Insitu assembly of Fe3O4@FeNi3 spherical mesoporous nanoparticles embedded on 2D reduced graphene oxide (RGO) layers as protective barrier for EMI pollution

Electromagnetic interference (EMI) is a major issue due to the increased use of electronic devices operating in the gigahertz frequency range. Consequently, to reduce electromagnetic pollution, materials with considerable magnetic and dielectric loss can be used for the attenuation of electromagnetic waves. In this paper, Fe3O4 FeNi3 spherical mesoporous nanoparticles embedded on reduced graphene oxide (RGO) layers have been synthesized using the hydrothermal reduction method. The specific surface area of Fe3O4@FeNi3/RGO nanocomposite was 67.4 m2/g with a pore size diameter of 3.4 nm (i.e., mesoporous range). Fe3O4@FeNi3/RGO nanocomposites show an enhanced absorption dominant shielding effectiveness (SE) value of 46.49 dB as compared to its binary counterpart Fe3O4@FeNi3, having SE value of 25.21 dB. The synthesized Fe3O4@FeNi3/RGO nanocomposite of thickness 1.42 mm has SER of ∼10.32 dB and SEA of ∼36.15 dB at 15 GHz. Furthermore, it is observed that shielding efficiency increases with increasing reduced graphene oxide (RGO) content in Fe3O4@FeNi3, which is owing to an excellent interconnected network between RGO and Fe3O4@FeNi3. The RGO sheets can create a comprehensive conductive network for the distribution of charges and can enhance dielectric loss because of the layered structure, greater specific surface area and large aspect ratio. Additionally, the mesoporous Fe3O4@FeNi3 hybrid embellished on the surface of RGO may be employed as a multi-pole polarisation centre, enhancing the electronic and space charge polarization of the composites, which is helpful for strong EM wave absorption. It was believed that these nanocomposites would pave the way for the development of RGO-based mesoporous nanocomposites as broadband, lightweight and effective shielding material for practical applications

Report from Working Group 2: Higgs Physics at the HL-LHC and HE-LHC

The discovery of the Higgs boson in 2012, by the ATLAS and CMS experiments, was a success achieved with only a percent of the entire dataset foreseen for the LHC. It opened a landscape of possibilities in the study of Higgs boson properties, Electroweak Symmetry breaking and the Standard Model in general, as well as new avenues in probing new physics beyond the Standard Model. Six years after the discovery, with a conspicuously larger dataset collected during LHC Run 2 at a 13 TeV centre-of-mass energy, the theory and experimental particle physics communities have started a meticulous exploration of the potential for precision measurements of its properties. This includes studies of Higgs boson production and decays processes, the search for rare decays and production modes, high energy observables, and searches for an extended electroweak symmetry breaking sector. This report summarises the potential reach and opportunities in Higgs physics during the High Luminosity phase of the LHC, with an expected dataset of pp collisions at 14 TeV, corresponding to an integrated luminosity of 3~ab$^{-1}$. These studies are performed in light of the most recent analyses from LHC collaborations and the latest theoretical developments. The potential of an LHC upgrade, colliding protons at a centre-of-mass energy of 27 TeV and producing a dataset corresponding to an integrated luminosity of 15~ab$^{-1}$, is also discussed

Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

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