Zobrazeno 1 - 10
of 7 713
pro vyhledávání: '"Diefenbacher, A."'
Autor:
Robert T. Tomczak
Publikováno v:
Acta Universitatis Carolinae: Historia Universitatis Carolinae Pragensis, Vol 61, Iss 2, Pp 109-112 (2022)
Book review on Michael Diefenbacher – Olga Fejtová – Zdzisław Noga (Hgg.), Krakau – Nürnberg – Prag. Stadt und Reformation. Krakau, Nürnberg und Prag (1500–1618) / Kraków – Norymberga – Praga. Miasto i reformacja. Kraków, Norymber
Externí odkaz:
https://doaj.org/article/f1d599aadbea4a768be0213289f57a00
Autor:
Butter, Anja, Diefenbacher, Sascha, Huetsch, Nathan, Mikuni, Vinicius, Nachman, Benjamin, Schweitzer, Sofia Palacios, Plehn, Tilman
Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph a starting simulation into the unfolded data. We show how to extend two morphing techniques, Schr\"odinger Bridges and Di
Externí odkaz:
http://arxiv.org/abs/2411.02495
Autor:
Krause, Claudius, Giannelli, Michele Faucci, Kasieczka, Gregor, Nachman, Benjamin, Salamani, Dalila, Shih, David, Zaborowska, Anna, Amram, Oz, Borras, Kerstin, Buckley, Matthew R., Buhmann, Erik, Buss, Thorsten, Cardoso, Renato Paulo Da Costa, Caterini, Anthony L., Chernyavskaya, Nadezda, Corchia, Federico A. G., Cresswell, Jesse C., Diefenbacher, Sascha, Dreyer, Etienne, Ekambaram, Vijay, Eren, Engin, Ernst, Florian, Favaro, Luigi, Franchini, Matteo, Gaede, Frank, Gross, Eilam, Hsu, Shih-Chieh, Jaruskova, Kristina, Käch, Benno, Kalagnanam, Jayant, Kansal, Raghav, Kim, Taewoo, Kobylianskii, Dmitrii, Korol, Anatolii, Korcari, William, Krücker, Dirk, Krüger, Katja, Letizia, Marco, Li, Shu, Liu, Qibin, Liu, Xiulong, Loaiza-Ganem, Gabriel, Madula, Thandikire, McKeown, Peter, Melzer-Pellmann, Isabell-A., Mikuni, Vinicius, Nguyen, Nam, Ore, Ayodele, Schweitzer, Sofia Palacios, Pang, Ian, Pedro, Kevin, Plehn, Tilman, Pokorski, Witold, Qu, Huilin, Raikwar, Piyush, Raine, John A., Reyes-Gonzalez, Humberto, Rinaldi, Lorenzo, Ross, Brendan Leigh, Scham, Moritz A. W., Schnake, Simon, Shimmin, Chase, Shlizerman, Eli, Soybelman, Nathalie, Srivatsa, Mudhakar, Tsolaki, Kalliopi, Vallecorsa, Sofia, Yeo, Kyongmin, Zhang, Rui
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few t
Externí odkaz:
http://arxiv.org/abs/2410.21611
Autor:
Bhimji, Wahid, Calafiura, Paolo, Chakkappai, Ragansu, Chou, Yuan-Tang, Diefenbacher, Sascha, Dudley, Jordan, Farrell, Steven, Ghosh, Aishik, Guyon, Isabelle, Harris, Chris, Hsu, Shih-Chieh, Khoda, Elham E, Lyscar, Rémy, Michon, Alexandre, Nachman, Benjamin, Nugent, Peter, Reymond, Mathis, Rousseau, David, Sluijter, Benjamin, Thorne, Benjamin, Ullah, Ihsan, Zhang, Yulei
The FAIR Universe -- HiggsML Uncertainty Challenge focuses on measuring the physics properties of elementary particles with imperfect simulators due to differences in modelling systematic errors. Additionally, the challenge is leveraging a large-comp
Externí odkaz:
http://arxiv.org/abs/2410.02867
Autor:
Tomczak, Robert T.
Publikováno v:
Acta Universitatis Carolinae Historia Universitatis Carolinae Pragensis. 61(2):109-112
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=1056859
Publikováno v:
2024 Mach. Learn.: Sci. Technol. 5 045044
Recently, combinations of generative and Bayesian machine learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural networks aim to quantify the uncertainty on the generated distribution ori
Externí odkaz:
http://arxiv.org/abs/2408.00838
Autor:
Huetsch, Nathan, Villadamigo, Javier Mariño, Shmakov, Alexander, Diefenbacher, Sascha, Mikuni, Vinicius, Heimel, Theo, Fenton, Michael, Greif, Kevin, Nachman, Benjamin, Whiteson, Daniel, Butter, Anja, Plehn, Tilman
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches ar
Externí odkaz:
http://arxiv.org/abs/2404.18807
Autor:
Lin Yang, Qiongliang Liu, Pramod Kumar, Arunima Sengupta, Ali Farnoud, Ruolin Shen, Darya Trofimova, Sebastian Ziegler, Neda Davoudi, Ali Doryab, Ali Önder Yildirim, Markus E. Diefenbacher, Herbert B. Schiller, Daniel Razansky, Marie Piraud, Gerald Burgstaller, Wolfgang G. Kreyling, Fabian Isensee, Markus Rehberg, Tobias Stoeger, Otmar Schmid
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-22 (2024)
Abstract Targeted (nano-)drug delivery is essential for treating respiratory diseases, which are often confined to distinct lung regions. However, spatio-temporal profiling of drugs or nanoparticles (NPs) and their interactions with lung macrophages
Externí odkaz:
https://doaj.org/article/09230b937a684643ae6ba3af933a693d
Machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements. Two main approaches have emerged in this research area: one based on discriminative models and one based on generative models. The mai
Externí odkaz:
http://arxiv.org/abs/2308.12351
Machine learning-based simulations, especially calorimeter simulations, are promising tools for approximating the precision of classical high energy physics simulations with a fraction of the generation time. Nearly all methods proposed so far learn
Externí odkaz:
http://arxiv.org/abs/2308.12339