Emmanouil Vourliotis

Many new physics models, e.g., leptoquarks, extra dimensions, extended Higgs sectors, supersymmetric theories, and dark sector extensions, are expected to manifest themselves in the final states with hadronic jets. Novel experimental techniques, including a dedicated scouting trigger stream and advanced machine learning techniques can be employed to identify such signals. This talk presents searches in CMS for new phenomena in the final states that include jets, focusing on the most recent results obtained using the full Run-II data-set collected at the LHC.

Many new physics models, e.g., leptoquarks, extra dimensions, extended Higgs sectors, supersymmetric theories, and dark sector extensions, are expected to manifest themselves in the final states with hadronic jets. Novel experimental techniques, including a dedicated scouting trigger stream and advanced machine learning techniques can be employed to identify such signals. This talk presents searches in CMS for new phenomena in the final states that include jets, focusing on the most recent results obtained using the full Run-II data-set collected at the LHC.

mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both threadand data-level parallelism. In the past few years the project transitioned from the R&D phase to deployment in the Run-3 offline workflow of the CMS experiment. The CMS tracking performs a series of iterations, targeting reconstruction of tracks of increasing difficulty after removing hits associated to tracks found in previous iterations. mkFit has been adopted for several of the tracking iterations, which contribute to the majority of reconstructed tracks. When tested in the standard conditions for production jobs, speedups in track pattern recognition are on average of the order of 3.5x for the iterations where it is used (3-7x depending on the iteration). Multiple factors contribute to the observed speedups, including vectorization and a lightweight geometry description, as well as improved memory management and single precision. Efficient vectorization is achieved with both the icc and the gcc (default in CMSSW) compilers and relies on a dedicated library for small matrix operations, Matriplex, which has recently been released in a public repository. While the mkFit geometry description already featured levels of abstraction from the actual Phase-1 CMS tracker, several components of the implementations were still tied to that specific geometry. We have further generalized the geometry description and the configuration of the run-time parameters, in order to enable support for the Phase-2 upgraded tracker geometry for the HL-LHC and potentially other detector configurations. The implementation strategy and high-level code changes required for the HL-LHC geometry are presented. Speedups in track building from mkFit imply that track fitting becomes a comparably time consuming step of the tracking chain. Prospects for an mkFit implementation of the track fit are also discussed.

The Large Hadron Collider (LHC) will be upgraded to Highluminosity LHC, increasing the number of simultaneous proton-proton collisions (pileup, PU) by several-folds. The harsher PU conditions lead to exponentially increasing combinatorics in charged particle tracking, placing a large demand on the computing resources. The projection on required computing resources exceeds the computing budget with the current algorithms running on single-thread CPUs. Motivated by the rise of heterogeneous computing in high-performance computing centers, we present Line Segment Tracking (LST), a highly parallelizeable algorithm that can run efficiently on GPUs and is being integrated to the CMS experiment central software. The usage of Alpaka framework for the algorithm implementation allows better portability of the code to run on different types of commercial parallel processors allowing flexibility on which processors to purchase for the experiment in the future. To verify a similar computational performance with a native solution, the Alpaka implementation is compared with a CUDA one on a NVIDIA Tesla V100 GPU. The algorithm creates short track segments in parallel, and progressively form higher level objects by linking segments that are consistent with genuine physics track hypothesis. The computing and physics performance are on par with the latest, multi-CPU versions of existing CMS tracking algorithms.

One of the most challenging computational problems in the Run 3 of the Large Hadron Collider (LHC) and more so in the High-Luminosity LHC (HL-LHC) is expected to be finding and fitting charged-particle tracks during event reconstruction. The methods used so far at the LHC and in particular at the CMS experiment are based on the Kalman filter technique. Such methods have shown to be robust and to provide good physics performance, both in the trigger and offline. In order to improve computational performance, we explored Kalman-filter-based methods for track finding and fitting, adapted for many-core SIMD architectures. This adapted Kalman-filter-based software, called "mkFit", was shown to provide a significant speedup compared to the traditional algorithm, thanks to its parallelized and vectorized implementation. The mkFit software was recently integrated into the offline CMS software framework, in view of its exploitation during the Run 3 of the LHC. At the start of the LHC Run 3, mkFit will be used for track finding in a subset of the CMS offline track reconstruction iterations, allowing for significant improvements over the existing framework in terms of computational performance, while retaining comparable physics performance. The performance of the CMS track reconstruction using mkFit at the start of the LHC Run 3 is presented, together with prospects of further improvement in the upcoming years of data taking.

mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both thread- and data-level parallelism. In the past few years the project transitioned from the R&D phase to deployment in the Run-3 offline workflow of the CMS experiment. The CMS tracking performs a series of iterations, targeting reconstruction of tracks of increasing difficulty after removing hits associated to tracks found in previous iterations. mkFit has been adopted for several of the tracking iterations, which contribute to the majority of reconstructed tracks. When tested in the standard conditions for production jobs, speedups in track pattern recognition are on average of the order of 3.5x for the iterations where it is used (3-7x depending on the iteration). Multiple factors contribute to the observed speedups, including vectorization and a lightweight geometry description, as well as improved memory management and single precision. Efficient vectorization is achieved with both the icc and the gcc (default in CMSSW) compilers and relies on a dedicated library for small matrix operations, Matriplex, which has recently been released in a public repository. While the mkFit geometry description already featured levels of abstraction from the actual Phase-1 CMS tracker, several components of the implementations were still tied to that specific geometry. We have further generalized the geometry description and the configuration of the run-time parameters, in order to enable support for the Phase-2 upgraded tracker geometry for the HL-LHC and potentially other detector configurations. The implementation strategy and high-level code changes required for the HL-LHC geometry are presented. Speedups in track building from mkFit imply that track fitting becomes a comparably time consuming step of the tracking chain.

The most recent CMS results from a search for supersymmetry (SUSY) with a compressed mass spectrum in leptonic final states will be presented. The search is targeting signatures with missing transverse momentum and two or three low-momentum (soft) leptons. The dataset used is collected by the CMS experiment during the Run-2 p-p collisions at $\sqrt{s} = {}$13 TeV at the LHC, and corresponds to an integrated luminosity of up to 137 fb$^{-1}$. The observed data are found to be in agreement with the standard model (SM) prediction and exclusion upper limits are set on the SUSY particles production cross section. The results are interpreted in terms of electroweakino and top squark pair production. In both cases, a small mass difference between the produced SUSY particles and the lightest neutralino is considered. A wino-bino and a higgsino simplified models are used for the electroweakino interpretation. Exclusion limits at 95% confidence level are set on $\tilde{\chi}_{2}^{0}$/$\tilde{\chi}_{1}^{\pm}$ masses up to 280 GeV for a mass difference between the $\tilde{\chi}_{2}^{0}$/$\tilde{\chi}_{1}^{\pm}$ and the lightest neutralino of 10 GeV for the wino-bino production. In the higgsino interpretation $\tilde{\chi}_{2}^{0}$/$\tilde{\chi}_{1}^{\pm}$ masses are excluded up to 210(150) GeV for a mass difference of 7.5(3) GeV. The results for the higgsino production are additionally interpreted in terms of a phenomenological minimal SUSY extension of the SM, excluding the higgsino mass parameter $\mu$ up to 180 GeV for bino mass parameter $M_1 = {}$800 GeV. Upper limits at 95% confidence level are set on the top squark pair production interpretation, excluding top squark masses up to 530 GeV in the four-body top squark decay model and up to 475 GeV in the chargino-mediated decay model for a mass difference between the top squark and the lightest neutralino of 30 GeV.

The major challenge posed by the high instantaneous luminosity in the High Luminosity LHC (HL-LHC) motivates efficient and fast reconstruction of charged particle tracks in a high pile-up environment. While there have been efforts to use modern techniques like vectorization to improve the existing classic Kalman Filter based reconstruction algorithms, Line Segment Tracking takes a fundamentally different approach by doing a bottom-up reconstruction of tracks. Small track stubs from adjoining detector regions are constructed, and then these track stubs that are consistent with typical track trajectories are successively linked. Since the production of these track stubs is localized, they can be made in parallel, which lends way into using architectures like GPUs and multi-CPUs to take advantage of the parallelism. The algorithm is implemented in the context of the CMS Phase-2 Tracker and runs on NVIDIA Tesla V100 GPUs. Good physics and timing performance has been obtained, and stepping stones for the future are elaborated.

The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) will produce particle collisions with up to 200 simultaneous proton-proton interactions. These unprecedented conditions will create a combinatorial complexity for charged-particle track reconstruction that demands a computational cost that is expected to surpass the projected computing budget using conventional CPUs. Motivated by this and taking into account the prevalence of heterogeneous computing in cutting-edge High Performance Computing centers, we propose an efficient, fast and highly parallelizable bottom-up approach to track reconstruction for the HL-LHC, along with an associated implementation on GPUs, in the context of the Phase 2 CMS outer tracker. Our algorithm, called Segment Linking (or Line Segment Tracking), takes advantage of localized track stub creation, combining individual stubs to progressively form higher level objects that are subject to kinematical and geometrical requirements compatible with genuine physics tracks. The local nature of the algorithm makes it ideal for parallelization under the Single Instruction, Multiple Data paradigm, as hundreds of objects can be built simultaneously. The computing and physics performance of the algorithm has been tested on an NVIDIA Tesla V100 GPU, already yielding efficiency and timing measurements that are on par with the latest, multi-CPU versions of existing CMS tracking algorithms.

Abstract The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) will produce particle collisions with up to 200 simultaneous proton-proton interactions. These unprecedented conditions will create a combinatorial complexity for charged-particle track reconstruction that demands a computational cost that is expected to surpass the projected computing budget using conventional CPUs. Motivated by this and taking into account the prevalence of heterogeneous computing in cutting-edge High Performance Computing centers, we propose an efficient, fast and highly parallelizable bottom-up approach to track reconstruction for the HL-LHC, along with an associated implementation on GPUs, in the context of the Phase 2 CMS outer tracker. Our algorithm, called Segment Linking (or Line Segment Tracking), takes advantage of localized track stub creation, combining individual stubs to progressively form higher level objects that are subject to kinematical and geometrical requirements compatible with genuine physics tracks. The local nature of the algorithm makes it ideal for parallelization under the Single Instruction, Multiple Data paradigm, as hundreds of objects can be built simultaneously. The computing and physics performance of the algorithm has been tested on an NVIDIA Tesla V100 GPU, already yielding efficiency and timing measurements that are on par with the latest, multi-CPU versions of existing CMS tracking algorithms.

Searches for supersymmetry (SUSY) models with a compressed mass spectrum are theoretically motivated but also pose experimental challenges. Two recent searches from the CMS Collaboration targeting leptonic final states that can originate from such models are presented. The first search investigates SUSY signatures with two opposite sign or three low momentum leptons, while the second probes the parameter space of top squark models, where the mass difference of the lightest SUSY particles is close to the mass of the top quark. Both searches are based on the full dataset collected by CMS during Run 2 of the Large Hadron Collider, corresponding to 137 fb$^{-1}$.

The most recent CMS results from a search for supersymmetry (SUSY) with a compressed mass spectrum in leptonic final states will be presented. The search is targeting signatures with missing transverse momentum and two or three low-momentum (soft) leptons. The dataset used is collected by the CMS experiment during the Run-2 p-p collisions at $\sqrt{s} = {}$13 TeV at the LHC, and corresponds to an integrated luminosity of up to 137 fb$^{-1}$. The observed data are found to be in agreement with the standard model (SM) prediction and exclusion upper limits are set on the SUSY particles production cross section. The results are interpreted in terms of electroweakino and top squark pair production. In both cases, a small mass difference between the produced SUSY particles and the lightest neutralino is considered. A wino-bino and a higgsino simplified models are used for the electroweakino interpretation. Exclusion limits at 95% confidence level are set on $\tilde{\chi}_{2}^{0}$/$\tilde{\chi}_{1}^{\pm}$ masses up to 280 GeV for a mass difference between the $\tilde{\chi}_{2}^{0}$/$\tilde{\chi}_{1}^{\pm}$ and the lightest neutralino of 10 GeV for the wino-bino production. In the higgsino interpretation $\tilde{\chi}_{2}^{0}$/$\tilde{\chi}_{1}^{\pm}$ masses are excluded up to 210(150) GeV for a mass difference of 7.5(3) GeV. The results for the higgsino production are additionally interpreted in terms of a phenomenological minimal SUSY extension of the SM, excluding the higgsino mass parameter $\mu$ up to 180 GeV for bino mass parameter $M_1 = {}$800 GeV. Upper limits at 95% confidence level are set on the top squark pair production interpretation, excluding top squark masses up to 530 GeV in the four-body top squark decay model and up to 475 GeV in the chargino-mediated decay model for a mass difference between the top squark and the lightest neutralino of 30 GeV.