JWST MIRI is already revolutionising mid-infrared astronomy. MIRI observes at wavelengths of 4.8-29 microns, and is designed to distinguish ultra-high-redshift galaxies from more nearby systems, to track the growth of heavily dust-enshrouded supermassive black holes at cosmic noon, and to trace the birth of stars. However, the very small projected areas on the sky of the MIRI detectors means that only very narrow, deep fields can be imaged. Much wider infrared survey data exists, but at a coarser angular resolution.
In the last half a decade Generative Adversarial Networks, denoising auto-encoders and other technologies have been used to attempt deconvolutions on optical data. We have developed an auto-encoder with a novel loss function to overcome this problem in the submillimeter wavelength range (Lauritsen+21 MNRAS, 507, 1546). This approach is successfully demonstrated on European Space Agency Herschel observatory SPIRE 500 micron COSMOS data, with the superresolving target being the James Clerk Maxwell Telescope SCUBA-2 450 micron observations of the same field. We reproduce the SCUBA-2 images with high fidelity using this auto-encoder.
This technique has been very successful in blank-field extragalactic surveys, in which the distant galaxies appear as unresolved point sources. However, we have found that the deconvolution struggles when there is extended structure in the images, due to interstellar cirrus from our Galaxy. This is not unexpected, because the deconvolution training sets did not incorporate this extended emission.
This project will therefore extend the deconvolution work to include interstellar cirrus, and also adapt the network to be trained and to operate at mid-infrared wavelengths. The project will then deconvolve lower-resolution data from the Spitzer and WISE space telescopes to attempt to approach the angular resolution of JWST. The principal astronomical science goal from developing these techniques will be to search for rare objects in the deconvolved data sets with the help of cross-correlation with existing imaging and catalogue data sets, particularly in identifying dust-obscured galaxies undergoing violent phases of supermassive black hole growth and/or star formation, in order to illuminate the processes driving the peak of galaxy stellar mass assembly at cosmic noon.
Interested students could explore where else and to what other data, including non-astronomical data, this new technique could be creatively applied. A useful outcome would be guidelines about data characteristics and how to apply the technology.
1. Lauritsen, L., et al., 2021, Superresolving Herschel imaging: a proof of concept using Deep Neural Networks, MNRAS 507, 1546 https://ui.adsabs.harvard.edu/abs/2021MNRAS.507.1546L/abstract
· 2:1 or above, MPhys or other integrated science masters in physics, astronomy, computer science or related disciplines
· First class honours BSc in physics, astronomy, computer science, or related disciplines
· 2:1 BSc in physics, astronomy, computer science, or related disciplines, plus a Masters-level qualification in a relevant area
For more information about the project, please contact the follwoing academisc:
Dr Jane M Bromley, Prof Stephen Serjeant and Dr Hugh Dickinson
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