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By studying charge trapping in germanium detectors operating at temperatures below 10 K, we demonstrate for the first time that the formation of cluster dipole states from residual impurities is responsible for charge trapping. Two planar detectors with different impurity levels and types are used in this study. When drifting the localized charge carriers created by α particles from the top surface across a detector at a lower bias voltage, significant charge trapping is observed when compared to operating at a higher bias voltage. The amount of charge trapping shows a strong dependence on the type of charge carriers. Electrons are trapped more than holes in a p-type detector, while holes are trapped more than electrons in an n-type detector. When both electrons and holes are drifted simultaneously using the widespread charge carriers created by γ rays inside the detector, the amount of charge trapping shows no dependence on the polarity of bias voltage. 

Abstract The detection of low-energy deposition in the range of sub-eV through ionization using germanium (Ge) with a bandgap of $$\sim $$ ∼ 0.7 eV requires internal amplification of the charge signal. This can be achieved through high electric field that accelerates charge carriers, which can then generate more charge carriers. The minimum electric field required to generate internal charge amplification is derived for different temperatures. We report the development of a planar point contact Ge detector in terms of its fabrication and the measurements of its leakage current and capacitance as a function of applied bias voltage. With the determination of the measured depletion voltage, the field distribution is calculated using GeFiCa, which predicts that the required electric field for internal charge amplification can be achieved in proximity to the point contact. The energy response to an Am-241 source is characterized and discussed. We conclude that such a detector with internal charge amplification can be used to search for low-mass dark matter. 

Abstract For the first time, electrical conduction mechanisms in the disordered material system is experimentally studied for p-type amorphous germanium (a-Ge) used for high-purity Ge detector contacts. The localization length and the hopping parameters in a-Ge are determined using the surface leakage current measured from three high-purity planar Ge detectors. The temperature dependent hopping distance and hopping energy are obtained for a-Ge fabricated as the electrical contact materials for high-purity Ge planar detectors. As a result, we find that the hopping energy in a-Ge increases as temperature increases while the hopping distance in a-Ge decreases as temperature increases. The localization length of a-Ge is on the order of $$2.13^{-0.05}_{+0.07}\mathrm{{A}}^\circ $$ 2 . 13 + 0.07 - 0.05 A ∘ to $$5.07^{-0.83}_{+2.58}\mathrm{{A}}^\circ $$ 5 . 07 + 2.58 - 0.83 A ∘ , depending on the density of states near the Fermi energy level within bandgap. Using these parameters, we predict that the surface leakage current from a Ge detector with a-Ge contacts can be much smaller than one yocto amp (yA) at helium temperature, suitable for rare-event physics searches. 

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.