Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Sydney Otten"'
Autor:
Melissa van Beekveld, Sascha Caron, Luc Hendriks, Paul Jackson, Adam Leinweber, Sydney Otten, Riley Patrick, Roberto Ruiz de Austri, Marco Santoni, Martin White
Publikováno v:
Journal of High Energy Physics, Vol 2021, Iss 9, Pp 1-33 (2021)
Abstract The lack of evidence for new physics at the Large Hadron Collider so far has prompted the development of model-independent search techniques. In this study, we compare the anomaly scores of a variety of anomaly detection techniques: an isola
Externí odkaz:
https://doaj.org/article/3f01283ca46b44fcb3ca0d6f11681b6e
Autor:
The DarkMachines High Dimensional Sampling Group, Csaba Balázs, Melissa van Beekveld, Sascha Caron, Barry M. Dillon, Ben Farmer, Andrew Fowlie, Eduardo C. Garrido-Merchán, Will Handley, Luc Hendriks, Guðlaugur Jóhannesson, Adam Leinweber, Judita Mamužić, Gregory D. Martinez, Sydney Otten, Roberto Ruiz de Austri, Pat Scott, Zachary Searle, Bob Stienen, Joaquin Vanschoren, Martin White
Publikováno v:
Journal of High Energy Physics, Vol 2021, Iss 5, Pp 1-46 (2021)
Abstract Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate. We describe a number of global optimisation a
Externí odkaz:
https://doaj.org/article/4e9c5f4589754b23acc9426eec8ca860
Autor:
Sydney Otten, Sascha Caron, Wieske de Swart, Melissa van Beekveld, Luc Hendriks, Caspar van Leeuwen, Damian Podareanu, Roberto Ruiz de Austri, Rob Verheyen
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-16 (2021)
Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to
Externí odkaz:
https://doaj.org/article/dee282af39824482b8165ce8053b6102
Autor:
Sydney Otten, Krzysztof Rolbiecki, Sascha Caron, Jong-Soo Kim, Roberto Ruiz de Austri, Jamie Tattersall
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 80, Iss 1, Pp 1-9 (2020)
Abstract We present a deep learning solution to the prediction of particle production cross sections over a complicated, high-dimensional parameter space. We demonstrate the applicability by providing state-of-the-art predictions for the production o
Externí odkaz:
https://doaj.org/article/caf0bafd24cb4104af44999556302eea
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 79, Iss 11, Pp 1-11 (2019)
Abstract Constraining the parameters of physical models with $$>5-10$$ >5-10 parameters is a widespread problem in fields like particle physics and astronomy. The generation of data to explore this parameter space often requires large amounts of comp
Externí odkaz:
https://doaj.org/article/429c0d17b124447ca206bf462afa3306
Publikováno v:
Monthly Notices of the Royal Astronomical Society, 496, 381-393
Monthly Notices of the Royal Astronomical Society, 496(1), 381-393. Oxford University Press
Monthly Notices of the Royal Astronomical Society, 496, 1, pp. 381-393
Monthly Notices of the Royal Astronomical Society, 496(1), 381-393. Oxford University Press
Monthly Notices of the Royal Astronomical Society, 496, 1, pp. 381-393
Since upcoming telescopes will observe thousands of strong lensing systems, creating fully-automated analysis pipelines for these images becomes increasingly important. In this work, we make a step towards that direction by developing the first end-t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0bf4b13042f6315947d56f730ccb780
https://doi.org/10.1093/mnras/staa1477
https://doi.org/10.1093/mnras/staa1477
Publikováno v:
Energy. 166:989-999
Energy scenarios provide guidance to energy policy, not least by presenting decarbonisation pathways for climate change mitigation. We review such scenarios for the example of Germany 2050, with a focus on the decarbonisation of heat generation and r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe97fdc42e197e27de683afaba15060a
https://doi.org/10.1016/j.energy.2018.10.114
https://doi.org/10.1016/j.energy.2018.10.114
Autor:
Roberto Ruiz de Austri, Melissa van Beekveld, Sydney Otten, Adam Leinweber, Sascha Caron, Paul Jackson, Martin White, Luc Hendriks, Riley Patrick, Marco Santoni
Publikováno v:
Journal of High Energy Physics, 2021, 9, pp. 1-33
Journal of High Energy Physics, 2021, 1-33
Journal of High Energy Physics, Vol 2021, Iss 9, Pp 1-33 (2021)
Journal of High Energy Physics
Journal of High Energy Physics, 2021, 1-33
Journal of High Energy Physics, Vol 2021, Iss 9, Pp 1-33 (2021)
Journal of High Energy Physics
The lack of evidence for new physics at the Large Hadron Collider so far has prompted the development of model-independent search techniques. In this study, we compare the anomaly scores of a variety of anomaly detection techniques: an isolation fore
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9bc4bc9cdde0967ce56e8087abb1c908
https://repository.ubn.ru.nl/handle/2066/237441
https://repository.ubn.ru.nl/handle/2066/237441
Autor:
Caspar van Leeuwen, Wieske de Swart, Sydney Otten, Roberto Ruiz de Austri, Melissa van Beekveld, Sascha Caron, Luc Hendriks, Rob Verheyen, Damian Podareanu
Publikováno v:
Nature Communications, 12, 2985-2985
Nature Communications, 12, 1, pp. 2985-2985
Nature Communications, 12:2985. Nature Publishing Group
Nature Communications
Nature Communications, Vol 12, Iss 1, Pp 1-16 (2021)
Nature Communications, 12, 1, pp. 2985-2985
Nature Communications, 12:2985. Nature Publishing Group
Nature Communications
Nature Communications, Vol 12, Iss 1, Pp 1-16 (2021)
Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e0f0d7944689bc1b8e2e6844bc33cfa
http://hdl.handle.net/2066/234128
http://hdl.handle.net/2066/234128
Autor:
Roberto Ruiz de Austri, Sydney Otten, Jong Soo Kim, Sascha Caron, Jamie Tattersall, Krzysztof Rolbiecki
Publikováno v:
European Physical Journal C, 80, 1-12
Digital.CSIC. Repositorio Institucional del CSIC
instname
European Physical Journal C, 80, 1, pp. 1-12
European Physical Journal C: Particles and Fields, Vol 80, Iss 1, Pp 1-9 (2020)
The European physical journal / C 80(1), 12 (2020). doi:10.1140/epjc/s10052-019-7562-1
European Physical Journal
Digital.CSIC. Repositorio Institucional del CSIC
instname
European Physical Journal C, 80, 1, pp. 1-12
European Physical Journal C: Particles and Fields, Vol 80, Iss 1, Pp 1-9 (2020)
The European physical journal / C 80(1), 12 (2020). doi:10.1140/epjc/s10052-019-7562-1
European Physical Journal
We present a deep learning solution to the prediction of particle production cross sections over a complicated, high-dimensional parameter space. We demonstrate the applicability by providing state-of-the-art predictions for the production of chargin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d928c3c3eb9b9b3655c6a4417d2e0bba
http://hdl.handle.net/2066/216756
http://hdl.handle.net/2066/216756