Weighing supermassive black holes
Understanding the formation and evolution of galaxies is central to much of contemporary astrophysics. The relations between black hole mass and various galaxy properties imply a tight connection between the growth of central supermassive black holes (SMBHs) and that of galaxies, and these relations now underlie a staggering number of observations and simulations. However, the number of reliable SMBH mass measurements is small, and the number of independent measuring methods even smaller. A team led by Prof Bureau has recently shown that the dense molecular gas of galaxies is the best tracer of their circular velocities, and thus of their masses. Most importantly, following the first SMBH mass measurement published in Nature, the team has now shown that these measurements are both much more accurate and much easier to carry out than with other methods. It is thus time to scale up those efforts and renew our knowledge of SMBHs.
As part of the WISDOM team (mm-Wave Interferometric Survey of Dark Object Masses), the student will use current mm/sub-mm telescopes to pursue a programme of SMBH mass measurements in a large sample of local galaxies spanning a range of morphological types, masses, and nuclear activities. This will primarily use ALMA, the largest ground-based telescope project in existence, on which WISDOM has received large allocations of observing time. There are thus much data in hand already, with more to come, and the tools necessary to model the velocity fields and estimate uncertainties have already been developed. The student will thus exploit a well-oiled machinery to make multiple measurements, and thus explore how SMBH masses and galaxy properties correlate, in addition to probing the nuclear-scale gas dynamics that allows SMBHs to be fed. The project will thus significantly increase the number of reliable SMBH masses available, and it will revolutionise our understanding of the co-evolution of SMBHs and galaxies.
Unravelling giant molecular clouds
Understanding how interstellar gas turns into stars is arguably the greatest remaining puzzle in galaxy formation. Stars form in dense gas clouds known as giant molecular clouds (GMCs), but how these emerge, what their structure is, or even whether they are long-lived or transient remains unclear. Previously restricted to our own Milky Way and nearby late-type (i.e. spiral) galaxies, with a new generation of telescopes studies of GMCs can now take an immense leap forward. By probing more diverse galaxies and hitherto inaccessible environments, new laboratories to study star formation are now available.
As part of the WISDOM team (mm-Wave Interferometric Survey of Dark Object Masses), the student will use current mm/sub-mm telescopes to study GMC populations in a large sample of galaxies spanning a range of morphological types, masses, and nuclear activities. This will primarily use ALMA, the largest ground-based telescope project in existence, on which WISDOM has received large allocations of observing time. There are thus much data in hand already, and the project aims to refine and develop the tools required to characterise individual GMCs and thus infer the properties of entire GMC populations. In particular, the student will for the first time probe individual clouds orbiting SMBHs, measuring their sizes, luminosities, and dynamics, and constraining their evolutionary histories. This is essential to establish whether the properties of GMCs are universal, or whether they depend on the SMBH and galaxy properties, in turn affecting their ability to turn into stars or otherwise feed the SMBHs. By significantly increasing the number of galaxies with GMC censuses, the project will revolutionise our understanding of GMC formation and evolution.
Machine learning supermassive black holes
Supermassive black holes are beautiful confirmation of our laws of physics as well as a crucial ingredient in our understanding of how galaxies form and evolve. Machine learning has become pervasive in our daily lives (eg when we talk on our phone), thanks to recent algorithmic breakthrough. In this project, students will explore how machine learning and AI can be used for a new approach to the dynamical modelling of galaxies and in particular to measure masses of supermassive black holes. The student will then extract the black hole masses from the stellar kinematics of a sample of galaxies. The measured masses will be used to update black holes scaling relations and try to improve our understanding of the role of black holes in galaxy evolution.
Supermassive black holes and galaxies: https://ui.adsabs.harvard.edu/abs/2013ARA%26A..51..511K
Machine learning: https://youtu.be/aircAruvnKk (basic idea) https://www.deeplearningbook.org/
Interstellar objects in a galactic context
Chris Lintott (Oxford) and Michele Bannister (U. Canterbury, New Zealand)
The discovery of the first interstellar objects observed travelling through the Solar System - 1I/‘Oumuamua and 2I/Borisov has created great interest. These small worlds are samples of the building blocks of planet formation that took place at other stars, and come close enough for the kind of detailed physical characterisation hitherto reserved for our own Solar System's comets and asteroids. The upcoming Vera Rubin Observatory’s LSST survey will offer the first chance to characterise this population. The aim of this PhD project is to build on a novel insight: we can use the EAGLE simulations of the star-formation history of analogues of the Milky Way, together with models of how planetesimals form, to understand the population of interstellar objects that Rubin will detect. This offers an opportunity for a truly novel test of our understanding of two very different areas of astrophysics — the structure and history of the Milky Way, and planet formation. The project would suit a student with broad interests in observational astronomy, and offers the chance to do something unique. The Rubin survey is currently scheduled to start in 2023, so there is also the chance to be directly involved in testing the predictions created in this PhD.
The obscured universe: tracing star formation and AGN activity across cosmic time
Dimitra Rigopoulou and Ismael Garcia Bernete
Determining the mechanisms that regulate star formation, the growth of supermassive black holes (SMBH) and how these two processes evolve with time and galaxy mass is fundamental to our understanding of how galaxies form and evolve. Feeding and feedback now form the new paradigm in galaxy evolution: by quenching star formation, AGN feedback is capable of shaping the galaxy luminosity function and create the observed bi-modal galaxy sequence. With its unparalleled sensitivity and unprecedented spectral and spatial resolution, the James Webb Space Telescope (JWST) will transform our understanding of galaxy evolution, providing a much more detailed look at the physics of star formation, SMBH growth and their interplay in galaxies near and far. Spectral features such as those from polycyclic aromatic hydrocarbon (PAH) molecules combined with atomic and ionic lines observed in the near and mid-infrared part of the spectrum can be used to infer the amount of recent and ongoing star formation, and measure AGN activity in a wide range of galaxies. Combining these new data with additional Atacama Large Millimetre Array (ALMA) CO observations, that probe the physical properties of the cold gas in and around galaxies and AGN, will enable us to identify the drivers and account for the physical processes that shape galaxy evolution from the early Universe to the present day. The project is particularly timely since the James Webb Telescope (JWST) is due for launch in late 2021 and is expected to probe NIR and MIR emission from nearby and distant galaxies and a variety of environments.
For more information and a list of publications related to the topic please contact:
Professor Dimitra Rigopoulou (email@example.com)
Dr Ismael Garcia-Bernete firstname.lastname@example.org
Beyond axisymmetry in models of galactic nuclei
The starting point for understanding the dynamical structure of galaxies or star clusters is the construction of an equilibrium model. These equilibria usually axisymmetric, but real galaxies can have interesting deviations from axisymmetry. Even galactic nuclei – which are dynamically very old – can have warps and lopsidedness. There are many dynamical mechanisms that have been proposed to explain such asymmetries, but few have been tested in any level of detail.The purpose of this project is to flesh out some of these scenarios and to test them against observations of real galaxies, the Milky Way and M31 in particular.
Most of the ongoing large-scale surveys of our galaxy focus on its stellar content, but the gas and dust between the stars offer a complementary probe of galactic structure and evolution. For example, interstellar gas can be traced through much of the galactic plane from its line emission, such as HI or CO. Comparing the observed joint (longitude,velocity) distribution against that predicted by hydrodynamical models allows us to constrain the galactic potential, as well as the three-dimensional distribution of the gas itself. Dust tends to occur where the gas is densest, but to map its three-dimensional distribution one has to rely on the "reddening" effect it has on stellar colours. We have developed a scheme that models the dust distribution as a Gaussian random field the values of which are constrained by these indirect reddening methods. These gas- and dust-mapping methods are still in their infancy and there is plenty of scope for a student interested in coupling Bayesian probability with some idealised models of astrophysical processes to develop them further.
Strong gravitational lenses: discovery and the structure of galaxies
Aprajita Verma, Matthias Tecza, Anupreeta More (ICUAA, India), Phil Marshall (SLAC/Stanford USA, Visiting Lecturer at Oxford), Chris Lintott
The phenomenon of gravitational lensing, predicted by general relativity, produces some of the most spectacular astronomical images seen. A single galaxy, group or cluster of galaxies can act as "cosmic telescopes" distorting space-time. They can amplify and magnify the light of distant galaxies lying behind them into multiple images, rings or arcs. The separation or deflection of the lensed images is determined by the total mass (dark and light) of the foreground lensing galaxy and are therefore one of the most direct tracers of mass in the galaxies. In fact, strong gravitational lenses have a diverse range of astrophysical and cosmological applications including weighing galaxies and placing constraints of the Hubble constant and dark energy.
With the advent of the forthcoming Euclid and Vera C. Rubin Observatories in the 2020s, the field of strong gravitational lensing will be transformed form the 1000s of systems know today by a factor of 100. This means that for any given use of SL we will no longer be limited by small samples but have the potential to cherry pick or use large samples with which to perform our analyses. However, generating samples of strong lenses that are complete and pure is highly challenging. Even machine learning (ML) assisted selection yielding samples that have high false positive rates (factors of 10-100). For Rubin, a combination of ML discovery algorithms and crowd-assisted visual inspection, that is currently a highly successful means of discovering lenses, will form part of the early survey tasks.
This DPhil project includes work on lens finding, and in-depth analysis of the mass distribution in newly discovered lenses, and the nature of the distant lensed sources, and will be tailored to the interests of the student. For lens finding, the student will work on ongoing Space Warps discovery projects including searches with current wide area surveys and preparations for the Rubin Legacy Survey of Space and Time including exploring active learning. On galaxy masses, we still lack a full physical picture of the dark and baryonic matter in galaxies. Gravitational lensing combined with stellar kinematic information can constrain the dark matter distribution in the centres of galaxies. We have integral field spectroscopy data of Space Warps and Galaxy Zoo discovered lens candidates in hand that will provide further insights on this matter. For the lensed background sources, the magnification and amplification due to lensing allow fainter galaxies to be studied on smaller scales than would be normally possible. This is a preview to the detailed galaxy science that will be achievable by the next generation of extremely large telescopes and we will explore expectations of what the ELT will achieve using simulations of lensed high redshift sources. The student will be expected to help write observing proposals, prepare observations, and liaise with citizen lensing enthusiasts participating in Space Warps (Paper 1 and 2, NPR Science Friday on the HSC search).
For further reading and more information, please contact email@example.com
The evolution of galaxies in the early universe with the next generation of telescopes
Dr Rebecca Bowler
At the cutting-edge of astronomy research is the study of the formation and evolution of the first galaxies. Through breakthrough observations in the past 30 years it has been possible to identify galaxies from when the universe was less than 500 million years old. These galaxies have unusual properties compared to the local universe, showing low chemical enrichment and dust obscuration, and irregular morphologies. This project aims to exploit the new Vera Rubin Observatory (VRO) and Euclid space-mission to discover and analyse galaxies at very high-redshifts (probing the first few billion years). The goal of the project is to understand when and how the most star-forming galaxies formed in the universe. The student will become an expert in the selection of high-redshift galaxies from multi-band photometry. They will then use the resulting samples to constrain the evolution of the number density of these sources (via the luminosity function). There is considerable flexibility in the direction of the project in later years, and the student would be encouraged to apply for follow-up data (e.g. with JWST, ALMA) as well as exploit archival data where available. At the end of the project the student would be in an excellent position to continue working with data from these next-generation facilities.
Testing galaxy evolution models with synthetic and real data sets
Dimitra Rigopoulou, Julien Devriendt and Niranjan Thatte
Cosmological simulations, like the New Horizon suite, can now achieve spatial resolutions of ~50 pc at redshifts z > 1, comparable to that achieved by the latest generation of mm/sub-mm telescopes (ALMA), and soon to be achieved by the ELT+HARMONI at near-infrared wavelengths. This provides a unique opportunity to test predictions of galaxy evolution models by comparing “mock observations” of simulated galaxy properties with ALMA and ELT observations.
Our method uses cosmological simulations that forward propagate primordial density fluctuations consistent with observations of the cosmic microwave background, creating individual galaxies at high spatial resolution, whose kinematic, morphology and dynamical properties are consistent with observed ensemble properties of the population at the corresponding redshift. As the input physics (e.g. star formation laws) for the simulation is well understood, the resulting objects provide dynamically stable mock galaxies consistent with physical laws and cosmological evolution models at the appropriate redshift. We have developed a method for post-processing these mock galaxies, computing gas emission line intensities using CLOUDY radiative transfer computations in each cell, to get realistic model galaxy observations with self-consistent kinematics and dynamics.
We are looking for a motivated DPhil student who has a keen interest in state-of-the-art numerical simulations, and is eager to work at comparing simulations with observations to test models of galaxy evolution. The successful candidate will gain expertise in radiative transfer, cosmological simulations, data reduction and analysis. The project work involves working with astronomical data sets, both real and simulated.