80 citations · 271 across the 13 of their papers we have counts for
13 papers
Anomaly detection for multijet scenarios
Gregor Kasieczka, Sung Hak Lim, Louis Moureaux +3
Signals of physics beyond the Standard Model continue to resist discovery at the LHC. Recent years have seen the proliferation of new anomaly detection techniques, promising discov…
Jet Classification Using High-Level Features from Anatomy of Top Jets
Amon Furuichi, Sung Hak Lim, Mihoko M. Nojiri
Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to…
GalaxyFlow: Upsampling Hydrodynamical Simulations for Realistic Mock Stellar Catalogs
Sung Hak Lim, Kailash A. Raman, Matthew R. Buckley +1
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 int…
Measuring Galactic Dark Matter through Unsupervised Machine Learning
Matthew R Buckley, Sung Hak Lim, Eric Putney +1
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…
Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions
Amit Chakraborty, Sung Hak Lim, Mihoko M. Nojiri +1
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…
Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
Amit Chakraborty, Sung Hak Lim, Mihoko M. Nojiri
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 interp…
Spectral Analysis of Jet Substructure with Neural Networks: Boosted Higgs Case
Sung Hak Lim, Mihoko M. Nojiri
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 an…
Monojet Signatures from Heavy Colored Particles: Future Collider Sensitivities and Theoretical Uncertainties
Amit Chakraborty, Silvan Kuttimalai, Sung Hak Lim +2
In models with colored particle that can decay into a dark matter candidate , the relevant collider process $pp\to \mathcal{Q}\bar{\mathcal{Q}}\rightarrow X\bar{X}…
Identifying a new particle with jet substructures
Chengcheng Han, Doojin Kim, Minho Kim +3
We investigate a potential of measuring properties of a heavy resonance X, exploiting jet substructure techniques. Motivated by heavy higgs boson searches, we focus on the decays o…
Identifying the production process of new physics at colliders; symmetric or asymmetric?
Sung Hak Lim
We propose a class of kinematic variables, which is a smooth generalization of min-max type mass variables such as the Cambridge- and , for measuring a mass spectrum o…
The 750 GeV Diphoton Excess May Not Imply a 750 GeV Resonance
Won Sang Cho, Doojin Kim, Kyoungchul Kong +4
We discuss non-standard interpretations of the 750 GeV diphoton excess recently reported by the ATLAS and CMS Collaborations which do not involve a new, relatively broad, resonance…
OPTIMASS: A Package for the Minimization of Kinematic Mass Functions with Constraints
Won Sang Cho, James S. Gainer, Doojin Kim +5
Reconstructed mass variables, such as , , , and , play an essential role in searches for new physics at hadron colliders. The calculation of these…
Constraints on composite quark partners from Higgs searches
Thomas Flacke, Jeong Han Kim, Seung J. Lee +1
In composite Higgs models, the generation of quark masses requires the standard model-like quarks to be partially or fully composite states which are accompanied by composite quark…