- Reconstructing the history of the Universe using cosmic structures, Dr Seshadri Nadathur
- Modelling the galaxy clustering of the DES and eBOSS surveys, Dr Santiago Avila
- Linking Galaxy Morphology to the motion of stars, Dr. Karen Masters
Project title: Reconstructing the history of the Universe using cosmic structures
Supervisor’s name: Dr Seshadri Nadathur (potentially also Dr Davide Bianchi)
Dates: by mutual agreement
Outline: The formation of large-scale structure in the cosmos is governed by the competing effects of gravitational collapse and the expansion of the Universe. The relative strengths of the two effects can be determined from the pattern of distortion seen in galaxy surveys along the line-of-sight direction – known as redshift-space distortions, or RSD. A particularly interesting effect of these distortions should be visible around vast regions of space, known as cosmic voids, containing relatively few or no galaxies. Measurement of the RSD effects in voids can help determine the properties of Dark Energy, which is driving the current accelerated expansion, and their evolution over time. This project offers the chance to examine the theory of RSD around voids, and to test the predictions against data from computer simulations as well as the SDSS galaxy surveys.
Skills required: Maths ability (ideally either have A-level Further Maths or have done mathematics courses up to 1st or 2nd year Physics level), basic programming ability/experience (in any language), some knowledge of statistics
Skills/knowledge to be gained: Introduction to standard aspects of cosmology theory, and to one of the most important modern analysis techniques. Data analysis and data visualisation techniques, programming skills in Python/C. This project will provide an introduction to large-scale structure research for a student who might want to pursue a research degree or PhD in cosmology, but would also be useful for anyone interested in data science more broadly.
Name of project: Modelling the galaxy clustering of the DES and eBOSS surveys
Supervisor’s name: Dr Santiago Avila
Dates: By mutual agreement
Ongoing and upcoming galaxy survey experiments (DES, eBOSS, DESi, LSST, Euclid) will observe hundreds of millions of galaxies over volumes exceeding the 1Gp3 scale. This represents a very powerful tool to study the Large Scale Structure (LSS) of the Universe and to constrain cosmological models. In order to model the LSS we rely in dark matter simulations which give us the correct distribution of dark matter halos. It is in these halos where galaxies form and live.
The Halo Occupation Distribution (HOD) modelling connects the unobservable halos with the galaxies, that are our direct observables, by populating the halos with a variable number of galaxies. Typically, the HOD model has been applied to model the observed galaxy clustering of a specific sample from a single experiment. In this project we will build an HOD model capable of reproducing simultaneously the clustering, abundance and cross-correlations of two galaxy samples obtained from different experiments: the Dark Energy Survey (DES) and the extended Baryon Oscillation Spectroscopic Survey (eBOSS).
Skills Required: Basic programming skills (C preferred, but not essential; python would also be useful)
Skills/knowledge to be gained:
Ability to modify and construct physical models.
Ability to deal with large amount of (astronomical) data
Improvement on programming
Name: Linking Galaxy Morphology to the motion of stars
Supervisor: Dr. Karen Masters
Dates: By mutual agreement, but likely avoiding mid July-end of August
Outline: The morphology of a galaxy should be providing information on the orbits of stars within it. As long as this is true, important clues to the formation history of galaxies is revealed by their morphologies. The Galaxy Zoo project (www.galaxyzoo.org) has provided quantitative visual morphologies for over a million galaxies (including the entire Sloan Digital Sky Surveys, or SDSS Main Galaxy Sample), and has been part of a reinvigoration of interest in the morphologies of galaxies and what they reveal about the evolution of galaxies. Meanwhile the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey has measured the dynamics of stars and interstellar gas for thousands of these galaxies. This summer 2017 project builds on the Ogden Summer Placement work of Alex Todd (2016) who ran a citizen science project to visually identify the kinematic maps of thousands of galaxies. The 2017 student will use the output of those data to test how well visual morphology correlates with the dynamics of stars and gas in a galaxy.
Skills Required: Some programme skills (ideally python) would be welcome, but can be developed during the placement.
Skills/knowledge to be gained: Data analysis and data visualisation techniques, programming skills in Python. This project will provide an introduction to galaxy evolution and dynamics research for a student who might want to pursue a research degree or PhD in astrophysics, but would also be useful for anyone interested in data science more broadly.