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Illustration of a dark body in the distant outer reaches of the solar 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

Title: Injection of Inner Oort Cloud Objects Into the Distant Kuiper Belt by Planet Nine
Authors: Konstantin Batygin and Michael E. Brown
First Author’s Institution: California Institute of Technology
Status: Accepted to ApJL

Ladies and gentlemen, welcome aboard the Astrobites Airlines with service from the Earth to Planet Nine. We are currently fourth in line for take-off, but you can learn more about other take-offs to Planet Nine here, here, and here. We are traveling at the speed of light and the duration of our flight will be about 70 hours. We ask that you please enjoy our long journey to the outer solar system.

Diagram illustrating the locations of the Kuiper Belt and the Oort cloud in our solar system.

Figure 1: The Kuiper Belt and Oort Cloud location. [ESA]

Our journey starts in the Kuiper Belt, a ring of icy bodies residing beyond Neptune’s orbit. Look around — these are distant Kuiper Belt Objects (KBOs) (look at Figure 1)! We can see (also in Figure 2) two distinct types of distant KBOs: some KBOs have dynamically stable orbits and some do not. Those that are unstable are destabilized by Neptune. The observed clustering of stable orbits needs to be affected by something so that it maintains orbital alignment against differential precession induced by Jupiter, Saturn, Uranus, and Neptune (huge planets that have huge gravity!). So, what is affecting the orbits of the stable KBOs? The authors of today’s paper think it could be Planet Nine!

Schematic showing the orbits of a set of distant Kuiper Belt Objects

Figure 2: Census of distant KBOs. The orbits of stable KBOs are depicted in purple and grey. Unstable ones are depicted in green. [Batygin & Brown 2021]

We Forgot That the Universe Is BIG!

The authors have been working on Planet Nine for a long time (their first paper hypothesizing the existence of this distant, unseen giant planet in our solar system was published in 2016)! During this time, they made some estimates on dynamical properties of the planet we are heading to right now. For example, Planet Nine might have a mass of 5 Earth masses, with a semi-major axis of 500 au, an eccentricity of 0.25, and an inclination of 20 degrees from the data that was observed (Planet Nine itself has not yet been observed). However, all this time, the authors treated the solar system as an isolated object, neglecting all the bodies that attain a heliocentric distance of over 10,000 au. But these bodies are still there! The authors’ assumption is valid for representing the evolution of objects with semi-major axes on the order of a few hundred au. More recent detections of trans-Neptunian objects (TNOs), however, increasingly point to a pronounced abundance of long-period TNOs with a heliocentric distance of over 10,000 au. This orbital domain borders the inner Oort cloud (IOC). More importantly, the population of debris in the IOC is stable, just like the KBOs mentioned above! So, the authors’ hypothesis is that some of these stable KBOs were injected into the Kuiper Belt from the outside, possibly due to the influence of Planet Nine.

The Tug-of-War Between Giant Planets and Stars

As we go further and further from the Sun on our spaceship, it is important to note that the Sun’s birth environment played an important role in shaping the solar system. After all, the Sun is the reason we have our solar system in the first place! The Sun, like any other star, was born in a big family of stars — a cluster. Now, it’s time to wear your glasses, because it’s simulation time!

The authors made an N-body simulation of the formation of our solar system including Jupiter and Saturn (they are significant because they are huge) and 100,000 planetesimals, spanning the 4.5−12 au range in the heliocentric distance in initial circular and coplanar orbits. They modeled the Sun’s birth cluster as a Plummer sphere. The Plummer sphere is often used in N-body simulations to “soften” gravity at small distance scales. This is needed to prevent the point particles from scattering too strongly off of one another on a close approach. Along with Jupiter and Saturn, they also modeled “passing stars” — members of the Sun’s family that might have affected the debris gravitationally. All of it, the concurrent growth of giant planets and the passing stars, affects the planetesimals. Think of it as a tug-of-war between Jupiter and Saturn on one side and the passing stars on the other side. Because these icy objects (a.k.a. planetesimals) don’t know where to go, they choose to “freeze” in place, thousands of au away from the Sun. These are what the IOC is formed of.

Have We Reached Planet Nine Yet?

Dear passengers, it’s the time for another simulation! In absence of Planet Nine, the IOC created by the tug-of-war between giant-planet scattering and the passing stars would essentially remain dynamically frozen over the main-sequence lifetime of the Sun. But that’s because Planet Nine wasn’t considered in the first simulation. Let’s see what happens when the authors add Planet Nine.

In this simulation, the authors accounted for the dynamics driven by Neptune, Planet Nine, and the passing stars as well as the effect of the galactic gravitational tidal field and the average effect of Jupiter, Saturn, and Uranus. They found that over the lifetime of the Sun, a significant fraction (that is, on the order of 20%) of the IOC gets injected into the distant Kuiper belt. The authors also found that these re-injected IOC objects exhibit orbital clustering, which is important for the Planet Nine hypothesis (see this previous bite for more details). However, the degree of clustering is considerably weaker. The data suggests that Planet Nine might be even more eccentric than we thought. So, our journey might take a little longer! Another key result of the simulation is that IOC objects display a very extended semi-major axis distribution, which might explain objects like the Goblin.

Three scenes modeling the simulated evolution of the solar system, illustrating body orbits.

Figure 3: Sequence of events modeled within this work. A population of trans-Neptunian objects forms while the Sun is still in its birth cluster. Subsequently, over the billion-year lifetime of the solar system, Planet Nine slowly affects these extremely long-period objects, mixing them into the observed census of Kuiper belt objects. [Batygin & Brown 2021]

We are happy that you chose us again for your journey. We are really excited to see what is really out there, far away in our solar system. Thank you for choosing Astrobites Airlines!

Original astrobite edited by Catherine Manea.
A Russian translation of this article is available on Astrobites, also written by Sabina Sagynbayeva.

About the author, Sabina Sagynbayeva:

I’m a graduate student at Stony Brook University and my main research area is planets. I’m currently working on planet formation using hydrodynamical simulations. I’m mainly interested in planet-disk interaction but nearly any topic related to planets is fascinating to me! In addition to doing research, I’m also a singer-songwriter. I LOVE writing songs, and you can find them on any streaming platforms.

An artist's impression of a rocky exoplanet is seen in the centre of the image, illuminated from the right by a large star. The planet is dark, almost black in colour, however small cracks cover its surface revealing glowing red underneath, as if the planet is made of magma that has cooled in places. The planet's atmosphere appears to be being blasted away from the planet by the 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: Water On Hot Rocky Exoplanets
Authors: Edwin S. Kite, Laura Schaefer
First Author’s Institution: University of Chicago
Status: Published in ApJL

Most Sun-like stars are thought to be home to a hot rocky exoplanet — which could mean that there are more than 300 million potentially habitable planets in our galaxy! However, whether any of these planets have atmospheres remains unknown. Unlike the Blue Marble we call home, the way that many of these planets form leaves them as dead rocks.

Most terrestrial planets larger than Earth (known as super-Earths) are thought to form as sub-Neptunes, consisting of a silicate magma ball surrounded by a thick atmosphere accreted from the planetary disc during formation. Because this atmosphere is dominated by light hydrogen molecules, it has a low mean molecular weight (µ, the average weight of each molecule in the atmosphere) and is later lost to space via atmospheric escape, leaving behind the bare super-Earth. While it’s possible for planets to later regain an atmosphere via volcanic activity or impacts from comets, what if there were a way for super-Earths to develop atmospheres while they evolve from sub-Neptunes?

Today’s paper explores a potential pathway that can not only generate super-Earth atmospheres, but could also allow them to be retained for billions of years.

When Magma Meets Air

The authors consider what happens to the products that form when a sub-Neptune’s magma reacts with its atmosphere. Iron oxides in the magma react with the atmospheric hydrogen, producing water, and iron, which sinks to the planet’s core. While some of this steam escapes into the atmosphere and mixes with the hydrogen, most of it dissolves and remains trapped in the magma, creating a planet made up of a slightly watery magma ball surrounded by a slightly higher-mean-molecular-weight atmosphere. But as the atmosphere begins to escape, what happens to the water?

diagram titled "pathways to a high-molecular-weight atmosphere"

Figure 1: A graphical representation of the potential pathways a sub-Neptune can take to become a super-Earth with a high-mean-molecular-weight (µatm) atmosphere. Blue atmospheres are hydrogen dominated, while green atmospheres are water dominated. The left-hand side shows a sub-Neptune losing its atmosphere to space, becoming a bare rock, and later regaining a high-µatm atmosphere. The right-hand side shows the pathway outlined in today’s paper, with a sub-Neptune evolving to a super-Earth with a water-dominated atmosphere via atmosphere–magma interactions. In each pathway, the sub-Neptune moves across the radius valley, decreasing in radius as it goes. [Adapted from Kite & Schaefer 2021]

Using models of planets, the atmospheres of each planet are removed in small steps, reassessing the equilibrium between the magma and the atmosphere each time. With each step, the pressure at the surface of the magma decreases, allowing some of the gases trapped there to escape. As the atmospheric loss continues, the model atmosphere gets thinner and thinner, while the large reservoir of water dissolved in the magma continues to be released. As outlined in Figure 1, over time the hydrogen will be completely lost, leaving behind a 150–500 km thick atmosphere and a water-dominated world! This kind of watery atmosphere can be referred to as being endogenic, as it originates from within the planet, as opposed to the exogenic atmospheres created by external processes, like being hit by an icy comet.

Water, Water, Everywhere?

The length of time for which a planet has a water-dominated atmosphere depends on how aggressive the atmospheric loss is. While smaller planets very close to their stars are at higher risk for atmospheric loss, planets at greater distances from their stars are safer and may never endure the process. Planets in between these extremes are able to keep hold of their newly acquired water-dominated atmospheres for varying lengths of time, but could potentially retain them for billions of years. So which planets can we expect to have watery envelopes?

When plotted on a graph of planetary radius vs. orbital period, the larger-radius sub-Neptunes and smaller-radius super-Earths are separated by a lack of planets often known as the radius valley. As a sub-Neptune loses its atmosphere its radius decreases, moving it down through the radius valley. The authors predict that, provided the planet has a long enough period and the interactions between magma and atmospheres are sufficiently efficient, the evolving planets that are able to retain water-dominated atmospheres should be found lining the radius valley in a “water belt”, as seen in Figure 2.

A plot of radius vs period showing planets near the radius valley

Figure 2: The “water belt” of super-Earths, shown in period–radius space for planets orbiting stars less than 3 Gyr old. The blue region shows the area occupied by sub-Neptunes, while the red region shows the area occupied by super-Earths. The yellow region in between is known as the radius valley. The water belt, where super-Earths with water dominated atmospheres may exist, is shown in green. The upper and lower dashed lines give the water-belt predictions for magmas with lower and higher amounts of iron oxides present. [Kite & Schaefer 2021]

Testing whether such planets exist could be relatively straight forward. Directly detecting the atmosphere of this kind of planet may be possible using a phase curve — a measurement of the light reflected and blocked by a tidally locked planet as it passes behind and in front of its host star. If the planet has retained the watery atmosphere, then heat can be more effectively distributed from the permanently illuminated day side to the cold, dark night side, leading to a smaller temperature difference between the two faces than would be the case for a bare, atmosphere-free rock. As endogenic atmospheres are likely to have smaller carbon-to-oxygen ratios than those on other super-Earths, observing the spectroscopic features of these atmospheres with the upcoming James Webb Space Telescope could also help distinguish between the two!

Original astrobite edited by Huei Sears.

About the author, Lili Alderson:

Lili Alderson is a first year PhD student at the University of Bristol studying exoplanet atmospheres with space-based telescopes. She spent her undergrad at the University of Southampton with a year in research at the Center for Astrophysics | Harvard-Smithsonian. When not thinking about exoplanets, Lili enjoys ballet, film and baking.


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: Finding Signs of Life in Transit: High-resolution Transmission Spectra of Earth-like Planets around FGKM Host Stars
Authors: Lisa Kaltenegger, Zifan Lin
First Author’s Institution: Cornell University & Carl Sagan Institute
Status: Published in ApJL

Anybody Out There?

One of the most fascinating topics in astronomy — and maybe in philosophy, as well — is the potential existence of other life out there in space: strange planets orbiting other stars, developing and evolving their own biology under unique circumstances.

Fortunately, life leaves so-called biosignatures for the keen observer to find; these biosignatures should be most prominent in the atmosphere of an inhabited planet. The presence of certain substances and molecules in an atmosphere suggests biological processes indicative of life. For instance, life creates large amounts of molecular oxygen (O2) by photosynthesis and small amounts of methane (CH4) on Earth. The simultaneous presence of these two molecules are strongly suggestive of biological processes, since on short timescales, these two species react to create carbon dioxide (CO2) and water (H2O) and must therefore constantly be replenished to remain detectable. The same goes for ozone (O3), which, if not replenished, decays into O2 within days. Water is often mentioned as an important secondary indicator for life as well as carbon dioxide. On their own, these molecules are not decisive, however in combination they can paint a clearer picture.

transmission spectroscopy

Figure 1: As a star’s light filters through a planet’s atmosphere on its way to Earth, the atmosphere absorbs certain wavelengths depending on its composition. [European Southern Observatory]

If a system is favorably inclined relative to an observer, stellar light passing through the exoplanet’s atmosphere can be analyzed after careful data reduction and calibration by spectroscopy; this is illustrated in Figure 1. The elements and molecules present in the atmosphere reveal themselves in the form of spectral lines and allow for a substantial analysis of the atmospheric composition.

New telescopes, such as the JWST and the ELT, make this highly detailed, so-called transmission spectroscopy possible. But where should we look to maximize our chances of actually finding life?

Simulating Biosignatures

The authors of today’s paper simulated the spectra of 12 Earth-like planets around FGK stars (between about 1,900 Kelvin cooler and 1,200 Kelvin hotter than the Sun) and 10 M dwarfs (about 1,900 Kelvin to 3,300 Kelvin cooler than the Sun) to a level of detail that will be achieved with upcoming spectrographs. In this way, researchers can prioritize exoplanets for atmospheric investigation, according to the expected signal strength of the biosignatures.

To conduct this simulation, a model is required. It takes into account the planet, its location, and any processes we know of that influence its atmosphere.

The habitable zone is largely defined by conditions that make liquid water possible. For the purposes of this work, the temperature on the simulated Earth-like planets was set to 288 Kelvin +/- 2%. To maintain this temperature, different stellar types have their habitable zones at different radial distances. Thus, a habitable planet around an M dwarf is much closer to its host star than a similar planet around a F star.

In terms of the simulated planet’s architecture, the authors decided to model the planets using Earth-like properties. The planets were simulated to have one Earth-radius, one Earth-mass, and similar rates of irradiance, outgassing, composition (70% ocean and 30% land made from basalt, granite, sand, grass, trees, and snow), surface pressure, and cloud coverage relative to modern Earth.

Now, let’s talk about our biosignature pairs O2+CH4 and O3+CH4. Ozone layer depth decreases for lower ultraviolet (UV) light environments, since it is this radiation that splits up O2 in the atmosphere, so that the two halves may combine with other O2 molecules and build O3. On the other hand, methane concentration increases with lower UV radiation since the molecules that methane reacts with on Earth are indirectly created with the help of UV light.

However, methane may be reduced by so-called space weather. This includes stellar activity, such as flares or stellar winds which send out charged particles at the planets, which then interact with the atmospheres. It is thus important to check the surroundings of the planet when searching for life on it, since non-biological environmental factors can be responsible for additionally increasing or decreasing biosignatures.

How Deep Can You Go?

The authors divided up the planetary atmospheres into 52 layers and simulated the width and strength of the spectral lines for each. There is a limit to the depth an observer may look into an atmosphere, since deeper layers deflect the light. Earth’s atmosphere, for example, could be probed to around 13 km above ground. Depending on the stellar type and thus the predominant wavelengths emitted by the star (shorter wavelengths coming from hotter stars are refracted more severely than the longer wavelengths emitted by cooler stars), planetary atmospheres can be probed to between 15.7 km (for planets orbiting F0V type stars) and 0 km (for planets orbiting M8V type stars) above their surface.

Clouds can heavily obscure spectral features of the layers below them. Because we do not know any details about cloud coverage on exoplanets, the authors included hypothetical spectra considering a 100% cloud coverage at a height of 6 km (the altitude of the middle layer of Earth clouds). This only affects the efficiency of detecting biosignatures for planets around M type stars, since they can theoretically be probed below this altitude.

Who Shows Their True Colors?

The overall strength of a spectral feature is determined by its abundance in an atmosphere, as well as the maximum depth an atmosphere can be probed to during a transit. The resulting simulated spectra are shown in Figure 2.

CO2, water, and oxygen show similar signal strengths across all modeled atmosphere spectra, however for water and oxygen, the detectability is strongly dependent on the maximum probable depth due to their location at relatively low altitude. Thus, these features on an Earth-like planet around a hot F-type star would be extremely difficult to find.

Stars with increased UV radiation (F types as well as active M dwarfs) show a high abundance of ozone in their planets’ atmospheres. Methane features can be best detected on planets in orbit around cooler stars with lower UV environments.

set of 12 plots showing simulated transmission spectra

Figure 2: Transmission spectra simulated by the authors for Earth-like planets hosting life in orbit around F stars (top row), G stars (second row), K dwarfs (third row), and M dwarfs (bottom row). The three columns display different wavelength ranges, from visible (left) through near-infrared (middle) to infrared (right) light. The most prominent spectral features are labeled with the names of their corresponding molecules. [Kaltenegger & Lin 2021]

Hence, the authors have shown that the biosignature pairs O2+CH4 and O3+CH4 become increasingly difficult to find in planets orbiting hotter stars. A potential way to increase methane levels would be to use a younger Earth model, when methane levels were much higher than in modern times. If we want to look for life around hotter stars, it could be feasible to search in younger systems where detection of methane may be more likely.

The highly detailed simulation conducted by the authors will be an excellent tool to prioritize systems to search for life. With thousands of exoplanets already confirmed, this may prove vital in conducting efficient searches and will maybe one day allow us to look upon our night sky and point to the one little, insignificant dot that we then know illuminates someone else’s home.

Original astrobite edited by Katy Proctor.
A German translation of this article is available on Astrobites, also written by Jana Steuer.

About the author, Jana Steuer:

I’m a second year PhD student at the LMU Munich, working for the University Observatory (USM), which owns the 2.1m Fraunhofer Telescope Wendelstein. My field of research is exoplanets. I hunt for traces of them in data from big surveys, like the TESS mission and then follow them up, using spectroscopy and photometry. Mainly, I focus on long period planets that may potentially harbor life. When I’m not planet hunting, I act as a DM for several Dungeons and Dragons groups and annoy people with facts from Tolkien’s Silmarillion. I enjoy kickboxing and learning about ancient human history.

Three images of M87 at different wavelengths

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: Broadband Multi-wavelength Properties of M87 during the 2017 Event Horizon Telescope Campaign
Authors: The Event Horizon Telescope Multi-wavelength Science Working Group
First Author’s Institution: N/A
Status: Published in ApJL

M87 is a galaxy of extremes — it is one of the brightest radio sources in the entire sky, one of the nearest galaxies that has a relativistic jet emitted from its nucleus, and one of our nearest extragalactic neighbors (a measly 53 million light-years away, in the Virgo Cluster). Also, in 2019, M87 made the news for hosting the subject of the first ever image of a black hole “shadow” (check out the Astrobites coverage of that historic event here).

M87 EHT image

The first detailed image of a black hole, M87, taken with the Event Horizon Telescope. [Adapted from EHT collaboration et al 2019]

This first direct image of a black hole at the heart of M87 was taken by the Event Horizon Telescope — a global interferometric network of radio dishes that granted the unprecedented resolution necessary to image the subtle structures surrounding the black hole. While this image provided us with invaluable information about black holes, there is still a significant amount of uncertainty on some of the characteristics of the pictured black hole, including details such as its exact spin and magnetic field configuration.

Reducing these uncertainties is imperative if we want to have a better understanding of M87, but radio observations alone cannot accomplish this. Luckily, input from other wavelengths can go a long way in complementing the radio observations. For example, previous multi-wavelength studies of M87 guided our understanding that M87 must have a non-zero spin.

A difficulty, however, is coordinating these observations. The emission from supermassive black holes (SMBHs) of M87’s size — and the jets that they launch — is known to fluctuate on timescales of a few weeks. Thus, to get a complete snapshot of a SMBH, you want to look at it in many wavelengths at roughly the same time. This is exactly what the authors of today’s paper accomplished.

This coordination of telescopes was no small feat. In total, 17 telescopes across as many orders of magnitude in frequency (from 1 GHz to 1018 GHz) came together to image the nucleus and jet of M87 in 2017. The schedule of the different observations from each telescope is displayed in Figure 1.

Colorful schedule showing when different telescopes observed M87

Figure 1: Schedule of when each telescope was observing M87 in 2017. The telescopes are ordered by frequency, with red being the lower frequencies (radio). Fermi-LAT normally operates in a survey mode, which is why there is data from every day. [The EHT MWL Science Working Group et al. 2021]

Finding a time for observations isn’t the only difficulty. Different telescopes have different sensitivities, and the types of technologies used to search for, say, X-rays, is drastically different than that needed to detect radio waves. Additionally, the different telescopes have dramatically different fields of view and hence probe different spatial scales. For example, interferometric radio observations can pick up subtle structures in the active galactic nucleus (AGN) and can even distinguish features within M87’s jet. On the other hand, gamma-ray telescopes have much lower spatial resolution, and, while they are able to detect emission from the AGN, they cannot distinguish emission from the region around the black hole from emission that is farther down the barrel of the jet. A compilation of the images of M87 in various wavelengths, highlighting the different angular scales, is shown in Figure 2.

Images of M87 at different wavelengths zoomed in on various scales

Figure 2: Compilation of the near-simultaneous observations of M87. Note the different angular scales, and how some of the radio observations on the left are able to differentiate features in the jet, whereas gamma-ray observations (right) cannot discriminate between these features. [EHT Collaboration; NASA/Swift; NASA/Fermi; Caltech-NuSTAR; CXC; CfA-VERITAS; MAGIC; HESS]

With all of these observations in hand (or rather, on hard drives), the authors of today’s paper tried to figure out what can be inferred about the physical properties of M87 and its jet. One way to do this is by comparing the amount of energy emitted in different wavelengths, which can be put together into a spectral energy distribution (SED). SEDs can be extremely informative, because different physical processes result in emission at different energies, which correspond to different wavelengths.

For example, if there are electrons in the jet, then they will emit synchrotron radiation as they spiral around magnetic field lines. This causes a bump in the SED at radio frequencies. Some of the synchrotron radiation can then actually interact with the same electrons, and get scattered up to very high energies, which can cause another bump in the SED at very high energies. Different predictions for, say, the distribution of electrons or the magnetic field strength will change the locations and magnitudes of these “bumps,” and so we can use the SED to infer characteristics about the composition of the jet. The SED from M87 is shown in Figure 3.

flux as a function of frequency from radio to gamma-rays

Figure 3: Broadband spectral energy distribution (SED) of M87 from 2017. The SED represents the amount of energy arriving at Earth at each of these frequencies. Different features in the SED can reveal valuable information about the environments producing the emission. [The EHT MWL Science Working Group et al. 2021]


From a deep look at M87’s SED, the authors come to the conclusion that a simple model of the emission — one that treats all of the emission as coming from the same location in the jet — cannot explain the entire SED. This lends evidence to the hypothesis that M87’s jet must have a more complex structure, and that the very high-energy gamma rays might be originating from a different region of the jet than the emission at lower frequencies.

Not only are the scientific takeaways from this work extremely informative, but it represents a massive success in uniting some of the most advanced telescopes in the world to create one of the most detailed snapshots of an AGN to date. These observations will serve as a cornerstone for future observations of M87, which will revolutionize our understanding of black holes and relativistic jets.

Original astrobite edited by Viraj Karambelkar.

About the author, Alex Pizzuto:

Alex is a PhD candidate at the Wisconsin IceCube Particle Astrophysics Center at the University of Wisconsin-Madison. His work focuses on developing methods to locate the universe’s most extreme cosmic accelerators by searching for the neutrinos that come from them. Alex is also passionate about local science outreach events in Madison, and enjoys hiking, cooking, and playing music when he is not debugging his code.

illustration of a star being torn apart and spread around a black hole

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: The 450 days X-ray monitoring of the changing-look AGN 1ES 1927+654
Authors: C. Ricci et al.
First Author’s Institution: Diego Portales University, Chile
Status: Accepted to ApJS

Accreting supermassive black holes at the centers of galaxies are some of the most powerful objects in the universe, outshining even their host galaxies. These active galactic nuclei (AGN) exhibit strong variability in their luminosity and emission lines. Some AGN show very broad emission lines in their spectra coming from high velocity gas orbiting near the black hole, while others only show narrow emission lines. AGN are typically classified based on the existence of these emission lines as Type-1 (broad emission) or Type-2 (no broad emission). Why some AGN don’t show broad lines in their spectra is still up for debate — and some AGN, known as changing-look AGN, have been observed to switch types!

The subject of today’s paper is a nearby AGN called 1ES 1927+654, originally classified as Type-2, that was actually observed in the act of changing types. In early 2018, this AGN was observed to become optically brighter and later develop broad hydrogen emission lines. Today’s paper presents a monitoring campaign for 1ES 1927+654 over a period of 450 days in X-ray (XMM-Newton, NuSTAR, NICER) and ultraviolet (UV; Swift) wavelengths, revealing some unusual X-ray properties.

The Case of the Missing Corona

Most X-ray emission in AGN comes from the corona, a region of hot plasma surrounding the accretion disk. In X-ray spectra, this emission is typically described as a power law with the addition of a blackbody from reprocessed emission.

The left panel of Figure 1 below shows the X-ray spectra of 1ES 1927+654 a few months after the appearance of optical broad emission lines (the changing-look event). This spectrum appears to be dominated by low energy X-rays producing blackbody emission and lacking the typically observed power law. Before the changing-look event, best-fit models of the AGN’s X-ray spectra had a significant contribution from a power law — so what happened to this corona emission?

two plots showing X-ray spectra

Figure 1: X-ray spectra of the changing-look AGN. Left: June 2018 observations from XMM-Newton shown as blue and orange crosses. The best-fit model (red curve over the data) is separated into components presented as different colored dashed and dotted lines: blackbody (cyan), power law (black), two gaussians (dark orange and green). The emission is dominated by the blackbody component. Right: December 2018 observations are shown as blue (XMM-Newton) and orange (NuSTAR) crosses. The emission shows more contribution from the power-law component. [Adapted from Ricci et al. 2021]

Even more curious, later observations in December 2018 (right panel above) and May 2019 show 1ES 1927+654 becoming brighter, with more of the power-law component appearing at higher energies. Figure 2 below gives a more detailed look at the relative contribution from the corona emission (the power law). This indicates that as the AGN becomes brighter, eventually the power-law component dominates the emission just as it did before the changing look event!

Plot showing proportion of power law to blackbody flux for different AGN luminosities

Figure 2: Ratio of the power-law and blackbody components of the X-ray emission vs the AGN luminosity for different observing dates. Data from previous work before the changing-look event is shown as green squares. Data from this paper is shown as blue circles (June 2018), red diamonds (December 2018) and black stars (May 2019). [Adapted from Ricci et al. 2021]

Other strange features of the observations are two unknown gaussian emission lines present in the spectra at 1 keV and 1.8 keV (Figure 1) as well as strong variability in the light curves (not shown here, see Figures 5 and 6 in the paper) that changes both in timescale and energy range for the different observing dates.

A Possible Tidal Disruption Event

While observations of changing-look AGN are becoming more common, this disappearing corona emission had not been observed before. It’s possible that due to a catastrophic event, the corona was actually destroyed.

One possible event that was proposed by the authors is that a star was tidally disrupted by the accretion disk, destroying the corona in the process. The reappearance of the power-law emission could also imply that the destroyed corona is in the process of forming again!

The authors suggest that these unusual X-ray characteristics could mean 1ES 1927+654 represents a new type of changing-look AGN. Future observations might begin finding more of these interesting AGN and help better understand supermassive black hole accretion.

Original astrobite edited by Ciara Johnson.

About the author, Gloria Fonseca Alvarez:

I’m a fourth year graduate student at the University of Connecticut. My research focuses on the inner environments of supermassive black holes. I am currently working on measuring black hole spin from the spectral energy distributions of quasars in the Sloan Digital Sky Survey. As a Nicaraguan astronomer, I am also involved in efforts to increase the participation of Central American students in astronomy research.

Hubble photograph of a young star cluster

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: Disentangled Representation Learning for Astronomical Chemical Tagging
Authors: Damien de Mijolla, Melissa Ness, Serena Viti, Adam Wheeler
First Author’s Institution: University College London, UK
Status: Accepted to ApJ

The question of galaxy formation and evolution is a big one in astronomy, and the Milky Way is a convenient testbed for studying this question. The Milky Way is composed largely of stars, gas, dust, and dark matter, and all of these components can be studied individually and collectively to inform our understanding of how galaxies form and evolve. Galactic archaeology is a subfield of astronomy that treats individual stars as fossils, using them as tools to study the evolution of our galaxy. The kinematic and chemical properties of a star hint at its ancestry. A star’s position in and motion through our galaxy, for example, can tell us in what portion of the galaxy it was born (e.g. thick disk, thin disk, halo, or bulge), whether it is a member of a certain cluster of stars, or whether it was part of a stellar population that was accreted by the Milky Way. The chemical composition of a star also contains a plethora of information about its history, and the use of stellar chemistry to infer a star’s origins is called chemical tagging. Today’s authors develop a new way to practice chemical tagging using a neural network that “disentangles” stellar spectra to bypass the precision issues often faced by chemical taggers. But first, let’s start with some background!

A diagram depicting chemical tagging.

Figure 1: A diagram depicting chemical tagging, the practice of using the chemical compositions of stars, which can be derived from their spectra, to trace their origins. The above diagram depicts the strongest form of chemical tagging, where stars are traced back to their specific birth cloud via their chemical profile. Weaker forms exist as well and involve tagging stars to general, large-scale Milky Way substructure, like the thin disk, thick disk, bulge, and halo. [Astrobites / Catherine Manea]

What’s the Idea Behind Chemical Tagging?

Most Milky Way star formation occurs in collapsing molecular clouds, and groups of stars born in the same cloud are called birth clusters. Some birth clusters are massive enough to remain gravitationally bound for several millions to billions of years, and these are the open clusters we observe today throughout the Milky Way disk. However, most stars are born in weakly bound associations that are quickly dispersed by the Milky Way potential.

The premise behind chemical tagging rests on the notion that the chemical composition of low- and intermediate-mass stars is largely constant throughout their evolution and generally reflects the chemical composition of their birth cloud. This means that, even when stars are dispersed from their birth sites, they retain their chemical composition and carry it with them for much of their life like a fingerprint. The new frontier of chemical tagging seeks to take advantage of this fact by using the chemical fingerprints of dispersed stars to tag them back to their birth cluster (see Figure 1).

The ability to tag stars back to their birth cluster relies on two assumptions: 1) that all stars born together possess the same chemical composition, and 2) that birth clusters have unique chemical profiles. Under these two assumptions, if one finds a group of chemically identical stars in the field, they were likely born together in the same molecular cloud. The validity of these two assumptions is still actively being studied, but studies have shown that many open clusters are extremely chemically homogeneous, supporting assumption (1).

Illustration of the Gaia spacecraft in front of the Milky Way

Illustration of the Gaia spacecraft, which is surveying roughly a billion stars. [ESA/Medialab]

We are at a ripe time in astronomy to practice chemical tagging. Thanks to massive spectral surveys like APOGEE, LAMOST, GALAH, RAVE, and Gaia-ESO, we have millions of spectra of Milky Way stars. Therefore, finding chemically similar stars in these data sets, and thus subsequently reconstructing dispersed birth clusters, is of great interest to those seeking to untangle the galaxy’s evolution.

How Do We Find Out the Chemical Compositions of Stars?

We can estimate the chemical compositions of stars by studying the light they emit. Though some studies have estimated certain chemical parameters, like iron content, from photometry alone, the best way to get precise chemical information for a star is through its spectrum. Stellar spectra can be generalized as black bodies, smooth curves that peak at a wavelength that corresponds with their surface temperatures. If we zoom into a stellar spectrum, however, we can find thousands of bumps and dips caused by specific ions in the atmosphere of the star absorbing and emitting the star’s light. Generally, we can look at the strength of each bump and dip (called a spectral line) to estimate how much of a certain element is contained in the atmosphere of the star.

The amount of a certain element in a star’s atmosphere is called an abundance. Classically, backing out abundances from a stellar spectrum is not a simple task: it requires one to model a stellar atmosphere, generate what the resulting spectrum would look like, and compare it to the observed spectrum in question. Most spectral surveys derive and report chemical abundances for their stellar targets. However, with each reported abundance comes an associated uncertainty, and in many cases, the abundance uncertainties are much larger than the spread in abundances of a group of stars born together. This means that it is difficult to group chemically similar stars when the reported abundances have really large uncertainties.

To get around abundance uncertainties, many people restrict their chemical tagging to solar twins, stars that have identical atmospheric parameters to the Sun. Stellar spectra are affected by not only the precise elements in the atmosphere of a star (which, again, create bumps and dips in the spectrum) but also the surface temperature (Teff), surface gravity (log g), rotation (v sin i), and microturbulence (vmic) of the star, among other physical factors. Thus, two chemically identical stars may still have different looking spectra if their atmospheric and physical parameters are different. By restricting chemical tagging to solar twins, however, people can compare apples to apples: any differences between the spectra of solar twins are entirely due to differences in the chemical composition of the stars. This kind of abundance work is called differential abundance analysis — it doesn’t require one to derive chemical abundances of each star to get a chemical similarity. One only needs the spectra of the two stars, and any differences in the strengths of each spectral line between both spectra indicate differences in abundance. This method entirely bypasses abundance uncertainties and allows one to achieve incredibly high precisions that are lower than the abundance differences between stars born in the same cluster.

The downside to typical differential abundance analysis is that one can only apply chemical tagging to stars with identical stellar parameters, which restricts the pool of stars dramatically. However, today’s authors find a way to achieve all the benefits of differential abundance analysis but without having to restrict it to solar twins. The authors call this method abundance-free chemical tagging, and it is ideal for chemically tagging large, diverse groups of stars to extremely high precision.

A Neural Network that Disentangles Spectra for High-Precision Chemical Tagging

The way the authors achieve this is by training a neural network (specifically, a conditional autoencoder) to learn the mapping between Teff and log g for a variety of different synthetic spectra with varying chemical abundances. This neural network is composed of two parts: a conditional encoder and a conditional decoder. The conditional encoder takes as input a batch of spectra with associated, preknown Teff and log g values. The conditional encoder then learns the mapping between Teff, log g, and the resulting spectra. It then reduces (disentangles) the input spectra into a lower-dimension vectors representing only chemical composition information, free of the effects of input Teff and log g. The conditional decoder takes the lower-dimension vectors, and the learned mapping scheme from the conditional encoder, to reconstruct (re-tangle) the input spectra. The neural network is fully trained when it is able to a) disentangle input spectra as fully as possible, and b) re-tangle the lower-dimension vectors into the original input spectra with minimal difference. Once the neural network is fully trained, one can feed it observed spectra with preknown Teff and log g and then get out spectra that all share a common (arbitrary) Teff and log g and only vary by their intrinsic differences in chemical abundance.

plots showing two artificial spectra and residuals

Figure 2: An example of the efficacy of the author’s method of disentangling temperature and surface gravity effects from stellar spectra. The top panel shows the artificial spectra of two stars with identical chemical compositions but differing effective temperatures and log g. Note that even though these two spectra belong to chemically identical stars, they vary dramatically due to effects caused by surface temperature and gravity differences between the stars. The center panel shows the effect of disentangling surface temperature and gravity effects from the spectra using the neural network created by the authors, leaving two spectra that look remarkably similar. The bottom panel shows the residuals of the two spectra, highlighting areas where the two disentangled spectra differ. [de Mijolla et al. 2021]

In short, with this method, the authors are able to disentangle the parameters they don’t care about in a spectrum from the parameters they do care about. In this case, they only want spectra that directly reflect the chemical composition of the star, without the distracting effects of surface temperature, surface gravity, and so on, which alter the shapes of lines significantly. After disentangling the spectra using the neural network, the authors are able to directly compare the spectra of different stars, and any visible differences in the two spectra are purely due to chemical differences in the stars (see Figure 2).

As with differential abundance analysis of solar twins, this method is able to bypass large abundance uncertainties common in classical abundance analysis. However, one is no longer restricted to solar twins: with this method, one can chemically tag the wide array of stars sampled by these massive spectroscopic surveys and use the full data sets to one’s advantage. In addition to allowing us to better probe large spectral surveys for chemically similar stars across a wide range of masses and physical characteristics, this method will also aid in the study of the chemical homogeneity of open clusters, globular clusters, stellar streams, and other cohesive groups of stars whose chemical abundance spread is of interest.

The authors note that their neural network was tested on artificial APOGEE data and was able to successfully retrieve 85% of the chemical pairs infused into their artificial data. The next step is to apply it to real survey data and see whether this method is effective at dealing with the dynamical range and unconstrained physical and systematic effects found in real spectral survey data. The authors finally conclude that their neural network architecture, called FactorDis, may be useful to fields outside of astronomy.

Original astrobite edited by Graham Doskoch.

About the author, Catherine Manea:

Catherine is a 2nd year PhD student at the University of Texas at Austin. Her research is in galactic archaeology, the practice of using the kinematic and chemical information of individual stars to study the evolution of our Milky Way. She is particularly interested in pushing chemical tagging, the practice of tracing stars back to their birth sites, to new limits.

Hubble Space Telescope infrared image of the host galaxy of FRB 180916.

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 High-Resolution View of Fast Radio Burst Host Environments
Authors: Alexandra G. Mannings et al.
First Author’s Institution: University of California, Santa Cruz
Status: Submitted to ApJ

First discovered in 2007, fast radio bursts (FRBs) have been the talk of the town for the last few years. As their name suggests, FRB sources emit very fast (millisecond-long) bursts of radiation at radio frequencies. Due to their very high dispersion measures (DMs), we know that FRBs are typically located in galaxies outside of the Milky Way, although the first galactic FRB was detected in March 2020. While the majority of FRBs are one-off events, some FRBs repeat, and two even repeat periodically! Even with the huge effort to study FRBs over the last few years, however, we still have much to learn about these objects.

In particular, while hundreds of FRBs have been discovered, we have only localized about a dozen FRBs to their respective host galaxies. Localizing FRBs is very important for auxiliary science, such as the missing baryon problem, and for determining what produces FRBs. By comparing FRB host environments with the hosts of other transients, we can hopefully determine whether FRBs might come from similar origins. This is what today’s authors explore!

Hubble for the Win!

The authors use infrared (IR) and ultraviolet (UV) observations from the Hubble Space Telescope to study the host galaxies of eight different FRBs. Six of these observations are newly presented, while the host galaxy of the infamous FRB 121102 (the first repeating FRB) and FRB 180916 (the first periodic FRB) were reported in previous studies.

So What Is Cooking for These Eight FRBs?

First, the authors locate the FRBs within their host galaxies. Of the eight FRBs, five are located within spiral galaxies as shown below in Figure 1. This is not unexpected, as ~60% of the observed galaxies in our universe are spiral galaxies. While they are located within the spiral arms of their hosts, the five FRBs are not actually located at the brightest points of the spiral arms (but note that they have huge error regions!).

Hubble Space Telescope images for each of the host galaxies. FRB 180916, FRB 191001, FRB 190608, FRB 190714, and FRB 180924 all reside in spiral galaxies.

Figure 1: Host galaxy images for each of the FRBs with the FRB location circled. The FRBs that reside in spiral arms are labelled using a blue “S.” [Mannings et al. 2021]

The authors are also able to convert the UV light at the position of the FRB to the star formation rate density at this position, and the IR light at the position of the FRB to the stellar mass surface density at this location. As shown below in Figure 2, only FRB 121102 and FRB 180916 lie in areas with a lot of star formation (and the star formation rate for FRB 180916 is an upper limit). The other FRBs tend to lie in more moderate, although slightly higher than average, regions. This is a bit surprising as young magnetars, a very popular origin for FRBs, are typically located very close to points of high star formation.

There is almost a perfect 1:1 correlation between the stellar mass surface density at the location of the FRB and the average for the FRB’s galaxy, suggesting that FRBs lie in typical surface mass density regions within their host galaxies.

two plots describing properties of the environment around 8 localized FRBs.

Figure 2: Left: Comparison between the star formation rate density for different FRBs as compared with the average for the host. There is no clear relation between the two, but we note that many of the observations are upper limits (indicated with triangles). Right: Comparison between the stellar mass surface density for different FRBs as compared with the average for the host. There is almost a perfect 1:1 relation between the two. [Mannings et al. 2021]

The authors also use the properties of the FRBs within their host galaxies to try to identify (or rule out) possible mechanisms for the origins of FRBs. In particular, they focus on five properties:

  1. The distance between the FRB and the center of the host galaxy, i.e., the radial offset.
  2. The distance between the FRB and the center of the host galaxy after normalizing by the size of the galaxy, i.e., the normalized radial offset.
  3. The brightness of the galaxy at the location of the FRB as compared to the brightness throughout the galaxy in the UV band. The UV light can be used as a proxy for star formation within a galaxy.
  4. The brightness of the galaxy at the location of the FRB as compared to the brightness throughout the galaxy in the IR band. The IR light can be used as a proxy for stellar mass within the host.
  5. The amount of light within the galaxy that is interior to the FRB’s location, known as the enclosed flux.

The authors compare these properties of FRBs within their hosts to that of six different transient phenomena: long duration gamma-ray bursts (LGRBs) short-duration gamma-ray bursts (SGRBs), Ca-rich transients, Type Ia supernovae (Type Ia SNe), core-collapse SNe (CCSNe), and super-luminous SNe (SLSNe). While it can be a bit difficult to remember all of these different transients, they can generally be lumped into three different categories: 1. Massive star explosions (LGRBs, CCSNe, SLSNE) 2. Explosions involving older stellars objects such as neutron star mergers (SGRBs) or white dwarfs (Type Ia SNe), and 3. Objects with unknown origins (Ca-rich transients).

From their comparisons, the authors find that the properties of LGRBs, SGRBs, SLSNe and Ca-rich transients are inconsistent with those of FRBs. The table below indicates which properties are inconsistent for each of these transients (marked with X). The authors cannot rule out CCSNe and Type Ia SNE as being associated with FRBs. This is similar to another recent result that finds FRBs are consistent with CCSNe (and hence magnetars born from CCSNe) but different from this result that finds it unlikely that Type Ia SNe are associated with every FRB.

LGRBs SGRBs Type Ia SNe CCSNe SLSNe Ca-rich transients
Radial Offset X X
Normalized Radial Offset X X
Brightness in UV X X
Brightness in IR X X


For the fifth property, the enclosed flux, the authors don’t compare the distribution from the FRBs to that of the other transients. Instead, they only conclude that the FRBs appear to trace the distribution of light within their host galaxies.

So Where Does This Leave Us? 

If there is one key transient associated with FRBs, then this work suggests that it is not LGRBs, SGRBs, SLSNe, or Ca-rich transients. However, there is one very important caveat to the authors’ work, which is that they group repeating and non-repeating FRBs together in their sample. So it is possible that there is not just one but multiple mechanisms responsible for the FRBs within their sample. Thus, the mystery of what produces FRBs continues…

Original astrobite edited by Viraj Karambelkar.

About the author, Alice Curtin:

I’m a second year MsC student at McGill University studying Fast Radio Burts and pulsars using the Canadian Hydrogen Mapping Experiment (CHIME). My work mainly focuses on characterizing radio frequency interference, investigating multi-wavelength counterparts to FRBs, and using pulsars as calibrators of future instruments. When not doing research, I typically find myself teaching physics to elementary school students, spending time with friends, or doing something active outside.

Active Galactic Nucleus

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: The Landscape of Galaxies Harboring Changing-Look Active Galactic Nuclei in the Local Universe
Authors: Sierra A. Dodd et al.
First Author’s Institution: University of California, Santa Cruz
Status: Published in ApJL

As we grow older and learn more about ourselves, we sometimes think about changing our appearance: a new hairstyle, clothes, or maybe even a tattoo! Active galactic nuclei (AGN) are, in this respect, no different. Over the course of a galaxy’s lifetime it will accrete gas and dust that will make its way to the centre, where its supermassive black hole (SMBH) resides. As the SMBH feeds on this material, it will emit huge amounts of radiation and become an AGN. However, certain conditions can cause disruptions in the AGN’s gas supply. Whilst the cause of these disruptions is unclear, we can see the effects in the AGN’s changing spectrum. Either they “turn on” as broad optical emission lines emerge or “turn off” as those lines disappear. Today’s authors are interested in identifying what kind of galaxies host these changing-look AGN (CL-AGN) to try to isolate the conditions that might trigger these changes.

Constructing the AGN Lookbook

Identifying CL-AGN involves a comparison between two sets of spectra of the same galaxy taken at two different times to find the changes in the broad emission lines. Different studies will approach this task in their own way but are ultimately following this principle. This is true for all identification techniques, but CL-AGN have only been identified as a phenomenon relatively recently, so very few have been found. To produce today’s sample of 17 CL-AGN, the authors have had to combine detections from three different studies that follow this broad approach but with their own unique characteristics. So, what one study calls a CL-AGN might be slightly different from another. In addition, all but two of these detections are turn-on AGN. The authors argue that the lack of turn-off AGN is due to the relative abundance of quiescent galaxies in the nearby universe. Within these galaxies, it is much easier to see the emergence of broad emission lines. As a result, their sample may not be particularly representative of the underlying CL-AGN population, but the authors are very forthcoming about these issues and have shown care in constructing their sample.

Their comparison sample is a set of 500,000 local galaxies with measured stellar masses, star formation rates and numerous other spectral properties. Most of these quantities are measured across the whole galaxy and are also broken down into bulge and disk components. These data, mostly drawn from the SDSS, will allow today’s authors to place the CL-AGN within the wider galaxy population and isolate the conditions that trigger this change.

Plot showing host galaxy distributions.

Figure 1: Distribution of host galaxies’ star formation rates and stellar masses. Blue circles show the distribution of CL-AGN compared to the underlying comparison galaxies (grey contours). Dashed lines indicate different star formation classifications: green-valley galaxies lie between the dashed blue and orange lines. [Dodd et al. 2021]


From their analysis, we can determine three key consistencies about the preferences of CL-AGN. Figure 1 shows that the CL-AGN are all consistent, within errors, of being hosted in green-valley galaxies. These are a rarer form of galaxy that lie in between the blue, actively star-forming galaxies and their red and dead counterparts. Green-valley galaxies are believed have recently undergone a burst of extreme star formation, possibly implying the presence of large amount of cold gas at the centre which could fuel the mostly turn-on activity seen in this sample.

Plot of galactic asymmetry vs. SMBH mass

Figure 2: Asymmetry of the host galaxy against SMBH mass. Blue circles show the tight distribution of CL-AGN compared to the underlying comparison galaxy distribution (grey contours) and merging galaxies (yellow diamonds). [Dodd et al. 2021]

Gas is often thought to be driven into galaxies through mergers, however figure 2 shows us that CL-AGN are extremely centrally concentrated, especially when compared to the shapes of merging galaxies. In addition, we see that the CL-AGN asymmetry range is much tighter than the wider galaxy population. To explore this idea further, the authors then compare the galaxies’ Sersic indices. This is a measure of the steepness of the galaxy light profile, with larger numbers indicating that stellar light is more centrally concentrated. Figure 3 shows us that the CL-AGN have much higher Sersic Indices than the galaxy population at large. Thus, the authors argue that CL-AGN preferentially reside in very symmetric and centrally concentrated galaxies.

Plot of galaxy concentration vs. SMBH mass

Figure 3: Sersic index (indicating the concentration of galaxy light) against SMBH mass. Both the squares and stars show how CL-AGN are distributed compared to the underlying comparison galaxy distribution (grey contours). [Dodd et al. 2021]

CL-AGN are a relatively novel phenomenon, and working with a sample constructed from multiple different studies may introduce some selection-based uncertainties. The authors admit that their sample may not be perfectly representative of the CL-AGN population, but despite this, they have found a fairly consistent set of properties. Their results imply that CL-AGN in the nearby universe most likely turn on when there is an abundant supply of cold gas and a high concentration of stellar mass in the central region of the host galaxy. Whilst the exact nature of the disruption is still a puzzle, these results will help focus future searches. With a better idea of where to look, strategies can be created that increase the sample of CL-AGNs and help us better understand what causes these transitions to occur.

Original astrobite edited by Alison Crisp.

About the author, Keir Birchall:

Keir is a PhD student studying methods to identify AGN in various populations of galaxies to see what affects their incidence. When not doing science, he can be found behind the lens of a film camera or listening to the strangest music possible.

Illustration of a long, thin, rocky body in the foreground against a backdrop of the galaxy.

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: Interstellar Objects in the Solar System: 1. Isotropic Kinematics from the Gaia Early Data Release 3
Authors: T. Marshall Eubanks, Andreas M. Hein, Manasvi Lingam et al.
First Author’s Institution: Space Initiatives Inc.
Status: Submitted to AJ

Comet 2I/Borisov

Hubble image of comet 2I/Borisov, captured just after the comet passed perihelion in December 2019. [NASA/ESA/D. Jewitt (UCLA)]

Three and a half years ago, astronomers discovered something in our solar system that had never been seen before: an object from another star system! Discovered by observatories in Hawaiʻi, this object (illustrated above) was given the name ʻOumuamua, which, in the Hawaiian language, roughly translates to “the first distant messenger.” Two years later, astronomers repeated this feat and discovered yet another interstellar object (ISO) — this one more cometary in appearance. This object was named after its discoverer, Borisov. These types of objects were expected to exist but eluded discovery until just now. So where do these objects actually come from? And how often should we expect to find them, now that we know they’re out there? We explore these questions in today’s astrobite.

An Elegant and Simple Model

The authors address these questions by calculating the “differential arrival rate” (Γ) of interstellar objects. Γ is defined as the number of objects that will pass within a given distance of the Sun every year, as a function of their velocity and perihelion (distance of closest approach to the Sun). Γ is the product of three numbers:

  1. the number density of ISOs (the number of ISOs per unit volume),
  2. the volume sampling rate of ISOs with a certain velocity and perihelion (the rate that a given volume samples ISOs of a certain type), and
  3. the probability distribution function of ISO velocities (the probability of an ISO having a particular velocity).

The first order of business is to calculate the number density of ISOs. For this, the authors turn to previous work, where astronomers used the discovery of ʻOumuamua to place a limit on the number of ISOs within a given volume. This estimate uses the detection rate, i.e., how many such objects are detected per year, divided by the amount of volume that the PanSTARRS observatory surveys per year (PanSTARRS was the first survey to detect ʻOumuamua). Even more time has passed since ʻOumuamua was discovered, and since the quality of surveys has improved as well, this work assumes there may be around half as many ISOs per unit volume as previously predicted.

Calculating the volume sampling rate is a bit more complicated, though it is conceptually straightforward. The amount of volume sampled at a given perihelion is just the cross sectional area enclosed by the perihelion multiplied by the velocity of an ISO (area * velocity = volume / time). However, it is imperative to account for “gravitational focusing,” the phenomenon whereby the Sun’s gravity alters the trajectories of smaller bodies passing through the solar system. The basic idea is that objects moving more slowly will be even more likely to be “focused” towards the Sun on their orbit, thus increasing the volume sampling rate for these types of objects. Nonetheless, this calculation only requires the assumption of a typical perihelion.

The last piece of the puzzle is to determine the probability distribution function of ISO velocities. This is the most difficult of the three necessary ingredients to obtain, and for this, we must consult the stars.

Consulting the Stars to Learn about Interstellar Objects

To recap, the authors want to calculate the differential arrival rate, Γ, of ISOs, or how many ISOs arrive in the solar system per unit time. To do this, they need to know their number density, the rate at which a given volume interacts with ISOs with a given velocity, and the probability of finding ISOs with that speed. Above we outlined how the authors determine the first two. However, if we have only ever discovered two ISOs (which are substantially different from each other), how can we possibly determine this last crucial ingredient, the probability distribution function of ISO velocities?

Here the authors make a basic assumption: the velocity distribution of ISOs relative to the solar system is probably similar to the velocity distribution of the host stars from which they originate. It is true that some objects may be kicked from their star systems at extreme velocities, but most are thought to exit with relatively low ejection velocities. This means all we need to do is measure the three-dimensional motion of a representative sample of stars near our solar system. Thankfully, the Gaia spacecraft, launched in 2013, has measured the precise motions of millions of stars, including several hundred thousand near (within 100 pc of) the Sun. After making some quality cuts, the authors use the precise motions of more than 70,000 nearby stars as a proxy for the velocity distribution of ISOs.

The velocity distribution of ISOs encodes the probability of finding an ISO with a particular velocity, since by definition, it describes how many ISOs have certain velocities. The authors combine this information with the two other components needed to calculate the arrival rate Γ of ISOs, and the result is given in Figure 1 below.

plot of arrival rate distribution function vs. velocity at infinity.

Figure 1: The number of interstellar objects that pass within 1 au of the Sun as a function of their velocity (“at infinity”, prior to entering the solar system). This distribution is Γ, the differential arrival rate. It shows that the majority of ISOs enter the solar system with velocities below 60 km/s. Also highlighted are the velocities of ʻOumumua and Borisov, two different objects discovered that came from beyond the solar system. Unsurprisingly, their velocities are near the median of this distribution. The authors also show the velocity of the local standard of rest (LSR); in this case, the LSR measures the relative velocity of the mean motion of material in the Milky Way at the Sun’s distance from the galactic center compared to the Sun. [Eubanks et al. 2021]

Finally, with the differential arrival rate (Γ) calculated, the authors deduce how many ISOs pass through the solar system with various velocities by integrating Γ across velocities (calculating the area under the red curve in Figure 1). In doing so, the authors predict that on average, 6.9 ISOs pass through the solar system within 1 au of the Sun every year. According to the authors’ estimates, the vast majority of these objects (92%) will have velocities below 100 km/s. Most objects will have velocities around 38 km/s, which is the median of the sample. Unsurprisingly, the observed ISO ʻOumuamua has velocities near the peak of this value. Though 2I/Borisov is likely substantially different from ʻOumuamua, it nonetheless shares a similar velocity, suggesting objects like it share similar velocity probability distributions. The authors’ results are neatly summarized in the table below.

Table of predictions for ISO properties.

Table 1: According to the type of interstellar object, this table summarizes the velocity range, arrival rate (integral of Γ), and fraction of the total population of detectable ISOs this type of object comprises. These numbers are tabulated for a perihelion of 1 au, meaning these objects would pass relatively close to Earth. Very few fast moving objects are expected to make this journey. [Eubanks et al. 2021]

As an added bonus to this analysis, the authors are able to estimate the probable origins within the galaxy of ISOs with different velocities. It turns out that, depending on where a star is located within the galaxy, it is likely to have a certain velocity relative to the Sun. Stars within the thin disk of the Milky Way move more coherently and are likely to have smaller velocities relative to the Sun (providing Type-1 ISOs, as per the authors’ analysis in the table above); stars within the thick disk have orbits that are more inclined and eccentric, and move even faster (providing Type-2 ISOs); stars within the Milky Way halo, mostly the debris of past accretion events, have even further disturbed and faster orbits relative to the Sun (providing Type-3 ISOs); and the fastest stars are not even bound to our galaxy (providing Type-4 ISOs). Reading off of the table above, we can see that the majority of ISOs will come from the galactic disk. The final group, the slowest moving of them all (Type 5), are objects that appear unbounded but likely originated from the Oort cloud. These objects, though deemed interstellar due to their unbounded orbits, are simply nudged into the inner solar system through gravitational interactions. With this information, the authors have not only predicted how often we should expect to find ISOs at different velocities, but also where they came from!

There is an incredible amount of science to be excited about when it comes to studying ISOs. These structures not only teach us about other star systems and the Milky Way galaxy, but also teach us about our own solar system by allowing comparison between their compositions and objects found more locally. Especially tantalizing is the prospect of actually rendezvousing with one these objects. Such an endeavor represents what might be our best chance at taking physical samples of material from other star systems on human timescales. With so much on offer, we have reason to suspect such an event may take place in our own lifetimes. One can hope!

Original astrobite edited by Alice Curtin and Ryan Golant.

About the author, Lukas Zalesky:

I am a PhD student at University of Hawaii’s Institute for Astronomy. I am interested in understanding the way galaxies form and evolve over billions of years, as well as gravitational lensing by galaxy clusters. Outside of research I spend my time with animals, exercising, practicing Zen, and exploring the beautiful island of Oahu.

Hubble photo of a large grand design spiral galaxy.

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 nearby repeating fast radio burst in the direction of M81
Authors: M. Bhardwaj et al.
First Author’s Institution: McGill University, Canada
Status: Accepted to ApJL

Fast radio bursts (FRBs) are some of the most mysterious phenomena in radio astronomy. First discovered in 2007, these short but energetic bursts of radio waves last on the order of milliseconds. In the decade since their discovery, catalogs of FRBs have grown to include sources of repeated bursts, bursts whose host galaxies have been identified, and even an FRB-like burst of radio waves within the Milky Way. Each year brings more tantalizing new discoveries.

There is not yet a consensus on the mechanisms behind FRBs. In recent years, new bursts have allowed astronomers to rule out certain burst models and lend credence to others. For example, neutron stars with extremely powerful magnetic fields, called magnetars, are currently a leading candidate, while studies of FRB environments indicates that indicate that gamma-ray bursts and some supernovae are less likely to be responsible. Ideally, finding FRBs close to Earth would allow us to narrow down the possibilities even more. Today’s paper presents the discovery of an FRB that has the potential to do just that.

Bursting with Excitement

One of the premier instruments for detecting FRBs is the interferometer on the Canadian Hydrogen Intensity Mapping Experiment (CHIME), which saw first light in 2017. As of early 2020, the CHIME/FRB project had already detected hundreds of bursts, including several repeating sources. One of those bursts, designated FRB 20200120A, would be followed by two more from the same location by the end of 2020. Maintenance on CHIME meant that the second burst yielded little information, but the first and third bursts raised some eyebrows for two reasons.

Waterfall plots of intensity data and baseband data for the three bursts detected by CHIME/FRB. The left half shows intensity data for all three busts, with clear peaks; the right half shows baseband data for the first and third bursts only. Many bands have been removed due to interference.

Figure 1: CHIME/FRB was able to detect three bursts from the source FRB 20200120, occurring in January, July, and November of 2020, respectively. Left: plots of intensity data for the three bursts. Right: plots of baseband data for two of the bursts — a record of the voltage measured by the telescope, which is useful for localization. Maintenance in July meant baseband data could not be recorded for the second burst. The white bands are regions where interference from artificial sources had to be removed. [Bhardwaj et al. 2021]

A Fast Radio Burst in the Neighborhood?

First, they exhibited surprisingly low dispersion measures for FRBs, at around 87.82 pc/cm3. Dispersion measures (DMs) describe how different frequencies in a radio signal are smeared out through interactions with free electrons in outer space. A higher DM means that there are more free electrons between us and the source, making DMs a convenient distance proxy. The DMs of each of these bursts placed them in an as-yet unexplored regime — neither firmly within the Milky Way nor firmly in extragalactic space. Second, the bursts were successfully localized to an area close to the galaxy M81, which, on cosmological scales, is just down the street from us. The localization region — the area in which astronomers are 90% confident the source lies — is large but overlaps with M81 as seen from Earth.

A radio source within the Milky Way’s disk along the line of sight to this repeating FRB (now referred to as FRB 20200120) would have a DM of no more than 40 pc/cm3 — far too low to match observations. Constraints on the electron density in the galactic halo, however, are much worse, with halo DM contributions ranging from as little as 30 pc/cm3 to as much as 80 pc/cm3. With the measured DM in this range, the bursts could indeed be coming from a halo object like a magnetar. From an astrophysical perspective, though, this would be odd; neutron stars are unlikely to be found in halos, and many are incapable of producing bursts this bright.

Image of M81 and the FRB 20200120 localization region from the Digital Sky Survey (DSS). The inset shows that while the FRB appears to be far from the galaxy it is actually still within M81's disk of neutral hydrogen, which isn't visible on the DSS image. Four sources exist within the localization region.

Figure 2: The 90% confidence localization region of FRB 20200120 is shown in red, superimposed on a Digital Sky Survey (DSS) image of the surrounding sky. Although the burst appears to be far from M81, at upper right, it is actually still located in the galaxy’s thick disk, which is shown more clearly by the inset map of 21 cm emission. The dotted lines correspond to the border of the DSS image. The labeled boxes within the localization region show the positions of the four known sources of interest in the area, which could be associated with the FRB. [Bhardwaj et al. 2021]

This makes the extragalactic hypothesis more appealing. If we assume a relatively low halo DM, then we have an excess of 18–23 pc/cm3, which could be accounted for by electrons in the intergalactic medium between the Milky Way and the source’s host galaxy, placing the host fairly close to us. The best candidate appears to be the M81 group of galaxies, which lies a mere ~3.6 Mpc from Earth and encompasses the large localization regions of the two well-studied bursts from FRB 20200120. In fact, the projected distance between the source and the center of M81 itself could be only 20 kpc, well within the galaxy’s disk. The authors estimated that the probability of a purely chance alignment between the galaxy and the source is roughly 1%.

A Toast to the Host

Plot showing probability of chance overlap of FRB and galaxy as a function of the dispersion measure excess. There are two models created using existing FRB catalogs, depending on whether baseband or intensity data is used.

Figure 3: The probability of an FRB randomly intersecting a galaxy like M81 depends strongly on the FRB’s DM excess. For a DM like that of FRB 20200120, this probability ends up being 0.7%. [Bhardwaj et al. 2021]

M81 is an exciting candidate host galaxy because of previously known activity within the FRB’s localization region. While no Milky Way satellite galaxy or globular cluster is along the line of sight to FRB 20200120, M81 has an HII region, a globular cluster, an X-ray source and a persistent radio source within the 90% confidence region — in other words, places where you might find a fast radio burst. There are nearby star-forming clumps, where massive stars — including many neutron star progenitors — are being born, so it could be a ripe environment for FRBs.

This isn’t to say that M81 is typical of an FRB host galaxy — it’s a massive early-type galaxy with an active galactic nucleus, which sets it apart from the other three known host galaxies of FRBs. Furthermore, a 20-kpc separation from the galaxy’s center would be the largest projected offset between an FRB and its host. That’s unusual — most magnetars and other theorized FRB sources lie near the centers of their host galaxies, or at least well within their disks.

FRB 20200120 is a tantalizing target for follow-up observations thanks to its proximity to Earth. Some FRB models predict that radio bursts should be accompanied by additional activity across the electromagnetic spectrum. If a magnetar is responsible for the bursts, it might be possible to detect a high-energy counterpart with existing X-ray or gamma-ray telescopes like the Swift Observatory. Such a detection would be an important leap in our understanding of fast radio bursts, particularly bursts from repeating sources.

Original astrobite edited by Sasha Warren.

About the author, Graham Doskoch:

I’m a first-year graduate student at West Virginia University, pursuing a PhD in radio astronomy. My focus is on neutron stars and pulsar timing, a method of detecting gravitational waves by monitoring arrays of pulsars over the course of many years. I’m an associate member of NANOGrav, and I’m starting to help with their ongoing timing efforts. I love running, hiking, reading, and just enjoying nature.

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