Imperial College London

Dr Paola Arrubarrena Tame

Faculty of Natural SciencesDepartment of Mathematics

Research Associate in Mathematics







Weeks BuildingSouth Kensington Campus





My research focuses on developing methods to analyze radio astronomy data using tools from rough path theory. Rough path models have the potential to help the development of measurement instruments and processing techniques used in new telescopes. Collaborating with the new SKA telescope and Cambridge, our aim is to improve detection sensitivity and focus on EoR signals.

Before arriving at Imperial College, I completed my PhD in experimental particle physics at the Ludwig Maximilian University of Munich (LMU). I primarily analyzed data taken with the ATLAS detector at CERN in the search for Dark Matter. I collaborated on developing models that applied machine learning techniques to improve our results.

The Alan Turing Institute and DataSig

I am a visiting researcher at the ATI which is UK's national institute for data science and artificial intelligence. 

I am also a member of DataSig, a project whose goal is to develop mathematical and computational tools based on rough path theory and machine learning environments.


Journal Articles

ATLAS Collaboration (2021), Search for R-parity violating supersymmetry in a final state containing leptons and many jets with the ATLAS experiment using $\sqrt{s}$ = 13 TeV proton-proton collision data, European Physical Journal C: Particles and Fields, 81 (11). 1023. ISSN: 1434-6044. 

DOI | Open Access Link

ATLAS Collaboration (2020), Search for new phenomena with top quark pairs in final states with one lepton, jets, and missing transverse momentum in pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector, Journal of High Energy Physics, 04 (2021) 174.

DOI | Open Access Link

Proceedings of Science

Z.P Arrubarrena Tame (2019), Search for direct top squark pair production on the 3-body decay mode with one-lepton final states in $\sqrt{s}$ = 13 TeV pp collisions with the ATLAS detector, Proceedings of Science PoS(EPS-HEP2019)623. 

Article | Poster