Search

In recent years, quantum computing has drawn significant interest within the field of high-energy physics. We explore the potential of quantum algorithms to resolve the combinatorial problems in particle physics experiments. As a concrete example, we consider top quark pair production in the fully hadronic channel at the Large Hadron Collider. We investigate the performance of various quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) and a feedback-based algorithm (FALQON). We demonstrate that the efficiency for selecting the correct pairing is greatly improved by utilizing quantum algorithms over conventional kinematic methods. Furthermore, we observe that gate-based universal quantum algorithms perform on par with machine learning techniques and either surpass or match the effectiveness of quantum annealers. Our findings reveal that quantum algorithms not only provide a substantial increase in matching efficiency but also exhibit scalability and adaptability, making them suitable for a variety of high-energy physics applications. Moreover, quantum algorithms eliminate the extensive training processes needed by classical machine learning methods, enabling real-time adjustments based on individual event data., Comment: 19 pages, 18 figures, 1 table

We present an in-depth study of two-component cold dark matter via extensive N-body simulations. We examine various cosmological observables including the temperature evolution, power spectrum, density perturbation, maximum circular velocity functions, and galactic density profiles. We find that a significant mass difference between the two components, coupled with the annihilation of the heavier into the lighter component, imparts warm dark matter (WDM)-like characteristics to the latter. This model benefits from the unique features of WDM, such as modifications to the matter power spectrum and density profiles, while avoiding stringent observational constraints on WDM mass. The two-component dark-matter model aligns with observational data and suggests new avenues for dark-matter detection in terrestrial experiments, particularly for light, sub-MeV DM candidates. Our findings provide a framework for understanding the small-scale structures and offer guidance for future particle physics and cosmological studies., Comment: 28 pages, 11 figures

Top quark polarization provides an important tool for studying its production mechanisms, spin correlations, top quark properties, and new physics searches. Unlike lighter quarks, the top quark's polarization remains intact until its decay, enabling precise spin measurements. While the down-type fermions from $W$ boson decay are known to be effective spin analyzers, charged leptons have typically been the main target for most analyses. In this paper, we investigate the relevance of global jet dynamics -- considering kinematics, jet charges, and particle multiplicity -- for hadronic top quark polarimetry. The formalism used allows for analytical derivations obtained throughout the manuscript, offering deeper insights into the corresponding phenomenology., Comment: 14 pages and 3 figures

Precision studies for top quark physics are a cornerstone of the Large Hadron Collider program. Polarization, probed through decay kinematics, provides a unique tool to scrutinize the top quark across its various production modes and to explore potential new physics effects. However, the top quark most often decays hadronically, for which unambiguous identification of its decay products sensitive to top quark polarization is not possible. In this Letter, we introduce a jet flavor tagging method to significantly improve spin analyzing power in hadronic decays, going beyond exclusive kinematic information employed in previous studies. We provide parametric estimates of the improvement from flavor tagging with any set of measured observables and demonstrate this in practice on simulated data using a Graph Neural Network (GNN). We find that the spin analyzing power in hadronic decays can improve by approximately 20% (40%) compared to the kinematic approach, assuming an efficiency of 0.5 (0.2) for the network., Comment: 7 pages and 3 figures

We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits within both the attention mechanism and the multi-layer perceptrons. The research addresses the critical challenge of computational efficiency and resource constraints in analyzing data from the upcoming High Luminosity Large Hadron Collider, presenting the architecture as a potential solution. In particular, we evaluate our method by applying the model to multi-detector jet images from CMS Open Data. The goal is to distinguish quark-initiated from gluon-initiated jets. We successfully train the quantum model and evaluate it via numerical simulations. Using this approach, we achieve classification performance almost on par with the one obtained with the completely classical architecture, considering a similar number of parameters., Comment: 14 pages, 8 figures. Published in MDPI Axioms 2024, 13(5), 323

The stransverse mass variable $M_{T2}$ was originally proposed for the study of hadron collider events in which $N=2$ parent particles are produced and then decay semi-invisibly. Here we consider the generalization to the case of $N\ge 3$ semi-invisibly decaying parent particles. We introduce the corresponding class of kinematic variables $M_{TN}$ and illustrate their mathematical properties. Many of the celebrated features of the $M_{T2}$ kinematic endpoint are retained in this more general case, including the ability to measure the mass of the invisible daughter particle from the stransverse mass kink. We describe and validate a numerical procedure for computing $M_{TN}$ in practice. We also identify the configurations of visible momenta which result in nontrivial ($M_{TN}\ne 0$) values, and derive a pure phase-space estimate for the fraction of such events for any $N$., Comment: 9 pages, 6 figures

Models based on vision transformer architectures are considered state-of-the-art when it comes to image classification tasks. However, they require extensive computational resources both for training and deployment. The problem is exacerbated as the amount and complexity of the data increases. Quantum-based vision transformer models could potentially alleviate this issue by reducing the training and operating time while maintaining the same predictive power. Although current quantum computers are not yet able to perform high-dimensional tasks yet, they do offer one of the most efficient solutions for the future. In this work, we construct several variations of a quantum hybrid vision transformer for a classification problem in high energy physics (distinguishing photons and electrons in the electromagnetic calorimeter). We test them against classical vision transformer architectures. Our findings indicate that the hybrid models can achieve comparable performance to their classical analogues with a similar number of parameters., Comment: 13 pages, 9 figures. Published version in a special issue "Computational Aspects of Machine Learning and Quantum Computing"

We explore extensive N-body simulations with two-component cold dark matter candidates. We delve into the temperature evolution, power spectrum, density perturbation, and maximum circular velocity functions. We find that the substantial mass difference between the two candidates and the annihilation of the heavier components to the lighter ones effectively endow the latter with warm dark matter-like behavior, taking advantage of all distinct features that warm dark matter candidates offer, without observational bounds on the warm dark matter mass. Moreover, we demonstrate that the two-component dark matter model aligns well with observational data, providing valuable insights into where and how to search for the elusive dark matter candidates in terrestrial experiments., Comment: 7 pages, 5 figures

This paper presents a comprehensive comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNN) and Quantum Neural Networks (QNN), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENN) and Deep Neural Networks (DNN). We evaluate the performance of each network with two toy examples for a binary classification task, focusing on model complexity (measured by the number of parameters) and the size of the training data set. Our results show that the $\mathbb{Z}_2\times \mathbb{Z}_2$ EQNN and the QNN provide superior performance for smaller parameter sets and modest training data samples., Comment: 13 pages, 7 figures

Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, greatly motivate the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, one can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we perform a fair and comprehensive comparison between classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their AUC scores, the quantum networks were shown to outperform the classical networks. However, seeing the computational advantage of the quantum networks in practice may have to wait for the further development of quantum technology and its associated APIs., Comment: 15 pages, 7 figures, 3 appendices

We study the top-Higgs coupling with a CP violating phase $\xi$ at a future multi-TeV muon collider. We focus on processes that are directly sensitive to the top quark Yukawa coupling: $t\bar{t}h$, $tbh\mu\nu$, and $t\bar{t}h\nu\bar{\nu}$ with $h\rightarrow b\bar{b}$ and semileptonic top decays. At different energies, different processes dominate the cross section, providing complementary information. At and above an energy of $\mathcal{O}(10)$ TeV, vector boson fusion processes dominate. As we show, in the Standard Model there is destructive interference in the vector boson fusion processes $t\bar{t}h\nu\bar{\nu}$ and $tbh\mu\nu$ between the top quark Yukawa and Higgs-gauge boson couplings. A CP-violating phase changes this interference, and the cross section measurement is very sensitive to the size of the CP-violating angle. Although we find that the cross sections are measured to $\mathcal{O}(50\%)$ statistical uncertainty at $1\sigma$, a 10 and 30 TeV muon collider can bound the CP-violating angle $|\xi|\lesssim9.0^\circ$ and $|\xi|\lesssim5.4^\circ$, respectively. However, cross section measurements are insensitive to the sign of the CP-violating angle. To determine that the coupling is truly CP violating, observables sensitive to CP-violation must be measured. We find in the $t\bar{t}h$ process the azimuthal angle between the $t+\bar{t}$ plane and the initial state muon+Higgs plane shows good discrimination for $\xi=\pm0.1\pi$. For the $tbh\mu\nu$ and $t\bar{t}h\nu\bar{\nu}$ processes, the operator proportional to $\left(\vec{p}_\mu\times\vec{p}_h\right)\cdot \vec{p}_t$ is sensitive to the sign of CP phase $\xi$. From these observables, we construct asymmetry parameters that show good distinction between different values and signs of the CP violating angle., Comment: v2: Matches published version, 33 pages, 11 figures, typos fixed, references added, discussion expanded, results unchanged; v1: 32 pages, 11 figures

Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To improve the accuracy of MC integration, a number of useful variance reduction algorithms have been developed, including importance sampling and control variates. In this work, we demonstrate how these two methods can be applied simultaneously, thus combining their benefits. We provide a python wrapper, named CoVVVR, which implements our approach in the Vegas program. The improvements are quantified with several benchmark examples from the literature., Comment: 20 pages, 2 figures, 3 tables

The Large Hadron Collider provides a unique opportunity to study quantum entanglement and violation of Bell inequalities at the highest energy available today. In this paper, we will investigate these quantum correlations with top quark pair production, which represents a system of two-qubits. The spacelike separation requirement for the two causally disconnected top quarks requires they fly relativistically away from each other, which motivates the use of the boosted top-tagging with the semi-leptonic top pair channel. Although measuring the spin polarization of the hadronic top quark is known to be challenging, our study indicates that it is feasible to reconstruct the spin density matrix of the two-qubit system using an optimal hadronic polarimeter. This is achieved with the aid of jet substructure techniques and NN-inspired reconstruction methods, which improve the mapping between subjets and quarks. We find that entanglement can already be observed at more than $5\sigma$ level with existing data, and violation of Bell inequalities may be probed above 4$\sigma$ level at the HL-LHC with 3 ab$^{-1}$ of data., Comment: 12 pages, 6 figures, 2 tables. Updated to match with the published version

A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10~TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work., Comment: 118 pages, 103 figures

We demonstrate the use of symbolic regression in deriving analytical formulas, which are needed at various stages of a typical experimental analysis in collider phenomenology. As a first application, we consider kinematic variables like the stransverse mass, $M_{T2}$, which are defined algorithmically through an optimization procedure and not in terms of an analytical formula. We then train a symbolic regression and obtain the correct analytical expressions for all known special cases of $M_{T2}$ in the literature. As a second application, we reproduce the correct analytical expression for a next-to-leading order (NLO) kinematic distribution from data, which is simulated with a NLO event generator. Finally, we derive analytical approximations for the NLO kinematic distributions after detector simulation, for which no known analytical formulas currently exist., Comment: 15 pages, 13 figures, 8 tables

We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential $\varphi$. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure., Comment: 36 pages, 10 figures. Submission to SciPost

This is the Snowmass2021 Energy Frontier (EF) Beyond the Standard Model (BSM) report. It combines the EF topical group reports of EF08 (Model-specific explorations), EF09 (More general explorations), and EF10 (Dark Matter at Colliders). The report includes a general introduction to BSM motivations and the comparative prospects for proposed future experiments for a broad range of potential BSM models and signatures, including compositeness, SUSY, leptoquarks, more general new bosons and fermions, long-lived particles, dark matter, charged-lepton flavor violation, and anomaly detection., Comment: 108 pages + 38 pages references and appendix, 37 figures, Report of the Topical Group on Beyond the Standard Model Physics at Energy Frontier for Snowmass 2021. The first nine authors are the Conveners, with Contributions from the other authors

We propose a novel experimental scheme, called DAMSA (Dump-produced Aboriginal Matter Searches at an Accelerator), for searching for dark-sector particles, using rare nuclear isotope accelerator facilities that provide high-flux proton beams to produce a large number of rare nuclear isotopes. The high-intensity nature of their beams enables the investigation of dark-sector particles, including axion-like particles (ALPs) and dark photons. By contrast, their typical beam energies are not large enough to produce the backgrounds such as neutrinos resulting from secondary charged particles. The detector of DAMSA is then placed immediate downstream of the proton beam dump to maximize the prompt decay signals of dark-sector particles, which are often challenging to probe in other beam-dump-type experiments featuring a longer baseline, at the expense of an enormous amount of the beam-related neutron (BRN) background. We demonstrate that BRN can be significantly suppressed if the signal accompanies multiple, correlated visible particles in the final state. As an example physics case, we consider ALPs interacting with the Standard Model photon and their diphoton decay signal at DAMSA implemented at a rare nuclear isotope facility similar to the Rare isotope Accelerator complex for ON-line experiment under construction in South Korea. We show that the close proximity of the detector to the ALP production dump makes it possible to probe a high-mass region of ALP parameter space that the existing experiments have never explored., Comment: 7 pages, 7 figures, 1 table

Kinematic variables have been playing an important role in collider phenomenology, as they expedite discoveries of new particles by separating signal events from unwanted background events and allow for measurements of particle properties such as masses, couplings, spins, etc. For the past 10 years, an enormous number of kinematic variables have been designed and proposed, primarily for the experiments at the Large Hadron Collider, allowing for a drastic reduction of high-dimensional experimental data to lower-dimensional observables, from which one can readily extract underlying features of phase space and develop better-optimized data-analysis strategies. We review these recent developments in the area of phase space kinematics, summarizing the new kinematic variables with important phenomenological implications and physics applications. We also review recently proposed analysis methods and techniques specifically designed to leverage the new kinematic variables. As machine learning is nowadays percolating through many fields of particle physics including collider phenomenology, we discuss the interconnection and mutual complementarity of kinematic variables and machine learning techniques. We finally discuss how the utilization of kinematic variables originally developed for colliders can be extended to other high-energy physics experiments including neutrino experiments., Comment: White paper submitted to Snowmass and being prepared as a review article in RMP. Comments and suggestions regarding missing content or references etc. are welcome and will be greatly appreciated

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., Comment: 43 pages, 18 figures, 3 tables, matched publish version

Constraining the Higgs boson properties is a cornerstone of the LHC program and future colliders. In this Snowmass contribution, we study the potential to directly probe the Higgs-top CP-structure via the $t\bar{t}h$ production at the HL-LHC, 100 TeV FCC and muon colliders. We find the limits on the CP phase ($\alpha$) at 95% CL are $|\alpha| \lesssim 36^\circ$ with dileptonic $t\bar t (h\to b\bar b) $ and $|\alpha| \lesssim 25^\circ$ with combined $t\bar t (h\to \gamma\gamma) $ at the HL-LHC. The 100 TeV FCC brings a significant improvement in sensitivity with $|\alpha| \lesssim 3^\circ$ for the dileptonic $t\bar t (h\to b\bar b) $, due to the remarkable gain in the signal cross-section and the increased luminosity. At future muon colliders, we find that the bounds with semileptonic $t\bar t (h\to b\bar b) \nu\bar\nu$ are $|\alpha| \lesssim 9^\circ$ for 10 TeV and $|\alpha| \lesssim 3^\circ$ for 30 TeV, respectively., Comment: 12 pages, 8 figures, contribution to Snowmass 2021

Intriguing signals with excesses over expected backgrounds have been observed in many astrophysical and terrestrial settings, which could potentially have a dark matter origin. Astrophysical excesses include the Galactic Center GeV gamma-ray excess detected by the Fermi Gamma-Ray Space Telescope, the AMS antiproton and positron excesses, and the 511 and 3.5 keV X-ray lines. Direct detection excesses include the DAMA/LIBRA annual modulation signal, the XENON1T excess, and low-threshold excesses in solid state detectors. We discuss avenues to resolve these excesses, with actions the field can take over the next several years., Comment: 57 pages, solicited white paper submitted to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021)

The perspective of designing muon colliders with high energy and luminosity, which is being investigated by the International Muon Collider Collaboration, has triggered a growing interest in their physics reach. We present a concise summary of the muon colliders potential to explore new physics, leveraging on the unique possibility of combining high available energy with very precise measurements., Comment: 21 pages, 7 figures; Contribution to Snowmass 2021

We study the potential of deep learning to resolve the combinatorial problem in SUSY-like events with two invisible particles at the LHC. As a concrete example, we focus on dileptonic $t \bar t$ events, where the combinatorial problem becomes an issue of binary classification: pairing the correct lepton with each $b$ quark coming from the decays of the tops. We investigate the performance of a number of machine learning algorithms, including attention-based networks, which have been used for a similar problem in the fully-hadronic channel of $t\bar t$ production; and the Lorentz Boost Network, which is motivated by physics principles. We then consider the general case when the underlying mass spectrum is unknown, and hence no kinematic endpoint information is available. Compared against existing methods based on kinematic variables, we demonstrate that the efficiency for selecting the correct pairing is greatly improved by utilizing deep learning techniques., Comment: 22 pages, 15 figures, 1 table, matches the published version

The study of the Higgs boson's properties is a cornerstone of the LHC and future collider programs. In this paper, we examine the potential to directly probe the Higgs-top interaction strength and CP-structure in the $t\bar t h$ channel with the Higgs boson decaying to bottom-quark pairs and top-quarks in the di-leptonic mode. We adopt the BDRS algorithm to tag the boosted Higgs and exploit the $M_2$-assisted reconstruction to compute observables sensitive to the CP-phase at the $t\bar{t}$ rest frame, where the new physics sensitivity can be enhanced. Performing a side-band analysis at the LHC to control the continuum $t\bar{t}b\bar{b}$ background, we find that the Higgs-top strength and CP-phase can be probed up to $\delta\kappa_t\lesssim 20\%$ and $| \alpha | \lesssim 36^\circ$ at 95% CL, respectively. We also derive that a similar analysis at a 100 TeV future collider could further improve the precision to $\delta\kappa_t\lesssim 1\%$ and $| \alpha| \lesssim 1.5^\circ$, where the CP-odd observables play a crucial role, boosting the sensitivity on the CP-phase., Comment: 17 pages, 10 figures, comments are welcome

The choice of optimal event variables is crucial for achieving the maximal sensitivity of experimental analyses. Over time, physicists have derived suitable kinematic variables for many typical event topologies in collider physics. Here we introduce a deep learning technique to design good event variables, which are sensitive over a wide range of values for the unknown model parameters. We demonstrate that the neural networks trained with our technique on some simple event topologies are able to reproduce standard event variables like invariant mass, transverse mass, and stransverse mass. The method is automatable, completely general, and can be used to derive sensitive, previously unknown, event variables for other, more complex event topologies., Comment: 6 pages, 7 figures

The dark matter interpretation for a recent observation of excessive electron recoil events at the XENON1T detector seems challenging because its velocity is not large enough to give rise to recoiling electrons of $\mathcal{O}({\rm keV})$. Fast-moving or boosted dark matter scenarios are receiving attention as a remedy for this issue, rendering the dark matter interpretation a possibility to explain the anomaly. We investigate various scenarios where such dark matter of spin 0 and 1/2 interacts with electrons via an exchange of vector, pseudo-scalar, or scalar mediators. We find parameter values not only to reproduce the excess but to be consistent with existing bounds. Our study suggests that the scales of mass and coupling parameters preferred by the excess can be mostly affected by the type of mediator, and that significantly boosted dark matter can explain the excess depending on the mediator type and its mass choice. The method proposed in this work is general, and hence readily applicable to the interpretation of observed data in the dark matter direct detection experiment., Comment: 17 pages, 5 figures, 2 tables. Comments are welcome

We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data., Comment: 58 pages, minor revision following comments from SciPost referees

Searching for two-body resonance decays is a central component of the high energy physics energy frontier research program. While many of the possibilities are covered when the two bodies are Standard Model (SM) particles, there are still significant gaps. If one or both of the bodies are themselves non-SM particles, there is very little coverage from existing searches. We review the status of two-body searches and motivate the need to search for the missing combinations. It is likely that the search program of the future will be able to cover all possibilities with a combination of dedicated and model agnostic search approaches., Comment: 17 pages, 1 figure, 14 tables

We examine the discovery potential for double Higgs production at the high luminosity LHC in the final state with two $b$-tagged jets, two leptons and missing transverse momentum. Although this dilepton final state has been considered a difficult channel due to the large backgrounds, we argue that it is possible to obtain sizable signal significance, by adopting a deep learning framework making full use of the relevant kinematics along with the jet images from the Higgs decay. For the relevant number of signal events we obtain a substantial increase in signal sensitivity over existing analyses. We discuss relative improvements at each stage and the correlations among the different input variables for the neutral network. The proposed method can be easily generalized to the semi-leptonic channel of double Higgs production, as well as to other processes with similar final states., Comment: 37 pages. Matched to published version. Revisions in section 1, 2. Replaced figures. Added references. Conclusions unchanged

We investigate the sensitivity of the 14 TeV LHC to pair-produced top partners ($T$) decaying into the Standard Model top quark ($t$) plus either a gluon ($g$) or a photon ($\gamma$). The decays $T\rightarrow tg$ and $T\rightarrow t\gamma$ can be dominant when the mixing between the top partner and top quark are negligible. In this case, the conventional decays $T\rightarrow bW$, $T\rightarrow tZ$, and $T\rightarrow th$ are highly suppressed and can be neglected. We take a model-independent approach using effective operators for the $T$-$t$-$g$ and $T$-$t$-$\gamma$ interactions, considering both spin-$\frac{1}{2}$ and spin-$\frac{3}{2}$ top partners. We perform a semi-realistic simulation with boosted top quark tagging and an appropriate implementation of a jet-faking-photon rate. Despite a simple dimensional analysis indicating that the branching ratios ${\rm BR}(T\rightarrow t\gamma)\ll {\rm BR}(T\rightarrow tg)$ due to the electric-magnetic coupling being much smaller than the strong force coupling, our study shows that the LHC sensitivity to $T\bar{T}\rightarrow t\overline{t}\gamma g$ is more significant than the sensitivity to $T\overline{T}\rightarrow t\overline{t}gg$. This is due to much smaller backgrounds attributed to the isolated high-$p_T$ photon. We find that with these decay channels and 3 ab$^{-1}$ of data, the LHC is sensitive to top partner masses $m_T\lesssim 1.4-1.8$~TeV for spin-$\frac{1}{2}$ and spin-$\frac{3}{2}$ top partners, respectively., Comment: 33 pages, 10 figures, 7 tables

We propose a novel kinematic method to expedite the discovery of the double Higgs ($hh$) production in the $\ell^+\ell^- b \bar{b} + E_T \hspace{-0.52cm} \big / ~$ final state. We make full use of recently developed kinematic variables, as well as the variables $\it Topness$ for the dominant background (top quark pair production) and $\it Higgsness$ for the signal. We obtain a significant increase in sensitivity compared to the previous analyses which used sophisticated algorithms like boosted decision trees or neutral networks. The method can be easily generalized to resonant $hh$ production as well as other non-resonant channels., Comment: 5 pages, 5 figures, matches the published version

We propose the idea of "Earth Shielding" to reject cosmic-ray backgrounds, in the search for boosted dark matter at surface neutrino detectors, resulting in the enhancement of the signal-to-background ratio. The identification of cosmic-originating rare signals, especially lacking features, at surface detectors is often considered hopeless due to a vast amount of cosmic-ray-induced background, hence underground experiments are better motivated to avoid such a challenge. We claim that surface detectors can attain remarkable sensitivities to even featureless signals, once restricting to events coming through the Earth from the opposite side of the detector location for the signals leaving appreciable tracks from which the source direction is inferred. By doing so, potential backgrounds in the signal region of interest can be substantially suppressed. To validate our claim, we study experimental reaches at several surface experiments such as SBN Program (MicroBooNE, ICARUS, and SBND) and ProtoDUNE for elastic boosted dark matter signatures stemming from the Galactic Center. We provide a systematic discussion on maximizing associated signal sensitivities., Comment: 23 pages, 8 figures

Constraining the Higgs boson properties is a cornerstone of the LHC program. We study the potential to directly probe the Higgs-top CP-structure via the $t\bar{t}h$ channel at the LHC with the Higgs boson decaying to a bottom pair and top-quarks in the dileptonic mode. We show that a combination of laboratory and $t\bar{t}$ rest frame observables display large CP-sensitivity, exploring the spin correlations in the top decays. To efficiently reconstruct our final state, we present a method based on simple mass minimization and prove its robustness to shower, hadronization and detector effects. In addition, the mass reconstruction works as an extra relevant handle for background suppression. Based on our results, we demonstrate that the Higgs-top CP-phase $(\alpha)$ can be probed up to $\cos\alpha< 0.7$ at the high luminosity LHC., Comment: 25 pages, 11 figures, 3 tables

Models with an orbifolded universal extra dimension receive important loop-induced corrections to the masses and couplings of Kaluza-Klein (KK) particles. The dominant contributions stem from so-called boundary terms which violate KK number. Previously, only the parts of these boundary terms proportional to $\ln(\Lambda R)$ have been computed, where $R$ is the radius of the extra dimension and $\Lambda$ is cut-off scale. However, for typical values of $\Lambda R \sim 10 \cdots 50$, the logarithms are not particularly large and non-logarithmic contributions may be numerically important. In this paper, these remaining finite terms are computed and their phenomenological impact is discussed. It is shown that the finite terms have a significant impact on the KK mass spectrum. Furthermore, one finds new KK-number violating interactions that do not depend on $\ln(\Lambda R)$ but nevertheless are non-zero. These lead to new production and decay channels for level-2 KK particles at colliders., Comment: V2: KK-top (mass and decays) updated, correcting a mistake in the literature

We investigate mono-top signatures arising from phenomenological models of fermionic top-partners, which are degenerate in mass and decay into a bosonic dark matter candidate, either spin-0 or spin-1. Such a model provides a mono-top signature as a smoking-gun, while conventional searches with $t\bar t$ + missing transverse momentum are limited. Two such scenarios: i) a phenomenological 3rd generation extra dimensional model with excited top and electroweak sectors, and ii) a model where only a top-partner and a dark matter particle are added to the SM, are studied in the degenerate mass regime. We find that in the case of extra dimension a number of different processes give rise to effectively the same mono-top final state, and a great gain can be obtained in the sensitivity for this channel. We show that the mono-top search can explore top-partner masses up to 630 GeV and 300 GeV for the 3rd generation extra dimensional model and the minimal fermionic top-partner model, respectively, at the high luminosity LHC., Comment: 8 pages, 6 figures

We advocate the use of on-shell constrained $M_2$ variables in order to mitigate the combinatorial problem in SUSY-like events with two invisible particles at the LHC. We show that in comparison to other approaches in the literature, the constrained $M_2$ variables provide superior ansatze for the unmeasured invisible momenta and therefore can be usefully applied to discriminate combinatorial ambiguities. We illustrate our procedure with the example of dilepton $t\bar{t}$ events. We critically review the existing methods based on the Cambridge $M_{T2}$ variable and MAOS-reconstruction of invisible momenta, and show that their algorithm can be simplified without loss of sensitivity, due to a perfect correlation between events with complex solutions for the invisible momenta and events exhibiting a kinematic endpoint violation. Then we demonstrate that the efficiency for selecting the correct partition is further improved by utilizing the $M_2$ variables instead. Finally, we also consider the general case when the underlying mass spectrum is unknown, and no kinematic endpoint information is available.

In models with universal extra dimensions (UED), the lightest Kaluza-Klein excitation of neutral electroweak gauge bosons is a stable, weakly interacting massive particle and thus is a candidate for dark matter thanks to Kaluza-Klein parity. We examine concrete model realizations of such dark matter in the context of non-minimal UED extensions. The boundary localized kinetic terms for the electroweak gauge bosons lead to a non-trivial mixing among the first Kaluza-Klein excitations of the ${\rm SU}(2)_W$ and ${\rm U}(1)_Y$ gauge bosons and the resultant low energy phenomenology is rich. We investigate implications of various experiments including low energy electroweak precision measurements, direct and indirect detection of dark matter particles and direct collider searches at the LHC. Notably, we show that the electroweak Kaluza-Klein dark matter can be as heavy as 2.4 TeV, which is significantly higher than $1.3$ TeV as is indicated as an upper bound in the minimal UED model., Comment: 29 pages, 10 figures, 1 table; v2: Fig 10 added, remarks on the potential influence of Sommerfeld enhancement added, typos corrected, references added, version published in JHEP