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Korea Polar Research Institute (KOPRI) installed an ionospheric sounding radar system called Vertical Incidence Pulsed Ionospheric Radar (VIPIR) at Jang Bogo Station (JBS) in 2015 in order to routinely monitor the state of the ionosphere in the auroral oval and polar cap regions. Since 2017, after two-year test operation, it has been continuously operated to produce various ionospheric parameters. In this article, we will introduce the characteristics of the JBS-VIPIR observations and possible applications of the data for the study on the polar ionosphere. The JBS-VIPIR utilizes a log periodic transmit antenna that transmits 0.5–25 MHz radio waves, and a receiving array of 8 dipole antennas. It is operated in the Dynasonde B-mode pulse scheme and utilizes the 3-D inversion program, called NeXtYZ, for the data acquisition and processing, instead of the conventional 1-D inversion procedure as used in the most of digisonde observations. The JBS-VIPIR outputs include the height profiles of the electron density, ionospheric tilts, and ion drifts with a 2-minute temporal resolution in the bottomside ionosphere. With these observations, possible research applications will be briefly described in combination with other observations for the aurora, the neutral atmosphere and the magnetosphere simultaneously conducted at JBS. 

A<sc>bstract</sc> A measurement is performed of Higgs bosons produced with high transverse momentum (p_T) via vector boson or gluon fusion in proton-proton collisions. The result is based on a data set with a center-of-mass energy of 13 TeV collected in 2016–2018 with the CMS detector at the LHC and corresponds to an integrated luminosity of 138 fb⁻¹. The decay of a high-p_THiggs boson to a boosted bottom quark-antiquark pair is selected using large-radius jets and employing jet substructure and heavy-flavor taggers based on machine learning techniques. Independent regions targeting the vector boson and gluon fusion mechanisms are defined based on the topology of two quark-initiated jets with large pseudorapidity separation. The signal strengths for both processes are extracted simultaneously by performing a maximum likelihood fit to data in the large-radius jet mass distribution. The observed signal strengths relative to the standard model expectation are$$ {4.9}_{-1.6}^{+1.9} $$ ${4.9}_{-1.6}^{+1.9}$and$$ {1.6}_{-1.5}^{+1.7} $$ ${1.6}_{-1.5}^{+1.7}$for the vector boson and gluon fusion mechanisms, respectively. A differential cross section measurement is also reported in the simplified template cross section framework. 

Abstract Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.