Linking Solid-State Astronomical Observations And Gas-Grain Models To Laboratory Data
Lead Research Organisation:
University College London
Department Name: Physics and Astronomy
Abstract
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Organisations
People |
ORCID iD |
| Serena Viti (Principal Investigator) |
Publications
Accurso G
(2017)
Radiative transfer meets Bayesian statistics: where does a galaxy's [C ii] emission come from?
in Monthly Notices of the Royal Astronomical Society
De Mijolla D
(2019)
Incorporating astrochemistry into molecular line modelling via emulation
in Astronomy & Astrophysics
Holdship J
(2018)
Bayesian Inference of the Rates of Surface Reactions in Icy Mantles
in The Astrophysical Journal
| Description | This was a short term grant (6 months of PDRA for UCL). We used them to hire the PI's ex PhD student (Dr Makrymallis) to apply the Bayesian and MCMC statistical packages he developed during his PhD to understand ices in the interstellar medium. We have a paper submitted in ApJ. We are now in the process of looking for funding to pre-commercialize the tools |
| Exploitation Route | this project developed statistical techniques in a field where no one had done it yet. The methodologies developed are applicable for a variety of sciences and the PDRA has now in fact moved on into the financial private sector |
| Sectors | Communities and Social Services/Policy,Digital/Communication/Information Technologies (including Software),Financial Services, and Management Consultancy,Healthcare |
| Description | The feasibility work performed during this short grant allowed us to become key contributors in the bid for the CDT in Data Intensive Science that STFC awarded to UCL: this DTC has ~20 industrial partners. |
| First Year Of Impact | 2017 |
| Sector | Education,Other |
| Impact Types | Economic |