Zobrazeno 1 - 10
of 1 129
pro vyhledávání: '"Mikuni, Vinicius"'
Autor:
Zhu, Huanbiao, Desai, Krish, Kuusela, Mikael, Mikuni, Vinicius, Nachman, Benjamin, Wasserman, Larry
In many experimental contexts, it is necessary to statistically remove the impact of instrumental effects in order to physically interpret measurements. This task has been extensively studied in particle physics, where the deconvolution task is calle
Externí odkaz:
http://arxiv.org/abs/2409.10421
Autor:
Dreyer, Etienne, Gross, Eilam, Kobylianskii, Dmitrii, Mikuni, Vinicius, Nachman, Benjamin, Soybelman, Nathalie
Detector simulation and reconstruction are a significant computational bottleneck in particle physics. We develop Particle-flow Neural Assisted Simulations (Parnassus) to address this challenge. Our deep learning model takes as input a point cloud (p
Externí odkaz:
http://arxiv.org/abs/2406.01620
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
There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators. We show that these two tasks can be unified by using maximum
Externí odkaz:
http://arxiv.org/abs/2404.18992
Autor:
Mikuni, Vinicius, Nachman, Benjamin
Machine learning has become an essential tool in jet physics. Due to their complex, high-dimensional nature, jets can be explored holistically by neural networks in ways that are not possible manually. However, innovations in all areas of jet physics
Externí odkaz:
http://arxiv.org/abs/2404.16091
Autor:
Bal, Aritra, Brandes, Tristan, Iemmi, Fabio, Klute, Markus, Maier, Benedikt, Mikuni, Vinicius, Aarrestad, Thea
Publikováno v:
Mach. Learn.: Sci. Technol. 5 025033 (2024)
Knowledge distillation is a form of model compression that allows artificial neural networks of different sizes to learn from one another. Its main application is the compactification of large deep neural networks to free up computational resources,
Externí odkaz:
http://arxiv.org/abs/2311.12551
Autor:
Buhmann, Erik, Ewen, Cedric, Kasieczka, Gregor, Mikuni, Vinicius, Nachman, Benjamin, Shih, David
Publikováno v:
Phys. Rev. D 109, 055015 (2024)
Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strategies for resonant anomaly detection have been developed, where sideband
Externí odkaz:
http://arxiv.org/abs/2310.06897
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
Autor:
Mikuni, Vinicius, Nachman, Benjamin
Diffusion generative models are promising alternatives for fast surrogate models, producing high-fidelity physics simulations. However, the generation time often requires an expensive denoising process with hundreds of function evaluations, restricti
Externí odkaz:
http://arxiv.org/abs/2308.03847