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simulation of the large-scale structure of the universe

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: A Detection of Cosmological 21-cm Emission from CHIME in Cross-Correlation with eBOSS Measurements of the Lyα Forest
Authors: CHIME Collaboration
Status: Published in ApJ

A Map of the Universe

Throughout the history of astronomy, we’ve been able to map the universe in a couple of different ways. Large surveys of galaxies, such as the Sloan Digital Sky Survey (SDSS) or the Dark Energy Spectroscopic Instrument survey, are extremely useful — they give astronomers a huge sample of galaxies to characterize, and the positions of those galaxies tell us about how the universe as a whole is set up. At extremely high redshifts, the cosmic microwave background shows us the structure of the universe right as it began. More recently, a totally separate technique has been developing: (spectral) line-intensity mapping. In this technique, you essentially take a very blurry picture of a large region of the universe at a wavelength that corresponds to a specific spectral line. Instead of targeting specific galaxies, you get all of the emission from that spectral line in that region of the universe. This way, you can get light from things that are much fainter than a traditional galaxy survey can see.

The fact that you’re targeting a specific spectral line is also important: because of the expansion of the universe, emission from different distances is redshifted to different observed wavelengths, and so the end product of a line-intensity mapping experiment is a 3D map of the universe instead of just a 2D picture. Because of the expansion of the universe, this also means you map the universe through time. Figure 1 shows a (very idealized) picture of what a single-wavelength slice of this could look like.

illustration of the evolution of the large-scale structure of the universe and the line-intensity mapping technique

Figure 1: A simulated line-intensity map from a single slice. [NASA / LAMBDA Archive Team]

Neutral About Hydrogen

The line-intensity mapping technique was originally developed for studies of the 21-cm hydrogen line, which is a spin-flip transition of neutral hydrogen mainly emitted by diffuse gas. This is a very powerful technique: it’s extremely difficult to observe neutral hydrogen gas in any other way, and a lot of the universe is made up of this gas, especially at high redshift!

Today’s article is also looking for this 21-cm emission, specifically by using the Canadian Hydrogen Intensity-Mapping Experiment (CHIME; shown in Figure 2). This experiment has been discussed in several astrobites over the past few years, both for its 21-cm work (x) and for its work with fast radio bursts (x, x, x).

photograph of the CHIME instrument

Figure 2: The CHIME instrument. [Wikipedia user Z22; CC BY-SA 4.0]

Fighting Foregrounds with Friends

Detecting 21-cm emission is significantly complicated by the fact that there are lots of things in the way! Between Earth and the distant galaxies astronomers are actually after, there are many other sources that emit around a wavelength of 21 cm, including Earth’s ionosphere and synchrotron emission from the Milky Way. These are “foregrounds,” and they’re far brighter than the 21-cm emission astronomers are looking for! Although you can work around foregrounds using 21-cm data alone, combining your 21-cm measurement with other measurements of the structure you’re trying to see (taken using other wavelengths of light) makes for a much more robust approach. Because the two measurements are taken using different wavelengths, it’s very unlikely that they’ll show the same foreground structure. When you use a statistical technique such as cross-correlation to combine the two, the foregrounds (more or less) disappear! This has already been done for the CHIME 21-cm data at low redshifts, but not at redshifts above z = 1.5 (9 billion years ago).

In this research article, the authors combine their 21-cm data with Lyα forest measurements from the extended Baryonic Oscillation Spectroscopic Survey (eBOSS), which is a part of SDSS. Importantly, the Lyα forest traces absorbing hydrogen gas, and the 21-cm data trace emitting hydrogen gas. Density determines whether gas absorbs or emits: denser gas tends to absorb radiation, and more diffuse gas tends to emit radiation. This means that on small scales, the two signals are actually expected to be anti-correlated (because the measurements are coming from different kinds of gas), and the cross-correlation signal will be negative.

A Negative Detection!

Indeed, after reducing the CHIME 21-cm data, the authors do detect anti-correlation! Figure 3 shows the cross-correlation signal. This is measured by introducing an artificial offset in the x-axis (in this case, the frequency axis) between the 21-cm measurement and the Lyα forest measurement, and then measuring how much these two signals correlate or anti-correlate at that offset. The amount of correlation is then shown as a function of this frequency offset. You can see a large negative spike right at zero offset, where the two signals should anti-correlate the most. The dotted black line shows a model the authors came up with for this cross-correlation, and the two agree quite well (the bottom panel shows the residuals between the signal and the model).

plot of the cross-correlation signal

Figure 3: Top: The cross-correlation signal shown as a function of offset between the two datasets in the frequency axis. The blue line is the actual CHIME x eBOSS data, and the dotted black line is a model of what the signal should look like, fit to the data in amplitude but nothing else. Bottom: The residuals between the data and the model, expressed in terms of their significance. [Adapted from CHIME Collaboration 2024]

This is very exciting — it’s the first detection of 21-cm radiation at a redshift greater than z = 1.5, and its cross-correlation with the Lyα forest looks pretty much as expected! There is a lot of information to be gained from this measurement. In particular, the amplitude in the y-axis of the cross-correlation signal is set by the spatial relationship between dense and diffuse hydrogen gas in the universe. However, the authors leave determining that exact relationship for a future work, because it requires some extremely detailed cosmological and hydrodynamic simulations. Also, even in this cross-correlation measurement, the authors found a lot of foreground emission! Improvements to the CHIME instrument and its data reduction will help get rid of this contamination in the measurement, but it’s still a very difficult problem. For now, it’s incredible that this faint but all-important signal has been detected so far away.

Original astrobite edited by Nathalie Korhonen Cuestas.

About the author, Delaney Dunne:

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

Multiwavelength image of the galaxy cluster SDSS J1531+3414

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: “Beads-on-a-string” Star Formation Tied to One of the Most Powerful Active Galactic Nucleus Outbursts Observed in a Cool-Core Galaxy Cluster
Authors: Osase Omoruyi et al.
First Author’s Institution: Center for Astrophysics ∣ Harvard & Smithsonian
Status: Published in ApJ

Billions of years ago, around 3.8 billion light-years away, there was a black hole outburst so explosive that we can still see remnants of it today — and it may be the most powerful event of its kind we have ever observed. The authors identified this outburst when they noticed a remarkable structure that formed from the wreckage, a 28 kiloparsec (91,000 light-year) arc of star formation that resembles beads on a string, as shown in Figure 1.

An image of a galaxy cluster surrounded by arcs created by gravitational lensing

Figure 1: SDSS 1531 image from Hubble’s Wide Field Camera 3, emission in the V-band (bottom left), and near-ultraviolet (bottom right). [Omoruyi et al. 2024]

The “beads on a string” structure lies near the center of SDSS J1531+3414 (hereafter SDSS 1531), a cool-core, strong-lensing galaxy cluster. In initial lower-resolution images from the Subaru Telescope, SDSS 1531 appeared to have one large central galaxy that was slightly bluer than expected, which was assumed to be an artifact of the strong gravitational lensing. The authors of this article obtained new observations in multiple wavelength ranges, revealing that there are instead two large central galaxies in the process of merging and that the blue excess is actually a result of the string of star formation.

Multiwavelength studies are especially illuminating, because each wavelength range can provide unique information about the underlying physical processes in a system. The Chandra X-ray Observatory was used to observe the hot intracluster medium. Radio observations from the Low Frequency Array (LOFAR) and the Very Large Array (VLA) were used to search for activity associated with the black hole outburst. The Gemini Multi-Object Spectrograph (GMOS) observed the warm ionized gas and searched for ionization sources. Finally, the Atacama Large Millimeter/submillimeter Array (ALMA) observed the cold molecular gas from which stars form.

From these observations, the authors were able to piece together a picture of the outburst, the lasting effect it had on its environment, and specifically how it created the string of star formation. Some 200 million years before SDSS 1531 was as it appears today (in reality, billions of years ago when light travel time is accounted for), a supermassive black hole at the center of one of the large merging galaxies was actively accreting material and emitting two extremely powerful jets in opposite directions, making it an active galactic nucleus. The jets blasted hot material away, forming massive cavities. One cavity was identified by Chandra and LOFAR observations because it lacks hot X-ray emission and is filled with radio emission. The symmetric cavity created by the other jet was not observed, but the authors proposed it may have since faded away if the jet blew into a less dense region of gas, or the motion of the surrounding gas pushed the cavity away from its original location.

This active galactic nucleus was not observed, so the authors know that it has since “turned off” and is no longer active. However, the major disruption caused by the active galactic nucleus outburst and the complex dynamics of the ongoing galaxy merger are likely the cause of the “beads on a string” star formation. The GMOS and ALMA observations revealed warm ionized gas and cold molecular gas along the edge of the star formation arc. The authors believe that hot gas pushed away by the outburst’s jets eventually cooled and is now falling back on to the merging galaxies. This cold gas then began to collapse to form stars, initiated by a cooling wake, strong ram pressure forces, or tidal interactions from the galaxy merger. See Figure 2 for an overview of these findings.

An image of the galaxy cluster, with added lines and labels

Figure 2: An overview of this article’s interpretation of SDSS 1531. The light blue “avocado” shapes represent the cavities formed by the active galactic nucleus jets. The light green dots show regions of star formation along the edge of the dark blue region of cold molecular gas. [Omoruyi et al. 2024]

To confirm their findings, the authors plan to obtain deeper observations and compare their work with simulations. This will allow them to verify the origin of the star formation, understand the complex interplay of gas in this system, and prove that billions of years ago an awesomely powerful black hole outburst boomed through SDSS 1531.

Original astrobite edited by Abbe Whitford.

About the author, Annelia Anderson:

I’m a 4th-year Astrophysics PhD student at the University of Alabama. My current research uses simulations to study the circumgalactic medium and inform interpretations of observations. Beyond research, I enjoy playing piano and guitar, cooking, spending time with my cat, and I hope to someday write astronomy children’s books.

illustration of a magnetar

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: Magnetar as the Central Engine of AT2018cow: Optical, Soft X-Ray, and Hard X-Ray Emission
Authors: Long Li et al.
First Author’s Institution: University of Science and Technology of China
Status: Published in ApJL

Over the past decade, several efforts to rapidly image the entire night sky on an approximately nightly basis have resulted in many unexpected discoveries. Several surveys, such as those of the Palomar Transient Factory, the Intermediate Palomar Transient Factory, the Zwicky Transient Facility, and the Asteroid Terrestrial-impact Last Alert System (ATLAS), have contributed to the ever-expanding and diverse transient zoo. Given this cadence and relatively shallow depth, these surveys have identified several classes of bright and fast transients. One class of transients that remain unexplained are luminous fast blue optical transients (FBOTs).

Although several FBOTs have been identified over the last decade, such as CSS161010 in 2016, this class of optical transient was largely overlooked due to their rarity until AT2018cow (i.e., “The Cow”) was discovered by the ATLAS facility in 2018. You can read about its discovery in this Astrobite! “The Cow” occurred in a relatively nearby galaxy at only 66 megaparsecs (~200 million light-years) away, and it was extremely bright, evolved quickly, and had a largely featureless, blue optical spectrum. The X-ray and optical spectra and light curves of AT2018cow did not align well with any models for known transients, leading to a slew of multi-wavelength observations in an attempt to uncover its origin. Since then, numerous theories have been proposed about the origin of FBOTs. However, there are some challenges since observations of a more recent FBOT, AT2023fhn (i.e., “The Finch”), has indicated that all FBOTs might not come from the same underlying astrophysical object, as discussed in this Astrobite! Despite this, the authors of today’s article have tackled this challenge and proposed a new explanation for “The Cow.”

Meet the Central Engine

The authors of today’s article are interested in creating a model that can explain the optical and X-ray properties of AT2018cow. Although there are many explanations currently in the literature, the authors are specifically interested in investigating the newborn magnetar scenario using a “two-zone” model. A magnetar is a highly magnetized pulsar, a type of neutron star born from the deaths of massive stars in supernovae.

In the first zone of the two-zone model, the magnetar has a Poynting-flux-dominated wind. This means the wind is highly magnetized and carries magnetic energy away from the magnetar. As the wind expands, magnetic dissipation converts magnetic energy into kinetic energy of the wind’s particles by reconnecting magnetic field lines. The particles in the wind gain so much kinetic energy that they become relativistic (travel near the speed of light). The electrons in the magnetar’s wind are charged, so they accelerate. They also interact with the magnetic field via the Lorentz force, which causes them to spiral around magnetic field lines and radiate away energy. This emission process is called synchrotron radiation.

The second zone the model considers is bulk material ejected external to the magnetar’s wind. The critical distinction between previous methods and today’s article is the inclusion of interaction between the emission from the magnetar’s wind and this ejecta. The authors assume that the ejected material is made of 20% hydrogen, 10% helium, and 70% oxygen to compute the opacity of the ejecta, which is vital to determining the resulting visible emission from the interaction of the wind’s radiation with the ejecta.

The authors of today’s article also calculate the emission from the ejecta heated by the wind’s radiation under the assumption that the radioactive decay of cobalt-56 and nickel-56 isotopes add energy to the ejecta. These isotopes are thought to contribute power to the optical emission from Type Ia supernovae. Initially, the core of the collapsing star is composed chiefly of nickel-56 that is ejected in the supernova, which goes on to decay to cobalt-56 and then finally to iron-56, radiating away energy that is absorbed and re-radiated by the ejecta as optical emission that we observe.

When adding all these contributions, the authors can predict the time evolution of the optical and X-ray emission that one might observe from such a system. The authors show an example of the spectrum from this system in Figure 1.

radiation spectrum of a newborn millisecond magnetar

Figure 1: The spectrum in units of kiloelectronvolts (keV) versus luminous intensity from the magnetar model for a particular magnetic field strength, magnetar spin period, etc. The synchrotron component is the dashed line from the magnetar’s wind. The thermal “BB” (blackbody) component, shown as a dotted line, comes from the energy injected into the ejecta by the magnetar wind and radioactive decay. The sum of the two components is the solid line, representing the observed spectrum. [Li et al. 2024]

How Does a “Young, Windy Magnetar Cow” Look?

Using their model, the authors compiled the optical and X-ray observations of AT2018cow and compared them with their predictions. The optical and X-ray light curves are shown in Figure 2, and the X-ray spectra are shown in Figure 3. The optical data align quite nicely until about 50 days, and the X-ray spectra at early (7 days) and late (46 days) fit well.

optical and X-ray light curves of AT2018cow

Figure 2: The optical (bolometric) and X-ray light curves in the soft and hard bands. The model explains the optical and X-ray properties well until about 50 days after the event when it over-predicts the X-ray flux and under-predicts the bolometric optical luminosity. [Li et al. 2024]

Although the model appears to explain some of the optical and X-ray properties of AT2018cow, the authors note some important differences that require further investigation. For example, the sudden drop in X-ray luminosity at ~50 days in Figure 2 could be explained by the eventual collapse of the magnetar into a black hole. This situation could happen if the magnetar were initially spinning at a high velocity. Then, as it “spun down” and lost its energy to the magnetar wind, it would slow down enough that the centrifugal force of the rotation plus the neutron degeneracy pressure could not hold the magnetar up against gravity anymore, leading to its collapse. Upon collapse, the wind would no longer be available as a source of energy to cause X-ray emission.

X-ray spectrum of AT2018cow

Figure 3: The observed X-ray spectrum of AT2018cow at 7.7 days (circles) and 46.7 days (squares). The model explains well the rising X-ray emission at the highest energies in the first epoch. Unfortunately, the second epoch does not constrain the thermal component of the emission very well. [Li et al. 2024]

This scenario is an attractive explanation of an exotic phenomenon. Still, it has an important problem: given the best-fitting model parameters, the magnetar’s mass must be fine-tuned to within one ten-thousandth of the postulated maximum neutron star mass. Given this, that scenario for the late-time drop in X-ray flux is unlikely, and this model likely requires additional modifications to explain all of the properties of AT2018cow. However, given its success with the optical and X-ray properties at early time, further investigation into central-engine-powered models for AT2018cow and other FBOTs is necessary to uncover the true origin of these unique transients.

Original astrobite edited by Sonja Panjkov.

About the author, Will Golay:

I am a graduate student in the Department of Astronomy at Harvard University and the Center for Astrophysics | Harvard & Smithsonian, advised by Edo Berger. I study radio emission from transient astrophysical objects like tidal disruption events.

illustration of our 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 astrobites.org.

Title: Friends Not Foes: Strong Correlation Between Inner Super-Earths and Outer Gas Giants
Authors: Marta L. Bryan and Eve J. Lee
Authors’ Institutions: University of Toronto and McGill University
Status: Published in ApJL

We have found exoplanet systems with small, close-in planets, like super-Earths, and we have also found exoplanet systems with distant gas giants like Jupiter. However, it is not clear how often these two kinds of planets form in the same planetary system. A system with a small, close-in planet and a distant giant planet would begin to resemble our own solar system, in that we have multiple rocky planets interior to multiple gas giant planets. We have found a few of these systems, and when astronomers find a few of something, they naturally want to compute an occurrence rate (see this bite).

Today’s article looks at the occurrence rates of small and large planets in the same system. Occurrence rates attempt to quantify how often a particular outcome is expected to be observed. In exoplanet science, this represents how often a particular kind of planet will be found if one randomly surveys stars to search for planets. In particular, today’s article looks to settle a debate. In the past, multiple teams have attempted to measure the occurrence rate of systems with a distant gas giant planet, given that the system has an inner small planet. We call this the conditional occurrence rate, because we want to know how often Planet Type A will be found in a chosen system if we already know (i.e., “on the condition”) that Planet Type B occurs in that chosen system.

Three past studies found a positive correlation between the conditional occurrence of these kinds of planets. In other words, if you find an inner small planet, you are more likely to find a distant giant than you would be if you randomly looked at a system with no inner small planet. However, other studies found no correlation (see this astrobite) or an anti-correlation — that is, you are less likely to find a distant gas giant in a system with an inner small planet than in a system with no inner small planet. It was recently found that accounting for the metallicity of the host stars results in a positive correlation instead of no correlation or a negative correlation. Metallicity is the measurement of how much of the star is composed of elements heavier than helium. Higher metallicity means a higher percentage of the star is made up of heavy elements, or “metals.”

Today’s article builds on the finding that host-star metallicity is an important metric for properly understanding the conditional occurrence rate of small, close-in planets and distant gas giant planets. The authors take all stars that have large publicly available radial velocity data sets and at least one confirmed small, close-in planet. They also purposefully cut out any host stars that are M dwarfs, since it has been demonstrated that M-dwarf stars have almost no distant gas giants (see this astrobite). Ultimately, they compile a sample of 184 systems.

To calculate the conditional occurrence rate, the authors have to take into account the completeness of the radial velocity data sets for finding distant giants. Completeness quantifies how sensitive the data are to finding a certain kind of planet (see this astrobite). For example, the radial velocities may have been sampled at times that are inopportune to finding a certain mass and period planet. To measure completeness for each of their 184 systems, the authors perform an injection/recovery test. They generate a planet with a given set of orbital parameters (period, eccentricity, inclination) and mass, then generate (inject) the expected radial velocity curve for that simulated planet at the same timestamps as the real observations. They then attempt to detect the simulated planet in the simulated data (recover).

They do this thousands of times for different simulated planets and repeat this for each of the 184 data sets. From this test, they can quantify how many distant giant planets these data sets might have missed and how many they almost definitely did not miss. Using these completeness maps, they generate an average completion map (see Figure 1), which they use to compute the conditional occurrence rate.

completeness map

Figure 1: The color bar shows the average completeness map for this sample (bright colors are more complete, dark colors are less complete) with the detected distant gas giant planets overlaid as blue dots. [Adapted from Bryan & Lee 2024]

When they split the 184 systems into metal-rich and metal-poor systems, they get different values for the conditional occurrence rate. For metal-rich stars (defined as more metallic than the Sun), they find a conditional occurrence of 28 (+4.9 / −4.6)%. For metal-poor stars, the rate is only 4.5 (+2.6 / −1.9)%. This is a big and statistically significant difference! This difference rigorously reaffirms the previous hypothesis that the conditional occurrence rate depends on the host-star metallicity.

But this is not quite enough — we also need to compare the conditional occurrence rate to the occurrence rate of distant gas giant planets, regardless of having an inner planet or not. If the rates are different, that tells us there is a relationship between the inner and outer system planets. Indeed the authors do find different rates! For metal-rich stars, they find that regardless of the inner system, distant giant planets occur for 12–13% of stars. (The authors used two data sets for this analysis.) Distant giant planets occur for 4–6% of metal-poor stars. Therefore, because the conditional occurrence is enhanced (28% vs. 12–13%, see Figure 2), this points to a positive correlation: if you find an inner small planet, you are more likely to find a distant gas giant than if you randomly searched for only distant gas giants!

plot of probability distributions for gas giants with and without small inner planets

Figure 2: The probability distribution of the conditional occurrence rates for systems with inner small planets (red) versus without considering any inner planets (black and gray). The red curve peaks at a higher occurrence rate than the black and gray curves, so having an inner small planet enhances the occurrence of having a distant gas giant. [Adapted from Bryan & Lee 2024]

This correlation has big implications for our understanding of exoplanet systems. It suggests that systems like our own solar system, with small planets interior to large planets, might be more common than not. This study is also the first to have used so many systems to look for correlation with metallicity. Previous studies have used very small samples, simply because getting the data to do this kind of work is difficult and time consuming. By bringing together many more systems and building on previous work, this article is able to make a more rigorous claim that there exists a positive correlation between small inner planets and outer giants.

Original astrobite edited by Samantha Wong.

About the author, Jack Lubin:

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

Simulation of the light emitted from the gas surrounding a supermassive black hole binary

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 First Robust Evidence Showing a Dark Matter Density Spike Around the Supermassive Black Hole in OJ 287
Authors: Man Ho Chan and Chak Man Lee
First Author’s Institution: The Education University of Hong Kong
Status: Published in ApJL

The nature of dark matter is still a big mystery to astronomers and physicists. Most of the evidence for dark matter comes from studying its gravitational effects on visible matter. We now know that dark matter makes up about 85 percent of the total matter in the universe and that it forms dark matter halos in which galaxies are embedded.

It is predicted that the supermassive black hole residing in the center of a galaxy can affect the shape of the dark matter density near the center of the galaxy. The dark matter gets redistributed to form a “spike” in the density distribution, causing the rate of dark matter annihilation to rise and resulting in strong emission of gamma rays. However, we have not detected any strong gamma-ray emission near a supermassive black hole, including the one in our galaxy — Sagittarius A* (Sgr A*).

In today’s article, the authors propose an alternative method to confirm the existence of a dark matter density spike at the center of a galaxy. They use data from a supermassive black hole binary called OJ 287. A binary consists of two black holes in orbit around each other. The binary will lose energy through gravitational waves, causing the orbit to decay and the orbital period to decrease. The authors use orbital data of OJ 287 to show the effects of a dark matter spike on the orbital period of the binary, providing strong evidence for the existence of a dark matter density spike around a supermassive black hole.

How Is the Supermassive Black Hole Binary Losing Its Energy?

Assuming the energy loss in the binary OJ 287 is dominated by gravitational waves, the measured orbital period decay rate suggests the two supermassive black holes will merge in about 12,000 years. The calculated total energy loss rate, however, turns out to be lower than the energy loss rate calculated due to gravitational wave emission. This suggests that there is some other mechanism causing the binary to lose energy.

The authors propose that the dark matter density spike can cause a drag force on the primary supermassive black hole. This drag force, called dynamical friction, can account for the additional energy loss rate. To test this, the authors compute the energy loss rate due to dynamical friction for a dark matter density distribution with a “spike-index” parameter to account for the density spike.

How “Spiky” Is the Dark Matter?

After accounting for the energy loss due to dynamical friction, the authors constrain the spike-index parameter by matching the calculated total energy loss rate to the observed value. This yields a narrow range of spike-index values that agree with the predicted value from a theoretical supermassive black hole growth model (see Figure 1).

plot of energy loss rate as a function of the spike index

Figure 1: The energy loss rate (dE/dt) of the binary with only gravitational radiation accounted for (green; GW only). The red curve shows the total energy loss rate with both gravitational waves and dynamical friction (GW + DF). The shaded region is the constrained total rate from observations. The blue dotted line indicates the spike index as predicted by an adiabatic supermassive black hole growth model. The dynamical friction from a dark matter density spike can account for the large orbital decay period observed. [Chan & Lee 2024]

It is possible that this discovery could be a coincidence, as the spike index derived from the assumed supermassive black hole growth model may not apply to a binary like OJ 287. Numerical simulations suggest that a supermassive black hole binary can scatter dark matter particles, decreasing the dark matter density and reducing the spike index. Therefore, the actual spike index could be lower, but there are other mechanisms that replenish the inner regions with dark matter. The complex interactions between dark matter and binary supermassive black holes are not well understood, but this result could provide an important clue to understand them better.

Future low-frequency gravitational wave observations like those from the Laser Interferometer Space Antenna (LISA) can further examine supermassive black holes similar to OJ 287 to verify this result and help us understand the interactions between dark matter and supermassive black holes better. Also, studying the dynamical orbits of stars around a supermassive black hole like Sgr A* in our own galaxy can be another way of verifying the existence of a dark matter density spike. Future accurate observations of stellar orbits around Sgr A* should help us constrain the dark matter density spike model and shed more light on the properties of dark matter.

Original astrobite edited by Megan Masterson.

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.

multi-wavelength image of Messier 51

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: X-ray View of Little Red Dots: Do They Host Supermassive Black Holes?
Authors: Tonima Tasnim Ananna (তনিমা তাসনিমঅনন্যা) et al.
First Author’s Institution: Wayne State University
Status: Published in ApJL

Among the fascinating objects observed by JWST are galaxies in the early universe known as little red dots (read more about them here and here!). These galaxies are believed to be rich in dust, which contributes to their distinct red hue. The infrared spectra of little red dots from JWST reveal the presence of strong emission lines. This emission can be a potential indicator of either 1) an active black hole or 2) vigorous star formation. That leaves us with a tantalizing question: which one is it!?

If the little red dots indeed host black holes, research suggests that the masses of these black holes must be between 10 million and 1 billion times the mass of the Sun. This poses a significant challenge — how can these black holes accumulate such large masses in such a short time from their births? This question has left astronomers puzzled, highlighting the need to understand if these little red dots do, in fact, hold such massive black holes.

In astronomy, akin to life, sometimes it helps to take a step back and observe something from a different perspective. Looking at the little red dots at a different wavelength of light can help understand what exactly is happening in these galaxies. Today’s authors set out to harness the power of multiwavelength astronomy and to look at these little red dots at X-ray wavelengths to see if they emit any X-ray signals. Even though both accreting black holes and star formation activity can lead to emission in the X-ray, the difference between the strength of the X-ray emission between the two is greater in the X-ray than in the infrared. So, obtaining the strength of X-ray emission from the little red dots should give us a good idea of whether the X-rays are caused by a black hole or by stellar processes.

The authors select a bunch of little red dots that are hidden behind a big galaxy. Since X-rays can be challenging to detect, a massive galaxy in front can act as a gravitational lens and magnify any X-ray signals from the little red dots. If little red dots indeed host really massive black holes, it is not unreasonable to expect X-ray signals from them.

Lo and Behold! There Are None!

plot of stacked X-ray signals from 21 little red dot galaxies

Figure 1: All 21 little red dots that were stacked to see if they emitted any X-ray signals. There does not appear to be a spike in the X-ray counts anywhere, indicating that there is no strong X-ray emission from the little red dots. [Adapted from Ananna et al. 2024]

The authors do not detect any X-rays from the little red dots. To further improve sensitivity, in case the X-ray signal is too weak to be determined from individual galaxies, the authors also stack the galaxies. X-rays were still undetected with significant confidence (Figure 1).

Although the authors do not detect any X-rays from the little red dots, they can still use the fact that they did not detect any X-rays to obtain conservative limits on the maximum mass of the black hole, if any, that might be present in the little red dots. The authors assume accretion at the Eddington limit, a theoretical maximum rate at which a black hole can grow by accretion of mass, and obtain black hole masses less than 1.5 x 106 M☉ (Figure 2). This is a couple of orders of magnitude lower than the black hole masses in little red dots determined by other works.

plot showing the derived upper limits on the black hole masses of the little red dot galaxies

Figure 2: The derived upper limits to the black hole masses of the little red dots, plotted against their stellar masses. The bottom-most thick dashed green line gives the average value (assuming Eddington limit) of the black hole mass. The black hole masses are not extremely over-massive unless the accretion is a fraction of the Eddington limit. [Ananna et al. 2024]

This mismatch may arise due to the method by which the black hole masses were estimated in these little red dots. Masses were determined using broad emission lines, which have been standardized based on nearby galaxies with active galactic nuclei and thus may not work for galaxies at much higher redshift. It is also possible that the X-rays were not detected because these galaxies are incredibly dusty, but the authors believe that to be very unlikely.

The Mystery of Little Red Dots Continues!

This work suggests that little red dots will not likely host extremely massive black holes. More data coming from JWST, including a detailed study of the spectra of these little red dots, can help us understand them better. This work primarily uses data from the Chandra X-ray Observatory, which has now unfortunately been defunded despite continuing to produce very significant scientific results. (Read here to learn how you can save Chandra!) The mysteries of the universe will ultimately be solved only by looking at it from every possible wavelength, and we need such multiwavelength telescopes to achieve this lofty goal!

Original astrobite edited by Janette Suherli.

About the author, Archana Aravindan:

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

spiral galaxy NGC 3432

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: CloudFlex: A Flexible Parametric Model for the Small-Scale Structure of the Circumgalactic Medium
Authors: Cameron B. Hummels et al.
First Author’s Institution: California Institute of Technology
Status: Published in ApJ

If you or I spent a long time sitting in a bathtub, we’d probably shrivel up and quickly become indistinguishable from a raisin. Galaxies, on the other hand, spend their lives bathed in a reservoir of gas known as the circumgalactic medium. In fact, the presence of the circumgalactic medium is necessary for the long-term growth and survival of the galaxy. More precisely, the circumgalactic medium provides the gas that regulates a galaxy’s ability to form stars — for example, as star formation depletes gas from the interstellar medium, inflows from the circumgalactic medium replenish that supply.

Our understanding of this diffuse gas reservoir has largely been driven by observing its effect on background light (i.e., along so-called quasar sight lines) through absorption features. These measurements have indicated the presence of gas in both hot (T ~ 106 K) and cool (T < 104K) phases. However, the presence of such a stark temperature difference between the various components of this intergalactic gas would produce fluid instabilities that could quickly drive the cold component to fragment into a collection of small “cloudlets.” Self-consistently modeling these structures in large numerical simulations is challenging because they require significant dynamic range — one must be able to resolve clouds on scales smaller than a few light-years while also modeling the large scale motions of gas on galactic (thousands of light-years) scales.

Inspired by this challenge, today’s authors set out to construct a simple model to efficiently describe the distribution and properties of these cool gas cloudlets embedded in broader cloud complexes (larger clouds composed of many cloudlets) within the circumgalactic medium. Leveraging such a model to predict observed line shapes will allow us to begin to constrain the detailed substructure of this galactic bathtub.

Constructing the Model

To construct their model, today’s authors populate these so-called cloud “complexes” with a collection of randomly sampled cloudlets and observe the resulting configuration. In detail, they statistically sample cloudlet properties based on a series of predetermined distributions. For example, the total mass of their cloud is set to be a million times the mass of our Sun. Then, based on their chosen parameters for the minimum mass of an individual cloudlet and the shape of their mass probability distribution (i.e., the function that tells you how likely it is for a cloudlet with a given mass to exist), they randomly generate cloudlets until the total mass of these objects equals the pre-set total mass. They combine this with probability distributions for the distance of the cloudlets from the center of the complex and the turbulent velocity of the cloudlets to produce a mock cloud complex structure, such as the three examples shown in Figure 1.

examples of cloudlet distributions

Figure 1: Three examples of cloudlet distributions generated using the model with the minimum cloudlet mass varied from 10-3 solar masses at left to 105 solar masses at right. Because these are 3D distributions in reality, what is shown here is the cumulative distribution along one direction, with the colors representing the density of the gas along the line of sight at any given point. [Hummels et al. 2024]

Observing the Circumgalactic Medium

In this manner the structure of the cloud complexes is determined by a choice of 11 parameters that can be flexibly varied to explore the effects of cloud structure on the resulting observations. The key observational probe that has been historically used to study the circumgalactic medium is the shape of absorption features in the spectra of distant, luminous quasars. Because the shape of these features is a direct result of the interaction of background light with the circumgalactic medium gas and the circumgalactic medium is thought to be a highly disordered, turbulent medium, observed features will in principle be sensitive to where in the medium the light passes and how dense the absorbing objects are. For example, light passing through the center of the cloud in the left panel of Figure 1 will likely interact with more cloudlets than light passing near the edge and thus the observed spectra should reflect this.

simulated absorption lines for 17 cloudlets

Figure 2: Top: A predicted magnesium-II absorption profile (black) generated from the cumulative effect of absorption in the 17 cloudlets intersected along the chosen sight line. The absorption profiles are shifted left and right based on the velocity of the cloudlets. Bottom: A depiction of the chosen sight line along the z axis with the observer at the left. The velocity vectors of the intersected cloudlets are shown with colored arrows. [Hummels et al. 2024]

In Figure 2, today’s authors demonstrate how they can use this model to predict an observed line profile (read: shape) — in this case for a magnesium-II line. This chosen line intersects 17 cloudlets as it passes through the complex (i.e., travels along the z direction in the lower panel). Each of these cloudlets has a different velocity that will cause it to contribute to a slightly different absorption feature in the spectrum because of the Doppler effect (the motion of the cloudlet causes the wavelength of the light it receives to stretch or shrink so that a given absorption line — which should have a fixed wavelength location — appears at a longer or shorter wavelength in the rest frame of the quasar), yielding the left–right offsets of the dips in the upper panel of Figure 2. Each cloudlet’s contribution to the absorption is then modeled by a so-called Voigt profile, which describes how the width of a spectral line will be determined by its internal temperature and density. The cumulative contribution of these profiles then produces the observed spectral feature.

Turning the Knobs

To understand the utility of such a framework, the authors then demonstrate how observed profiles respond to variations in the chosen parameters, such as minimum cloudlet mass. To this end, they generate 10,000 random sight lines through a given cloud complex (i.e., they do the procedure shown in Figure 2 10,000 times) and analyze the properties of the absorption in aggregate (such as by counting the number of intersected absorbers). One key metric to quantify these effects is the observed equivalent width, which basically measures the strength/depth of an absorption feature — a sight line that passes through a high density of absorbers will have a larger equivalent width than one that intersects only a few. For example, from Figure 3, they show that decreasing the minimum cloudlet mass, which should increase the total number of cloudlets (as we saw in the leftmost panel of Figure 1), increases the number of intersected cloudlets, as you might expect. Because these cloudlets are much more common, there are more sight lines that will pass through a lower density column of gas, yielding a broader distribution of column densities. Despite these effects, the overall distribution of observed equivalent widths is roughly independent of minimum cloudlet mass, but the fraction of sight lines that yield a given equivalent width is significantly different between the different minimum masses.

Figure 3: Left three panels: The distribution of the number of intersected cloudlets, column density along the line of sight, and equivalent width for different choices of the minimum cloudlet mass (different colors). Rightmost panel: The fraction of sight lines yielding equivalent widths greater than the value on the horizontal axis. [Adapted from Hummels et al. 2024]

This cloud complex model can be applied in aggregate to “simulate” the distribution of clouds in the halo around the galaxy. That is, several of these complexes will populate the circumgalactic medium around a galaxy, so a given sight line will intersect a number of complexes, each of which is composed of a collection of cloudlets, as we’ve seen. Applying this procedure to such a distribution allows one to predict observed line profiles and thus equivalent width distributions, a key observable used to probe the circumgalactic medium.

Comparing the observed and predicted distributions and the sensitivity of these distributions to underlying parameters will shed light on the detailed structure of this complex medium.

Original astrobite edited by Jessie Thwaites

About the author, Sahil Hegde:

I am an astrophysics PhD student at UCLA working on using semi-analytic models to study the formation of the first stars and galaxies in the universe. I completed my undergraduate at Columbia University, and am originally from the San Francisco Bay Area. Outside of astronomy you’ll find me playing tennis, surfing (read: wiping out), and playing board games/TTRPGs!

NGC 1277 as seen by Hubble

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: RUBIES: Evolved Stellar Populations with Extended Formation Histories at z ∼ 7 − 8 in Candidate Massive Galaxies Identified with JWST/NIRSpec
Authors: Bingjie Wang et al.
First Author’s Institution: Pennsylvania State University
Status: Published in ApJL

When JWST was launched, scientists were hopeful that the telescope would be able to answer hundreds of questions about the early universe. Luckily, it has been able to answer many of these questions. And even more luckily, observations of the early universe with JWST have been able to generate even more questions we don’t know the answers to!

One new conundrum scientists are running into is that it looks like some galaxies in the early universe have what we call evolved stellar populations. This means that even though these galaxies are only a couple hundred million years old (super young, right?), they already have ~100-million-year-old stars, meaning that these galaxies had to make a large amount of stars in only a short span of time. We’re not used to seeing this many stars form this fast — in fact, we expect that star formation should be slow this early in the universe. So how do we reconcile this?

The authors of today’s article look in more depth at the spectra of three such galaxies to figure out if these galaxies have really been forming stars for millions of years already. These three galaxies are located at redshifts of z = 6.7–8.4, which places them just 600 million years after the Big Bang.

plot of spectra of three galaxies

Figure 1: Balmer break at rest wavelength 3645 Angstroms (Å) observed in the galaxy spectra. The strength of the break (the relative flux difference before and after the break) is 1.71. [Wang et al. 2024]

Balmer Breaks Create Questions

First, the authors looked to confirm that evolved stellar populations do in fact exist in these galaxies. One foolproof way to do this is to look for a Balmer break in the galaxies’ spectra. The Balmer break comes from the atmospheres of older stars, indicating that the galaxy contains a population of older stars and has had little star formation for at least the past 100 million years. Excitingly, the authors confirm that there is in fact a Balmer break in the spectra of these three galaxies, as shown in Figure 1. Unfortunately, the Balmer break only tells you that there are older stars, not necessarily how old they are or how many of them there are.

Stellar Populations or Active Galactic Nuclei?

The authors then turn to understanding the finer details of the stellar population, specifically its formation history. The authors found evidence of broad lines in their emission-line spectra, signaling that there might be active galactic nucleus activity in at least two of these galaxies. Broad lines come from Doppler broadening, where photons emitted by an active galactic nucleus have a wide distribution of velocities, resulting in a broad emission line. However, the authors aren’t entirely ready to say conclusively that there are active galactic nuclei in the spectra.

In the local, low-redshift universe, we’re pretty good at understanding how these broad lines relate to active galactic nuclei because we have hundreds of spectra to test. But at high redshift, we don’t have a lot of data. We aren’t sure why supermassive black holes act the way they do early in the universe, and we don’t want to assume they act the same as the ones we observe in the local universe. So researchers have to rely on testing theoretical models against high-redshift galaxy observations.

To test how much of the light in these galaxies is coming from active galactic nuclei or evolved stellar populations, the authors test three different models: 1) The light is mostly from stars. 2) It’s from a mixture of stars and active galactic nuclei. 3) It’s mostly from active galactic nuclei. The authors create the model spectra using Prospector and compare the modeled spectra to the observed spectra to see how well they match.

Model Comparisons

The results of the modeling are shown in Figure 2. The most important thing the authors are testing here is what these different levels of contribution from the active galactic nucleus mean for the formation history of the stellar population. For instance, the model with the maximal stellar contribution means that the stellar population had to form earlier, because there need to be enough stars to create all the light seen. The minimal stellar model can form stars a bit later because the active galactic nucleus contributes more to the light seen, so you don’t need as many stars producing a lot of light. However, there is no way to verify which model is more accurate at this stage, so the authors turn to theorizing how each model could fit into the evolutionary stages of later galaxies.

comparison of modeled spectra to observed spectra

Figure 2: Left: Each model compared to the spectrum for one of the observed galaxies. Right: Predicted star formation rate as a function of time since Big Bang for each model. As seen, models with higher active galactic nucleus contribution predict later star formation. [Wang et al. 2024]

Progenitors of Massive Quiescent Galaxies?

To find out how their models fit into the evolution of galaxies, the authors then compare the different suggested star formation histories of the medium and maximal stellar contribution models with the projected histories of massive quiescent galaxies that exist at z ~ 3-5. These massive quiescent galaxies lack an explanation for how they got so massive yet have such slow star formation now. As can be seen in Figure 3, the star formation rate traced across the age of the universe for the massive galaxies (black, blue, green, and yellow distributions) matches much better in the maximal stellar contribution model. The authors posit that these galaxies could create a lot of stellar mass in a short time, then have their star formation slowed greatly to become these massive quiescent galaxies. With this explanation, the authors note that these galaxies would be a relatively small fraction of all the galaxies in the early universe.

comparison of star formation histories of massive quiescent galaxies and the RUBIES sample galaxies

Figure 3: Comparison of medium (purple) and maximal (red) stellar models with the projected formation histories of a sample of massive quiescent galaxies at z ~ 3-5. [Wang et al. 2024]

Of course, there is still the complication that there’s convincing evidence that there are active galactic nuclei in this sample of galaxies. Also, having this many stars form this early is still an uncomfortable thought for many astronomers. The jury is still out on these galaxies and how much star formation they had early on, but future studies looking into the far infrared can hopefully distinguish between light from stars and active galactic nuclei and lend more insight into what makes up these galaxies. And hopefully with JWST we can find some more answers while exploring even more questions.

Original astrobite edited by Amaya Sinha.

About the author, Caroline von Raesfeld:

I’m a second-year PhD student at Northwestern University. My research explores how we can better understand high-redshift galaxy spectra using observations and modeling. In my free time, I love to read, write, and learn about history.

Illustration of gravitational waves emanating from a pair of black holes approaching a merger

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 Metallicity Dependence and Evolutionary Times of Merging Binary Black Holes: Combined Constraints from Individual Gravitational-Wave Detections and the Stochastic Background
Authors: Kevin Turbang et al.
First Author’s Institution: Free University of Brussels and University of Antwerp
Status: Published in ApJ

The Gravitational Wave Ocean

Before the final few seconds of the gravitational wave signal from a merger washes up on LIGO–Virgo–KAGRA detectors on Earth, the black holes or neutron stars that caused the powerful waves were actually orbiting around each other for much, much longer — possibly for hundreds of millions of years. The gravitational waves emitted prior to the pair’s final hurrah are too quiet to be picked up via ground-based interferometry with current detectors, and they instead wash together in a background hum like waves out at sea.

The length of time between two black holes forming and their eventual merging — otherwise known as delay time — is a useful signature for telling us where the black holes came from and the lives of the stars from which they were born. Fortunately, we don’t have to sit around for a billion years watching the cosmos play at spinning black holes around each other (though it would be fun to help if you were an interstellar cat). The distribution of time delays can be calculated using the number of merging black holes per given time at a certain redshift (called the merger rate), something that we can measure with current detections of merging black holes with ground-based detectors; as long as we know the rate of star formation and how this varies with the metallicity of the forming stars.

Instead of waiting for one binary system to merge somewhere in the cosmos, we can understand the time-delay distribution by detecting many black holes that have formed and merged at different points in time. The different information that multiple detections give us allows us to paint a picture of this time-delay distribution without waiting the lifetime of a black hole binary. There’s one crux, however: with the limited sensitivity of current ground-based gravitational wave detectors, we only really resolve merging black holes out to a redshift of around 1, about 8 billion years ago, when the universe had already started slowing its pace of forming stars. The black holes merging closer than this distance make loud enough gravitational waves that they stand out from the noise, making them resolvable, while beyond this distance gravitational waves that are quieter and harder to detect are lost in detector noise, and hence are unresolved. This means we can only understand the merger rate at small redshifts with resolved detections, while minimising the information we can get about the time-delay distribution.

Heading for Choppy Waters

Fortunately, today’s authors have a trick up their sleeve. By combining the information from the current detections loud enough to individually identify and the information from the background hum of merging black holes too weak to detect, the so-called stochastic background, they can help bypass this problem. The stochastic background is made up of unresolved signals from many more inspiralling black holes — a random sea of gravitational waves — and provides vastly more information about the black hole population at higher redshifts. The authors show that by using information about the stochastic background, we can extract information about the shape of the time-delay distribution (i.e., whether most black holes take a long time to merge or whether most merge relatively quickly after forming), the minimum time a black hole binary takes to merge, and the maximum metallicity environment that can create merging black holes. However, this background has so far been too quiet to detect. Today’s authors show that predicting these parameters using only resolved signals doesn’t tell us much about the time-delay distribution, but things change significantly when including different models of the stochastic background.

Even though we haven’t detected this stochastic background, we can still make predictions about the time-delay distribution because we know that the stochastic signal needs to be quieter than we can currently hear in the detectors. Therefore, there should be fewer merging binaries making up the background, making it not loud enough to meet the detection threshold. This means we have an estimate of the energy per unit frequency of the stochastic background, without having even detected it.

The authors’ model assumes the time-delay probability distribution is a power law with slope κ. A steeply positively sloped time-delay distribution would mean that there is very little probability of any objects merging quickly after they have formed and it’s very likely that black holes take a long time to merge. A steeply negative slope would mean that it’s very likely that black holes merge quickly after forming. Using the resolved detections and this stochastic background estimate, the authors find κ is strongly constrained to be negative. Whether the slope would be positive or negative is determined by the low-redshift black hole population — a positive slope would mean that since every greater time delay is more probable than the smaller time delay before, every day there would be more and more black holes merging. Fortunately, we don’t need to explain an ever-increasing number of merging black holes, as measurements at low redshifts show that there are fewer merging black holes closer to us as far as our resolved detections reach. Meanwhile, they can’t place any constraints on the minimum delay time or the maximum sensitivity. This is a consequence of the estimated stochastic background being much weaker than what we can detect with the current observations.

Our Next Great Voyages

With the next big set of improvements to the LIGO detectors currently being implemented comes two possibilities: either this stochastic background is detected, or it remains too quiet to be picked up. The authors show that either of these cases will be more informative about the time-delay distribution than what the current observations can say. By simulating a stochastic background for each of these future cases and adding it to the current resolved detections, they can say more about the astrophysics of the time delay and metallicities of the black hole mergers in the universe.

For the case of a future detection of the stochastic background, the slope of the time-delay distribution is likely to be highly negative, while the metallicity parameter and the minimum time delay are both constrained to smaller values, as seen in Figure 1. This means that a universe with a loud stochastic background has shorter delay times with lots of black holes merging in the early universe and at low metallicities.

corner plot of the three astrophysical parameters of the time-delay distribution

Figure 1: A corner plot of the three astrophysical parameters of the time-delay distribution (from left to right: the time delay power-law slope κ, log of the maximum metallicity in solar metallicities, and log of the minimum time delay in billions of years) measured with current resolved gravitational wave observations, and a stochastic background detected with future LIGO–Virgo–KAGRA detectors. The three panels at the top of each column show the 1D probability distributions of the three parameters, where the dotted line is the current constraints on these parameters. The three panels in the bottom left represent the joint probability distributions between pairs of parameters, showing the correlations between the parameters. [Turbang et al. 2024]

On the other hand, a non-detection at higher sensitivities means that the time-delay slope is more likely to be weakly negative, with larger values of maximum metallicity and minimum time delay. In this case, there are fewer probable values of the time-delay power-law slope (we can understand this as a narrower probability distribution of κ in Figure 2, meaning a smaller uncertainty on this value), while there is a larger uncertainty on the maximum metallicity and minimum time-delay parameters. Essentially, this result is because a shallower slope of the time delay (a more even distribution of delay times across cosmic time) is the only way to get a stochastic background that is too quiet to be detected with future sensitivities. A quiet background means that there are fewer farther away black holes merging to contribute to the background, and so there can’t have been many more black holes merging at the start of the universe as there are now.

corner plot of the three astrophysical parameters of the time-delay distribution in the case of a non-detected stochastic background

Figure 2: The same as Figure 1 but considering a non-detected stochastic background at future detector sensitivity. In this case the value of κ is still negative but closer to zero, meaning a less steep distribution of black hole time delays. There is a wider range of possible maximum metallicities and minimum time delays. [Turbang et al. 2024]

So how do we tell how long it takes for two black holes in the universe to merge? No matter if we see it or not, the stochastic background will give us more information when the next sets of gravitational wave observations are gathered. With the currently ongoing fourth observing run of the LIGO–Virgo–KAGRA detectors, hopefully it won’t be long before we can sail the stochastic seas, so get to your seats for the great black hole race of the universe!

Original astrobite edited by Evan Lewis.

About the author, Storm Colloms:

Storm is a postgraduate researcher at the University of Glasgow, Scotland. They work on understanding populations of binary black holes and neutron stars from the gravitational wave signals emitted when they merge, and what that tells us about the lives and deaths of massive stars. Outwith astrophysics they spend their time taking digital and film photos and making fun doodles of their research.

emission shell surrounding the X-ray binary CI Camelopardalis

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: Discovery of an [O III] Emission Shell Around the X-ray Binary CI Cam
Authors: Robert Fesen et al.
First Author’s Institution: Dartmouth College
Status: Published in RNAAS

From Be Star to X-ray Binary

Among the spectral classes of normal stars that we know and love, there is one particularly eccentric type called the Be stars. Unfortunately, no, the “e” there does not stand for eccentric. Much like the B-type stars, Be stars are incredibly hot and massive objects. However, what sets them apart is the notable presence of excess emission features in their spectral profiles, in contrast with the absorption lines typically seen in the spectra of regular B stars. Thus, the “e” stands for emission.

Our star today, CI Camelopardalis (CI Cam), was initially cataloged as a Be star, but when it was later found that the spectrum was particularly rich in forbidden emission lines, it was reclassified as a B[e]star. The brackets designate the forbidden lines that come from an improbable transition between metastable energy levels in atoms or ions, which can only be observed in low-density astrophysical environments. As an example of this notation, the doubly ionized oxygen forbidden line is written as [O III].

Artist’s illustration of an X-ray binary system

Figure 1: Artist’s illustration of an X-ray binary system that consists of a normal star and a compact object. The gravity of the compact object pulls matter off the normal star creating an accretion disk, shown in blue. [European Space Agency, NASA and Felix Mirabel (the French Atomic Energy Commission & the Institute for Astronomy and Space Physics/Conicet of Argentina)]

CI Cam underwent a dramatic X-ray outburst in 1998, which not only revealed the presence of an unseen compact companion, but also firmly established the system as an X-ray binary. An X-ray binary is a fascinating duo of a normal star and a compact object, which could be a white dwarf, a neutron star, or a black hole (see Figure 1). The two objects orbit closely around a common center of mass, emitting intense X-ray radiation. This high-energy emission is produced by the accretion process in which material from the star gets pulled onto the compact object by its immense gravitational field. As this material spirals inward, it heats up to temperatures in the millions of degrees, causing it to radiate very brightly in the X-ray part of the electromagnetic spectrum. Interestingly, the exact nature of CI Cam’s compact companion remains unclear to this day.

The Discovery

In a serendipitous finding, today’s authors uncovered a previously unknown shell of emitting gas surrounding the CI Cam system. This shell, spanning about 8 arcminutes by 12 arcminutes on the sky, is brightest in the forbidden [O III] emission line at 5007 Angstroms (within the optical or visible light regime). The discovery unfolded from the optical data of the large supernova remnant G150.3+4.5 (G150) that was observed as part of a limited wide-field imaging survey of optical emission associated with galactic remnants. This survey was led by Robert Fesen from Dartmouth College (the first author of today’s article). The emission shell was spotted adjacent to the remnant’s filaments along its northwestern edge (see Figure 2).

Zoomed out and detailed views of the newly discovered emission shell

Figure 2: Composite image showing the emission shell around CI Cam and the western portion of the galactic supernova remnant G150.3+4.5. Blue represents the [O III] emission and red represents the Hα emission. [Adapted from Fesen et al. 2024]

The presence of [O III] emission indicates the existence of extremely hot ionizing sources that can provide the high-energy photons needed to doubly ionize oxygen atoms. Typically, strong [O III] emissions are seen to have filamentary or arc-like structures tracing the interfaces between ionized and neutral gas regions. These emissions can also be enhanced by the presence of shocks, in addition to photoionization by hot sources. An emission shell featuring strong [O III] emission around CI Cam with faint Hα emissions (see Figure 3) implies the presence of high-velocity shocks of about 90 to 150 km/s, potentially driven by CI Cam’s outflows into the surrounding interstellar medium.

Two grayscale views of the shell in O III and H alpha filters

Figure 3: Monochromatic images of the emission shell around CI Cam as seen in [O III] (top) and in Hα (bottom). [Adapted from Fesen et al. 2024]

By adopting CI Cam’s newly refined Gaia distance of 4.1 kiloparsecs (about 13,000 light-years), the physical size of the shell, assuming it is indeed the result of CI Cam’s outflows, spans approximately 9.5 x 14.3 parsecs (31 x 47 light-years). To put this into perspective, the longer side of the nebula is roughly equivalent to a hundred billion times the distance between New York City and Los Angeles via the I-40. This dimension is comparable to the dimensions of nebulae observed around Wolf–Rayet stars, which are known to generate such large structures through their powerful stellar winds and outflows.

Due to the similarity in [O III] brightness between the shell and the supernova remnant G150’s filaments, their close proximity raised the question of a potential physical association between the two objects. However, the authors found no direct evidence in the morphology linking the shell to the remnant on the imaging data. They also took into account the available distance estimates for G150 derived from radio and γ-ray observations, which suggested a much closer distance compared to CI Cam. The authors argued that if G150 were actually at CI Cam’s distance, it would have to be a much larger and more ancient supernova remnant, which seems highly unlikely given its bright optical filaments that suggest a relatively young age. The authors then concluded that the apparent proximity of the shell to G150 is likely just a chance alignment with Cl Cam.

While X-ray binaries are primarily studied in the X-ray, optical observations offer a complementary perspective, helping us to understand the details of the binary’s interactions with its surrounding medium. That the size of the emission shell around CI Cam is comparable to those created by powerful Wolf–Rayet stars provides evidence for the strong stellar winds and the transient outburst that CI Cam has unleashed over an extended period. This, in turn, promises to shed light on the mass-loss mechanisms and accretion processes in this class of objects. This discovery not only highlights the significance of wide-field imaging surveys in unveiling previously unknown structures around peculiar objects, but also opens up a new observational window for advancing our understanding of how such systems enrich and shape their cosmic neighborhoods.

Original astrobite edited by Ivey Davis.

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.

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