Sydney Otten

Radboud University Nijmegen — Netherlands

Physics and Astronomy · Physical Sciences

13h-index596citations26works1.02yr avg
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Research Topics

Particle physics theoretical and experimental studies(11), Computational Physics and Python Applications(9), Generative Adversarial Networks and Image Synthesis(4), Particle Detector Development and Performance(4), Astrophysics and Cosmic Phenomena(3)

Publications26 total

Large physics models: towards a collaborative approach with large language models and foundation models
The European Physical Journal C·2025· 2 citedOpen Access
Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models
arXiv (Cornell University)·2025Open Access
Combining outlier analysis algorithms to identify new physics at the LHC
Journal of High Energy Physics·2021· 15 citedOpen Access
Event generation and statistical sampling for physics with deep generative models and a density information buffer
Nature Communications·2021· 33 citedOpen Access
A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
Journal of High Energy Physics·2021· 26 citedOpen Access
A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
Data Archiving and Networked Services (DANS)·2021· 7 citedOpen Access
Combining outlier analysis algorithms to identify new physics at the LHC
Radboud Repository (Radboud University)·2021· 54 citedOpen Access
Untitled
UvA-DARE (University of Amsterdam)·2021· 99 cited
Differentiable strong lensing: uniting gravity and neural nets through differentiable probabilistic programming
Monthly Notices of the Royal Astronomical Society·2020· 35 citedOpen Access
Inferring astrophysical X-ray polarization with deep learning
arXiv (Cornell University)·2020· 4 citedOpen Access
Inferring astrophysical X-ray polarization with deep learning
arXiv (Cornell University)·2020· 2 citedOpen Access
Deep-learning enhancement of large scale numerical simulations
arXiv (Cornell University)·2020· 1 citedOpen Access
'Learning the production cross sections of the Inert Doublet Model' training data set.
2020Open Access
'Learning the production cross sections of the Inert Doublet Model' training data set.
Zenodo (CERN European Organization for Nuclear Research)·2020Open Access
DeepXS: fast approximation of MSSM electroweak cross sections at NLO
The European Physical Journal C·2020Open Access
\nDeepXS: fast approximation of MSSM electroweak cross sections at NLO
Radboud Repository (Radboud University)·2020· 29 cited
ttbar data with up to 4 jets and 2 leptons
Figshare·2019Open Access
ttbar data with up to 4 jets and 2 leptons
Zenodo (CERN European Organization for Nuclear Research)·2019Open Access
Event Generation and Statistical Sampling for Physics with Deep\n Generative Models and a Density Information Buffer
arXiv (Cornell University)·2019· 38 citedOpen Access
Event Generation and Statistical Sampling with Deep Generative Models
arXiv (Cornell University)·2019· 5 citedOpen Access
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Frequent Co-authors

Roberto Ruiz de Austri(14), S. Caron(12), Melissa van Beekveld(11), Luc Hendriks(11), Caspar M. van Leeuwen(6), Damian Podareanu(6), Wieske de Swart(5), Rob Verheyen(5), Adam Leinweber(4), Jong Soo Kim(3), Christoph Weniger(3), T. Golling(3), M. J. White(3), P. Jackson(2), Riley Patrick(2), Krzysztof Rolbiecki(2), Jamie Tattersall(2), Csaba Balázs(2), Barry M. Dillon(2), Ben Farmer(2)