In a previous publication, we studied the parameter space of the phenomenological minimal Super- symmetric standard model with a light neutralino thermal dark matter ( M x 01 <= M h / 2) and observed that the recent results from the dark matter and collider experiments put strong constraints on this scenario. In this work, we present in detail the arguments behind the robustness of this result against scanning over the large number of parameters in phenomenological minimal Supersymmetric standard model. The Run 3 of LHC will be crucial in probing the surviving regions of the parameter space. We further investigate the impact of light staus on our parameter space and also provide benchmarks that can be interesting for Run 3 of LHC. We analyze these benchmarks at the LHC using the machine learning framework of XGBOOST. Finally, we also discuss the effect of nonstandard cosmology on the parameter space.

In this paper, we propose a dedicated forward detector, FOREHUNT (FORward Experiment for HUNdred TeV), for 100 TeV FCC-hh for the detection of light long-lived particles (LLP) coming from $B$-meson decay. We calculate the signal acceptance as a function of mass and proper decay length of the LLP for 100 TeV and interpret our result in terms of model parameters for models of dark Higgs scalar and heavy neutral leptons. We also compare the sensitivity with proposed transverse detectors like MATHUSLA, CODEX-b for HL-LHC, and DELIGHT (Detector for long-lived particles at high energy of 100 TeV) for FCC-hh. Our analysis reveals that if the LLP has low decay length < 10 m, a forward detector like FOREHUNT is the best option to look for the decaying LLP, while DELIGHT is preferable for higher decay lengths.

Top tagging has emerged to be a fast-evolving subject due to the top quark's significant role in probing physics beyond the standard model. For the reconstruction of top jets, machine learning models have shown a significant improvement in the tagging and classification performance compared to the previous methods. In this work, we build top taggers using $N$-Subjettiness ratios and several observables of the Energy Correlation functions as input features to train the eXtreme Gradient BOOSTed decision tree (XGBOOST). It is observed that the performance of the taggers depends on how well the top jets are matched to their truth-level partons. Furthermore, we use SHapley Additive exPlanation (SHAP) framework to calculate the feature importance of the trained models. It helps us to estimate how much each feature of the data contributed to the model's prediction and what regions are of more importance for each input variable. Finally, we combine all the tagger variables to form a hybrid tagger and interpret the results using the Shapley values.

We explore the parameter space of the phenomenological minimal supersymmetric standard model with a light neutralino thermal dark matter (m_{χ[over ˜]_{1}^{0}}≤m_{h}/2) that is consistent with current collider and astrophysical constraints. We consider both positive and negative values of the higgsino mass parameter (μ). Our investigation shows that the recent experimental results from the LHC as well as from direct detection searches for dark matter by the LUX-ZEPLIN Collaboration rule out the Z-funnel region for the μ>0 scenario. The same results severely restrict the h-funnel region for positive μ; however, the allowed points can be probed easily with few more days of data from the LUX-ZEPLIN experiment. In the μ<0 scenario, we find that very light higgsinos in both the Z and h funnels might survive the present constraints from the electroweakino searches at the LHC, and dedicated efforts from experimental collaborations are necessary to make conclusive statements about their present status.

A bstract In this paper, we study the prospect of ECAL barrel timing to develop triggers dedicated to long-lived particles decaying to jets at the level-1 of HL-LHC. We construct over 20 timing-based variables, and identify two of them which have better performances and are robust against increasing PU. We estimate the QCD prompt jet background rates accurately using the “stitching” procedure for varying thresholds defining our triggers and compute the signal efficiencies for different LLP scenarios for a permissible background rate. The trigger efficiencies can go up to $$ \mathcal{O}\left(80\%\right) $$ O80% for the most optimal trigger for pair-produced heavy LLPs having high decay lengths, which degrades with decreasing mass and decay length of the LLP. We also discuss the prospect of including the information of displaced L1 tracks to our triggers, which further improves the results, especially for LLPs characterised by lower decay lengths.

We study the pair production of the long-lived mediator particles from the decay of the SM Higgs boson and their subsequent decay into standard model particles. We compute the projected sensitivity, both model-independently and with a minimal model, of using the muon spectrometer of the CMS detector at the HL-LHC experiment for ggF, VBF, and Vh production modes of the Higgs boson and various decay modes of the mediator particle, along with dedicated detectors for LLP searches like CODEX-b and MATHUSLA. Subsequently, we study the improvement with the FCC-hh detector at the 100\,TeV collider experiment for such long-lived mediators, again focusing on the muon spectrometer. We propose dedicated LLP detector designs for the 100\,TeV collider experiment, DELIGHT (\textbf{De}tector for \textbf{l}ong-l\textbf{i}ved particles at hi\textbf{gh} energy of 100\,\textbf{T}eV), and study their sensitivities.

We explore the parameter space of the phenomenological Minimal Supersymmetric Standard Model (pMSSM) with a light neutralino thermal dark matter (m_χ̃_1^0≤ m_h/2) that is consistent with current collider and astrophysical constraints. We consider both positive and negative values of the higgsino mass parameter (μ). Our investigation shows that the recent experimental results from the LHC as well as from direct detection searches for dark matter by the LUX-ZEPLIN (LZ) collaboration rule out the Z-funnel region for the μ>0 scenario. The same results severely restrict the h-funnel region for positive μ, however, the allowed points can be probed easily with few more days of data from the LZ experiment. In the μ<0 scenario, we find that very light higgsinos in both the Z and h funnels might survive the present constraints from the electroweakino searches at the LHC, and dedicated efforts from experimental collaborations are necessary to make conclusive statements about their present status.

This work is a study of some possible background sources in the XENON1T environment which might affect the energy spectrum of electronic recoil events in the lower side and might contribute to the observed excess. We have identified some additional possible backgrounds, like 41Ca, 49V, 63Ni, 106Ru and 125Sb coming from cosmogenic production, where the former two emit monoenergetic X-rays and the latter three have β decays, or isotopes, like 210Pb, from the decay chain of 222Rn emanated in liquid xenon from the materials, or isotopes, like 137Cs, produced due to neutron capture. We perform a χ2 fitting of the ER spectrum from these backgrounds along with tritium to the observed excess events by varying their individual rates to understand whether they can be present to contribute to the low energy excess or their presence is constrained from the data. We also study the possibility of simultaneous presence of more than one such backgrounds, and how this affects the rates required by individual backgrounds to explain the excess.

We examine the capacity of the Large Hadron Collider to determine the mean proper lifetime of long-lived particles assuming different decay final states. We mostly concentrate on the high luminosity runs of the LHC, and therefore, develop our discussion in light of the high amount of pile-up and the various upgrades for the HL-LHC runs. We employ model-dependent and model-independent methods in order to reconstruct the proper lifetime of neutral long-lived particles decaying into displaced leptons, potentially accompanied by missing energy, as well as charged long-lived particles decaying ihnto leptons and missing energy. We also present a discussion for lifetime estimation of neutral long-lived particles decaying into displaced jets, along with the challenges in the high PU environment of HL-LHC. After a general discussion, we illustrate and discuss these methods using several new physics models. We conclude that the lifetime can indeed be reconstructed in many concrete cases. Finally, we discuss to which extent including timing information, which is an important addition in the Phase-II upgrade of CMS, can improve such an analysis.

Triggering long-lived particles at the first stage of the trigger system is very crucial in LLP searches to ensure that we do not miss them at the very beginning. The future High Luminosity runs of the Large Hardron Collider will have increased number of pile-up events per bunch crossing. There will be major upgrades in hardware, firmware and software sides, like tracking at level-1 (L1) as well as inclusion of the MIP timing detector. The L1 trigger menu will also be modified to cope with pile-up and maintain the sensitivity to physics processes. In our study we found that the usual level-1 triggers, mostly meant for triggering prompt particles, will not be very efficient for LLP searches in the 140 PU environment of HL-LHC, thus pointing to the need to include dedicated L1 triggers in the menu for LLPs. We consider the decay of the LLP into jets and develop dedicated jet triggers using the track information and if available, the regional timing information at L1 to select LLP events. We show in our work that these triggers give promising results in identifying LLP events with moderate trigger rates.

Sophisticated machine learning techniques, like computer vision, are state of the art in modern day research. These technologically advanced algorithms have promising potential in search for physics beyond Standard Model in Large Hadron Collider (LHC). Most of the computer vision tasks are surrounded around convolutional neural networks (CNN), which can provide powerful tools for differentiating between patterns of calorimeter energy deposits by prompt particles of Standard Model and long-lived particles predicted in various models beyond the Standard Model. We demonstrate the usefulness of CNN by using a couple of physics examples from well motivated BSM scenarios predicting long-lived particles giving rise to displaced jets. Our work suggests that modern machine-learning techniques have potential to discriminate between energy deposition patterns of prompt and long-lived particles, and thus, they can be useful tools in such searches.

Sophisticated machine learning techniques have promising potential in search for physics beyond Standard Model in Large Hadron Collider (LHC). Convolutional neural networks (CNN) can provide powerful tools for differentiating between patterns of calorimeter energy deposits by prompt particles of Standard Model and long-lived particles predicted in various models beyond the Standard Model. We demonstrate the usefulness of CNN by using a couple of physics examples from well motivated BSM scenarios predicting long-lived particles giving rise to displaced jets. Our work suggests that modern machine-learning techniques have potential to discriminate between energy deposition patterns of prompt and long-lived particles, and thus, they can be useful tools in such searches.