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of 45
pro vyhledávání: '"Lim, Sung Hak"'
Publikováno v:
JHEP 07 (2024) 146
Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to less interpretable models, emphasizing the need to understand the dec
Externí odkaz:
http://arxiv.org/abs/2312.11760
We present a novel, data-driven analysis of Galactic dynamics, using unsupervised machine learning -- in the form of density estimation with normalizing flows -- to learn the underlying phase space distribution of 6 million nearby stars from the Gaia
Externí odkaz:
http://arxiv.org/abs/2305.13358
Publikováno v:
Monthly Notices of the Royal Astronomical Society, Volume 533, Issue 1, September 2024, Pages 143-164
Cosmological N-body simulations of galaxies operate at the level of "star particles" with a mass resolution on the scale of thousands of solar masses. Turning these simulations into stellar mock catalogs requires "upsampling" the star particles into
Externí odkaz:
http://arxiv.org/abs/2211.11765
Measuring the density profile of dark matter in the Solar neighborhood has important implications for both dark matter theory and experiment. In this work, we apply autoregressive flows to stars from a realistic simulation of a Milky Way-type galaxy
Externí odkaz:
http://arxiv.org/abs/2205.01129
Autor:
Lim, Sung Hak, Nojiri, Mihoko M.
We introduce a jet tagger based on a neural network analyzing the Minkowski Functionals (MFs) of pixellated jet images. The MFs are geometric measures of binary images, and they can be regarded as a generalization of the particle multiplicity, which
Externí odkaz:
http://arxiv.org/abs/2010.13469
Publikováno v:
J. High Energ. Phys. 2020, 111 (2020)
Deep neural networks trained on jet images have been successful in classifying different kinds of jets. In this paper, we identify the crucial physics features that could reproduce the classification performance of the convolutional neural network in
Externí odkaz:
http://arxiv.org/abs/2003.11787
Publikováno v:
J. High Energ. Phys. 2019, 135 (2019)
Classification of jets with deep learning has gained significant attention in recent times. However, the performance of deep neural networks is often achieved at the cost of interpretability. Here we propose an interpretable network trained on the je
Externí odkaz:
http://arxiv.org/abs/1904.02092
Akademický článek
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Autor:
Lim, Sung Hak, Nojiri, Mihoko M.
Publikováno v:
JHEP10(2018)181
Jets from boosted heavy particles have a typical angular scale which can be used to distinguish them from QCD jets. We introduce a machine learning strategy for jet substructure analysis using a spectral function on the angular scale. The angular spe
Externí odkaz:
http://arxiv.org/abs/1807.03312
Publikováno v:
Eur. Phys. J. C (2018) 78: 679
In models with colored particle $\mathcal{Q}$ that can decay into a dark matter candidate $X$, the relevant collider process $pp\to \mathcal{Q}\bar{\mathcal{Q}}\rightarrow X\bar{X}+$jets gives rise to events with significant transverse momentum imbal
Externí odkaz:
http://arxiv.org/abs/1805.05346