Overview
Our group works on a variety of astronomy topics, ranging from studies of the early stages of low mass star formation, to AGN and starbursts. While our research interests span a wide range of topics, they are all centred around the role of molecules in space, especially in the dense gas of the interstellar medium. Core to our research is the interpretation of molecular observations using astrochemical and radiative transfer models. In recent years we have also been concentrating on devising novel techniques for astrochemistry involving machine learning.
Galactic
Low and High Mass star formation in our own Galaxy
The interstellar medium (ISM) is made of a mixture of gas, mainly hydrogen, and dust grains. The ISM is organised in large structures, 1-100 parsecs, of gas and dust differing in densities and temperatures, known as interstellar clouds. Dense interstellar clouds called molecular clouds are the sites for star formation. Infrared and submillimeter observations reveals that dense clouds have a rich chemistry with more than 200 identified atomic and molecular species.
Understanding the processes by which stars and planets form in the cold and dark interiors of these molecular clouds is one of the most fascinating challenges of modern astrophysics and is what our group is studying.
In particular, we focus on the modelling and on the interpretation of sub millimeter molecular data by using astrochemical and radiative transfer models created by our group (see below).
Extra-Galactic
Developing tools to bridge observables and quantities of interest
Galaxies present a combination of energetic processes, including UV-photons, cosmic and X-rays, and shocks, all contributing toward the processing of the gas and dust in their interstellar medium (ISM).
Dense molecular clouds can survive (and possibly even form) in environments dominated by powerful active galactic nuclei (AGN) or in “hostile” environments associated with young massive starburst. How AGN are fuelled and how the energy generated by the AGN affects the ISM have important implications for the co-evolution of galaxies and black holes and are the questions that our group is interested in answering.
Galaxy evolution strongly depends on feedback mechanisms whereby star formation is inhibited at some point in the galaxy's lifetime either by AGN or by stellar feedback through stellar winds and supernovae. Each of these processes triggers a specific chemistry which gives rise to a particular combination of molecular lines. Hence, molecular line emission is an extremely powerful tool to trace the gas that fuels star formation and black holes accretion, determine the influence of the newly formed stars on their environments, and ultimately understand how galaxies form, evolve, and interact with each other.
Our group uses observational data from world-class submillimeter instruments, especially ALMA, combined with state of the art chemical and statistical models, to determine the nature of the dense gas in nearby galaxies.
Statistics
Machine Learning and Bayesian Inference
The use of statistics and machine learning is key to the MOPPEX project. As our modelling ambitions grow so do the computational costs. In order to counter this, the group are working on techniques such as emulation to replace computationally intensive parts of our models with neural networks which can produce the same outputs for much lower cost.
Further, if we combine high quality observations of the kind acquired with ALMA with state of the art models, the inference done should be of an equivalent standard. Tools based on MCMC methods of Bayesian inference are being developed by the group to allow users to extract probability distributions for the value of parameters of interest which take into account the uncertainties in the observations and modelling to give a clear view of what can be inferred from our data.
