospheres. This allows for a better understanding of their composition as you don’t have to deal with pesky and still relatively poorly constrained cloud models.  Today’s work used observations from the Immersion Grating Infrared Spectrometer (IGRINS) instrument on the Gemini South telescope, observing the planet before and after its secondary eclipse — when the planet moves behind the host star. Figure 1 gives details about the system and shows the phases of the two observations.

schematic of the WASP-121 planetary system

Figure 1: Schematic of the system studied in this article. The blue and red highlighted regions show the phases of the planet’s orbit during the observations. Something fun about this diagram: it’s fully to scale, which really demonstrates just how close the planet is to its host star. [Adapted from Smith et al. 2024]

The observations of WASP-121b consisted of high-resolution spectra capturing the thermal emission of the planet. These data were then compared against models that included volatile (water, carbon monoxide, hydroxide) and refractory (Fe, Mg, Ca) species. Being able to constrain all these different compounds with the same observations is actually a pretty rare feat, as volatile and refractory species generally have to be studied in different portions of the electromagnetic spectrum. However, it’s possible to observe refractory species with high-resolution spectroscopy in the near-infrared, which is exactly what IGRINS provides. This is handy because it allows for direct comparisons without needing to worry about systematic effects from using different instruments.

Ratios to the Rescue

Posterior distributions of the C/O and R/V ratios

Figure 2: Posterior distributions (which can be understood as the probability for each value) of the C/O and R/V ratios in the atmosphere of WASP-121b. By comparing the values to the stellar abundance we can constrain where in the protoplanetary disk the ultra-hot Jupiter formed. [Adapted from Smith et al. 2024]

Thus, by comparing IGRINS observations against planetary atmosphere models, the researchers were able to place constraints on the presence of both volatile and refractory species in WASP-121b’s atmosphere. However, getting absolute abundance measurements was a trickier task given relative unknowns about other contributions to the continuum of the spectrum. So instead of trying to constrain absolute abundances, the research team calculated ratios. The two ratios that are important in this article are carbon to oxygen (C/O) and refractory to volatile (R/V). Not only are ratios better constrained than straight up abundances, they can also help place where in the protoplanetary disk WASP-121b formed. The C/O ratio will increase past the soot line as oxygen incorporates into silicates, and it will further increase past the snow line as oxygen freezes into water ice. The R/V ratio tracks the behavior of solid accretion, and if the R/V ratio is high, this suggests that the planet formed interior to the snow line where most of the accreted solids did not contain volatile material. Thus, a combination of the C/O and R/V ratios allows us to determine the location of planet formation relative to the water snow line and the soot line. Figure 2 shows the posterior distribution (which is a semi-complicated Bayesian statistical result that essentially just means probability) for the C/O and R/V ratios.

Overall, WASP-121b has high values for both the C/O and R/V ratios. This suggests that the planet was formed exterior to the soot line but interior to the water snow line, which is kind of an unexpected result. As discussed above, it’s thought that massive gas giants form exterior to the snow line, as they need the ices to build a big enough core. Though surprising, it’s not impossible for this ultra-hot Jupiter to have formed interior to the snow line. One interpretation is that the planet actually started as a super-Earth (exoplanet categories are so silly sounding — this means a planet between the sizes of Earth and Neptune) that could form interior to the snow line but still be big enough to sustain the runaway gas accretion that got it up to Jupiter size. And then over time, it would have migrated in towards the host star, heating up until it became the roasted marshmallow we know and love. While this is one explanation, there’s still a lot to understand about this formation pathway and more study is definitely needed. Overall, these results provide an interesting challenge to our understanding of planet formation. They also demonstrate the usefulness of near-infrared spectroscopy when it comes to constraining the atmospheres of ultra-hot Jupiters.

Original astrobite edited by Lucas Brown.

About the author, Skylar Grayson:

Skylar Grayson is an Astrophysics PhD Candidate and NSF Graduate Research Fellow at Arizona State University. Her primary research focuses on active galactic nucleus feedback processes in cosmological simulations. She also works in astronomy education research, studying online learners in both undergraduate and free-choice environments. In her free time, Skylar keeps herself busy doing science communication on social media, playing drums and guitar, and crocheting!

Illustration of the orbits of moons of Saturn

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: Irregular Moons Possibly Injected from the Outer Solar System by a Stellar Flyby
Authors: Susanne Pfalzner, Amith Govind, and Frank W. Wagner
First Author’s Institution: Jülich Supercomputing Center; Max Planck Institute for Radio Astronomy
Status: Published in ApJL

Irregular Moons

Our solar system has many moons that are “regular.” They have circular, prograde orbits with low inclination, meaning they orbit their host planet in the same direction and plane as the planet orbits the Sun. These regular moons formed, along with their planets, from the protoplanetary disk. But there are some moons in the solar system with strange orbits, and we’re not sure yet why. These “irregular moons” orbit the outer, giant planets (Jupiter, Saturn, Uranus, and Neptune) on eccentric, inclined, and often retrograde (backwards) orbits.

Luckily, we have a clue. Irregular moons resemble a class of objects that have similarly strange orbits around the Sun out beyond Neptune, called trans-Neptunian objects (TNOs), so they may share a common origin. (When Pluto was demoted from planet status, it was reclassified as a dwarf planet, which is a type of TNO.) One origin theory is that TNOs and irregular moons are a result of the giant planet migration; as the giant planets drifted away from the Sun, they could have kicked TNOs into skewed orbits and captured some in their path as irregular moons. However, this theory cannot explain TNOs that are too distant to have been gravitationally influenced. Another theory is that a star passed through the outer solar system shortly after the solar system’s formation, gravitationally jumbling TNOs, some of which were captured by giant planets. This theory seemed too far-fetched, and it was mostly dismissed until recently, when the Atacama Large Millimeter/submillimeter Array showed that such stellar flybys are actually more common than we thought. Today’s article explores the stellar flyby theory using simulations to demonstrate that such a scenario could create the irregular moons we see today.

The Stellar Flyby Simulation

In a previous article, the authors ran a range of stellar flyby simulations to find the specific scenario that best reproduces the population of TNOs we find beyond Neptune. The best model was a parabolic flyby of a 0.8-solar-mass star at an inclination of 70° and a closest approach of 110 au from the Sun. In today’s article, the authors studied the effect this flyby has on the region near the giant planets, within Neptune’s orbit.

The simulation begins with a disk of undisturbed TNOs on circular orbits. For comparison, the authors modeled two disk sizes, 150 au and 300 au. As is commonly done in simulations for computational simplicity, the TNOs are represented by test particles. The flyby star is flung through the disk, and test particles are moved according to their gravitational interactions with the flyby star and the Sun. As the star passes through the disk, it pulls particles out of their orbits, creating the complex structure shown in Figure 1. The authors then let the simulation run for a billion years to see where the test particles ended up and how they interacted with the giant planets.

Snapshot of the simulation 200 years after the flyby star’s closest passage

Figure 1: Snapshot of the simulation 200 years after the flyby star’s closest passage, to the right of the Sun. The turquoise particles are those that will end up within Neptune’s orbit after the interaction has settled. [Pfalzner et al. 2024]

Comparing the Simulation to Observations

Distribution of injected TNO perihelions

Figure 2: Distribution of injected TNO perihelions for the 300 au disk (solid line) and the 150 au disk (dotted line). Click to enlarge. [Adapted from Pfalzner et al. 2024]

Immediately after the flyby, around 7.2% of the TNOs from the original disk end up inside Neptune’s orbit on highly eccentric orbits. Figure 2 shows the distribution of injected TNO perihelions, as well as the radii of the giant planets’ orbits. For both disk models, more TNOs end up near Saturn’s orbit than Jupiter’s, which matches observations — Saturn has 122 irregular moons while Jupiter has 87. It’s hard to say if this result makes sense for Uranus and Neptune, because it is difficult to detect moons at greater distances.

Most of the injected TNOs after the flyby had prograde orbits, and a significant fraction were at high inclinations. But interestingly, retrograde orbits dominated in the region inside 10 au. Around Jupiter’s orbit at 5.2 au, TNOs were 30% more likely to be retrograde than prograde, while around Saturn’s orbit at 9.5 au, it was 20% more likely.

Distribution of injected TNO perihelions for prograde and retrograde orbits

Figure 3: Distribution of injected TNO perihelions for prograde (blue) and retrograde (red) orbits, at 12,000 years (solid line) and a billion years (dashed line) after the stellar flyby. After a billion years, more TNOs with retrograde orbits remain. Click to enlarge. [Adapted from Pfalzner et al. 2024]

As the simulation progressed over the next billion years, 85% of the injected TNOs were eventually ejected. Figure 3 shows the distribution of prograde versus retrograde orbits as the simulation evolved. TNOs with retrograde orbits were more likely to avoid ejection than those with prograde orbits. The high fraction of resulting TNOs with retrograde orbits matches observations, as the giant planets have mostly retrograde irregular moons.

Finally, observed irregular moons are similar to observed TNOs in color, ranging from gray to red, except they lack very red objects. The original pre-flyby disk had a color gradient from red near the center to gray on the edges. Figure 4 shows the regions of the original disk that ended up near the giant planets, none of which cover the extremely red region.

The original TNO disk

Figure 4: The original TNO disk. The colored regions show the position of TNOs from the original disk that were injected inside Neptune’s orbit, none of which were extremely red. The colorbar represents the inclination of those resulting orbits. [Adapted from Pfalzner et al. 2024]

So far, the authors have shown that a stellar flyby could have perturbed TNOs to create the population of giant planet irregular moons. If a TNO orbiting the Sun on an eccentric, inclined, or retrograde orbit was gravitationally captured by a planet, its post-capture orbit would also be eccentric, inclined, or retrograde. The authors haven’t modeled the planets capturing the moons, but they reference another article that demonstrates that moon capture in such a scenario is likely. The plausibility of the stellar flyby and the similarities between TNOs and irregular moons demonstrate that this theory is promising and worth further investigation.

Original astrobite edited by Kylee Carden.

About the author, Annelia Anderson:

I’m an Astrophysics PhD candidate at the University of Alabama, using simulations to study the circumgalactic medium. Beyond research, I’m interested in historical astronomy, and hope to someday write astronomy children’s books. Beyond astronomy, I enjoy making music, cooking, and my cat.

Images of six "little red dot" galaxies from JWST

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 Small Sizes and High Implied Densities of “Little Red Dots” with Balmer Breaks Could Explain Their Broad Emission Lines Without an Active Galactic Nucleus
Authors: Josephine F. W. Baggen et al.
First Author’s Institution: Yale University
Status: Published in ApJL

Since its launch, JWST has been on a roll with observations that continue to shape our understanding of the universe. Recently, JWST saw a bunch of galaxies in the early universe that could each host a massive black hole. These galaxies are rightfully called “little red dots” as they are compact (i.e., have small radii) and appear red in the infrared as observed by JWST. However, we are not entirely sure about the identity of the little red dots. The spectra of these galaxies seem to contain features that suggest the presence of an accreting supermassive black hole or AGN. AGNs are known to produce X-ray emission, and we have not detected X-rays from the little red dots. So, what exactly are the little red dots? Do they have an AGN, or are they simply a collection of very massive and compact galaxies?

A recent finding showed that three little red dots have a relatively old and evolved stellar population. We know this because of the presence of a Balmer break — a jump in the galaxy spectrum at the Balmer line — which is predominantly seen in older stars. Does this mean that these galaxies are dominated by older, evolved stars, and does that rule out the presence of AGNs? The authors of today’s article argue that the three little red dots with a Balmer break have no AGNs and are just massive, compact galaxies. Have they truly unmasked the little red dots? Let’s find out.

These Are Some Extremely Dense Galaxies

The authors find that the galaxies are extremely compact, with small half-light radii around 100 parsecs (326 light-years), similar to ultra-compact dwarf galaxies in the local universe. While the galaxy spectra can give us an estimate of the stellar mass, the contribution from the AGN alone can be a significant fraction of a galaxy’s mass and must be accounted for. The authors fit three models to the galaxy spectra: 1) assuming most of the galaxy’s light comes from an old, evolved stellar population with no contribution from an AGN, 2) assuming maximum contribution from an AGN and minimal from the stars, and 3) a model that lies somewhere in between. The three models yield very different galaxy mass estimates, with the first model estimating 100 billion solar masses in stars, and the second model estimating just a billion solar masses.

The detection of the Balmer break points toward a source that has light mostly dominated by stars. Assuming the AGN contribution to the mass is minimal, the stellar mass density would be quite high. The authors show this in Figure 1, where the model with no AGN component is shown in purple and exhibits a very high stellar mass density near the galaxy’s center compared to other galaxies. This could be taken as evidence against the no-AGN model, but there is another observation we should consider.

plot of stellar mass density versus galactocentric radius

Figure 1: The stellar mass density profile — stellar mass density versus the distance from the center — for the three different models. The purple profile is for the no-AGN model. The black dotted line shows the profiles of galaxies from the local universe. The cyan and purple dotted lines show the profiles at higher redshifts. The no-AGN model’s stellar mass profile indicates a higher central density than other models. [Baggen et al. 2024]

What About the Broad Emission Line?

One of the clearest signatures of an AGN in the galaxy spectra comes from broad emission lines, which result from fast-moving gas near the supermassive black hole moving both toward and away from us. This causes a Doppler broadening of the emission lines. This associated velocity dispersion was measured in the three little red dot galaxies in today’s article.

The authors argue that the calculated velocity dispersion can be explained by the dynamics of gas and stars in the galaxy itself, without the need for an AGN. This is because of the galaxy’s extreme density, which can produce a spectral line width that is similar to that of an AGN’s broad emission line. In Figure 2, the authors show a plot of the observed velocity dispersion versus the predicted velocity dispersion, assuming it is the gas and stars present in the galaxy that are contributing to the Doppler broadening. The predicted and observed velocity dispersions seem to match very well for the model with no AGN contribution. This also provides a natural explanation for the lack of X-ray signatures in these galaxies, since we would expect to see X-ray emission if AGNs were present but no such emission from an ordinary stellar population.

plot of the observed vs predicted stellar velocity dispersion

Figure 2: Left: A plot of the observed versus predicted stellar velocity dispersion assuming the kinematics of the gas and stars in the dense galaxy contributing to the broad line emission. In the no-AGN model, the three little red dots (indicated by the purple square, rectangle, and pentagon) can explain the observed stellar velocity dispersion. Right: A schematic figure showing that the observed H-beta line widths can be accounted for by the velocity dispersion calculated from the kinematics. [Baggen et al. 2024]

Verdict: We Need More Evidence

However, further investigation is needed to determine if this scenario can explain every aspect of the little red dots. Additionally, the origins of such extremely dense galaxies are uncertain, and we need to understand how they evolve into the “normal” galaxies with lower central densities and larger sizes that we see today. The authors stress that the explanation of the little red dot spectra being dominated by AGNs rather than stars is still likely, and further observations are needed to uncover the truth.

Original astrobite edited by William Smith.

About the author, Pranav Satheesh:

I am a second-year graduate student in physics at the University of Florida. My research focuses on studying supermassive binary and triple black hole dynamics using cosmological simulations. In my free time, I love drawing, watching movies, cooking, and playing board games with my friends.

NGC 4151

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: XRISM Spectroscopy of the Fe Kα Emission Line in the Seyfert Active Galactic Nucleus NGC 4151 Reveals the Disk, Broad-line Region, and Torus
Authors: XRISM Collaboration
Status: Published in ApJL

Today we’re going to be taking a high-resolution look at X-rays from close to a supermassive black hole! But before we get into the astrophysics of today’s article, we first need to discuss the instruments that were built to do this science. More than 50 years ago now, charge-coupled devices (CCDs) began revolutionizing astronomy, and they continue to be one of the most commonly used detectors on telescopes. CCDs rely on the photoelectric effect, through which an incoming photon can liberate electrons in some material (semiconductors in the case of CCDs). These electrons are trapped by strong potential wells and electric charge can be applied to move the charge along and read this signal (check out this Astrobite for more details). CCDs are particularly powerful in the X-ray band, where the number of electrons trapped in each pixel scales roughly with the photon energy. This means that you get energy information (i.e., a spectrum) for free with CCDs! However, CCDs have limited spectral resolution, meaning they can’t determine this energy very precisely and therefore cannot resolve and unlock the power of narrow emission and absorption lines.

X-Ray Microcalorimetry and 20/20 Vision

cartoon of a microcalorimeter

Figure 1: Schematic showing how a microcalorimeter works. An X-ray photon with energy E will produce a spike in the temperature of the absorber of E/C, where C is the heat capacity of the absorber. The thermometer is extremely sensitive to small changes in temperature, which means that we can get very accurate energies for each of the incoming X-ray photons. Therefore, a microcalorimeter can produce an X-ray spectrum with the best energy resolution of any current instrumentation. [NASA]

There are other ways to get better spectral resolution in the X-ray, including using gratings that will disperse your spectrum, as is commonly done with optical spectroscopy. However, even these techniques can’t reach the high spectral resolution needed; instead, new technology called a microcalorimeter has been engineered to solve this long-standing issue. As the name suggests, this instrument detects incoming photons by measuring tiny (micro) changes to the temperature (calorimetry) of the detector. Figure 1 shows the basic set-up of a microcalorimeter and how the energy of the photon is encoded in the strength of the resulting temperature fluctuation. In order to detect tiny changes to the temperature, microcalorimeters need to be extremely cold, 50 millikelvin to be precise! This is a huge engineering feat, but one that has recently been achieved by the X-ray Imaging and Spectroscopy Mission (XRISM)! XRISM is a JAXA/NASA collaborative mission, and it has two instruments on board: a CCD camera called Xtend and a microcalorimeter called Resolve. It was launched in September 2023, and its first science results are just starting to roll in!

Now, XRISM isn’t actually the first X-ray microcalorimeter to fly, but it’s the first to live through its commissioning phase! Although the X-ray microcalorimeter has been in the works since the 1990s, previous X-ray microcalorimeters have been cut from missions, lost to launch failures, and left unable to operate due to loss of coolant for the detector. In 2016, JAXA successfully launched and operated the first X-ray microcalorimeter on the Hitomi Satellite. However, unfortunately, shortly after taking a beautiful spectrum of the Perseus Cluster, one of the best-studied galaxy clusters in the local universe, communication was lost with the satellite and never recovered. XRISM’s Resolve instrument has been the most successful X-ray microcalorimeter so far, and it has allowed us to start looking at the universe with 20/20 X-ray vision!

Supermassive Science with XRISM

Today we’re going to put on our high-resolution X-ray spectroscopy glasses to look at one of the first XRISM targets: NGC 4151, one of the most well-known active galactic nuclei in the local universe. An active galactic nucleus consists of a supermassive black hole that is gobbling down gas from its surroundings through a process known as accretion. While we’ve known about active galactic nuclei for more than 50 years now, we still don’t really understand how they are fueled and what the structure is around them. XRISM can unlock this information indirectly by resolving some of the key X-ray emission and absorption lines. In particular, the most prominent emission line in the X-ray spectrum of an active galactic nucleus is a neutral iron Kα line at 6.4 kiloelectronvolts (keV), which arises from material around the supermassive black hole being illuminated by the light from the accretion process. This line holds the keys to probing the structure of the surrounding gas, as its dynamics can tell us about the structure of the accretion disk and trace gas in the torus that is thought to connect the local host galaxy to the accretion flow.

Figure 2 shows the XRISM Resolve spectrum of NGC 4151 from two separate observations. The spectrum shows a prominent 6.4 keV line that is resolved, meaning that the measured width of the line is greater than the instrument’s resolution limit. Additionally, the line cannot be fit with a single emission line and instead requires multiple lines, signaling multiple physical scales contributing to this emission line. The right panels of this figure highlight that there are three distinct components to this emission line with broad (magenta), intermediate (dark blue), and narrow (cyan) widths. Since gas that is closer to the black hole will be moving faster than more distant gas, the authors can use these line widths to estimate where this gas is located. They find that these three lines range from about 100 gravitational radii (about 100 times the size of the black hole) to about 10,000 gravitational radii. Determining the multi-scale nature of this line has been extraordinarily difficult to detect with other instruments due to their limited energy resolution!

XRISM resolve spectra of NGC 4151

Figure 2: XRISM Resolve spectra of NGC 4151. The left panels show the spectrum in the 5.8-7.2 keV range from two separate observations, with the data in black and the best fit total model in red. The right panels show a zoom in on the iron Kα 6.4 keV line with the three different components for the line also shown. The magenta model corresponds to the widest line, arising potentially from a warped disk, the dark blue model corresponds to the intermediate width line coming from the inner edge of the broad line region (BLR), and the cyan model corresponds to the most narrow line that arises from the inner edge of the dusty torus. [XRISM Collaboration et al. 2024]

Together these three components to the iron Kα line provide a compelling picture for the nuclear structure, which is shown in Figure 3. There are some additional pieces of evidence from the data that support this model as well. For example, the broadest line (magenta) shows variability on timescales of less than a day. This timescale corresponds roughly to the distance light could travel before reaching the magenta part of this figure, supporting the idea that there is a broad component associated with the disk. In addition to the location of the emitting gas, the dynamics and density can be constrained using the energy and shape of the line, respectively. In this source, the line is at the rest-frame energy and the shape is relatively symmetric, which together suggest that the emission comes from relatively optically thin gas that has not been accelerated to high velocities. Together, these diagnostics give one of the most in-depth pictures of supermassive black hole environments to date and will be crucial for testing our models of black hole feeding!

Schematic highlighting where each of the iron Kα emission lines arise from

Figure 3: Schematic highlighting where each of the iron Kα emission lines arise from. The magenta component corresponds to the broadest line, potentially from a warp in the disk. The dark blue component corresponds to the intermediate-width line and arises from the inner edge of the broad line region (BLR). The cyan component corresponds to the narrowest line and arises from the inner edge of the active galactic nucleus torus. [XRISM Collaboration et al. 2024]

What’s Next?

These XRISM observations are rich with information, and today’s article focused only on the 6.4 keV emission line. The authors are planning a series of further articles, including on the active galactic nucleus winds traced by the absorption lines (i.e., the major dips seen at ~6.7 and ~7 keV in the left panels of Figure 2), comparisons of the emission lines with optical emission lines, and looking for faint evidence of broader emission from even closer to the supermassive black hole. The next obvious steps are also to observe more active galactic nuclei to test whether this multi-zone emission is a common occurrence in active galactic nuclei. One thing’s for sure, this 20/20 vision is sure to reveal new secrets about the lives and environments of supermassive black holes!

Original astrobite edited by Roel Lefever.

About the author, Megan Masterson:

I’m a 4th-year PhD student at MIT studying transient accretion events around supermassive black holes, including tidal disruption events and changing-look active galactic nuclei. I primarily use multi-wavelength observations to study from the inner accretion flow to the obscuring material in these transients. In my free time, you’ll find me hiking, reading, and watching women’s soccer.

illustration of Betelgeuse

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.

Betelgeuse within the constellation of Orion

Figure 1: A schematic of the Orion constellation, with a small pink arrow indicating the location of Betelgeuse in Orion’s left shoulder. Click to enlarge. [Wikipedia]

Betelgeuse (Figure 1) is one of the most famous stars in the sky. As the closest red supergiant to Earth, it provides a unique opportunity to study the final stages of massive stellar evolution. In 2020, it captured our attention with a substantial, unexpected decrease in brightness that astronomers thought could be a sign of an impending supernova. Though we have since figured out that this “Great Dimming” was likely caused by a dust cloud forming from ejected mass, the event highlighted just how much we still don’t understand about the behavior of this enigmatic star.

Red supergiants like Betelgeuse are known to experience radial pulsations: repeated expansion and contraction of the star’s outer layers that cause periodic fluctuations in brightness. Like standing waves on a string, these pulsations include both fundamental and higher-order modes, with the fundamental mode dictating the longest possible pulsation period. For Betelgeuse, astronomers have measured a fundamental-mode period of about 420 days. However, they have also observed a long secondary period of 2,100 days, the origin of which is a mystery. Or is it?

Today’s bite covers two articles that independently reach the same conclusion: Betelgeuse’s long secondary period is caused by the presence of a low-mass binary companion! Such a small star would be drowned out by Betelgeuse’s intrinsic brightness and variability, explaining why we haven’t detected it before. But as today’s articles show, careful analysis of radial velocity and astrometry data can tease out signatures of Betelgeuse’s hidden buddy.

Article 1: One Long-Secondary-Period Mechanism to Rule Them All

Title: A Buddy for Betelgeuse: Binarity as the Origin of the Long Secondary Period in α Orionis
Authors: Jared A. Goldberg, Meridith Joyce, and László Molnár
First Author’s Institution: Flatiron Institute
Status: Published in ApJ

In Article 1, the authors use light curves from the American Association of Variable Star Observers (AAVSO) and radial velocities from STELLA to test the plausibility of eight different explanations for Betelgeuse’s long secondary period. Light curves are a record of how a star’s brightness (or magnitude) changes over time, and radial velocities record how quickly a star is moving towards or away from us. For Betelgeuse, both datasets show the ~2,100-day long secondary period. The authors measure a phase offset between the datasets of about −2 radians, meaning that the radial velocity curve lags behind the light curve by about half an orbit. With these data in hand, they are able to rule out all proposed long-secondary-period mechanisms that don’t involve a binary companion. For example:

  1. Is the long secondary period really the fundamental mode? No! If this were the case, Betelgeuse would need to have a much larger radius than what we’ve measured. The ~420-day fundamental mode aligns much more closely with our observations.
  2. Is the long secondary period caused by the motion of convective cells on Betelgeuse’s surface? No! Convection would introduce random variability that we don’t observe. Plus, we expect convective motions on the surfaces of all luminous giant stars, but we know that not all of these stars show long secondary periods like Betelgeuse does.
  3. Is the long secondary period caused by starspots coming in and out of view? No! Betelgeuse’s long-secondary-period variations are stronger at bluer wavelengths than redder wavelengths, which starspots can’t replicate. We’d also expect Betelgeuse’s magnetic activity cycle to have a longer period than the long secondary period.

The authors conclude the long secondary period is most likely caused by binarity. However, a small companion passing in front of Betelgeuse would not cause noticeable dimming compared to the variability from pulsations. Since red supergiants like Betelgeuse have dusty circumstellar environments, previous work suggested that a hypothetical companion could drag a cloud of dust along with it to produce more significant dimming. This theory would also explain why long-secondary-period variations are stronger at bluer wavelengths, where dust scatters more light than at redder wavelengths.

There’s just one problem: the measured phase offset of −2 radians suggests that the companion would be in front of Betelgeuse when the system reaches maximum brightness, not behind it as the above theory suggests. To address this, the authors propose that rather than dragging dust along, the companion somehow destroys or modifies dust to cause an increase in Betelgeuse’s brightness as it passes in front of the larger star (see Figure 2). However, they note that future work is necessary to determine exactly how this could occur.

observed and modeled light curve and radial velocity curve for Betelgeuse

Figure 2: A sketch showing how Betelgeuse’s light curve variability (top graph) and radial velocity variability (bottom graph) can be explained by the presence of a binary companion. The light curve is noticeably out of phase with the radial velocity curve, as found in both articles. The middle row of drawings shows what an observer on Earth would see at each of the four points labeled on the two graphs. Betelgeuse is shown as a red circle, its hypothetical companion is shown as a black circle, and its circumstellar dust is shown as curly orange and red lines. Note that at maximum brightness (Point C), the companion is in front of Betelgeuse instead of behind it. [Goldberg et al. 2024]

From their radial velocity data, the authors estimate a lower mass limit of 1.17 ± 0.07 M☉ (just a little bigger than our Sun!) and an orbital separation of 1,850 ± 70 R☉ for Betelgeuse’s companion. As expected, they find that such a small star would be nearly impossible to detect so close to Betelgeuse, which is much brighter, larger, and more variable than its hypothetical buddy.

Article 2: Finding Binarity in a Century’s Worth of Data

Title: Radial Velocity and Astrometric Evidence for a Close Companion to Betelgeuse
Authors: Morgan MacLeod et al.
First Author’s Institution: Center for Astrophysics | Harvard & Smithsonian; Institute for Theory and Computation
Status: Published in ApJ

In Article 2, the authors leverage more than 100 years of radial velocity data — including the radial velocities from Article 1 and several other, independent datasets — to search for a binary companion to Betelgeuse. First, they construct a model including the unseen companion, a flexible Gaussian process kernel to represent variability from other sources, and a noise term. Then, they fit their radial velocities with this model to retrieve the period, phase, and amplitude of the long-secondary-period signal (see Figure 3).

plots of observed and modeled radial velocity data for Betelgeuse

Figure 3: Results of fitting a model (blue lines) to 100 years of radial velocity data (black points). The top panel shows the full model, including the flexible kernel used to fit all variability sources other than the long secondary period (labeled “GP” here for “Gaussian process”). The middle panel shows just the sinusoidal component of the fit that represents the long secondary period, which remains a good fit over the whole baseline. The bottom panel shows the residuals after the full model (from the top panel) is subtracted from the data. [MacLeod et al., in press]

The authors find that their century-long dataset is well-described by a single periodic signal, implying that the long secondary period is stable over time. This supports binarity as the long-secondary-period mechanism, since other mechanisms like convection would show random variations over such a long timescale. By comparing their radial velocities with the AAVSO light curve (which provides coverage all the way back to 1920!), the authors also find a phase offset of about one-half of an orbit, in good agreement with the results from Article 1.

Using their fitted parameters, the authors derive a mass of 0.60 ± 0.17 M☉ and an orbital separation of 1,818 ± 6 R☉ for Betelgeuse’s hidden companion. This mass is significantly smaller than the estimate from Article 1, which the authors attribute to differences in the fitted long-secondary-period amplitudes between the articles. Betelgeuse’s radial velocity curve includes multiple sources of variability from radial pulsations, the signature from the binary companion, and other random changes. As a result, the true amplitude of the long-secondary-period variations is highly uncertain, even when the period is well constrained.

In addition to radial velocities, the authors also investigate Betelgeuse’s long secondary period with astrometry. Astrometric data record the relative positions and motions of the stars, which are expected to follow predictable patterns. An unseen binary companion will introduce an extra “wobble” in this pattern by tugging the visible star back and forth as it moves through space. These wobbles can be modeled to determine the orbital parameters of the binary, which should agree with the parameters derived from radial velocity data.

The authors fit a model to their astrometry and find two prominent periods: 1) the long secondary period at ~2,100 days and 2) an unexpected periodicity at ~1,650 days (about four times the fundamental mode). They also find a variability amplitude that is higher than their fitted radial velocity amplitude, implying a companion mass of 2.1 ± 0.5 M☉. However, their data are fit equally well by a binary model or a single-star model that includes a significant noise term. This is likely due to the fact that Betelgeuse’s radial extent is larger than its expected astrometric wobble, making precise astrometric measurements difficult. The authors conclude that additional observations are needed to constrain the binarity of Betelgeuse with astrometry, noting that their radial velocity results are more trustworthy given the longer baseline, higher cadence, and higher precision of the radial velocity data.

Using their measured parameters of the Betelgeuse system, the authors calculate the evolution of the hypothetical binary due to tides. Just like the Moon’s gravitational pull creates tides on Earth, the stars in a binary system pull on each other as well, dissipating angular momentum from the system and moving closer together in a process called “orbital decay.” In the case of Betelgeuse, the authors find that tides will cause runaway orbital decay over the next 10,000 years, where the system will be unable to stabilize as the orbital separation shrinks. This means Betelgeuse will interact with and eventually swallow its tiny companion!

Staying Hidden… for Now

Despite using different datasets and fitting methods, both of today’s articles find evidence that the ~2,100-day long secondary period in Betelgeuse’s light curve is likely due to the presence of a previously undetected, low-mass binary companion. Direct detection of such a companion is nearly impossible with current instruments, but further study of the dust around Betelgeuse could help constrain the companion’s properties. If confirmed, Betelgeuse’s binarity would have significant implications for both its evolution and for other evolved stars with long secondary periods, which may also host hidden binary buddies!

Original astrobite edited by Lindsey Gordon.

About the author, Alexandra Masegian:

Alexandra is a second-year PhD student in astronomy at Columbia University and the American Museum of Natural History. She is broadly interested in stellar astrophysics, especially evolved stars and binaries. Outside of work, she enjoys cooking, reading and writing science fiction, and visiting national parks.

Stellar bow shock

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: Probing the Low-Velocity Regime of Nonradiative Shocks with Neutron Star Bow Shocks
Authors: Stella Koch Ocker and Maren Cosens
First Author’s Institution: California Institute of Technology and Observatories of the Carnegie Institution for Science
Status: Published in ApJL

Neutron stars are fascinating remnants of massive stars that have undergone a supernova explosion. These stellar remnants often move at incredible speeds through space, producing bow shocks, the regions where the fast-moving neutron star collides with interstellar gas. Imagine a cosmic wind so powerful that it creates a shock wave in space, much like a speedboat cutting through water. These powerful shock waves hold clues to study non-radiative shocks, which play an important role in heating plasma and accelerating particles, such as cosmic rays. Today’s article took a closer look at the properties of three neutron-star bow shocks in unprecedented detail, revealing new insights into the hidden physics behind these cosmic collisions.

What Are Bow Shocks?

Image of the LL Orionis bow shock taken with the Hubble Space Telescope

Figure 1: Image of the LL Orionis bow shock taken with the Hubble Space Telescope. [NASA and The Hubble Heritage Team (STScI/AURA); Acknowledgment: C. R. O’Dell (Vanderbilt University)]

A bow shock forms when a fast-moving object, like a neutron star, passes through a medium — in this case, the interstellar medium, the gas and dust that fills the space between stars. The interaction between the neutron star’s wind and the interstellar medium causes form a shock wave, which resembles the bow wave that forms at the front of a boat moving through water (for example, see Figure 1).

In the context of neutron stars, the bow shock is non-radiative, meaning it does not emit much in the form of light or heat. However, the shock does produce a particular type of emission called Hα (hydrogen alpha), which occurs when neutral hydrogen atoms in the interstellar medium are excited and emit light at a specific wavelength in the optical wavelength range. Observing this Hα emission is one of the main ways astronomers can study neutron-star bow shocks.

Understanding the Shock’s Velocity and Structure

Today’s authors focused on three known neutron-star bow shocks (see Figure 2): J0742−2822, J1741−2054, and J2225+6535 (also known as the “Guitar Nebula”). Using integral field spectroscopy, a technique that captures both the spatial and spectral information of an object, they were able to observe these bow shocks in detail. For their observations, they used the Keck Cosmic Web Imager (KCWI) on the Keck II Telescope in Hawaii. Unlike traditional spectroscopy, which provides a one-dimensional spectrum of light from a single region, integral field spectroscopy collects spectra across a two-dimensional field, allowing the astronomers to map the shock properties. This allows astronomers to study the shock shape, velocity structure, and Hα emission intensity in exquisite detail, giving a more complete picture of how these shocks behave.

neutron-star bow shock images

Figure 2: KCWI data of the three neutron-star bow shocks, showing the morphologies of each bow shock at different velocity slices. [Ocker & Cosens 2024]

Studying the relative contributions to the Hα emission is crucial to unlocking the detailed shock physics.  There are two main components to the Hα emission: a narrow line that represents the ambient gas in the interstellar medium and a broad line produced by the shock itself. The ratio between these two lines, the broad-to-narrow line intensity ratio (Ib/In), provides crucial information about the velocity of the shock and the processes occurring within it, including the electron-ion temperature and the particle energy distribution.

The study revealed that the Ib/In values for all three neutron-star bow shocks indicated low shock velocities, all below 200 kilometers per second. This is notably different from the much higher velocities seen in supernova remnants, where shocks can exceed 1,000 kilometers per second. These results suggest that neutron-star bow shocks operate in a distinct low-velocity regime, and current models, which are designed for higher-velocity shocks, may not fully capture the behavior of these slower shocks. To better understand the temperature ratios between electrons and ions, as well as how particles are accelerated in this regime, new models are needed.

Why Is the Low-Velocity Regime Important?

Understanding the low-velocity regime of non-radiative shocks is important for several reasons:

  • Cosmic-Ray Acceleration: Non-radiative shocks are believed to accelerate particles to very high speeds, contributing to the population of cosmic rays — high-energy charged particles that travel through space. Studying how these shocks operate at different velocities helps scientists understand how cosmic rays are produced and what role neutron stars might play in this process.
  • Energy Transfer in Shocks: Non-radiative shocks are also key to understanding how energy is transferred between different types of particles, such as electrons and protons. In faster shocks, the temperature of electrons and protons can differ significantly, but in slower shocks, like those studied here, the temperatures might be more equal. Understanding this balance provides insight into the physics of shock waves and how they heat and accelerate particles.
  • Astrophysical Modeling: Most models of non-radiative shocks are based on high-velocity shocks in supernova remnants. However, the findings from this study suggest that these models need to be expanded to include slower shocks, which behave differently and require new theoretical approaches.

This study provides critical new insights into the enigmatic nature of neutron-star bow shocks, particularly in the unexplored low-velocity regime. By probing these slow shocks, we unlock a deeper understanding of how astrophysical plasmas are heated and how particles are accelerated to cosmic-ray speeds — shedding light on some of the most powerful processes in the universe. The findings challenge existing models of non-radiative shocks, emphasizing the need for new theory to capture the unique behavior of these slower shocks. As a result, this research not only reshapes our understanding of cosmic rays but also paves the way for exciting new directions in astrophysics, with potential breakthroughs on the horizon.

Original astrobite edited by Megan Masterson.

About the author, Janette Suherli:

Janette is a PhD student at University of Manitoba in Winnipeg, Canada. Her research focuses on the utilization of integral field spectroscopy for the studies of supernova remnants and their compact objects in the optical. She is also the current chair of Graduate Student Committee for the Canadian Astronomical Society (CASCA). She grew up in Indonesia where it is summer all year round! Before pursuing her PhD in astrophysics, Janette worked as a data analyst for a big Indonesian tech company, combating credit card fraud.

illustration of planets colliding

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: Accelerating Giant Impact Simulations with Machine Learning
Authors: Caleb Lammers et al.
First Author’s Institution: Princeton University
Status: Published in ApJ

Planet Formation

In the nebular hypothesis view of planet formation, planets form out of a protoplanetary disk, starting out as small dust grains, some of which combine and grow to form planetesimals and eventually planets. Part of this process is the giant-impact phase, in which the planetesimals experience frequent, violent collisions, leading to the growth of what will eventually become fully fledged planets.

Numerically modeling the giant-impact phase is complicated by the computational difficulty of running simulations of many bodies over long timescales. Machine learning has already been adopted to improve and speed up planetary simulations, such as with the Stability of Planetary Orbital Configurations Klassifier (SPOCK) package. SPOCK’s first incarnation (SPOCKI) predicts whether a compact planetary system is stable over 1,000,000,000 orbits based on the results of a shorter 10,000-orbit integration, and an extension (SPOCKII) uses those results to predict when a planetary system will destabilize.

Creating a Machine Learning™ Framework

Today’s article extends previous work by using machine learning to predict the outcomes of planet–planet collisions in three-planet systems with two subtasks: 1) predicting which planet pair will collide and 2) predicting the orbital configuration of the resulting system. The authors use supervised learning: giving a model a training set with inputs and correct outputs so that the model can learn the mapping from inputs to outputs. Predicting collisions (subtask 1) requires classification, producing an outcome from a set of discrete options or categories (in this case, which planets collide). Predicting resulting orbital configurations (subtask 2) requires regression, producing numerical values describing the orbit of the post-collision system. The authors use a training set of more than 500,000 N-body simulations integrated with the REBOUND package. They initialize tightly packed three-planet systems with randomized initial conditions and integrate the systems for 10,000,000 orbits (of the innermost planet) with mass and momentum conservation. The authors only keep the systems with mergers between 10,000 and 10,000,000 orbits so the machine learning framework does not concern itself with non-merging systems.

The authors use separate multi-layer perceptron models independently trained on 80% of the training set (leaving the rest as a validation set) to complete both subtasks, following the schematic shown in Figure 1. The collision classifier takes as inputs the orbital elements of the three planets after they’ve completed 10,000 orbits, generates the probabilities of planet-pair collisions, and samples the probabilities to determine which pair of planets collide. The orbital outcome regressor takes the orbital elements of the three planets and the choice of which planets collide to predict the new orbital elements (semi-major axis, eccentricity, and inclination) of the resulting system.

schematic of the machine learning framework

Figure 1: A schematic of the machine learning model in which a classifier predicts which pair among three planets collides and a regressor predicts the orbital configuration of the two resulting planets. [Lammers et al. 2024]

Piecing Together the Giant-Impact Emulator

The authors combine their machine learning model with SPOCKII to create an iterative emulator to model the giant-impact phase, with a schematic shown in Figure 2. The emulator takes in overly packed, multi-planet systems with randomly initialized configurations, groups the systems into trios of planets, uses SPOCKII to predict when the systems will destabilize, merges the most unstable trio, and then repeats until stability is achieved. The authors also run N-body simulations for 500 ten-planet systems for comparison with the results of the emulator. As shown in Figure 3, there is close agreement with masses, spacings, inclinations, and most system-level properties.

demonstration of the iterative model of the emulator

Figure 2: A schematic of the giant-impact emulator in which a multi-planet system is broken into trios. The machine learning model shown in Figure 1 predicts the results of a collision in the most unstable trio, and the process repeats until a stable system is formed. [Lammers et al. 2024]

comparison of the properties of the N-body and machine learning–based planetary systems

Figure 3: A comparison of the properties of the N-body (red) and machine learning–based (blue) planetary systems resulting from giant impacts. The machine learning–based framework produces largely similar results except that machine learning tends to produce systems that are less dynamically excited (bottom right). [Lammers et al. 2024]

In comparison with the N-body simulations, the machine learning–based emulator is ~10,000 times faster. The collision classifier model predicts probabilities with a scatter of ~10% and minimal offsets from the N-body-derived probabilities. The orbital outcome regressor predicts orbital configurations for the validation set almost at the accuracy limit set by chaos inherent in N-body dynamics.

Planet formation is a messy, unsolved problem. Until the discovery of exoplanetary systems, we had only the solar system and its single formation outcome to study. It turns out that nature produces a beautiful mosaic of planetary systems, many of which bear little resemblance to our own. The giant-impact phase has long been a bottleneck in simulating planet formation due to computational constraints, and today’s article presents a truly exciting advancement in modeling planet–planet collisions. The model is limited in that it breaks multi-planet systems into trios of adjacent planets and models collisions and scattering only within trios, though the authors expect this is a minor effect. The model is publicly available and will certainly expand the frontier of possible planet formation analyses.

Original astrobite edited by Nathalie Korhonen Cuestas.

About the author, Kylee Carden:

I am a second-year PhD student at The Ohio State University, where I am an observer of planets outside the solar system. I’m involved with the Roman Space Telescope, a small robotic telescope called DEMONEXT, and exoplanet atmospheres. I am a huge fan of my cat Piccadilly, cycling, and visiting underappreciated tourist sites.

Jellyfish galaxy ESO 137-001

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: Dark-Matter-Free Dwarf Galaxy Formation at the Tips of the Tentacles of Jellyfish Galaxies
Authors: V. Lora et al.
First Author’s Institution: Institute of Nuclear Sciences, Mexico (UNAM)
Status: Published in ApJL

When Jellyfish Fly

Most galaxies are part of a galaxy cluster, which is exactly what it sounds like — a large collection of galaxies that are gravitationally bound to the larger cluster, much like how stars are gravitationally bound to a larger galaxy. In addition to the galaxies themselves, there is also gas between the galaxies in the cluster, referred to as the intracluster medium. When a disk-like galaxy moves through the intracluster medium in a galaxy cluster, some of the gas within the galaxy (the interstellar medium) gets stripped away from the galaxy. This creates long gaseous tails (or, if you will, tentacles), giving the galaxy an uncanny resemblance to a jellyfish!

Jellyfish galaxies, and their tentacles in particular, have been studied for decades. Astronomers have investigated how much of the gas in the tentacles comes from the intracluster medium versus the interstellar medium, as well as where and how star formation occurs within the tentacles. Interestingly, astronomers have found star-forming regions in the tentacles that have similar masses and sizes to ultra-compact dwarf galaxies. Today’s authors look to reproduce those results computationally and better understand how this dwarf galaxy formation channel works.

Hanging On by a Tentacle

The authors use data from the IllustrisTNG50 simulation, a cosmological simulation large enough to form dozens of galaxy clusters with enough resolution to accurately model features such as the arms of spiral galaxies. The authors identify a set of jellyfish galaxies within this simulation, then make additional cuts to

  • ensure the galaxies have obvious tentacles;
  • find locations of star formation within the tentacles; and
  • eliminate galaxies where tentacle-like features could be due to interactions with other galaxies.

These cuts leave only one galaxy with a mass of ~400 billion solar masses; compare this to the mass of the Milky Way, which is typically reported as ~1 trillion solar masses. (However, a 2023 study found that the Milky Way mass was closer to ~200 billion solar masses.)

The authors identify a star-forming site within one of the tentacles of this galaxy, highlighted in Figure 1. This both supports the observational evidence and suggests that this may be a new type of dwarf galaxy (more on this in a moment). Additionally, by tracking the galaxy’s history prior to the infall, they determine that the galaxy loses gas but not stars. This means that the gas in the tentacle came from the galaxy, but the stars are forming in the tentacle rather than being relocated from the galaxy. This is a consequence of ram-pressure stripping, the primary physical phenomenon that creates the tails of jellyfish galaxies. Another important finding about the dwarf galaxy candidate is that it lies well outside the dark-matter halo of the jellyfish galaxy, which has important ramifications for its status as a dwarf galaxy candidate.

Visualizations of a star-forming region in the tentacle of a jellyfish galaxy

Figure 1: Different visualizations of the selected galaxy. The top panel shows neutral gas (green), dark matter (white), and star formation (rainbow). The bottom panel shows the dark matter (white) and stellar mass (rainbow). The dwarf candidate is circled in magenta in both panels. [Lora et al. 2024]

Dark-Matter-Deficient Dwarfs

The authors perform additional analysis on the dwarf galaxy candidate. First, they determine that the gas and stars are gravitationally bound, meaning that they can be thought of as a single system much like how a galaxy is thought of as a single system. They also look at the dark-matter content of the dwarf galaxy candidate and find that none of it is gravitationally bound, making this a dark-matter-free dwarf galaxy. Furthermore, they estimate the mass and size of the dwarf galaxy candidate to be ~200 million solar masses and ~1–1.5 kiloparsecs. Based on these findings, the authors conclude that this system represents a new kind of dwarf galaxy, which they dub a ram-pressure-stripped dwarf galaxy; additionally, ram-pressure-stripped dwarf galaxies are unique among dwarf galaxies because they lack a dark-matter halo due to their creation via ram pressure stripping.

Plots of the star formation rate and the oxygen abundance

Figure 2: Star formation rate (top panel) and oxygen abundance (proxy for metal concentration, bottom panel) of the ram-pressure-stripped candidate (magenta). [Lora et al. 2024]

The authors also analyze the star formation and metallicity of the ram-pressure-stripped dwarf, shown in Figure 2. They find a high star formation rate compared to other star-forming regions created via ram pressure stripping. They also find that the ram-pressure-stripped dwarf is very metal rich compared to other dwarf galaxies of similar size and mass; this is because the jellyfish galaxy is also rich in metals, so the gas stripped into the tentacle to form stars has a higher concentration of metals.

Today’s authors have found evidence of a new type of dwarf galaxy, which they call a ram-pressure-stripped dwarf galaxy. These dwarf galaxies form via ram pressure stripping in the tentacles of jellyfish galaxies and are characterized as being gravitationally self-bound, hosting star formation, and lacking a dark-matter halo. The authors hope to continue studies of ram-pressure-stripped dwarf galaxies, noting that other cosmological simulations that can resolve smaller amounts of mass may lead to more discoveries of ram-pressure-stripped dwarfs with lower masses.

Original astrobite edited by Amaya Sinha.

About the author, Brandon Pries:

I am a graduate student in physics at Georgia Institute of Technology (Georgia Tech). I do research in computational astrophysics with John Wise, using machine learning to study the formation and evolution of supermassive black holes in the early universe. I’ve also done extensive research with the IceCube Collaboration as an undergraduate at Michigan State University, studying applications of neural networks to event reconstructions and searching for signals of neutrinos from dark matter annihilation.

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