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representative-color composite image of supernova remnant W49B

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: Bumpy Declining Light Curves Are Common in Hydrogen-Poor Superluminous Supernovae
Authors: Griffin Hosseinzadeh et al.
First Author’s Institution: Steward Observatory, University of Arizona
Status: Published in ApJ

Space is constantly alight with supernovae — so much so that astronomers are scrambling to keep up! As a result, interest and competitive resources can fall off after a supernova has been named and identified, making it difficult to observe superluminous supernovae months after the dazzling heights of their light curves.

Brighter than a typical supernova, superluminous supernovae reach blinding absolute magnitudes of −20 or more. Miraculously, their super-powered light curves remain bright for hundreds of days before decreasing to a known slope called a radioactive tail.

The authors argue that this period of time after the initial explosion is well worth the study, because some superluminous supernovae don’t cool off without a fight. Instead, they display unanticipated bumps and wiggles months after the peaks of their light curves. Shared qualities amongst the supernovae that show wiggles could even shed light on the mechanisms powering these behemoths.

What’s Haunting the Cosmic Graveyard?

The source behind monstrous superluminous supernova explosions is a topic of hot debate, especially when it comes to the subclass that have spectra devoid of emission lines of hydrogen. There are two main competing theories. The first is that there is a super-powered neutron star called a magnetar at the center. With the most extreme magnetic fields in the universe, reaching a magnetic field strength of 1015 Gauss, these colossal corpses would serve as a “central engine” driving the explosion’s brightness and prolonging its light curve.

plot of multiwavelength light curves for a superluminous supernova

Figure 1: An example superluminous supernova, SN2011ke. Bumps begin about 40 days after the explosion in nearly every observed wavelength. In the residual panels, the authors subtract the underlying blackbody to make this clearer. Click to enlarge. [Adapted from Hosseinzadeh et al. 2022]

You can tell that something’s not quite right with a magnetar if its cooling phase, or the decrease of its light curve, doesn’t follow a smooth, well-behaved luminosity trend of L ∝ t-2. Observed late-time bumps in superluminous supernovae certainly disrupt this picture, as in Figure 1. To explain bumpy behavior with just a central engine, one would require that material falls back onto the magnetar surface and alights in a violent flare.

Others posit that hydrogen-poor superluminous supernovae — subclass SLSN-I — once had hydrogen on their surfaces, but they dropped that hydrogen like a pair of lost glasses. Surely it must be around here somewhere… crunch. The unaware explosions trample over the circumstellar material as it expands. This interaction would serve as multiple powder kegs prolonging the light curve in a manner similar to the magnetar model. Yet, when astronomers take spectra during SLSN-I bumps, they do not find the smoking-gun evidence of narrow emission lines that would indicate interaction with hydrogen-rich material.

That Ghost Has Footprints!

The authors collected a total of 34 SLSNe-I with plenty of optical data well after their peaks. Of these 34, they find that 44–76% exhibit bumps at about 50 days or more past explosion. Why the broad uncertainty range? Because, unfortunately, the consistency of data coverage limits how sure the authors can be that these bumps exist. Among their sample, they investigate what these bumps have in common and if there are relationships between the overall light curve and the bumps.

The authors reason that there are five main characteristics of a magnetar: magnetic field (B), spin period (P), ejecta mass (Mej), ejecta velocity (vej), and the time it takes for the explosion to reach its peak brightness (trise); and there are four main characteristics of a bump: duration (Δtbump), the time the bump occurs (tbump), the temperature at which it is emitting (Tbump), and amplitude. The authors cross-compare each of these characteristics by mixing and matching their axes and plotting all 34 superluminous supernovae in Figure 2. Then, they check for correlation, or a clear and obvious trend, between the quantities on each set of axes.

multi-panel plot of bump properties as a function of supernova properties

Figure 2: Which properties exhibit a trend? The authors believe the rise time (trise) of the explosion and the time the bump occurs (tbump) are very weakly correlated. To convince yourself, find the panel with the red text (last column, third row) and ask yourself how much you think increasing trise increases tbump, and vice versa. [Hosseinzadeh et al. 2022]

The authors find a mild correlation between how quickly the main peak rises and the time at which the bump appears (Figure 2, panel with red text). This correlation could indicate that the ejecta reaches a temperature in the range of 6000–8000K — which includes the temperature at which ionized oxygen recombines… suspiciously the same element that dominates the supernova ejecta mass! Could this mean that these bumps are indeed caused by recombination of ionized oxygen, since there’s so much of it? If this occurs at the site of the magnetar, it would ultimately favor the magnetar accretion model.

If the bumps are instead caused by interaction with circumstellar material, the authors determine it would be an optically thin shell with an average mass of 0.034 solar mass and a thickness of about 8.1 x 1015 cm. That’s not a whole lot of material, spread about a whole lot of space!

histogram of the number of supernova light curve bumps

Figure 3: Histogram of superluminous supernova bumps and the maximum depth from which photons would have originated. Bumps in the shaded region are sourced from a maximum depth too shallow to be consistent with a central engine origin. [Adapted from Hosseinzadeh et al. 2022]

The authors also debate explosion mechanisms using the timing of the wiggles and inner ejecta thickness (not to be confused with the farther-out circumstellar material). If bumps are caused by changes at the very center of the explosion, such as in an accreting magnetar, then those photons must climb their way out of dense material to reach us. This would require a minimum amount of time that depends on the number of photon collisions, or the opacity of the material. A comparison of these depths and bump times is illustrated in Figure 3: the shaded region indicates where observed bumps appear too quickly to be explained by a photon originating in the center of the explosion. In the unshaded region, the photon could have come from the central engine or interaction with circumstellar material.

What remains unanswered, however, is whether or not all 34 supernovae must be explained by the same mechanism. If the answer is yes, then those meddling supernovae in the shaded region rule out a central engine entirely! If the answer is no, then it seems that since most bumps exist in the unshaded region, one could argue that there is diversity among the superluminous supernova mechanisms; perhaps those powered without central engines are simply rarer.

Original astrobite edited by Lina Kimmig and Briley Lewis.

About the author, Lindsay DeMarchi:

Lindsay DeMarchi is currently a graduate student at Northwestern University. She is obsessed with gravity and uses multi-messenger methods to analyze the final moments of stellar collapse.

simulated image of heat flow around a hot jupiter exoplanet

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: Reassessing the Evidence for Time Variability in the Atmosphere of the Exoplanet HAT-P-7 b
Authors: Maura Lally and Andrew Vanderburg
First Author’s Institution: Cornell University, Northwestern University, and University of Texas at Austin
Status: Published in AJ

It would make sense, given the ever-changing atmospheres of Earth and the other solar system planets, that exoplanets would also be host to their own kinds of “weather.” But, despite the many characterised exoplanet atmospheres, confidently detecting a changing atmosphere remains extremely challenging. Previous attempts to assess the variability of exoplanet atmospheres have used phase curves — a measure of the changes in observed stellar flux over a planet’s entire orbit as it passes in front of and behind its star (see, for example, Figure 1). For tidally locked planets like hot Jupiters, different parts of the atmosphere will be visible as the planet moves around its star, so the shape of the phase curve can provide information about the planet’s atmosphere. By monitoring how the phase curve varies over time, you can decipher if the atmosphere is varying.

Figure 1: An overview of the Kepler data analysed in the article. Top: the entire light curve of HAT-P-7, split into 60 chunks each containing 10 transits. Middle: A zoom in on 70 days of observations. Bottom: An example phase curve along with the positions of the planet as it completes its orbit. [Lally & Vanderburg 2022]

The first planet to be assessed in this way was HAT-P-7b, a hot Jupiter that was observed by the Kepler mission for more than four years. The original study (Armstrong et al., 2016, which was covered by this astrobite!) found that the hottest part of the planet’s atmosphere shifts east and west by up to 41 degrees in latitude away from the point at which the star appears directly overhead, likely thanks to strong but changing wind patterns. However, phase curve analysis is hard, especially if the host star itself is changing over time, and theoretical work has struggled to explain hotspot offsets as large as HAT-P-7b’s. The authors of today’s article, therefore, take another look at the mysterious weather of HAT-P-7b.

Back to Basics

As an initial test, the authors first analysed the many phase curves in the original Kepler data to see if they obtained the same hotspot offset variability as Armstrong et al. To ensure that the result wasn’t influenced by any analysis choices, the authors repeated their methods on different outputs of the Kepler data reduction pipeline and tested different model choices to compare the results.

As seen in Figure 2, the authors’ offset measurements were comparable to those of Armstrong et al., although the offset was only seen to vary by up to 30 degrees — a result that was robust for all their analysis choices. But is this variability actually coming from the planet’s atmosphere? To determine if the host star could be causing the phase curves to vary, the authors injected simulated non-varying planetary phase curve signals into the light curves of a selection of stars similar to HAT-P-7 and extracted the resulting hotspot offsets. This test showed that similar variability could be recovered from the injected light curves, meaning that it’s possible that HAT-P-7b’s hotspot offset might not be varying after all! Given that Kepler was known to be a very well-behaved instrument, the stars themselves must be varying and contributing additional noise to the phase curves.

Figure 2: The measured hotspot offsets from HAT-P-7b’s phase curve over the course of the observations. The values calculated by Armstrong et al. are shown in pink open circles, while the values the authors calculated for a selection of phase curves are shown in red filled circles. Both analyses are a good match, indicating that the respective methodologies are unlikely to be causing the variability. [Adapted from Lally & Vanderburg 2022]

Tuning Out the Noise

To understand what sources of noise might be impacting HAT-P-7b’s phase curve, the authors produced the power spectrum of the light curve of HAT-P-7. They then compared it to a white noise spectrum, as shown in Figure 3, to identify at what frequencies significant sources of noise were occurring.

Figure 3: The power spectrum of HAT-P-7’s light curve (grey), showing how sources of noise are occurring over a range of frequencies. An equivalent white noise spectrum is shown in blue, highlighting that HAT-P-7 has significant excess noise at lower frequencies. [Lally & Vanderburg 2022]

Figure 3 shows that HAT-P-7’s light curve has a significant amount of excess noise at timescales similar to the planetary period, which could explain the observed variations in the hotspot offset. Noise due to supergranulation — changes in stellar brightness as bright warm bubbles appear on the surface of the star over long timescales — is particularly dominant around the period of HAT-P-7b, and could very easily be affecting the hotspot offset measurements.

With all this evidence to hand, the authors conclude that the varying offset measurements of HAT-P-7b are likely not related to atmospheric variability, and supergranulation is the culprit of the changing phase curves. Although Kepler is now decommissioned, the analysis performed in today’s article provides a helpful tool for verifying past and future claims of exoplanet weather from it or any other telescope, including JWST.

Original astrobite edited by Yoni Brande.

About the author, Lili Alderson:

Lili Alderson is a second-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.

illustration of a giant impact

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: Large Impacts onto the Early Earth: Planetary Sterilization and Iron Delivery
Authors: Robert I. Citron and Sarah T. Stewart
First Author’s Institution: University of California, Davis
Status: Published in PSJ

The early Earth wasn’t your typical summer break vacation destination. During the Hadean Eon (4.5–4.0 billion years ago), Earth was an extremely hostile environment and was frequently bombarded by asteroids. Yet, somehow, life on Earth could have emerged during this time.

This story begins with a noteworthy event during the Hadean Eon: a now long-gone Mars-sized planet called Theia slammed into Earth. The massive impact blew off a large amount of debris that started to circle the new Earth–Theia merger and eventually formed the Moon. This proposed sequence of events is known as the Giant Impact Hypothesis (see Figure 1). Theia’s impact melted Earth’s entire crust several kilometers deep, creating an environment not very favorable for any form of life that we know of and very likely sterilizing anything already present on Earth. After all the turmoil of the impact settled down, life on Earth (in principle) could have formed shortly after, a mere 4.5 billion years ago. Reality, as it turns out, may have run less smoothly.

illustration of the giant impact hypothesis

Figure 1: An illustration of the Giant Impact Hypothesis. The planet Theia crashed into Earth, and the debris from the collision led to the formation of the Moon. [Aparna Nathan, SITNBoston]

As a deeply studied subject, the origin of life has been described by several proposed hypotheses. A popular hypothesis states that it all began with the formation of amino acids and RNA molecules. Because our present-day atmosphere and oceans are oxidizing (noticed iron rusting? Maybe some fire?), spontaneously forming these molecules is difficult. But what about all those lifeforms we see today? They needed to come from somewhere. Well, to actually form these amino acids and RNA molecules on a global scale, we would need a reducing atmosphere or ocean. And how better to create one than slamming a huge rock full of reducing material (like iron) into Earth?

Reducing an Atmosphere 101

After the Theia impact, but still during the Hadean Eon, Earth endured a large amount of impacting asteroids of varying sizes, which were sling-shotted to the inner solar system by Jupiter and Saturn. The authors of today’s article wanted to know which objects under which conditions can actually create a (temporarily) reducing atmosphere or ocean on Earth, ultimately opening the way to forming the building blocks of life. Instead of really slamming various rocks on Earth, the authors ran smoothed particle hydrodynamics (SPH) simulations of large objects (the projectiles) colliding with an Earth-like planet (ominously called the target). To account for several possible scenarios, illustrated in Figure 2, the authors varied the impacting object’s mass, velocity, and the angle at which it strikes Earth.

diagrams of an object impacting earth

Figure 2: Effect of different impact angles of a large object colliding with Earth, with the distinction between the atmosphere (blue), the mantle (orange), and the core (dark gray). Different angles lead to different degrees of surface melting (red). [Citron & Stewart 2022]

Now, when such an object impacts Earth, a lot happens in a short span of time. To know what goes where, the authors kept track of the mantle (made of forsterite) and core (consisting of an iron–silicon alloy) of both the simulated impacting object and Earth. Part of the iron-rich core material — the stuff we need to start large-scale reduction of Earth’s atmosphere or oceans — gets scattered in the atmosphere by the impact. How much of this iron enters Earth’s atmosphere depends strongly on how the impact occurred (which is controlled by object mass, impact velocity, and impact angle). A 24-hour time lapse of the impact in one of the simulations is shown in Figure 3.

simulation snapshots

Figure 3: Simulation of a smaller object colliding with Earth. Here, the object mass is 25% of the Moon’s mass, the impact velocity is 1.5 times Earth’s escape velocity, and the impact angle is 45°. The mantle and core materials of Earth and the impacting object are color-coded to see where they eventually land, if at all. This simulation shows that the object is shattered on Earth’s surface; the colliding object’s mantle material — forsterite — is mainly scattered around Earth or resides on its surface, while the heavier core material from the object — iron — mainly sinks in large chunks to Earth’s interior. Part of the iron from the object, however, remains scattered in the atmosphere, where it will act as a reducing agent. The spatial dimensions are expressed in Earth radii. [Citron & Stewart 2022]

By analyzing their simulations, the authors found that only the largest of the asteroids during the Hadean Eon — the Moon-sized ones — could have delivered enough iron to fully reduce Earth’s oceans and atmosphere. But there’s an additional problem now: the impact of an object so huge would easily vaporize ocean-sized bodies of water (the authors find that even an object with a radius of 0.2 times the Moon’s radius could vaporize Earth’s early ocean) and would even melt most of Earth’s surface, creating almost post-Theia impact circumstances that weren’t very life-supporting. Considering all this, it looks like these Hadean asteroids did not really help early life on its way.

However, this study has shown that it takes a larger object than previously estimated to fully sterilize the early Earth’s exterior by melting its whole surface; such an object would need to have more than 25% of the Moon’s mass. As objects this size were rare even during the Hadean Eon, the chances of a mass extinction by space rocks are lower than previously expected. Even the ocean-evaporating asteroids do not necessarily sterilize Earth if early life occurred under the planet’s surface. Moreover, remember the Moon-sized asteroids needed to fully reduce the atmosphere and oceans? Turns out we don’t need that kind of overkill (pun intended). Multiple smaller objects slamming into Earth could reduce the atmosphere or ocean enough to create favorable conditions for spontaneous RNA formation.

In any case, if life emerged from a post-impact world, it would be due to the right asteroids at the right time. Too small, and the kick-starter for life wouldn’t occur. Too large, and any progress made so far would be wiped out. Considering the fact that you are reading this post, it seems our very, very far forebears weren’t out of luck!

Original astrobite edited by Sarah Bodansky.

About the author, Roel Lefever:

Roel is a first-year astrophysics PhD student at Heidelberg University. He works on massive stars and simulates their atmospheres and outflows. In his spare time, he likes to hike and bike in nature, play (a whole lot of) video games, play and listen to music (movie soundtracks!), and read (currently The Wheel of Time, but any fantasy really).

illustration of exoplanetary systems

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: Continuous Habitable Zones: Using Bayesian Methods to Prioritize Characterization of Potentially Habitable Worlds
Authors: Austin Ware et al.
First Author’s Institution: Arizona State University
Status: Published in ApJ

With more than 5,000 exoplanets discovered (roughly 30 of which are expected to be habitable), how can astronomers prioritize which to study in the search for extraterrestrial life? Today’s article explores the “continuous habitable zone”: planetary orbits that allow for water to be liquid long enough for detectable life to develop.

Habitable Worlds

With JWST beginning observations and the Habitable Exoplanet Observatory (HabEx) and Large UV/Optical/IR Surveyor (LUVOIR) space telescopes on the horizon, transit spectroscopy from space is set to dramatically increase our ability to characterize exoplanet atmospheres. Astronomers are working to prioritize which potentially habitable exoplanets are the best to search for life on. The habitable zone defines the region surrounding a star in which a planet could host liquid water, which is typically assumed to be a requirement for life. Stars evolve, though, and planets that are habitable at one point in time may not always be or have been.

Life takes time to develop and become detectable. Let’s consider Earth’s history as a benchmark. The Great Oxidation Event, during which biologically produced molecular oxygen accumulated in Earth’s atmosphere, occurred roughly 2 billion years after Earth’s formation. This is adopted as the length of time needed for life to make a detectable impact on a planet’s atmosphere. Today’s article presents a method to estimate the likelihood that a planet has resided in its star’s habitable zone for at least 2 billion years, defining the region where this could occur as the 2-billion-year continuous habitable zone (CHZ2).

So, how do we determine the habitable zone of a star? The article discussed two frameworks:

  1. Optimistic habitable zone: Regions that receive an amount of radiation from their star less than Venus did 1 billion years ago and more than Mars did 3.8 billion years ago ago. These “recent Venus” (RV) and “early Mars” (EM) limits are chosen because observations suggest liquid water existed on those planets until 1 or 3.8 billion years ago, respectively.
  2. Conservative habitable zone: A greenhouse effect model. The inner edge is defined by the “runaway greenhouse,” where stellar flux will vaporize an ocean. The outer edge is defined as the “maximum greenhouse,” where Rayleigh scattering dominates over the greenhouse effect of carbon dioxide.
diagram of the optimistic and conservative habitable zones for a range of stellar temperatures

Figure 1: The habitable zone for a range of stellar temperatures, showing Venus, Earth, Mars, and a selection of potentially habitable exoplanets. [Chester Harman]

Statistical Modeling

Using Bayesian statistics, the authors created an equation for the probability of a planet residing in the CHZ2 as a function of its host star’s mass, metallicity, and age. They assigned ages to the host stars based on evolutionary tracks from the Tycho stellar modeling code and found that their estimates aligned well with previous measurements based on stellar spins.

The authors used their framework to evaluate nine potentially habitable exoplanets as well as Venus, Earth, and Mars. All stars considered were relatively Sun-like (between 0.5 and 1.1 solar masses), with Earth-like and super-Earth terrestrial planets (radii < 1.8x Earth’s and mass < 10x Earth’s). Figure 2 shows the results for the solar system and the authors’ best exoplanet candidate.

plot of solar system planets and one exoplanet and their likelihood of being within the continuous habitable zone

Figure 2: CHZ2 probabilities for two stars. Line styles indicate the habitable zone model: three conservative model versions for different planet masses and the RV/EM optimistic model. Left: The Sun, with the orbits of Venus, Earth, and Mars indicated. Right: KIC-7340288, showing the best candidate examined in the article with ~90% CHZ2 probability under all models. [Adapted from Ware et al. 2022]

What This Means for the Future

The authors conclude with proposals for future work on this topic, including extending the analysis to lower-mass stars. They also estimated ages for nearly 3,000 Transiting Exoplanet Survey Satellite (TESS) continuous-viewing-zone stars, which are the best candidates for TESS to find habitable zone planets around, to apply a similar framework to in the future. The exoplanets determined here to have a high CHZ2 probability will be ideal for follow-up with JWST. The method will also be valuable in target selection for future exoplanet characterization missions like HabEx and LUVOIR.

As shown in Figure 2 above, the method used in this article determined Mars to be in the CHZ2, while we know it not to be currently habitable. This indicates the need for additional model parameters in the Bayesian analysis to improve accuracy. This includes further stellar and planetary properties important to their evolution, including stellar oxygen-to-iron ratios, planetary composition, and stellar activity.

Original astrobite edited by Jana Steuer.

About the author, Macy Huston:

I am a fourth-year graduate student at Penn State University studying astronomy and astrophysics. My current work focuses on technosignatures, also referred to as the Search for Extraterrestrial Intelligence (SETI). I am generally interested in exoplanet and exoplanet-adjacent research. In the past, I have performed research on planetary microlensing and low-mass star and brown dwarf formation.

simulation of galaxies during the epoch of reionization in the early 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: Reionization with Simba: How Much Does Astrophysics Matter in Modeling Cosmic Reionization?
Authors: Sultan Hassan et al.
First Author’s Institution: Flatiron Institute and University of the Western Cape, South Africa
Status: Published in ApJ

While a tired trope to be sure, the hero’s journey to conquer the darkness and bring in an age of light is a memorable one. Today, our hero isn’t a person but succeeds in that illuminating quest all the same!

The authors of today’s article consider one particular question: how do we model the re-emergence of light sources in the early universe during the time of cosmic reionization? Namely, does the way we model the sources of ionizing photons (high-redshift stars and galaxies) impact observables on the large scales relevant for cosmological observations?

That’s a bit of a mouthful, but we’ll chew through it slowly and methodically in this bite!

Out of the Darkness

Before we can talk about reionization, we have to understand what brought about the dark age of the universe in the first place. After the hot Big Bang, the universe was initially fully ionized (all atoms were stripped of their electrons) up until it cooled to the point where hydrogen atoms “recombined” (free electrons paired up with lone protons) at a redshift (z) of roughly 1,000 (Figure 1). After recombination, the universe was filled with neutral hydrogen and was in a sense “dark,” since the cooling universe had no sources of ionizing photons to liberate electrons from the neutral hydrogen (HI).

However, during this dark age the seeds of revolution (ahem, structure formation) were slowly growing until eventually the first stars and galaxies formed inside dark matter halos, providing new sources of ionizing photons. These light-bringers then proceeded to make Swiss cheese out of the dark universe, creating holes filled with ionized hydrogen (HII) as illustrated by the bubbles at the center of Figure 1. You can get an instant and visceral feel for this process by watching this wonderful movie of a simulation of reionization.

diagram of the evolution of the universe over time

Figure 1: A schematic view of reionization within the larger cosmic timeline. Blue represents (opaque) neutral hydrogen, while black represents fully ionized hydrogen. The transition between these two regimes proceeds around redshift z = 10 by way of reionized bubbles around source stars and galaxies. [NAOJ]

This story, like many heroic journeys, neglects an enormous amount of real-world complexity. To accurately model reionization at the level necessary for upcoming surveys, astrophysicists need to answer a host of questions: What is the detailed morphology of reionization? How does reionization depend on the characteristics of large-scale structure? On the processes of galaxy formation? What about the nature of the ionizing sources? Today’s article explores some of these questions using the Simba suite of galaxy-formation simulations. In particular, the authors delve into different ways to model the sources of ionization.

Getting Straight to the Source (Modeling)

The authors set out to understand whether or not the details of how stars and galaxies produce ionizing photons affect observables on large (“cosmological”) scales. To do this, they used the Simba simulations, which include a host of galaxy-formation physics as well as gas hydrodynamics, and accounted for radiative transfer of photons in post-processing. Specifically, the authors tested whether it was possible to notice a difference in the morphology of reionization or in the distribution of ionized hydrogen in the simulation with different choices of source modeling. The results of this comparison are shown in Figures 2 and 3, and we’ll walk through them one at a time.

Figure 2 shows the visual morphology of reionization by displaying the spatial distribution of the ionization fraction xHII (blue is ionized, red is neutral) in the simulation. Each row of Figure 2 corresponds to a different model of ionizing photons. The models contain ionizing photon sources with different properties, in different numbers, or a larger degree of scatter, but each model contains similar overall amounts of photons. The columns correspond to increasing time (and therefore increasing mean global ionization fraction) from left to right.

plots of ionization maps for all models tested

Figure 2: Simulation output maps of ionized hydrogen fraction (xHII) as a function of time (left to right) for several choices of reionization source models (rows). Small features are different by eye in the different models but the overall morphology on larger scales remains the same. [Hassan et al. 2022]

From a quick glance, it is clear that as we look down a single column, the Swiss-cheese structure of ionized bubbles looks broadly similar across source modeling choices. Not much changes on large scales, even though the smaller bubbles or detailed edge features may be changing significantly. The authors take this to mean that source modeling choice doesn’t have a significant impact on large scales, but for a quantitative confirmation of this finding they turn to the power spectrum of ionized hydrogen, which describes the spatial distribution of HII.

Figure 3 shows the power spectra of ionized hydrogen at the redshifts considered in Figure 2, as well as their residuals in the lower panel. The different modeling choices correspond to the different curves in the figure. The curves are broadly in agreement with each other over most scales for most choices of source modeling. In particular, on large scales (log k < 0.0) the models agree quite well — quantitatively corroborating that the choice of source modeling does not impact the large-scale spatial distribution of the ionized hydrogen.

power spectra of the models in the previous figure

Figure 3: Reionization power spectra as reionization progresses in time from high to low redshift z (from left to right). For several choices of source modeling considered by today’s authors, the large-scale (low k) power is similar. [Hassan et al. 2022]

The conclusion of this article is concrete: the authors suggest that future large-scale reionization modeling can safely use more efficient methods than expensive simulations like the ones included here. This follows from the finding that changes in the source modeling and associated scatter in the ionization rate and halo mass relation does not affect large scales as shown in Figures 2 and 3. If large-scale reionization does not depend on the details of astrophysical source modeling, this will definitely make the lives of astrophysicists studying large-scale reionization easier — without the need to simulate these details, researchers can run less costly simulations to extract information about the high-redshift universe on scales relevant for cosmology!

Original astrobite edited by Alice Curtin.

About the author, Jamie Sullivan:

I am a third-year astrophysics PhD student at UC Berkeley and part of the Berkeley Center for Cosmological Physics. My current research focuses on measuring and modeling large-scale structure to constrain cosmological parameters. I completed my undergraduate at UT Austin, and I’m originally from the Washington, DC, area.

illustration of an exoplanet on a grazing orbit around its host star

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

Title: Accurate Modeling of Grazing Transits Using Umbrella Sampling
Author: Gregory J. Gilbert
Author’s Institution: University of Chicago
Status: Published in AJ

Today’s author uses umbrellas to accurately model the planets that “graze” their stellar hosts.

Planets That Graze on Their Stars

Roughly 75% of all known exoplanets were discovered via transit surveys. These surveys monitor many stars at once to look for dips in brightness that could be caused by a planet passing, or “transiting,” in front of a star. Although rare, some of these planets only “graze” their host stars, meaning that they only partially transit their parent star’s disk (check out this astrobite to learn more about a specific case of a grazing planet).

In astronomical terms, “grazing” planets are defined as those that have an impact parameter that is larger than the ratio of the planet’s radius to the star’s radius. The impact parameter is defined as the distance between the center of the stellar disk and the center of the planetary disk at conjunction, where conjunction is the point in a planet’s orbit where it is most closely aligned with its star, as viewed from Earth. A perfectly centered transit has an impact parameter of 0 while a transit in which only half of the planetary disk passes in front of the stellar disk has an impact parameter of 1.

plot of flux over time for different impact parameters

Figure 1: The impact parameter (the distance between the centers of the stellar and planetary disks at conjunction) changes the shape of a transiting planet’s light curve. On this plot, the flux, or brightness, of the star normalized to 1 is on the y-axis. The time before and after the transit in hours is on the x-axis. Planets that have a high impact parameter graze the disk of their host star during their transit, making it more difficult to characterize a planet using its light curve. [Gilbert 2022]

Figure 1 demonstrates how the shape of the light curve from a transiting planet changes as a function of the impact parameter. The depth of the dip in a light curve allows astronomers to estimate the planet’s radius relative to the star, but this estimation becomes more difficult if the planet is grazing. For example, the light curve of a smaller, non-grazing planet could look the same as the light curve from a larger, grazing planet. One therefore needs to simulate grazing transits even in cases where it is unlikely that the planet grazes its host star.

However, today’s author shows that standard Monte Carlo methods, which are frequently used by exoplanet scientists to model grazing planets, can lead to unreliable results! Identical runs of the same model can return differing results, or results where it is not obvious that the model is wrong (Figure 2). When dealing with a handful of planets, one can let the simulation run for a longer period of time or add additional data, such as the spectrum of the star, to the model. However, for larger samples, a more efficient method is needed. What can astronomers do instead?

plots of posterior distributions

Figure 2: Plots of the posterior distributions from four identical Monte Carlo simulations. The parameters explored are the impact parameter, b, and the ratio of the planet’s radius to the star’s radius, r. Although the simulations are identical at the start, they devolve into four wildly different scenarios. In Panel A, the simulation is mostly consistent with a non-grazing planet (b < 1). In Panel B, the simulation fails to explore entirely whether the planet is grazing or not. In Panel C, the simulation gets caught at the boundary between a grazing and non-grazing planet. In Panel D, the simulation has a bimodal posterior distribution that barely explores whether the planet is grazing at all. [Gilbert 2022]

Umbrella-ella-ella

They can use umbrella sampling! Umbrella sampling is a technique that has been used in other scientific fields for decades, but not by astronomers until recently (specifically, Matthews et al. (2018) was the first to introduce umbrella sampling to the field of astronomy). This technique splits a distribution into sub-regions, draws samples from each of these sub-regions independently, and recombines these samples into a single posterior distribution (Figure 3). The author finds that this technique returns more reliable results than those from standard Monte Carlo methods (Figure 4).

plots of posterior distributions

Figure 3: On the top left, the target distribution is split into three sub-regions, each of which is assigned a function. On the top right, after sampling from each of these sub-regions independently, each sub-region is assigned a biased distribution. On the bottom left, the three unbiased sub-distributions are shown. On the bottom right, the three unbiased sub-distributions are combined into a single posterior distribution. [Gilbert 2022]

plots of posterior distributions

Figure 4: Posterior distributions of radius, impact parameter, and transit duration for a mini-Neptune orbiting a K-dwarf star. The vertical dashed lines represent ground-truth values for this system. These plots demonstrate how standard Monte Carlo methods fail to properly explore the parameters of grazing planets and show that umbrella sampling produces more robust results! [Gilbert 2022]

A good deal of math is needed to properly weight the sub-regions relative to one another; these calculations are described in detail in the article, and a step-by-step tutorial can be found on the author’s GitHub. Nonetheless, the math is worth it — this technique can be used to explore any complicated distribution, so it can be used in fields beyond exoplanet science. This means you should get out your umbrellas, ‘cause it’s gonna be raining grazing planets!

Original astrobite edited by Jana Steuer.

About the author, Catherine Clark:

Catherine Clark is a PhD candidate at Northern Arizona University and Lowell Observatory. Her research focuses on the smallest, coldest, faintest stars, and she uses high-resolution imaging techniques to look for them in multi-star systems. She is also working on a Graduate Certificate in Science Communication. Previously she attended the University of Michigan, where she studied astronomy and astrophysics as well as Spanish. Outside of research, she enjoys spending time outdoors hiking and photographing, and spending time indoors playing games and playing with her cats.

composite image of cygnus a

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: Evidence for Strong Intracluster Magnetic Fields in the Early Universe
Authors: J. Xu and J. L. Han
First Author’s Institution: Chinese Academy of Sciences and University of Chinese Academy of Sciences
Status: Published in ApJ

Mysteries of the Magnetic Macrocosm

Magnetic fields are everywhere, from the vast, pristine emptiness of cosmic voids to the dense, galaxy-packed environments of massive clusters. Wherever we find plasma — the hot, ionized fluid making up 99.9% of the universe’s visible matter — we find magnetic fields shaping and stirring said plasma. Needless to say, magnetic fields have their fingers (or, rather, their field lines) in many pies. Yet, for a wide range of astrophysical situations, the question still remains: where did these fields come from?

Galaxy clusters — associations of hundreds to thousands of galaxies held together by the glue of gravity — are no exception to this magnetic mystery. Today’s article seeks to understand the origin of magnetic fields in the intracluster medium, the ultra-hot plasma permeating the space between cluster-bound galaxies. At surface level, knowledge of the magnetic fields in the intracluster medium is necessary for understanding the rich spectrum of radiation emitted by galaxy clusters (see, for instance, these three astrobites). On a grander scale, however, these clusters — as the largest gravitationally bound bodies in the universe — can also provide key insight into the history of our cosmos. By tracing the growth of intracluster fields back through cosmic time, we can probe how magnetic fields influenced the formation of structure in the infant universe and catch a glimpse of the earliest magnetic fields in existence. This is precisely what today’s authors set out to do.

Faraday Forecasts of Faraway Fields

So, how does one study magnetic fields that are millions to billions of light-years from Earth? Today’s authors leverage the power of Faraday rotation: when a polarized light wave passes through a magnetic field, the wave is rotated through an angle that depends on the strength of the field (as illustrated in this cartoon). Therefore, by observing the change in the polarization angle of incoming light and calculating the so-called rotation measure, one can deduce the strength of the magnetic field along the light’s path. This technique is invaluable in radio astronomy and has been used extensively to study the magnetic backdrop of our universe.

If we’re going to be using Faraday rotation to explore intracluster magnetic fields, all we need now is some radiating object to shine light through a galaxy cluster and into our telescopes. There’s one slight complication, though: the incoming light is sensitive to magnetic fields along its entire path of propagation — if we’re looking at light from a distant galaxy cluster, the wave will be rotated not only by the intracluster fields, but also by intergalactic fields between the cluster and the Milky Way and by galactic fields within the Milky Way. How, then, do we isolate the rotation due solely to intracluster fields?

image of radio galaxy hercules A

Figure 1: Composite photo of the radio galaxy Hercules A and its two prominent radio lobes from the Hubble Space Telescope and the Very Large Array. [NASA, ESA, S. Baum and C. O’Dea (RIT), R. Perley and W. Cotton (NRAO/AUI/NSF), and the Hubble Heritage Team (STScI/AURA)]

The authors ingeniously sidestep this issue by looking at close by pairs of light sources; by looking at the difference in Faraday rotation between two light sources embedded in the intracluster medium, we probe only the intracluster fields separating the two sources — the intergalactic and galactic contributions cancel out! Serendipitously, the universe has provided us with an abundance of double light sources in the form of radio galaxies, whose bright pairs of lobes naturally arise as material ejected from these galaxies interacts with the surrounding intracluster medium (see Figure 1). Figure 2 illustrates, schematically, the authors’ strategy to probe intracluster magnetic fields via the rotation measures of radio lobes.

cartoon of a person looking through the intergalactic medium toward a double-lobed radio source

Figure 2: Schematic diagram showing the observation of a radio galaxy embedded in the intracluster medium. Light emitted from the two radio lobes (labeled RM1 and RM2 to indicate their different rotation measures) passes through the magnetic fields of the intracluster medium, the intergalactic medium, and the Milky Way before reaching the observer (far left). [Xu & Han 2022]

Baffling B-fields from Bygone Bodies

Since the authors are interested in the evolution of intracluster fields across the lifetime of the universe, they comb through archived radio telescope data from both the NRAO VLA Sky Survey (NVSS) and from recent literature to obtain rotation measures for double-lobed radio galaxies across a wide range of redshifts (in this context, redshift just tells us how far into the past we’re looking). When compiling their data set of lobe pairs, the authors make careful cuts based on the distances between the lobes and the locations of the lobes relative to the Milky Way so as to minimize rotation measure contamination from intergalactic and galactic fields — when we take the difference between the rotation measures of a given pair of lobes, we want this difference to reflect only the contribution from intracluster fields.

plots of rotation measure difference as a function of redshift

Figure 3: Plots of the pairwise rotation measure (RM) differences (top two rows) and the statistical dispersion in these differences (bottom two rows, showing two different ways of quantifying the dispersion) vs. redshift for the radio lobe data set analyzed by the authors. Blue points represent pairs of lobes from the NVSS catalog, while red points represent pairs compiled from the literature. The right column shows a subset of the data with RM measurement uncertainties below a certain threshold. Click to enlarge. [Xu & Han 2022]

Ultimately, the authors select 387 pairs of lobes from NVSS and 197 pairs from the literature, with redshifts as high as 3 (meaning that the light we’re seeing from the farthest lobe is almost 11.5 billion years old). Plotting the pairwise rotation measure differences (and the statistical dispersion in these differences) yields Figure 3. To high confidence, the authors conclude that the rotation measure differences in higher-redshift clusters are statistically higher than those in lower-redshift clusters, thus implying that intracluster fields were stronger in the past.

The authors go a step further and use these rotation measures to estimate the typical intracluster field strength for clusters that existed more than seven billion years ago (roughly half the age of the universe) — but this only leads to more confusion: there was too little time between the beginning of the universe and the formation of these clusters for their strong magnetic fields to have grown via typical channels like dynamos. Thus, the authors conclude that strong magnetic fields must have existed in the early universe, prior to the formation of these clusters. While intracluster fields will provide useful constraints on the growth of magnetic fields in the early universe, the ultimate origin of these fields continues to elude us.

And thus, the universe’s grand magnetic mystery lives on.

Original astrobite edited by Catherine Manea.

About the author, Ryan Golant:

I am a second-year astronomy Ph.D. student at Columbia University. My current research involves the use of particle-in-cell simulations to study magnetic field growth in gamma-ray burst afterglows and closely related plasma systems. I completed my undergraduate at Princeton University, and I’m originally from Northern Virginia. Outside of astronomy, I enjoy learning about art history, playing violin and video games, and watching cat videos on the internet.

hubble image of spiral galaxy UGC 2885

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: Detection of a Superluminous Spiral Galaxy in the Heart of a Massive Galaxy Cluster
Authors: Ákos Bogdán et al.
First Author’s Institution: Center for Astrophysics | Harvard & Smithsonian
Status: Published in ApJ

Galaxy clusters contain hundreds of galaxies in a huge variety of shapes and sizes, ranging from irregular dwarf galaxies to giant ellipticals. The most luminous member of a cluster is known as the brightest cluster galaxy. Each brightest cluster galaxy is different, but there are some properties that they tend to have in common — most brightest cluster galaxies are found at the very centre of their host cluster and are large, elliptical galaxies, containing little gas and forming very few new stars.

The reason why most brightest cluster galaxies look so similar is well understood, as it is thought that these large galaxies form via a series of galaxy mergers. These are violent cosmic events that slowly increase the size of the galaxy, whilst also destroying any delicate disk or spiral arms that the galaxy may have (click here to see a simulation of two merging spiral galaxies). Additionally, mergers can lead to gas being expelled from a galaxy, resulting in the gas-poor, quenched brightest cluster galaxies that we see today.

However, today’s article presents an exciting twist to this story by presenting data from three galaxy clusters that do not appear to follow this trend, including one brightest cluster galaxy that doesn’t fit with our current theories at all.

Suspicious Spirals

The authors begin by introducing seven superluminous spiral galaxies, a recently discovered class of huge galaxies with spiral or lenticular shapes. The great size of these galaxies is what motivates the main question of today’s article: could these superluminous spiral galaxies actually be brightest cluster galaxies, despite not looking like them?

To answer this, we can look at the amount of X-ray radiation surrounding these galaxies. X-rays are emitted by the intracluster medium, a vast cloud of incredibly hot gas that fills a cluster, occupying the space between galaxies: if a cluster was a tasty chocolate chip muffin, the intracluster medium would be the cake, filled with chocolate chip galaxies. Using the X-ray telescope XMM-Newton, the authors found no X-ray emission surrounding two of their galaxies. However, as Figure 1 shows, the remaining five have large amounts of X-rays being produced nearby. This indicates the presence of the intracluster medium, meaning that these galaxies are nearby to a galaxy cluster.

x-ray observations of seven superluminous disk galaxies

Figure 1: X-ray observations of the region surrounding each of the seven superluminous disk galaxies. Regions of stronger X-ray emission are represented by lighter colour, and the centre of each X-ray region (i.e., the cluster centre) is marked by a green cross. The position of each superluminous disk galaxy is shown by the green circle. Note that the two galaxies in the bottom right (J11380 and J09354) have no associated clusters, and that the top-left galaxy (J16273) is located at the centre of its cluster. [Adapted from Bogdán et al. 2022]

It’s unusual to find spiral galaxies inside of clusters, but not unheard of. However, what makes this work so exciting is that in three of these clusters, there is not a single other galaxy that is brighter than the superluminous spiral — in other words, they are the brightest cluster galaxy. Finally, one of these galaxies (J16273 in Figure 1) is not only the brightest galaxy in the cluster, but is found directly in the cluster centre, in exactly the position that we would usually expect to find a brightest cluster galaxy!

Galaxy Mergers, but Not as You Know Them

The fact that J16273 is the brightest galaxy in a cluster and lives right in the cluster centre makes it look like a fairly typical brightest cluster galaxy. However, brightest cluster galaxies are elliptical because of the large numbers of galaxy mergers that they experience. How can we explain why this one is so different from all of those that we’ve seen before?

Surprisingly, one explanation is mergers themselves. The authors suggest that J16273 was previously a regular, elliptical brightest cluster galaxy that recently merged with a smaller gas-rich galaxy. Under the right conditions, this merger could spin up the elliptical galaxy, with the remnants of the gas-rich galaxy forming a brand new spinning disk.

In order to really understand these giant spiral galaxies, future work will need to look at many more than just seven of them. The authors acknowledge this and suggest that eROSITA, an ongoing X-ray survey of the sky, will be able to look at many more of these galaxies and determine whether they live in clusters, groups, or alone. eROSITA is due to release its first data at the end of 2022 and should help us to solve the mystery of how these huge spirals ended up in places we never expected to find them.

Original astrobite edited by Katy Proctor.

About the author, Roan Haggar:

I’m a PhD student at the University of Nottingham, working with hydrodynamical simulations of galaxy clusters to study the evolution of infalling galaxies. I also co-manage a portable planetarium that we take round to schools in the local area. My more terrestrial hobbies include rock climbing and going to music venues that I’ve not been to before.

composite X-ray and optical image of the galaxy 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: COSMOS2020: Ubiquitous AGN Activity of Massive Quiescent Galaxies at 0 < z < 5 Revealed by X-ray and Radio Stacking
Authors: Kei Ito et al.
First Author’s Institution: The Graduate University for Advanced Studies and National Astronomical Observatory of Japan
Status: Published in ApJ

While most passive or “dead” galaxies we see today have had fairly passive lives, distant passive galaxies in the early universe may have had a more active path to passivity. Detailed studies of nearby quiescent galaxies have revealed they follow a simple evolutionary track: a burst of star formation early on in their life followed by a quiet existence with low rates of star formation. In contrast, recent discoveries have uncovered a new population of quiescent galaxies that get quenched faster and earlier on than should be possible if following this simple evolutionary track (for example, the distant quiescent galaxies covered in this astrobite and that astrobite). The existence of so many quiescent galaxies so early on in the universe is a problem for galaxy evolution models, and the intense starburst phase and rapid suppression of star formation has been difficult to reproduce with cosmological simulations.

A big unresolved question related to this problem is how the burst of star formation gets suddenly shut off or quenched in these galaxies. Are the streams of gas from the cosmic web that fuel star formation getting cut off? Or is the gas flowing in and being expelled by some feedback mechanism? One such possible feedback mechanism is triggered by the galaxy’s central supermassive black hole as it funnels in material and creates a disk of hot, luminous gas and dust around it, forming an active galactic nucleus (AGN). The AGN devours some of the gas and radiation, wind, and jets eject the rest.

In today’s article, the authors leverage the extensive multiwavelength COSMOS2020 catalog to explore the AGN activity in quiescent galaxies across cosmic time through two primary AGN signatures: X-ray and radio emissions. However, many of these galaxies and the possible AGN within them, especially those farthest away, are faint enough that they are not individually detected in X-ray and radio surveys. To both overcome this faintness and to focus on typical (rather than extremely bright) sources, the authors use a technique called stacking to characterize the average properties of a quiescent galaxy sample and a comparison star-forming galaxy sample. Beyond comparing the stacks of quiescent galaxies and star-forming galaxies, the authors create a grid of stacks spanning stellar mass (basically, how big the galaxy is) and redshift (how far away and therefore how early on in the universe the galaxy is) to investigate trends along these axes.

Galaxy Pancakes 

To better understand the stacking technique and the grid of stacks, imagine each galaxy is a pancake. Some pancakes are regular (quiescent) and the ones that have a little more going on are buttermilk (star-forming). Now let’s say all of the pancakes have berries in them, but eating a single pancake won’t get you a full serving of fruit. So, to portion out a daily fruit intake you make stacks of pancakes on each plate, separating out regular and buttermilk.

Besides the regular and buttermilk types of pancakes, let’s say they also come in different sizes, from silver dollar to the size of the plate — this represents the stellar mass axis. And of course, the pancakes weren’t made simultaneously: the stacks of pancakes made earlier are farther down the table from where you’re sitting, and the newer ones are right in front of you, similar to how more distant (i.e., higher redshift) galaxies represent conditions earlier in the universe than nearby galaxies.

To build their grid of galaxy pancake stacks, the authors used observations at wavelengths at which the galaxies were detected individually (optical and infrared) and redshifts from the COSMOS2020 catalog to decide which galaxies were star-forming versus quiescent as well as how massive each was. The authors then used observations at wavelengths at which the galaxies were not individually detected (X-ray and radio) to place stacked observations in a grid of stellar mass and redshift. The resulting sample is the largest, highest-redshift sample of typical quiescent galaxies created so far.

Taking an X-ray

plot of image stacks showing brighter and fainter detections in a grid of redshift, stellar mass, and quiescent versus star-forming galaxies

Figure 1: The grid of galaxy stacks showing the average X-ray detection for two different X-ray bands. The red images show the regular pancake quiescent galaxies and the blue images show the buttermilk pancake star-forming galaxies, with redshift (z) increasing from top to bottom and stellar mass increasing from left to right in each color bin. Click to enlarge. [Ito et al. 2022]

The first stacking analysis the authors conducted was with X-ray data, with some representative stacks shown in Figure 1.

Beyond identification of some general trends, physically interpreting these stacks requires understanding what is causing the X-ray emission. X-ray emission comes from two main sources in galaxies: X-ray binaries, which contain a dense stellar remnant energetically drawing material from a star in its orbit, and AGN. Returning to our analogy, the fruit content in the pancakes could come from whole berries scattered around the pancake (X-ray binaries) or from a berry jam filling in the center (AGN).

But if you only know the average amount of fruit in each pancake stack, how can you tell if it’s in the form of whole berries or a jam filling? Based on known relations between the star formation rate and stellar mass in a galaxy and the amount of X-ray binaries expected, the authors determined the relative contribution from X-ray binaries and AGN. With these models, they found that X-ray binaries could explain most of the X-ray emission for the star-forming galaxy stacks. On the other hand, for quiescent galaxies, the average X-ray emission in each stack was 5–50 times higher than expected from just X-ray binaries, implying that much of the X-ray emission came from AGN. Additionally, they found the biggest difference between the star-forming and quiescent samples in the highest redshift bin, providing hints that AGN may have a role in quenching star formation early in the universe.

Tuning In to the Radio

To further verify their findings, the authors then stacked data from the other major signature of AGN: radio emission. Similar to X-rays, radio emission comes from two main sources in galaxies: one related to ongoing star formation and one related to AGN. Taking an empirically known correlation between star formation rate and radio luminosity, the authors determined that the quiescent galaxy stacks have 3–10 times higher radio emission than expected from just star formation, while the star-forming galaxy stacks could be explained primarily by star formation. Consistent with the X-ray result, this suggests that faint AGN are ubiquitous in quiescent galaxies.

How to Quench a Pancake

How does this AGN feedback mechanism work to quench galaxies? In nearby galaxies, we know that quenching tends to occur with more active AGN. This is due to two processes: quasar-mode feedback and radio-mode feedback. In quasar-mode feedback, wind from a bright AGN expels gas from the galaxy and suppresses star formation. In radio-mode feedback, a typically fainter AGN heats the gas in and around the galaxy with radio jets, which prevents gas from cooling and forming stars. In this way, radio-mode feedback maintains quiescence rather than just reducing the possible star formation by tossing out fuel. The authors note their faint, typical sample is probably mostly undergoing radio-mode feedback, with some non-AGN environmental quenching coming into play at lower redshifts.

So what do these stacks tell us about galaxy evolution? The ubiquitous AGN signatures in both X-ray and radio give us an interesting clue about quenching: everyday quiescent galaxy pancakes are often filled with AGN berry jam, and feedback from faint AGN within them are likely the culprit for shutting off star-forming buttermilk berry galaxy pancakes so suddenly and early in the universe.

Original astrobite edited by Alice Curtin.

About the author, Olivia Cooper:

I’m a second-year grad student at UT Austin studying the obscured early universe, specifically the formation and evolution of dusty star-forming galaxies. In undergrad at Smith College, I studied astrophysics and climate change communication. Besides doing science with pretty pictures of distant galaxies, I also like driving to the middle of nowhere to take pretty pictures of our own galaxy!

photograph of a globular 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 astrobites.org.

Title: Detection of a 100,000 M⊙ black hole in M31’s Most Massive Globular Cluster: A Tidally Stripped Nucleus
Authors: Renuka Pechetti et al.
First Author’s Institution: Liverpool John Moores University, UK
Status: Published in ApJ

Intermediate-Mass Black Holes

Stellar-mass black holes — those with masses of tens of solar masses (M☉) — are thought to result from the collapse of massive stars. The formation of supermassive black holes — those with millions to billions of solar masses — is less clear. Given their large masses, there is not enough time for stellar-mass black holes to grow into the supermassive black holes that we know existed fairly early in the history of the universe. One possibility is that the “seeds” that grow into supermassive black holes lie somewhere in between 102 and 105 M☉, or what we’d call intermediate-mass black holes.

Despite their importance, intermediate-mass black holes remain elusive and their existence has not been quite confirmed. The best way to measure black hole masses is using the motions of stars around them, but this tactic may not work for intermediate-mass black holes since they have a smaller sphere of influence than supermassive black holes. Today’s article takes a look at a possible intermediate-mass black hole in a globular cluster in neighboring galaxy M31, also known as the Andromeda Galaxy.

Pinning Down the Mass

The globular cluster B023-G078 is the most massive cluster in Andromeda, and the velocity of stars within the cluster seems to indicate the presence of a massive central object. The authors of the article use images of the cluster from the Hubble Space Telescope and spectroscopic observations from Gemini to determine if this central mass could be an intermediate-mass black hole.

plot of root mean square velocity as a function of radius in arcseconds and parsecs

Figure 1: Root-mean-square velocity of stars in the cluster vs. radial distance to the center of the cluster. Points in red show the observations from the Gemini telescope. The black line shows the best-fit model for a massive black hole. The blue line shows the model assuming there is no black hole. [Adapted from Pechetti et al. 2022]

The authors use the Hubble images to come up with models for the mass of the black hole. They use a method called Jeans anisotropic modeling, which fits the Jeans equations to observations of a star cluster or galaxy. The high resolution of the Gemini data (and the proximity of the cluster) allows them to get information on the motion of individual stars within the cluster. Using integral field spectroscopy, the authors determine the root-mean-square velocity of stars at different distances from the center of the cluster, which depends on the central mass. The authors then compare their models to the observed velocities, shown in Figure 1.

The best-fit models give the central object a mass of 9 x 104 M☉, placing it firmly in intermediate-mass black hole territory!

It is possible that the central mass is actually several stellar-mass black holes rather than one intermediate-mass black hole. The main difference between the two possibilities would be that a collection of many black holes would look more extended than a single compact object. The authors investigate this possibility using their models, but any conclusions may require higher resolution observations.

However, there is something else that can give us a clue if this is indeed an intermediate-mass black hole: the origin of the globular cluster.

Remnants of a Small Galaxy? 

Because of the wide spread of metallicity measured for stars in the cluster, the authors consider the possibility that B023-G078 is the remnant of a small galaxy that underwent a merger with Andromeda, making it a stripped nuclear star cluster. The idea is that as small galaxies merge into larger galaxies (what is known as a minor merger), tidal forces pull apart parts of the galaxy, including the nuclear star cluster at the center that houses a massive black hole, leaving behind a globular cluster.

Given the mass of the cluster (~106 M☉), the authors estimate that the original galaxy had a mass of ~109 M☉. (For comparison, the mass of the Milky Way is ~1011 M☉.) Since the mass of a central black hole typically scales with the mass of the galaxy, this mass estimate means this nucleus is a good place to look for an intermediate-mass black hole.

The combination of the mass of the black hole from modeling and the evidence that this cluster is a stripped nuclear star cluster leads the authors of the article to favor the idea that there is indeed an intermediate-mass black hole in the cluster!

Original astrobite edited by Alex Pizzuto.

About the author, Gloria Fonseca Alvarez:

I’m a fifth-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 properties 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.

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