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elliptical galaxy

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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.

spectrum of galaxy C1-23151

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.

stellar population synthesis model

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.

quiescent galaxy comparison

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.


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

Title: The 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.

Drake equation

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.

habitable zone occurrence rates

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.

W3/W4/W5 complex

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

Title: 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.


Figure 2: The different reaction pathways to forming PGIM from H2CNCCH and hydrogen. Carbon atoms are represented by black circles, nitrogen by blue, and hydrogen by white. Note that different reaction pathways and branching are represented by different colored lines, and both E- and Z-propargylimine can form. [Lupi et al. 2020]

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.


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.

amino acids

Figure 3: Imines are chemical precursors to amines, classified by carbon bonded to NH2. Amines are chemical precursors to amino acids, which are classified by the NH2 and COOH groups. Amino acids are the building blocks that make up our DNA. In this image “R” indicates any group of atoms. [Abygail Waggoner]

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.

brown dwarf

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

Title: 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.

LOFAR observations

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.

circumbinary transit

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

Title: 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

Kepler-154 system

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.

TOI 1469 multi-planet system

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.

normalized PDF for TESS systems

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.


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

Title: 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.

star formation rates

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 = 2 and = 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.

halo mass v. redshift

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.

perseus cluster gas

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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 10Kelvin, 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.

perseus cluster temperature

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.

temperature maps

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.

M dwarf planet

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Title: The High-Energy Radiation Environment Around a 10 Gyr M Dwarf: Habitable at Last?
Authors: Kevin France, Girish Duvvuri, Hilary Egan, et al.
First Author’s Institution: University of Colorado Boulder
Status: Accepted to AJ

There are lots of stars out there in the universe, and a large chunk of those are M dwarfs. These are the smallest and reddest stars, coming last in the sequence of spectral types (O, B, A, F, G, K, and last but not least: M). Bonus: since they’re so small and dim, it’s actually easier to find smaller, terrestrial planets around them! Given that M dwarfs are so plentiful and we have a good shot at peering into their habitable zones, it makes sense that we’d want to think about what life on a planet around an M dwarf would be like.

flaring dwarf star

Artist’s rendering of a flaring dwarf star. [NASA’s Goddard SFC/S. Wiessinger]

But there’s a catch. M dwarfs are also known to be very active stars, flaring and giving off a lot of ultraviolet light and X-rays that are bad news for biological life. This stellar activity is so strong that it drives atmospheric escape, stripping these rocky planets of their atmospheres, which are critical for habitability. Extreme ultraviolet light (known as EUV or XUV) is particularly good at stripping away an atmosphere, and young M dwarfs give off more of this since they spend a longer time in their pre-main sequence evolution phase. So, the beginning of these stars’ lives are extreme, ruining chances for a planet to be habitable. What about older M dwarfs? Planets around M dwarfs could have a do-over on their atmosphere, gaining a “secondary atmosphere” created by gases released through impacts or volcanos. Do M dwarfs mellow with age, quieting down all that radiation and making it possible for their planets’ secondary atmospheres to stick around long enough for life to arise?

Today’s paper seeks to answer these questions by observing a nearby old M dwarf for its UV and X-ray activity, and then computing what would happen to the atmosphere of an Earth-like planet in its habitable zone.

The Search for the Atmosphere Killers

The authors used the Hubble Space Telescope (for UV observations) and the Chandra X-ray Observatory to observe Barnard’s Star, a nearby old M star. Barnard’s Star is only about six light-years away, making it one of our closest neighbors in space. It’s only 16% the size of the Sun, but about twice as old. It’s also known to host a cold (around –300°F!) super-Earth about three times the size of our planet, discovered using the radial velocity method.

The average UV luminosity of Barnard’s star is among the lowest ever measured for an M dwarf, but it still emits more XUV than the Sun, as shown in Figure 1. They also measured a weak (but non-zero) X-ray flux, also among the lowest observed on an M dwarf. Barnard’s Star still flared just about as frequently as younger M dwarfs, but the flares on the older star were lower intensity (still more intense than a star like our Sun, though!). Another atmosphere-harming event is the CME, or “coronal mass ejection”, which releases high energy particles from the star; the authors found that these events have similar energies to solar flares, but are much more frequent. There is a caveat on this, though: M dwarfs have been theorized to have stronger magnetic fields, which may keep CMEs from traveling far from the star and impacting planets, so there’s a bit of uncertainty on the effect of CMEs on an atmosphere discussed here.

sun v barnard

Figure 1: Sun (black) vs. Barnard’s star (red). Barnard’s star shows more extreme ultraviolet! [France et al. 2020]

The Verdict on the Atmosphere

Now that we know a bit more about the environment around an old M dwarf, what would happen to a planet’s atmosphere? The authors estimated the atmospheric escape from a hypothetical Earth-like planet in the habitable zone of Barnard’s Star that encounters this observed high-energy radiation.

First, to make sure their models made sense, they tested them on the Sun/Earth system to see if they could reproduce what we observe in our own solar system. Then, they moved on to look at the thermal and ion escape from our hypothetical planet. Thermal escape happens when particles are hot enough, and therefore moving fast enough, to exceed the escape velocity of the planet. Around Barnard’s Star, our hypothetical planet would lose its atmosphere in about 11 million years. Or, you can think about it as losing 87 times the Earth’s atmosphere in a billion years (for context, Earth is over 4 billion years old!).

They also looked at ion escape, which is actually the main way Earth loses atmosphere. This is a bit more complicated, since it requires a plasma interaction model. Their simulations showed that in a normal, quiescent (not flaring) state, Barnard’s Star only slightly increases atmospheric escape compared to Earth. However, when a flare happens, there is much more atmosphere loss, as seen in Figure 2. One thing to note is that the hypothetical planet here is unmagnetized; magnetism could make a difference, as it does on Earth, shielding from some of these high energy particles. The big takeaway here, though, is that atmospheric loss around old M dwarfs will be dominated by the flare periods.

ion loss

Figure 2: These simulations for show ion escape for three scenarios: base (unmagnetized Earth around the Sun), quiet (unmagnetized Earth-like planet in Barnard star habitable zone in quiescent conditions), and flare (same planet around Barnard star but during flare). The color bar corresponds to the amount of oxygen ions lost. [France et al. 2020]

Can Life Find a Way?

Flares might actually have a positive effect on life in a different way. Other work has shown that near-UV (NUV) photons might drive the formation of precursor molecules to RNA; Barnard’s Star has a little less NUV radiation than is needed for this in its quiet state, but flaring could be enough to support these prebiotic pathways. Also, now that we know flares might be an issue for keeping an atmosphere, we might want to extend our search for habitable planets out farther from the star; there’s a possibility of an “extended habitable zone” farther out from the star where the radiation is less extreme!

Although they’re less active, this paper has shown that even old M dwarfs can lose a lot of atmosphere, particularly due to flares. We still need to learn more about the flare cycles, since that seems to be a key parameter in atmospheric retention and M dwarf habitability!

About the author, Briley Lewis:

Briley Lewis is a second-year graduate student and NSF Fellow at the University of California, Los Angeles studying Astronomy & Astrophysics. Her research interests are primarily in planetary systems – both exoplanets and objects in our own solar system, how they form, and how we can create instruments to learn more about them. She has previously pursued her research at the American Museum of Natural History in NYC, and also at Space Telescope Science Institute in Baltimore, MD. Outside of research, she is passionate about teaching and public outreach, and spends her free time bringing together her love of science with her loves of crafting and writing.

AGN illustration

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

Note: the title of this article has been changed from its original version.

Title: The Role of Active Galactic Nuclei in the Quenching of Massive Galaxies in the SQuIGGLE Survey
Authors: Jenny E. Greene et al.
First Author’s Institution: Princeton University
Status: Published in ApJL

To butcher an apocryphal quote about cars, galaxies can be any colour, as long as it’s red or blue. If you were to plot the magnitude and colour of a large sample of galaxies you would see that they fall into two groups: one is actively star-forming and filled with young blue stars, and the other has long since finished making new stars so is left with only the older, redder populations. Between these two monolithic groups, however, there is an elusive class of galaxies. Post-starburst galaxies (PSBs) are objects that are thought to have had large amounts of star-formation shut off very rapidly in a quenching event. This quick transition means that PSB samples are generally quite small, so the origins of quenching are still quite uncertain. However, studying PSBs is still thought to be the best route to understanding what causes galaxies to transition from blue to red.


Figure 1: This Hubble Ultra-Deep Field image reveals around 10,000 galaxies that are red, blue, and shades in between. [NASA/ESA/H. Teplitz & M. Rafelski (IPAC/Caltech)/A. Koekemoer (STScI)/R. Windhorst (Arizona State University)/Z. Levay (STScI)]

Today’s authors are looking inside the galaxy for their quenching trigger. They focus on the galaxy’s central supermassive black hole. Emission from active galactic nuclei (AGNs) is thought to inject huge amounts of energy back into their host galaxies. Such huge injections are believed to either create strong winds that eject star-forming material from the host galaxy or heat the gas so much as to prevent it from cooling and collapsing to form new stars. Today’s paper searches for signs of nuclear activity in a sample of PSBs to see if AGNs could be responsible for their quenching.

Connecting the Dots with SQuIGGLE

The authors showcase a brand-new galaxy survey dedicated to studying quenching activity at intermediate redshifts. The Studying of Quenching in Intermediate-z Galaxies: Gas, anguLar momentum and Evolution (SQuIGGLE) survey contains thousands of massive galaxies found in SDSS DR14. From this survey they use 1,207 PSBs at redshifts between 0.5 and 0.94. In addition, they construct a separate sample of galaxies in a similar mass and redshift regime from the LEGA-C survey to act as a comparison.

AGNs in this sample are identified in the optical part of the spectrum. This is typically done using the BPT diagnostic, where two optical emission line ratios are compared to distinguish between AGNs and star-formation as the primary source of ionisation. Due to the high redshift of the AGNs in this sample, however, some of the emission lines used in the BPT diagram do not appear in their spectra, rendering one of these ratios unusable. Instead, the authors turn to the mass–excitation diagram, which is based on the BPT diagram but replaces the lost emission line ratio with stellar mass. Enhancement of the remaining BPT ratio, called the excitation axis, can be caused by lower metallicity, but this only occurs in lower-mass galaxies, as they typically host younger stars. Given these are fairly high mass galaxies, we know that their metallicity is higher, so any increase in the excitation axis is due to AGN activity. This means the authors can identify AGNs by looking for enhancement in the excitation axis within these relatively high mass galaxies. Alongside this, they also apply a spectral signal-to-noise threshold to make sure these excitation detections are real.

Do AGN Quench Star Formation?

Taking these criteria together, the authors find a sample of 64 AGNs in the PSB sample, leading to an AGN fraction of about 5%. Only five AGNs were found in the comparison sample, leading to an overall AGN fraction of 1.4%. This reveals that AGNs are more likely to be found in PSBs than in a sample of regular galaxies of similar mass and redshift.

AGN fraction

Figure 2: Comparing the AGN fraction as a function of galaxy age for the PSBs (taken from SQuIGGLE) and the normal galaxy sample (taken from LEGA-C). Dn4000 (4,000-angstrom break) describes the age of the galaxy, with a higher Dn4000 corresponding to an older galaxy. [Adapted from Greene et al. 2020]

These results are broken down further to identify how trends may vary with host galaxy properties. Most interestingly, they look at how AGN fraction varies with stellar age, measured using a quantity called the 4,000-angstrom break (Dn4000). It gives us an indication of the relative contributions made to a galaxy’s spectrum by the shorter-lived, blue stars and their longer-lived red counterparts. Once quenching has occurred, Dn4000 increases as the short-lived, blue stars start to die and cannot be replaced, leaving behind only longer wavelength emission from red stars. Figure 2 shows the results of this breakdown of AGN fraction with galaxy age.  It clearly shows that younger PSBs have an extremely enhanced AGN fraction compared to older ones: AGNs are ten times more likely to appear in the youngest PSBs!

AGN fractions appear to peak around the time of the quenching event where AGN-driven winds could force gas out of the host galaxy. In doing this, the AGN also removes sources of future fuel, causing the large drop in AGN fraction as the galaxies get older. Such a strong correlation between AGN fraction and the galaxy’s age suggests AGN activity could play a role in quenching galaxies. Whilst this correlation is compelling, it isn’t definitive. This makes the follow-up work being done by the authors all the more important: they are searching these AGNs for signs of outflows, which, if found, would suggest that star formation really is quenched by AGN-driven winds.

About the author, Keir Birchall:

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

millisecond pulsar

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Title: Gravitational-wave constraints on the equatorial ellipticity of millisecond pulsars
Authors: The LIGO Scientific Collaboration, the Virgo Collaboration
First Author’s Institution: Northwestern University
Status: Submitted to ApJ

Neutron stars represent one of matter’s weirdest manifestations. They have a mass of a little more than that of the Sun packed into a space the size of a big city — and getting to know their size, shape, and structure can unlock the most fundamental questions in atomic physics. What makes up a neutron star? Are they rigid or squishy? Are they perfectly spherical? If they have deformities, what is the tallest “mountain” they can support?

The first direct detection of gravitational waves by LIGO in 2015 gave us one of the best tools for studying neutron stars. Gravitational waves are radiated whenever matter moves in an asymmetric manner, which changes its quadrupole moment with time. For us to be able to detect these waves, they need to emanate from the asymmetric motion of extremely massive and dense matter. The first detection of gravitational waves was radiation from a pair of black holes spiraling into one another. Since then, most of the gravitational wave events detected by LIGO–Virgo have similarly been black hole binaries.

neutron star merger

Artist’s impression of the collision and merger of two neutron stars. [NSF/LIGO/Sonoma State University/A. Simonnet]

However, one might argue that neutron stars are much more diverse and interesting gravitational wave sources. The first confirmation of the existence of these waves was provided by the Hulse–Taylor binary: a system featuring a pulsar (a rapidly rotating neutron star giving off radio pulses) orbiting another neutron star. This week, we just passed the third anniversary of GW170817, an event where for the first time, LIGO and Virgo “heard” two neutron stars colliding. The collision resulted in a kilonova explosion that was observed using electromagnetic telescopes.

Neutron stars can exist in pairs and do the tango like the binaries mentioned above, but the cool thing is that they can also radiate gravitational waves while being single!

Any physical deformation, like a “mountain” on the neutron star crust, will give rise to a large quadrupole moment since neutron stars rotate extremely fast. The particular kind of neutron stars studied here are called millisecond pulsars: entire stars that complete one rotation within a few tens of milliseconds, much less than the blink of an eye. Even if the pulsar were perfectly spherical on the outside, it may have internal deformities in its core — a possibility that very little is known about. Or, it may be slightly elliptical in shape and wobble asymmetrically as it spins, which can also give rise to gravitational wave radiation.

All of the above mechanisms of lone neutron star gravitational waves have a tantalizing characteristic: their frequency is almost entirely constant. This is because it is determined by the frequency of rotation of the neutron star. These gravitational waves are thus known as continuous waves, distinguishing them from the transient, chirping waves produced by colliding binaries.

The search for continuous waves from pulsars is promising because data analysts know which frequencies to dig out from the data for the pulsars that astronomers have already seen through radio telescopes. This enables targeted searches for known millisecond pulsars in LIGO and Virgo data (Figure 1).

GW frequencies of known pulsars

Figure 1: The gravitational wave frequencies (dashed vertical lines) of known pulsars used in this search, compared with the power spectral density (PSD), also known as the “noise bucket of sensitivity” of the LIGO and Virgo gravitational wave observatories. The spikes in the PSD correspond to known continuous noise sources, such as the 60-Hz power line in the US. Can you see why it is such a nuisance for the Crab pulsar? [LIGO–Virgo Collaboration 2020]

LIGO–Virgo’s third observing run did not detect continuous waves from any pulsar directly. The downside of continuous wave searches is that the expected strength of these gravitational wave signals is far less than those from compact binary mergers. Assuming that continuous waves are constant in frequency, only long stretches of data spanning several years can build enough signal above the noise threshold. However, even a non-detection can tell us a lot about what the structure of the pulsar is (or more importantly, isn’t!)

It isn’t quite true that rotation speeds of pulsars are absolutely constant. Indeed, if an elliptical, wobbly pulsar radiates gravitational waves, it would invariably lose energy and slow down (called spin-down). Other factors, like magnetic fields or internal dynamics, can dominate this slowing down process as well. Pulsar spin-down has already been measured, but it takes place over very long timescales, effectively ensuring that pulsar frequency is constant over the period of a LIGO–Virgo observing run.

Knowing the spin-down rate helps us probe an interesting aspect of pulsars. Assuming that a pulsar slows down entirely due to radiation of gravitational waves and no other processes, conservation of energy equates the spin-down to the expected strength of gravitational waves detected. The energy of these gravitational waves is related to the degree of deformation or ellipticity of the pulsar. The observed spin-down limit thereby constrains the degree of asymmetry of the neutron star mass distribution as it rotates.

pulsar ellipticity

Figure 2: Constraints on the mass quadrupole moment Q22 and ellipticity for one of the pulsars in the study. The area under the curve between two values of quadrupole moment is the probability that the true value lies within that range; smaller values imply increasingly perfect spheres. The black vertical line represents the spin-down limit for the pulsar, and the colored vertical lines correspond to 95% confidence that the ellipticity is below a certain value. When the upper limit measurements (colored vertical lines) of the quadrupole moment (or ellipticity) lie to the left of the black lines, the spin-down limit has been surpassed. [Adapted from LIGO–Virgo Collaboration 2020; reference here]

For the very first time, LIGO and Virgo achieved a level of sensitivity that enabled them to detect possible signals from the pulsar J0711–6830 weaker than its known spin-down limit (Figure 2). That means the authors could constrain its ellipticity or limit the size of its mountains to a greater extent than previous observations. As a result of not detecting any gravitational waves, we now know that this pulsar is less deformed from a perfect sphere than the width of a human hair!

Before Galileo pointed his telescope towards it, most scientists believed that the Moon was a perfect sphere. It is fascinating today to be able to correctly identify perfect spheres over a hundred times smaller than the Moon, situated over 300 light years away from us.

About the author, Sumeet Kulkarni:

I’m a third-year PhD candidate at the University of Mississippi. My research revolves around various aspects of gravitational wave astrophysics as well as noise characterization of the LIGO detectors. It involves a lot of coding, and I like to keep tapping my fingers on a keyboard even in my spare time, creating tunes instead of bugs. I run a science cafe featuring monthly public talks for the local community here in Oxford, MS, and I also love writing popular science articles. My other interests include reading, cooking, cats and coffee.

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