This page shows a sample of projects available as part of the 4IR-CDT. We also welcome open applications that specify an area of interest rather than an exact project choice.
Digital Pulse Shape Processing for Fission Data from nTOF
This project concerns the measurement of fission data from the Neutron Time-of-flight (n_TOF) facility at CERN. Neutrons of a broad spectrum of energies are produced by spallation reactions of protons incident on a lead target. The neutrons are timed relative to the proton pulse and are then used to induce fission on an actinide target in experimental area of the n_TOF facility. The SpectromeTer for Exotic Fission Fragments (STEFF) is a 2E-2v detector system, developed at the University of Manchester, which used to study the resulting fission fragments. It allows measurement of the velocities and energies of both fragments from the fission event. Mass may be measured either independently in each arm (Ev), or, by ignoring the momentum and kinetic energy contributions of the emitted fast neutrons and target effects, by 2E or 2v methods. STEFF also includes an array of 12 (5"x4") NaI scintillation detectors for gray detection. The fission fragments stop in two Bragg detectors, the outputs of which are digitized as a function of time. The digitized traces may be used to measure energy loss (dE/dx) and range of the fission fragments and hence are used to determine atomic-number distributions. At n_TOF, the digital acquisition system runs in a mode triggered by the proton pulse, with a second-level trigger based on the detection of a fission event. Since all signal channels are digitized, the resulting data sets are large (~1014 bytes) and require extensive numerical post-processing to determine the times and amplitudes of the detected fission fragments and g-rays. This project will involve the development of efficient digital processing algorithms, including filters for noise reduction, to extract the required parameters from the data as a part of a study of the neutron-induced fission process. The measured properties of fission are used in studies of fission dynamics but also have applications in the nuclear-energy sector.
Machine Learning for Radio Astronomy
The Square Kilometre Array (SKA) is predicted to be one of the most extreme big data machines in next generation science. Aside from the huge data rates within the telescope itself, the system will produce output data products that can run to several peta-bytes in size. The challenges produced by these data sizes both at an instrumental level and at an analysis level are tremendous and necessitate a shift in the way that astronomers must approach them. In this project the student will start by looking at specific aspects of radio telescope calibration that might be improved by using machine learning approaches: instrumental phase calibration and ionospheric calibration. The initial approach will explore Gaussian Process Modelling (GPM) to optimise calibration as well as improve on propagation of uncertainties from calibration into science data products. The project will also look at novel ways of incorporating complementary external sources of data to predict calibration solutions, as well as considering classification of observing conditions in a machine learning context. The student will demonstrate these techniques using real data from the eMerlin telescope, which has comparable baseline lengths to SKA1 and provides an excellent analogue.
Understanding Blazar Jets - The OVRO 40-m Blazar Monitoring Program
Active Galactic Nuclei (AGN) are powered by accretion onto rotating super-massive black holes which create relativistic jets along the spin axis, though the detailed mechanism of this process still remains elusive. In the cases where the jets are aligned at a small angle to the line of sight, relativistic beaming dramatically boosts the apparent luminosity and variability; these objects are collectively known as blazars. One avenue for progressing our understanding of jet production is provided by the Fermi Gamma-ray Space Telescope, which continuously monitors all gamma-ray bright blazars, allowing an unprecedented opportunity for the systematic study of blazar jets. The exact location of the gamma-ray emission region and its proximity to the central black hole remain subjects of debate, with two main
competing classes of models for the GeV emission region. Testing these models requires a multiwavelength approach, combining the Fermi observations with supporting radio observations to search for correlations in the light curves at different frequencies.
This project is centred around the radio monitoring of 1500 blazars with the 40-m telescope at the Owens Valley Radio Observatory (OVRO, California) and then producing a joint analysis with Fermi data. The 40-m now has 8 years of intensity observations, which now offers a very powerful data set for correlation studies. In addition, a new polarisation sensitive receiver is currently being commissioned on the 40-m, and so a novel set of additional information will shortly be available for incorporation into the analysis. This project offers the possibility for the student to take a Long Term Attachment for 3+ months to Caltech, Pasadena, in order to visit the OVRO site, to contribute to the 40-m observing, and to learn about the low-level data reduction. The project will be co-supervised by the lead of the Caltech team, Prof Tony Readhead.
Big Data in Astro and Agri
You will work on the latest massive datasets in both astronomy and agriculture. This project combines these two separate disciplines and develops cutting-edge tools common to both. The unique research environment in Manchester brings together (i) cosmology from large optical and radio surveys, including measuring the shapes of a billion galaxies from the Dark Energy Survey with (ii) the development of novel sensors for agriculture, including hand-held devices for assessing crop health across the world. You will capitalise on new data from the European Space Agency's Sentinel satellites, by porting expertise across from astronomy and vica versa. As part of your thesis you will carry out research work on each of astronomy and agriculture, using common methodologies, as well as the opportunity to develop new techniques in collaboration with computer scientists. An example of potential commonality you could focus on is the challenge of measuring galaxy distances from multi-waveband information (photometric redshifts) and identifying crop stress from hyperspectral imaging.
Constraining Cosmology Using Galaxy Clusters Detected by the Simons Observatory
The Simons Observatory is a next generation CMB experiment which will be built in the Atacama Desert in Chile with the goal of measuring the intensity and polarisation with high fidelity across significant regions of the sky. As a by-product catalogues of tens of thousands of galaxy clusters will be created by Sunyaev-Zeldovich decrement. These samples will be close to mass limited allowing them to be used to constrain cosmological parameters such as the neutrino mass and those describing the nature of dark energy. The project will involve setting up a pipeline to simulate the observations, extract the clusters from this simulated data and then constraining cosmological parameters. This will require the development of component separation, source extraction and MCMC methods.
The Radio Milky Way
The SKA will be the most sensitive radio survey instrument ever built. This makes it hard to predict what it will actually detect, as we have never observed to such faint levels. The uncertainty is especially true within the Galaxy where a multitude of objects will contribute to the radio emission. This project aims to model the stellar radio emission of the Milky Way down to nano-Jy levels, focusing on the radio emission from the stars. The radio emission includes ionized winds, HII regions, the radio photospheres of cool
giants, interacting binaries such as symbiotic stars, and flare stars. A radio HR diagram will be constructed, covering high-mass O/B stars, with radio emission from HII regions and stellar winds, WR stars, to low-mass post-AGB stars and planetary nebulae. The distribution of stars in the galaxy is modeled using the Besancon computer model. The project will show what the SKA may expect to see in different directions within the Milky Way.
The GAIA Galaxy
GAIA is measuring accurate distances to up to 10% of all stars in the Galaxy using astrometric parallaxes. The first data release contained over a million stars with good distances. Later releases will increase this hundred-fold or more. We have developed method to calculate the actual stellar temperature and luminosities by combining the GAIA data with large photometric catalogues. This has
produced the first real HR diagram of stars within a kiloparsec of the Sun. This project will extend this to much of the Galaxy using the next two data releases. The project is highly data-intensive and requires computer proficiency. The student will correlate the catalogues for cross-calibration, and fit stellar atmosphere models to each of the up to 1 billion stars. This will give a unique view of the Milky Way
galaxy. The result will be used to identify post-AGB stars in the galaxy, a very fast phase that sun-like stars pass through at the end of their lives. Models for the speed of evolution through this phase differ by a factor of 3, the largest uncertainty in stellar evolution of low mass stars. The project aims to resolve this discrepancy.