arXiv subjects

Jun Seung Pi

Publications and source records attributed to Jun Seung Pi.

4 recordsLinked to original sources

arXiv2025

Hunting and identifying coloured resonances in four top events with machine learning

We study prospects to search for pair or singly produced colour octet or colour sextet scalars which decay into two top quarks at the LHC. We focus on the same-sign lepton final state. We train a neural network comprising a simple multilayer perceptron combined with a convolutional neural network to optimize the separation of signal and background events. For LHC operated at 14 TeV and a luminosity of 3 ab$^{-1}$ we find an expected discovery reach of $m_8=1.8$ TeV and $m_6=1.92$ TeV for pair produced colour octets and sextets, respectively, and an expected exclusion reach of $m_8=2.02$ TeV and $m_6=2.14$ TeV. In a second step, we retrain the same network architecture to discriminate between signal processes. The network can clearly distinguish between the different colour representations. Moreover, we can also determine whether there is a significant contribution from single production to pair production for the same final state. The methodology can be applied to BSM candidates of different spin and colour representations.

hep-ph↗

arXiv2025

Echoes of Self-Interacting Dark Matter from Binary Black Hole Mergers

Dark matter (DM) environments around black holes (BHs) can influence their mergers through dynamical friction, causing gravitational wave (GW) dephasing during the inspiral phase. While this effect is well studied for collisionless dark matter (CDM), it remains unexplored for self-interacting dark matter (SIDM) due to the typically low DM density in SIDM halo cores. In this work, by considering BH mergers within SIDM spikes, which can arise from models with a massive force mediator, we show that the GWs emitted are dephased in a distinct manner. To incorporate the feedback of the BH orbital motion that can significantly modify the DM profiles, we use $N$-body simulations to analyze GW dephasing in binary BH inspirals within CDM and SIDM spikes. By tracking the binary's motion in different DM environments, we show that the Laser Interferometer Space Antenna (LISA) can observe GW dephasing arising from SIDM spikes in particular scenarios. Our results indicate that these observations offer a possibility of distinguishing between binary-BH inspirals in different DM environments.

astro-ph.CO↗

arXiv2023

Uncovering doubly charged scalars with dominant three-body decays using machine learning

We propose a deep learning-based search strategy for pair production of doubly charged scalars undergoing three-body decays to $W^+ t\bar b$ in the same-sign lepton plus multi-jet final state. This process is motivated by composite Higgs models with an underlying fermionic UV theory. We demonstrate that for such busy final states, jet image classification with convolutional neural networks outperforms standard fully connected networks acting on reconstructed kinematic variables. We derive the expected discovery reach and exclusion limit at the high-luminosity LHC.

hep-ph↗

arXiv2022

Portraying Double Higgs at the Large Hadron Collider II

The Higgs potential is vital to understand the electroweak symmetry breaking mechanism, and probing the Higgs self-interaction is arguably one of the most important physics targets at current and upcoming collider experiments. In particular, the triple Higgs coupling may be accessible at the HL-LHC by combining results in multiple channels, which motivates to study all possible decay modes for the double Higgs production. In this paper, we revisit the double Higgs production at the HL-LHC in the final state with two $b$-tagged jets, two leptons and missing transverse momentum. We focus on the performance of various neural network architectures with different input features: low-level (four momenta), high-level (kinematic variables) and image-based. We find it possible to bring a modest increase in the signal sensitivity over existing results via careful optimization of machine learning algorithms making a full use of novel kinematic variables.

hep-ph↗