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8 December 2020

Warning: Too Much Radiogenic Heat Could Hurt Exoplanet Habitability

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: Radiogenic Heating and its Influence on Rocky Planet Dynamos and Habitability
Authors: Francis Nimmo et al.
First Author’s Institution: University of California Santa Cruz
Status: Published in ApJL

Rocky planets are thought to start as hot masses of material accreting from a disk of gas and dust around their young host star. Whereas the primary heat source early on comes from accretion, and orbital dynamics can lead to further heating through tidal squeezing, the ongoing thermal evolution of many rocky planets is likely controlled by radiogenic heat production. In particular, the radioactive isotopes of uranium (U) and thorium (Th) have long half-lives and so may be significant in deciding the long-term geodynamic history. The authors of today’s bite argue that the exact concentrations of such elements in a planet’s mantle could decide the presence and strength of that world’s magnetic field.

Dynamo theory holds that magnetic fields are generated by circulation of conductive fluids. In the case of Earth, convection of hot liquid metals in the outer core may be generating our magnetic field (see Figure 1). This outer core dynamo shuffles heat outward from the planet’s interior and its efficiency is controlled by the temperature of the overlying mantle. Thus, our magnetic field can be linked to heat production in the mantle that is mainly due to the decay of radiogenic elements. But what would it mean if our planet happened to have more or less of these elements?

Figure 1: Simplified cross-section of the common interpretation for Earth’s interior. The thin layer at the Earth’s surface is the crust (brown), below that is the mantle (red), which extracts heat from the liquid outer core (yellow), which convects to produce the magnetic field and gradually solidifies to form the inner core (white). [universe-review.ca]

In general, the composition of a planet should be similar to that of its host star since they coalesced from the same stuff. Therefore, we should be able to measure the elemental abundance of a star and say something about its planets. However, concentrations of some elements can vary significantly from star to star due to the different processes that produce them. So-called r-process elements like U and Th are likely distributed unevenly throughout the galaxy because they only form under the extreme conditions of rare processes like neutron star merger events. For understanding radiogenic heat production in the mantle of a planet, the presence of U and Th is important in terms of its concentration relative to the bulk mass of silicates. The ratio of europium to magnesium (Eu/Mg) serves as a good proxy for this — useful since U and Th are hard to detect in the spectra of stars. Given typical measurements of Eu and Mg, the authors consider that radiogenic heat production in the mantle of similar planets may vary from roughly 30% to 300% of the Earth’s 15 terawatts.

The models at the center of today’s paper are relatively simple compared to more computationally expensive 2D or 3D models, but are sufficient to see how changing a parameter like mantle heat production could affect a planet’s evolution. They consider the timeline of three identical Earths, where the only difference is having less U and Th (Figure 2a), Earth-like concentrations (Figure 2b), or more U and Th (Figure 2c). All cases assume that plate tectonics contributes to heat transfer, because previous work suggests magnetic dynamos are more likely under conditions conducive to plate tectonics, despite its presence not being a certainty (see Venus, for example). In Figure 2, the authors use entropy production as a proxy for the likelihood and intensity of a dynamo. The entropy production rate determines the presence of a dynamo based on whether it exceeds the adiabatic entropy rate, where the adiabat defines the expected temperature and pressure conditions for the mantle. Dynamo convection is at first due to extraction of heat into the mantle that gradually declines, but rapidly increases again after the core cools enough to begin solidifying. This extra burst of activity is due to “compositional buoyancy” where solidification of the core releases light elements into the fluid above.

As a good starting point, the trend predicted by the model for normal Earth (Figure 2b) matches geologic observations that the Earth has had an active magnetic field for over 3.5 billion years, though it turned off or weakened at least once for a few million years. In fact, it seems that Earth was just on the threshold for having a consistently active dynamo, based on how the entropy production may have briefly dipped below the threshold around one billion years ago. In the case of less radiogenic heat than normal Earth (Figure 2a), solid core formation starts earlier and the dynamo is easily maintained. In the case of more radiogenic heat (Figure 2c), the dynamo may turn off for hundreds of millions of years because a high-temperature mantle isn’t as effective at extracting heat from the core. So, opposite to what you might expect, the authors find that more radiogenic heat in the mantle leads to less core heat flux, less dynamo, and a smaller solid core.

Figure 2: Model results for (a) 0.33, (b) 1, and (c) 3 times Earth’s U and Th concentrations. The upper panels show decreasing heat flow over time (solid lines) and the onset of inner core formation (dashed green line). The lower panels show the entropy production rate over time, which generally decreases until inner core formation begins. The dynamo is thought to operate only when the total entropy rate (black) is greater than the adiabatic entropy rate (red). [Nimmo et al. 2020]

A more thorough view of the effect of radiogenic heat can be seen in Figure 3. The concentration of radiogenic elements could affect the habitability of the planet based on whether they are of low enough abundance to allow for a magnetic dynamo. Though some disagree, it is generally thought that a magnetic field helps shield a planet from solar particles which may otherwise erode the atmosphere. On the other hand, higher radiogenic heat in the mantle is expected to cause more volcanism, which likely releases much of the volatiles that allow for a thick, comfy atmosphere. The authors point out that their model probably misses some of the complex feedbacks that may occur here, especially with the many unknowns about plate tectonics, but ultimately argue that the abundance of r-process elements (as seen from stellar Eu/Mg ratios) should be seen as another important factor to consider in the search for habitable exoplanets.

Figure 3: Rate of entropy production (indicated by color) for a varying fraction of radiogenic elements compared to normal Earth (in log scale) over time. Solid black lines indicate a reference temperature and the dashed red lines show the trajectory of three modeled scenarios through time (the author’s Figure 1 is referenced as Figure 2 in this astrobite). Note the black region where too much radiogenic heat kills the dynamo. [Nimmo et al. 2020]

Interestingly, it has been found that lower quantities of radiogenic isotopes are present farther from the galactic center. Also, older stars are found to have smaller amounts of these heavy elements — however, today’s authors expect the random distribution due to r-process rarity to ultimately have the strongest influence on U and Th abundances. The more we learn about what makes Earth’s systems work, the more we will know about what to look for in our searches of the skies for habitable worlds. This paper paves the way for future observations and modeling to expand our view of the complicated interactions that feed into planetary geodynamics and possibly life in the universe.

Original astrobite edited by Spencer Wallace.

About the author, Anthony Maue:

Anthony is a PhD student at Northern Arizona University in Flagstaff studying planetary geology. In particular, his research focuses on Titan’s fluvial processes through analyses of Cassini radar data, laboratory experiments, and terrestrial field analog studies. Outside of school, Anthony enjoys skiing, cycling, running, music and film.

2 December 2020

Solar Wind Takes Up Mercury’s Mantle

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 Solar Wind Prevents Reaccretion of Debris after Mercury’s Giant Impact
Authors: Christopher Spalding and Fred C. Adams
First Author’s Institution: Yale University
Status: Published in PSJ

Mercury is a bit of an oddball compared to the other terrestrial planets. Because of its proximity to the Sun, Mercury doesn’t have an atmosphere, only a “surface-bound exosphere” of gas particles on ballistic trajectories. Under the surface, Mercury has an iron core that extends to more than 80% of its radius, compared with just 50% for Earth.

Many theories have been proposed to explain how Mercury ended up as the planet with the largest core compared to its size. One idea is that Mercury formed with a silicate mantle that was blasted away by asteroid impacts. Another puts forth that as the planets formed from the protoplanetary disk orbiting the Sun, high temperatures sorted out the silicates and iron, so Mercury formed in a region of the disk bereft of silicates to begin with. A third theory states that high temperatures took over after Mercury formed, vaporizing its mantle but not the iron core. The fact that many close-in exoplanets have been found over the last decade with rocky mantles casts considerable doubt on the latter theory.

It is, in fact, a corollary of the first theory that the authors of today’s paper tested. Specifically, they hypothesize that as asteroid impacts knocked pieces of Mercury’s mantle into orbit, the powerful solar wind removed the debris before it could coalesce back onto the surface.

Every Day Is a Windy Day in Space

In 1957, Eugene Parker realized something funny happened when he tried to solve the fluid equations to understand how the Sun’s atmosphere works. At very far distances, he found a discontinuity — the pressure was much lower than realistically possible. His solution was so revolutionary it took three tries to get published: the solar corona is not static but constant expands into space. Parker’s solar wind is composed of supersonic protons traveling at 400 km/s, and it dominates the interplanetary environment as far as the heliopause. Parker’s other major discovery was the spiraling solar magnetic field.

It’s believed that the young Sun had a solar wind about 100 times stronger than today, which is what makes the work in today’s paper possible.

The Giant Impact of Giant Impacts

Mercury’s early history was likely dominated by giant impacts (similar to those that might have formed the Moon), which blasted large amounts of its silicate mantle into space. The pebble-sized debris, left to orbit, would gradually reaccrete onto the surface of Mercury within about ten million years.

But the strong solar wind from the young Sun can push on the debris just enough to modify the particles’ orbits, either accelerating the debris toward the outer solar system or dragging it in toward the Sun. Figure 1 shows a schematic of this system.

To test whether the solar wind could be responsible for facilitating the loss of Mercury’s mantle, the authors first looked for an analytical solution by directly solving equations of motion. Despite the simplifications required, they believed the results would be conceptually insightful. They then followed up with a detailed numerical simulation, relying on high-performance computing.

Figure 2: Radial (top) and azimuthal (bottom) velocities of the solar wind as a function of radius for the Sun at ages 3, 10, and 30 million years. Radial velocity increases monotonically but azimuthal velocity reaches a maximum close to the Sun. Super-Keplerian azimuthal winds can accelerate particles outward or inward, depending on orientation. Mercury’s semi-major axis is 0.39 AU. [Spalding & Adams 2020]

In the analytical incarnation, the authors looked for the amount of acceleration the solar wind can impart on centimeter-sized debris orbiting Mercury. Close to the Sun, the solar magnetic field locks the solar wind to the solid body rotation of the Sun. The result is the wind has an azimuthal velocity (circulating around the equator) in addition to its outward, radial velocity. The azimuthal velocity was recently confirmed by the Parker Solar Probe.

Though the azimuthal velocity decreases with distance, at Mercury’s location, it is sufficient to impact orbiting debris with a force, as shown in Figure 2.

The authors added solar wind acceleration to the orbital equations of motion and looked for the decay timescale of the semi-major axis and eccentricity. They varied the age of the Sun, strength of the solar wind, debris launch angle, and starting orbit. In most cases, the solar wind causes debris to decay within about one million years, which is significantly shorter than the ten million years it takes the debris to reaccrete onto the surface, a promising indication for their hypothesis.

Figure 3: Results of numerical simulations with and without solar wind. Of the 110 starting particles, many times more collide back with Mercury in the absence of a solar wind, indicating the wind’s role in stripping collisional debris. [Spalding & Adams 2020]

Many researchers would be satisfied with an analytical solution that supports the hypothesis, but these authors wanted to follow up with a computational approach. Simulations can easily handle more robust physics and perform better control tests. The authors run N-body simulations of centimeter-sized debris orbiting Mercury with and without the solar wind, tracking each particle to see if it either collides back with Mercury or escapes for good.

Figure 3 shows the results, indicating that the presence of even a weak solar wind significantly reduces the number of particles that recoalesce onto the planet’s surface.

Beyond the Solar System

With a combination of analytical and computational methods, the authors conclude that a strong solar wind during the period of heavy impacts on Mercury could have removed ejected material from orbit within less than a million years. Over time, this resulted in Mercury’s silicate mantle being lost into the Sun or toward the outer solar system, leaving behind the iron core.

The authors offer the possibility of utilizing this work in the study of exoplanets. As space physicists learn more and more about the solar wind and heliosphere, attention has turned to astrospheres, heliospheres around stars other than the Sun. Some detections of close-in exoplanets indicate they are iron-enriched like Mercury, leading to the possibility that their composition can be used as an indirect probe of stellar wind characteristics.

Original astrobite edited by Haley Wahl and Wynn Jacobson-Galan.

About the author, Will Saunders:

I am a third year Ph.D. student at Boston University, where I study planetary atmospheres. I work with Prof. Paul Withers at BU and Dr. Mike Person at MIT using stellar occultations to measure waves in the atmospheres of Mars and Uranus. I received my Bachelors in Physics from the University of Pennsylvania. I am so excited about founding and co-hosting the podcast astro[sound]bites. Check us out on astrosoundbites.com, Apple Podcasts, Google Play, SoundCloud, and Spotify. In my free (pandemic) time, I enjoy biking, outdoor dining, and walking around Boston.

1 December 2020

Do We Have the Abell-ity to Find a Recoiling Black Hole in A2261-BCG?

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: Chandra Observations of Abell 2261 Brightest Cluster Galaxy, a Candidate Host to a Recoiling Black Hole
Authors: K. Gültekin et al.
First Author’s Institution: University of Michigan
Status: Accepted to ApJ

Galaxies can come in many different shapes and sizes, from dwarf galaxies that contain only a few hundred million stars to giant spirals like the Andromeda Galaxy that house over a trillion stars. Their centers can also vary from noisy black holes emitting large jets of radiation (also known as active galactic nuclei) to two spiraling supermassive black holes from two merged galaxies. Today’s paper looks at a bright cluster galaxy and tries to figure out exactly what is at its center.

An Odd Galaxy

Figure 1: Mrk 739, seen here, is an example of a galaxy merger observed before the two supermassive black holes have merged at the center of the newly-formed galaxy. [SDSS]

Though galaxies can look different, the galaxy cluster Abell 2261’s brightest cluster galaxy, A2261-BCG, is particularly strange looking. It has a very large center in its stellar surface brightness profile — a profile that describes the brightness of the galaxy as a function of distance from the center — showing that its core is very large and flat. Interestingly, the core is offset from the center. Cores like this are sometimes the result of a supermassive black hole merger. When two galaxies collide, their central black holes can sink to the center of the merger and become a binary (as seen in Figure 1). Stars interacting with this supermassive black hole binary can even end up being flung out of the center, taking some energy from the binary with it and causing the binary’s orbit to shrink. This is called “scouring” a core in the galaxy. As the binary moves closer together, it emits gravitational radiation in the form of gravitational waves, further shrinking its orbit. This energy emission will eventually cause the supermassive black hole binary to merge, and can lead to a recoil of the merged black hole at speeds of up to several thousand kilometers per second (!), pushing it slightly away, or offset, from the center.

Astronomers expect that A2261-BCG should host a very massive black hole (which is possibly the result of a merged binary) at the center due to the size of its core. In this paper, the authors look for evidence of a recoiling or ejected black hole in AA2261-BCG, which would indicate that the black hole is a result of a merged binary.

Searching Radio and X-ray Observations

In order to test the theory that this supermassive black hole was once two, the authors focused on four stellar knots, areas of high star density, near the center. If the black hole was recoiling, it would take a clump of stars with it; the black hole would lie at the center of this clump. In previous works, this team used the Very Large Array (VLA) telescope in New Mexico to look for radio emission coming from the stellar knots, but the only activity they found was evidence of old jet activity. They then used the Hubble Space Telescope to look at the stellar velocity distributions of three of the knots to see if there was a massive object around, but didn’t find any conclusive evidence.

In this work, they use X-ray observations taken with the Chandra telescope to look for evidence of accretion onto a large black hole at the center of A2261-BCG. If found, it would point to the fact that the black hole has never recoiled or that the recoil is very slight.

Were They Abell to Figure Out What’s at the Center?

The team analyzed new Chandra observations and performed image and spectral fitting on this data combined with archival data to determine the profile of the gas being emitted from the center. They found evidence of a previous dynamical disturbance, which matches their optical observations, but showed no point-source emission arising from the optical center of the galaxy. The observations show no 10¹⁰ solar mass black hole at any of the stellar knots (which is seen in Figure 2 by the absence of any excess emission from the stellar knots), which raises the question of just where the black hole is. One possibility is that it is at the center of the galaxy and is just accreting at such a low rate that it cannot be detected in X-rays. Another possibility is that the black hole traveled farther than 10 kpc away from the center, but looking farther away from the center could increase the chance of more X-ray noise.

Figure 2: Images of the center of the galaxy. Left: the Hubble image showing the four stellar knots in the white contours at the center, with the red circle showing the optical center and the red box showing the location of the radio emission. Middle: Chandra data showing the center. Right: the residuals (the difference between the two). The colors show the amount of X-ray emission. Each panel shows no excess emission at any of the locations. [Gültekin et al. 2020]

The evidence of a radio source implies that at some point there was a sufficient amount of material falling onto the black hole to produce jets, but lack of evidence of a bright X-ray core supports the idea that this is relic emission and not current emission.

While the center should show some evidence for a supermassive black hole, the team finds that either there is no 10¹⁰ solar mass black hole at the center, or that it is accreting at a very low level. Further observations with the upcoming James Webb Space Telescope would allow two-dimensional spectral characterization of the core of the galaxy to help determine whether there is a black hole at the center of A2261-BCG.

Original astrobite edited by Brent Shapiro-Albert.

About the author, Haley Wahl:

I’m a third year grad student at West Virginia University and my main research area is pulsars. I’m currently working with the NANOGrav collaboration (a collaboration which is part of a worldwide effort to detect gravitational waves with pulsars) on polarization calibration. In my set of 45 millisecond pulsars, I’m looking at how the rotation measure (how much the light from the star is rotated by the interstellar medium on its way to us) changes over time, which can tell us about the variation of the galactic magnetic field. I’m mainly interested in pulsar emission and the weird things we see pulsars do! In addition to doing research, I’m also a huge fan of running, baking, reading, watching movies, and I LOVE dogs!

24 November 2020

Red Dead Evolution

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 Rapid Build-up of Massive Early-type Galaxies. Supersolar Metallicity, High Velocity Dispersion and Young Age for an ETG at z = 3.35
Authors: Paolo Saracco, Danilo Marchesini, Francesco La Barbera, et al.
First Author’s Institution: INAF – Osservatorio Astronomico di Brera, Italy
Status: Accepted to ApJ

For the majority of galaxies in the universe, several billions of years must pass for dramatic, measurable changes to occur in their stellar populations and morphologies. Moreover, in the standard cosmological paradigm, ΛCDM, the most massive structures typically form last from the buildup of smaller constituents (this is known as hierarchical growth). However, recent studies have found something surprising: some galaxies are able to accumulate incredible stellar mass (>10¹¹ M_☉) and deplete their gas reservoirs — becoming “quiescent”, or no longer forming stars, within the first two billion years after the Big Bang. These massive systems must form and evolve through novel avenues in order to assemble so much mass and exhaust their gas supplies so quickly. In this astrobite, we dive into the mystery of massive quiescent galaxies at high redshift: where did they come from and how did they form?

One Galaxy Is Worth One Thousand Words

While the diversity of galaxies throughout the universe is extreme (even during a single epoch), in-depth analyses of a few representative members are capable of shedding light on entire galactic populations. For rare sub-groups, such as massive quiescent galaxies (MQGs) at high redshift, detailed studies of single objects can yield especially insightful results sparking further research. In this work, the authors sought to measure key properties of a previously discovered MQG with the catchy name of C1-23152 in order to explore more general questions about this population of galaxies, like those raised in the introduction. This galaxy is located at z = 3.35, when the universe was only 1.9 billion years old. Previous analyses of this galaxy used a combination of imaging, where one group of astronomers used the Hubble Space Telescope to measure structural properties, as well as shallow spectroscopy to confirm the redshift and some basic features. In this work, the team led by Paolo Saracco devoted 17.3 hours of total effective integration time with the Large Binocular Telescope (LBT) in Arizona to obtain a detailed near-infrared spectrum. The goal in obtaining this spectrum, shown in Figure 1, was to conclusively establish this galaxy’s stellar age, metallicity, and velocity dispersion, while also constraining its star-formation history.

Figure 1: LBT spectrum of galaxy C1-23151. In dark grey is the observed spectrum; in black is a smoothed version of the spectrum; and in red is the best stellar population synthesis model used to infer the physical properties of the galaxy. Prominent emission and absorption lines are labeled in red, while grey vertical bars demarcate portions of the spectrum that are not used in the analysis due to the presence of strong emission lines or sky transmission. In the bottom panel, an SDSS spectrum of a local quiescent galaxy is shown for comparison. [Saracco et al. 2020]

What Was Measured?

Essentially all physical properties of galaxies are in one way or another encoded in the light they emit. The key then is to work backwards: using the light we observe through photometry and spectroscopy, what must the underlying physical properties be? Using the high quality LBT spectrum (Figure 1) of galaxy C1-23152, the authors performed both absorption line fitting (ALF) and full spectrum fitting (FSF). In both cases, synthetic stellar population synthesis models with known physical parameters (e.g., stellar age, metallicity, mass) are compared with the observed spectrum. During ALF, as the name suggests, only the absorption lines are compared with those of models, as the strengths of these lines are easily measured and robust. Conversely, during FSF, the entire spectrum is compared with models. Combining the two approaches is a method to combat systematic issues arising from the model-fitting procedures and yields related but different estimates of the galaxy’s physical properties. Both of these methods employ a code that tries to fit multiple stellar populations of different ages to the observed spectrum (see Figure 2 to see how this works). Lastly, the team performed standard spectral energy distribution (SED) modeling, where archival photometry from the UltraVISTA survey was used to constrain the SED of the galaxy.

Figure 2: Demonstration of a multi-component stellar population synthesis model. In this example, a galaxy is synthesized from two stellar populations: a younger population accounting for 75% of the flux (35% of the mass) and an older population accounting for 25% of the flux (65% of the mass). Using the same code to analyze C1-23152, the team attempted to tease out the different stellar populations with ALF and FSF. The right panel shows the inferred ages of the different components of this synthetic galaxy and demonstrates the robustness of the team’s method in extracting correct ages for the underlying populations. [Saracco et al. 2020]

The team’s analysis revealed C1-23152 is indeed an early-type (quiescent) galaxy that contains an active galactic nucleus (AGN) and has a total stellar mass of ~ 2×10¹¹ M_☉. It attained its morphology and ceased its star formation during the last 600 million years prior to observation (at 3.35 < z < 4.6). During the recent 450 million years prior to observation, the galaxy was likely forming stars at a dramatic rate, creating more than 400 solar masses worth of stars per year (400× the current average star formation rate in local galaxies). The team measured a super-solar metallicity, which suggests that star formation was taking place rapidly, without time for the gas supplies to replenish. It is likely that the star formation ceased roughly 150 million years prior to observation.

The Art of Inductive Reasoning

So how does this specific finding relate to massive quiescent galaxies in general? On the one hand, the fast formation time suggested by ALF and FSF, combined with the high surface mass density as measured through its morphology, points to a dissipative stellar-mass growth cycle that did not involve mergers, which would ultimately lower the surface mass density. The authors stress that density appears to be a principal driver in quenching star formation, which has been suggested by other astronomers. Theoretical models can form galaxies like C1-23152 relatively easily so long as the progenitor galaxy is dense. The greater-than-solar metallicity also suggests a fast, dissipative growth of stars. It is likely that the AGN in the galaxy played some role in the quenching process, although it is not clear from the data what that role is. Assuming this galaxy continues to evolve through well-understood pathways, such as major and/or minor mergers, it will likely resemble the most massive systems observed today (corresponding to z = 0). A comparison of how this galaxy compares with others like it is given in Figure 3, where different evolutionary scenarios are also illustrated.

Figure 3: Comparison of C1-23152 with other similar quiescent galaxies. From left to right, the velocity dispersion, surface-mass density, and stellar mass of each galaxy is plotted on the x-axis while its effective radius (the radius that encloses half of the light) is plotted on the y-axis. Local quiescent galaxies are shown in grey; local quiescent galaxies with high velocity dispersions are shown as black triangles; red squares illustrate quiescent galaxies that are both compact and have high velocity dispersions; two other MQGs at z > 3 are shown in green and cyan; the large black circle indicates the galaxy in question, C1-23152. The different colored arrows indicate different evolutionary paths the galaxy is likely to traverse from z = 3.35 to today. [Saracco et al. 2020]

C1-23152 provides an interesting laboratory for understanding the growth of formation of massive quiescent galaxies in the early universe. With the detailed analysis of its spectrum, the authors confirmed many of the previously reported properties of this galaxy and were able to infer possible formation scenarios that fit into our theoretical understanding of the way galaxies form and change over time, although this galaxy is quite an extreme example. Nevertheless, each in-depth study of interesting and unique galaxies, like C1-23152, brings us closer to a full understanding of galaxy formation and evolution, with all of the intricacies included.

Original astrobite edited by Wynn Jacobson-Galan.

About the author, Lukas Zalesky:

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

17 November 2020

Around Half of Sun-Like Stars Could Host Potentially Habitable Worlds

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 Occurrence of Rocky Habitable Zone Planets Around Solar-Like Stars from Kepler Data
Authors: S. Bryson, M. Kunimoto, R. Kopparapu et al.
First Author’s Institution: NASA Ames Research Center
Status: Accepted to AJ

When we look up on a clear night and contemplate the seemingly innumerable stars, it’s difficult not to wonder just how many other worlds like the Earth might be out there. In ancient times, long before the first unambiguous discovery of an extrasolar planet (a planet orbiting a star other than the Sun), it was believed there were essentially two possibilities, both put forward by philosophers during the Classical period in Ancient Greece: Aristotle (384–322 B.C.) declared “There cannot be more worlds than one”, while Epicurus (~460–370 B.C.) held the opposing view that “There are infinite worlds both like and unlike this world of ours”. Over two millennia later, and thanks to the painstaking work of countless generations of astronomers, we now know for sure which of these views is closer to the truth. Now, a team of astronomers led by Steve Bryson of NASA’s Ames Research Center have taken us one step closer in our quest to discover other worlds like the Earth, using data obtained with the Kepler Space Telescope and the Gaia mission. Their results suggest that around half of Sun-like stars in our galaxy might host rocky, potentially habitable planets within their habitable zones.

NASA’s Kepler Space Telescope was launched in 2009, and one of its primary missions was to discover rocky planets in or near the habitable zones of their host stars (roughly, the region where liquid water can exist on a planet’s surface), and to estimate the fraction of stars that might host such planets. During the nine and a half years over which it was active, Kepler detected 2,393 confirmed exoplanets around the 530,506 stars it observed — and for most of that time it stared at a single patch of sky only about the size of a hand at arm’s length. To detect planets, Kepler made use of the transit method, which relies on measuring the tiny dip in brightness that occurs when a planet passes in front of its host star as we view it from the Earth. As can be imagined, this dip in brightness is pretty small: about 1% for a giant exoplanet similar to Jupiter, and only about 0.1% for a rocky, Earth-like exoplanet. NASA retired the space telescope in 2018 after it ran out of fuel, but that hasn’t stopped astronomers from continuing to pore over the data and make new discoveries.

The number of potentially habitable planets per solar system in our galaxy is a key term in the Drake equation, which is a probabilistic formula used to estimate the number of detectable civilisations residing within the Milky Way. None of the terms in the equation are known exactly, and most are only rough estimates based on observation. For this reason, much of the research carried out at The SETI Institute (The Search for Extraterrestrial Intelligence) focuses on finding reliable constraints for these terms.

Figure 1: An illustration of the various terms in the famous Drake equation. The number of potentially habitable planets per solar system is one of the key terms in the equation. [University of Rochester]

Bryson and collaborators performed a detailed statistical analysis after combining the Kepler planet candidate catalogue with data from ESA’s Gaia mission. Previous studies that attempted to estimate similar planet occurrence rates have only considered the planet’s distance from the star, whilst this is the first study of its kind to consider the “instellation flux”, or the amount of energy falling on a planet from its host star. This was possible thanks to the inclusion of data from Gaia, which was designed to construct an ultra-precise 3D map of the positions and motions of stars in the Milky Way, and also to provide information on stellar properties — such as their luminosity and effective temperature. This allowed the researchers to carry out their analysis in an entirely new way that was more representative of the actual diversity of host stars in our galaxy.

The researchers then estimated the occurrence rates using a range of models, stellar populations and computation methods. They limited their analysis to exoplanets similar in size to the Earth (radii between 0.5 to 1.5 times that of the Earth) and therefore likely to be rocky, and stars with a similar age and temperature to the Sun (between about 4,800 K and 6,300 K). They also considered two scenarios using either a conservative or optimistic definition of the inner and outer habitable zone boundaries.

Figure 2: The resulting distributions of the habitable zone occurrence rate for a range of models and stellar populations. The medians and 68% credible intervals are shown above the distributions. The top panels incorporate uncertainties on planet radius, stellar instellation and stellar effective temperature, whilst the bottom panels do not incorporate these uncertainties. Left panels consider the conservative habitable zone; right panels, the optimistic habitable zone. The plots show how very similar results were obtained for models 1 and 2 for both stellar populations. [Bryson et al. 2020]

From their analysis, the authors estimate that the average number of planets per star with a planet radius between 0.5 and 1.5 Earth radii, and within the star’s habitable zone, is between 0.37 and 0.60 for the conservative habitable zone. For the optimistic habitable zone, they estimated between 0.58 and 0.88 planets per star. This means that, even using the most conservative estimate, there could be as many as 300 million potentially habitable planets in our galaxy, and most likely many more! They also showed that there are likely to be at least four potentially habitable planets within about 30 light-years of the Sun, and the closest is likely to be at most about 20 light-years away.

Although the team carried out a very careful analysis of the data, the uncertainties on their estimates are still quite large due to the small number of rocky planets actually detected by Kepler. Future work will likely help to refine these estimates even further. Knowing just how common different types of exoplanets are could help guide the design of future space missions searching for potentially habitable exoplanets.

Original astrobite edited by Will Saunders.

About the author, Jamie Wilson:

I am a PhD student at the Astrophysics Research Centre, Queen’s University Belfast. My work focuses on the characterisation of exoplanet atmospheres in order to better understand their chemical compositions, formation conditions and evolutionary histories. When not doing science I can usually be found playing drums and touring with my band.

10 November 2020

How Do Imines Form in Space Clouds?

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: Methanimine as a Key Precursor of Imines in the Interstellar Medium: The Case of Propargylimine
Authors: Jacopo Lupi, Cristina Puzzarini, and Vincenzo Barone
First Author’s Institution: Scuola Normale Superiore, Italy
Status: Submitted to ApJ

What Even Is an Imine?

Perhaps one of the biggest questions we can ask is, where does life come from? Many astrochemists seek to answer this question by investigating the history and evolution of molecules that are biologically significant. It turns out imines (pronounced like “I means”) are an important group of molecules that can eventually form DNA. Imines are distinguished by a carbon atom double-bonded to a nitrogen atom, which is then bonded to hydrogen, or “C=NH,” where carbon can be bonded to any other groups of atoms.

Figure 1: Z- and E-propargylimine. The CNH bonds in red are what classify these molecules as imines. Note that the Z- and E- configurations are different molecules. The slightly different positioning of the N–H bond makes these molecules isomers. [Abygail Waggoner]

Currently, only six imines have been detected in the interstellar medium (ISM). The most recent detection, published earlier this year, is of Z-propargylimine, shown in Figure 1 (check out this website to learn how chemists name molecules), in G+0.693-0.027. With an increasing number of known imines in the ISM, many astrochemists are trying to understand how they form. Traditionally, larger molecules (like Z-propargylimine), are assumed to have formed in the ice layers of dust grains. However, recent studies have shown that some imines can form in the gas phase of molecular clouds.

Today’s paper uses computational chemistry to determine if the newly detected propargylimine (PGIM) can form via a similar route in the gas phase, or if this large, complex imine is more likely to form in interstellar ices.

Chemistry with Computers

The authors of today’s paper use computational chemistry that uses quantum mechanics to determine the structure and energy of different molecules. The software they used, Gaussian, is commonly used to determine if a chemical reaction is possible and exactly how a set of reactants form a product.

Today’s paper explores many different ways to form PGIM, and they find that the simplest imine, methanimine (CH₂NH), is a possible precursor for PGIM. Methanimine can react with either CN or CCH to form CH₂NCCH, which then follows a reaction pathway presented in Figure 2 to form either E- or Z-propargylimine. As you can see in Figure 2, these reaction pathways can get pretty complex.

The type of reaction the authors identified is known as an addition-elimination reaction. Basically, once CH₂NCCH is formed from methanimine, a hydrogen atom will be “added” by reacting with CH₂NCCH, then the nitrogen and terminal carbon will “switch” spots. Lastly, the hydrogen atom is lost, or “eliminated,” thus forming PGIM.

In addition to the kinematic study shown above, the authors derived the individual rate constants for each step in the reaction pathway shown in Figure 2. The calculated rate constants suggest that the proposed addition-elimination reaction is indeed possible in gas-phase interstellar conditions.

CNH to DNA

So, why is it important that PGIM can form in the gas-phase in the ISM? Well, as can be seen in Figure 2, chemical reactions and reaction pathways are very complex, and many different molecules can be formed many ways. While this study focused on the production of PGIM, the results suggest that other complex imines could form from smaller, less complex imines via a similar pathway in the gas-phase.

Like we discussed at the beginning of today’s bite, imines are considered a biological precursor to DNA (Figure 3), so it is important to understand their formation to also understand the origins of life in the universe. Traditionally we assume that large carbon-based molecules, like imines, form in the ice. So, the discovery of a possible gas-phase formation route is a new and exciting pathway that could tell us more about the origins of life as we know it.

Original astrobite edited by Huei Sears.

About the author, Abygail Waggoner:

I am a second year chemistry graduate student at the University of Virginia and NSF graduate fellow. I study time variable chemistry in protoplanetary disks. When I’m not nerding out about space, I’m nerding out about fantasy by reading or playing games like dungeons and dragons.

3 November 2020

Don’t Change the Station! Substellar Objects Are Up Next

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: Direct radio discovery of a cold brown dwarf
Authors: H. K. Vedantham et al.
First Author’s Institution: ASTRON, Netherlands Institute for Radio Astronomy
Status: Submitted to ApJL

Brown Dwarfs: The Middle School of Celestial Objects

What do you get when you have too much mass to form a planet, but not enough mass to form a star? A brown dwarf! First theorized in the 1960s and observed in the 1990s, brown dwarfs — a subclass of ultra-cool dwarfs — are substellar objects around 13–80 times the mass of Jupiter (or 10–90 times, depending on who you ask). They are special because, though they are thought to form in a similar manner to stars, they aren’t massive enough to trigger sustained hydrogen fusion in their cores. Instead, they are thought to fuse deuterium or lithium. This means that, unlike our Sun or other stars, they will gradually cool and fade rather than becoming white dwarfs, neutron stars, or black holes.

Despite not being stars, brown dwarfs are still self-luminous — meaning they emit energy in the form of light rather than just reflecting it back from a host star — and therefore can have spectral classifications like stars. Depending on how much light they emit and their temperatures, brown dwarfs are classified as either L, T, or Y type. Each class shows different dominant absorption lines: L dwarfs are water- and carbon-monoxide-dominated, T dwarfs are methane-dominated, and Y dwarfs are potentially ammonia-dominated.

The Study

Like stars, some brown dwarfs are known to have strong magnetic fields, and even instances of potential aurorae. In addition to being observable by some optical instruments, this magnetic activity allows some brown dwarfs to be detectable in the radio and — if the magnetic field activity is strong enough — X-ray bands. However, radio observations of these objects have previously been performed primarily to follow-up known brown dwarfs. The authors of today’s paper use the Low Frequency Array (LOFAR) to make the first direct radio discovery of a brown dwarf, BDR 1750+3809. They specifically looked at circularly polarized radio sources in the LOFAR Two-meter Sky Survey (LoTSS), because known brown dwarfs have highly circularly polarized radio emission. They followed up the LoTSS data with near-infrared observations using the Wide-field Infrared Camera (WIRC) at Palomar, and the NIRI imager at Gemini-North. They also obtained a spectrum using NASA’s Infrared Telescope Facility (IRTF).

Using all of these follow-up observations, the authors were able to determine several characteristics of BDR 1750+3809:

It has strong methane absorption bands, indicating it is likely a T dwarf
The approximate distance to the object, calculated using the distance modulus, is around 57–74 pc (186–241 light-years)
It has a larger luminosity than expected. This is likely caused by the viewing geometry or by a companion object that is either large or close to BDR 1750+3809, similar to the Jupiter–Io system.

Most importantly, though, the detection shows that radio observations can be used to blindly discover these objects.

Figure 1: Six graphs of LOFAR radio signals (and non-detections) from BDR 1750+3809 are shown. The graphs on the left show the total intensity of the signal, while the graphs on the right show only the intensity of the circularly polarized signals. The three different observation dates are noted on the graphs. [Vedantham et al. 2020]

Why Does It Matter?

This discovery is important not only as evidence of a way to discover more brown dwarfs, but also as a potential window into learning more about the properties of exoplanet magnetospheres. Both brown dwarfs and planets are thought to have exclusively dipolar magnetic fields, meaning they have two poles of equal and opposite strength like a bar magnet or Earth’s magnetic field. However, because of technological constraints and the fact that Earth’s ionosphere blocks many low-frequency radio waves, signals from exoplanet magnetic fields are currently hard to detect (although one was detected via its aurorae earlier this year). This low-frequency brown dwarf observation — comparable to what is expected from gas giant exoplanets — indicates that instruments such as LOFAR do have the sensitivity necessary to make radio detections of exoplanet magnetospheres. If learning about the magnetic field itself isn’t exciting enough, keep in mind that a magnetic field strong enough to shield a planet from stellar radiation is a requirement for habitability as we know it. The more we can determine about an exoplanet’s magnetosphere, the more we can speculate about the possibility of it sustaining life!

Original astrobite edited by Aaron Pearlman.

About the author, Ali Crisp:

I’m a third year grad student at Louisiana State University. I study hot Jupiter exoplanets in the galactic bulge. I am originally from Tennessee and attended undergrad at Christian Brothers University, where I studied physics and history. In my “free time,” I enjoy cooking, hiking, and photography.

20 October 2020

X Marks the Region

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: Hidden Worlds: Dynamical Architecture Predictions of Undetected Planets in Multi-planet Systems and Applications to TESS Systems
Authors: Jeremy Dietrich and Dániel Apai
First Author’s Institution: The University of Arizona, Tucson
Status: Published in AJ

Fans and writers of science fiction alike spend countless hours crafting intricate star systems, replete with planets, moons, and a menagerie of space-faring civilisations. The success of missions such as the Kepler Space Telescope (hereafter Kepler) and the Transiting Exoplanet Survey Satellite (TESS) have shown that our solar system is just one of many multi-planet systems present throughout the Milky Way. However, our ability to accurately determine the “planetary architecture” (the orbital configuration of the planets) of a given extrasolar system is severely lacking. Knowing how planets are configured in different extrasolar systems would greatly aid our understanding of how planets form, and how planetary systems evolve (e.g., via planetary migration).

Exoplanets are inherently difficult to detect, and one of the primary means of detecting them involves measuring transits, the tiny dimming of a star as a planet moves in front of it. To better understand stellar systems, instead of considering each exoplanet individually, we can consider the entire population of exoplanets at once through statistical inference — a method that has only recently become viable thanks to the wealth of data from modern exoplanet surveys. Today’s paper presents a statistical framework — DYNAmical Multi-planet Injection TEster (DYNAMITE) — designed to predict the presence of exoplanets that have so far eluded detection.

Fire in the Hole!

The core method at the heart of DYNAMITE is to determine the likelihood of finding an additional planet in an existing multi-planet system, based on the overall statistics of an existing representative population. The authors consider a combined probability density function (PDF) over the inclination, orbital period, and planetary radius, with the key assumption being that each of these parameters has its own independent distribution. Each of these initial PDFs were based on transiting planet data from Kepler, with the range of orbital periods restricted from 0.5 to 730 days, planetary radii from 0.5 to 5 Earth radii, and inclinations between 0 and 180 degrees. Monte Carlo methods (means of approximating something through repeated random sampling) are then used to sample the full probability distributions and “inject” new planets into the system. In order to come up with sensible results, the planetary system must be dynamically stable. This stability depends on the orbits of the innermost and outermost planets, their masses, and the mass of the parent star. It is difficult to accurately determine the masses of exoplanets via the common transit method, so the authors make use of a mass–radius relation to estimate the masses from the planetary radii.

Sweet Spot

Figure 1: The probability distribution function for the Kepler-154 system with the 9.92-day-period planet removed (outlined cross). Blue spikes indicate individual Monte Carlo injections. Green circles indicate the relative sizes of the known planets. Click to enlarge. [Dietrich & Apai 2020]

The model underwent rigorous testing for sensitivity and robustness. Several test scenarios included removing a known planet to see if the model could reproduce it, and removing a planet whilst altering the remaining planets. Figure 1 shows an example of the PDF as a function of orbital period for the Kepler-154 system with the known planet at P = 9.92 days (Kepler-154 f) removed. Of the total Monte Carlo predictions that inject a new planet inside the orbit of the outermost planet, 97% correspond to the region of the removed planet. As for the radius, 67% of the models predictions lie within three standard errors, while the spread is more substantial for the inclination (43%). The mean injections match the known planet’s parameters quite well (as in Figure 1 where the peak is just below the known value for the period), but the authors nevertheless state that since DYNAMITE is primarily aimed at helping guide future observations, it is not designed to provide exact predictions, but rather a likely range of values.

Speculative Execution

One of DYNAMITE’s major applications lies in the analysis of systems with candidate planets — planets that are suspected to be there but have not yet been definitively confirmed. TOI 1469 is used as an example to illustrate the iterative nature of the statistical model. Figure 2 shows the various stages of DYNAMITE for the TOI 1469 (HD 219134 / Gliese 892) system. This system is known to have two transiting planets, with at least three non-transiting planets. Starting with only the two known transiting planets, the PDF peaks at around 12.5 days. A planet is inserted here, and the model is run again. Now the PDF peaks near the known planet at around 23 days (HD 219134 f has a period of 22.72 +/- 0.02 days), so we insert another planet here and execute the model again. Proceeding in this manner, the model predicts another planet at ~46 days (corresponding to HD 219134 f with orbital period 46.86 +/- 0.03 days), while in the last iteration the model predicts a fourth planet at ~87 days, corresponding to the unconfirmed candidate planet.

Figure 2: Probability distribution functions of the orbital period for each iteration of DYNAMITE for the TOI 1469 multi-planet system. [Dietrich & Apai 2020]

To Probability Space and Beyond

predicted planet period-radius likelihoods — Figure 3: 2D normalised probability densities in log radius and log period. Brighter regions correspond to a higher probability of a predicted planet. Multi-planetary systems are marked along with their TESS TOI identifiers. Ellipses indicate one standard error. [Dietrich & Apai 2020]

Another purpose of DYNAMITE is to analyse the newly identified multi-stellar systems discovered by TESS and identify the systems most likely to contain additional planets so that they can be surveyed again. A sample of known multi-stellar systems from the ExoFOP-TESS archive was tested with the statistical model. Figure 3 shows the overall results of the model using the period ratio model from Kepler, while Figure 4 shows the exact PDF for each TESS system for the orbital period and planetary radius.

With the ability to predict the locations of hitherto undetected planets, future surveys can be more focused and targeted. Studying these systems in detail, and confirming whether or not these additional planets are present, allows us to constrain and refine models of planetary architectures, our knowledge of the mechanisms that govern the evolution of planetary systems, and, ultimately, our understanding of how exoplanets form.

Figure 4: The normalised PDF for each TESS sample for orbital period (left) and planetary radius (right). Red dots indicate known planets, with the size of each dot representing that planet’s relative size. Darker regions highlight the most likely regions in which to find additional planets. [Adapted from Dietrich & Apai 2020]

About the author, Mitchell Cavanagh:

Mitchell is a PhD student in astrophysics at the University of Western Australia. His research is focused on the applications of machine learning to the study of galaxy formation and evolution. Outside of research, he is an avid bookworm and enjoys gaming, languages, and code jams.

13 October 2020

The Starry, Dusty Origin of a Distant Galaxy 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: Emergence of an Ultra-Red, Ultra-Massive Galaxy Cluster Core at z = 4
Authors: Arianna S. Long et al.
First Author’s Institution: UC Irvine
Status: Published in ApJ

Galaxy clusters are vast entities. They contain 100 to 1,000 galaxies, making them the largest gravitationally stable structures in the cosmos. One of the assumptions of our understanding of the universe is that structures form hierarchically — smaller gravitational objects form first, followed by the largest. Therefore, to form large objects like clusters, smaller objects such as galaxies need to collide and merge together over a long period of time.

Although we generally know how clusters form, the specific process by which they grow is not yet well understood. The progenitors of clusters, known as protoclusters, are typically found at redshifts greater than z = 2 (the higher the redshift, the farther back it is in time), when the universe was about one third of its current size. Unlike the clusters we observe today, protoclusters do not appear to have an established population of “red and dead” elliptical galaxies, which makes them harder to identify. Instead, protoclusters are usually identified by overdensities of star forming galaxies known as Lyman-alpha emitters, which are typically studied using data in the optical and ultraviolet (UV) wavelength ranges. You can check out other Astrobites on the intriguing properties of protoclusters here, here, and here.

The Hunt for Protoclusters

Today’s paper offers an alternative method for identifying protoclusters via a certain galaxy population known as dusty star forming galaxies (DSFGs). These galaxies contain substantial amounts of dust, which obscures their optical/UV light, but allows them to glow in infrared (IR). DSFGs are capable of forming stars in a short period of time at higher redshifts, which allows them to become the large red elliptical galaxies we see in clusters later on. DSFGs are also typically found in the vicinity of other DSFGs, which suggests they are pivotal to protocluster evolution.

Although protoclusters with DSFGs have been observed before, these have almost all been at redshifts below z = 3. Above this, a handful of protoclusters have been found, but studies are considerably more limited in resolution and the ability to spectrally classify such galaxies.

The authors of today’s paper present insights into the gas, dust, and stellar properties within a distant (z = 4!) protocluster containing 11 tightly bound DSFGs, known as the Distant Red Core (DRC). For the first time, this system has new high resolution HST (optical) and Spitzer (infrared) observations, bolstered with existing ALMA (submillimetre), Gemini (near infrared), and Herschel (far infrared/submillimetre) data.

Different components of the protocluster are shown in Figure 1 using the Spitzer, Gemini and HST data. Combining multiwavelength data is crucial to ensuring that the DRC and potential other members comprise a genuine physical system, and to bypass issues where two or more galaxies appear as one in an image due to blending effects. Within some of these, we can see evidence of merging (e.g., in the long elliptical structure of DRC8). The authors note that although there are 11 in total, only 10 are spectroscopically confirmed to be at z = 4 (all except DRC 5).

Figure 1: The Spitzer data highlights the protocluster core within the green box in the leftmost image, while the pink circles labeled C–G show the positions of other potential protocluster members. The Gemini image (middle) zooms in on the protocluster core, with each of the 10 components circled in green. The HST image on the right confirms the appearance of 11 DRC components. [Long et al. 2020]

Outstanding in the Field?

Each of the galaxies in the DRC have approximately similar masses (>10¹⁰ solar masses). Interestingly, they exhibit minimal differences in star formation rate and mass when compared with other galaxies at z ~ 4 that aren’t in clusters (known as the “field”). The authors demonstrate that the DRC members fit neatly within one standard deviation of the measured main sequence (MS) relation at z = 4 (shown in Figure 2). Previous studies have also expected to find more gas in galaxies in cluster environments compared to field galaxies, but the results do not confirm this.

Figure 2: Star formation rate of galaxies from various studies, including the DRC objects, as a function of their stellar mass. The majority of DRC objects (given by the circled numbers) are shown to be within the bounds of the main sequence (MS) relation. This broadly agrees with galaxy populations from various other studies. [Long et al. 2020]

Weighing It Up

Finally, the authors infer the total mass of the protocluster. This is difficult, as it includes not just the individual stars in galaxies, but the underlying dark matter distribution encompassed within the cluster halo. As we cannot see dark matter, some assumptions are required to determine the total mass. Fortunately, a well-known relationship exists between the stellar and total mass of galaxies, assuming stars in galaxies accurately trace the dark matter content. By summing the masses of the individual galaxy halos, and correcting to avoid double counting halos that overlap, the protocluster mass can be computed. As shown in Figure 3, the DRC is already at least as massive as protoclusters between redshifts of z = 2 and z = 3. Based on this data, the DRC is on course to evolve to above 10¹⁵ solar masses by the present day, which would make it one of the largest clusters in our cosmos.

Figure 3: Estimated total mass of the DRC using three different methods (purple dotted lines) compared to bounds on the allowed mass of protoclusters (red stars) over a range of redshifts. The DRC is at the same mass scale as a population of known protoclusters despite being considerably farther away. For comparison, the grey band shows the estimated growth for a Coma-like cluster over time. [Long et al. 2020]

Our current model of hierarchical structure formation allows us to predict the maximum possible mass of protoclusters at various times. The authors show the DRC is large enough that it is on the boundary of being excluded by our accepted cosmological model. Further observations of the DRC will be able to tell us whether such a massive protocluster really is causing a problem (it wouldn’t be the first time). The emergence of the DRC at z = 4 shines a new light on early cluster formation, while also leaving room for many exciting questions that will hopefully be answered in future protocluster studies.

About the author, Sunayana Bhargava:

I’m a 4th year PhD student at the University of Sussex, interested in X-ray and optical observations of galaxy clusters to learn more about dark matter and large-scale structure. When I’m not working, I’m usually trying to write in coffee shops or hiking.

6 October 2020

Analyzing Thermal Spectra with Machine Learning

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 Novel Machine Approach to Disentangle Multi-Temperature Regions in Galaxy Clusters
Authors: Carter L. Rhea, et al.
First Author’s Institution: University of Montreal, Canada
Status: Accepted to AJ

Galaxy clusters are among the largest gravitationally bound structures in the universe. One of their defining characteristics is that they tend to be embedded within a large reservoir of superheated gas, known as the intracluster medium (ICM). With temperatures up to 10⁸Kelvin, the ICM is a strong emitter of X-ray radiation. The resulting spectrum is dominated by thermal bremßtrahlung radiation: radiation emitted when charged particles are decelerated. Characterising this thermal emission provides useful insights into the physical processes within the cluster, such as galaxy merging and active galactic nucleus (AGN) activity, as well as various physical parameters including temperature and metallicity. In order to obtain these parameters, one must first fit the observed spectra. However, the ICM is not necessarily uniform. Different regions are often characterised by multiple thermal components, requiring a mix of temperatures rather than a single temperature model to reproduce the observed spectra. The authors of today’s bite propose a new machine-learning method to systematically estimate the different underlying thermal components in ICM spectra. As this approach is not reliant on any particular physical model, it is both efficient and portable.

The Component and The Forest

The authors’ machine-learning approach features two key techniques; principal component analysis (PCA) and random forests. The idea of PCA is to break large, multi-dimensional datasets into their principal components; these are a series of orthonormal basis vectors such that each vector points in a direction of maximal variance. This is analogous to solving for eigenvectors, and the data processing can be thought of as a change of basis. PCA is extremely useful for machine learning because it structures the data in a way that best highlights relevant features (while discarding those that are redundant/irrelevant). This improves the learning capability and efficiency of machine-learning techniques. The authors use a random forest of decision tree classifiers to classify the processed data (i.e. the data after having been transformed via PCA). In a decision tree, the dataset is recursively partitioned until each subset corresponds to a specific class or category. Since decision trees are quite unwieldy and prone to overfitting, it is often beneficial to train several thousand at once (i.e. a random forest). Given an input corresponding to a region of X-ray emission, the goal is to output the number of unique thermal components needed to describe the region. The authors create the training data using synthetic X-ray spectra based on observations taken from the Chandra observatory.

The King of Mycenae

The authors applied their machine-learning method to the Perseus cluster, which is known to have regions with multiple temperature components. Figure 1 shows that the overwhelming majority of the Perseus cluster consists of two-component thermal emission (blue), with some regions of four-component (yellow) and single-component (indigo) emission. This verifies previous conclusions, based on Chandra observations, that the Perseus cluster cannot be accurately modelled with a single temperature component.

Figure 1: A smoothed image of the X-ray emission from the Perseus cluster (left), compared to a Voronoi tessellation map of the predicted single component (indigo), double component (blue) and quadruple component (yellow) regions. There is a very small triple component (green) region in the brightest cluster galaxy (BCG). [Rhea, et al. 2020]

Mapping the Components

Having established that there are two main temperature components, the authors next calculated temperature maps. Figure 2 shows each of these components. Overall, each component corresponds to gases at different temperatures; the first component is characterised by a relatively cooler gas (of around 2 keV), while the second corresponds to a hotter gas (of 4 keV). These also correspond to soft and hard X-ray emission. What is encouraging is that these components are distributed differently: the cool gas is mostly uniform while the hot gas is more uneven. Some regions with a low first-component temperature have a high second-component temperature (and vice versa). Thus only by combining these different components can one accurately model the thermal nature of X-ray emission throughout the ICM.

Figure 2: Temperature maps (Voronoi) highlighting the first (left) and second (right) thermal components (for regions with exactly two components). Colour denotes the mean temperature of the gas. [Rhea, et al. 2020]

Onwards and Upwards

One of the major benefits of this machine-learning approach is that it is not solely restricted to Chandra data; it can be used with other X-ray missions such as Athena and eROSITA. The authors expect that future, high resolution surveys will result in improved classifications. This is since the random forest classification is sensitive to many factors including resolution, time epochs (since CCDs degrade over time), and selection biases in the choice of training data (e.g. redshift, column densities). The authors have demonstrated that a new machine-learning technique is capable of extracting the multiple thermal components in ICM X-ray emission, confirming that the Perseus cluster is indeed best characterised by more than one component. As future surveys yield stronger constraints on ICM emission, it will be possible to model physical processes in greater detail, ultimately improving our understanding of galaxy clusters and the evolution of galaxies contained within.

About the author, Mitchell Cavanagh:

Mitchell is a PhD student in astrophysics at the University of Western Australia. His research is focused on the applications of machine learning to the study of galaxy formation and evolution. Outside of research, he is an avid bookworm and enjoys gaming, languages, and code jams.

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