Reconstruction of tracks of charged particles with high precision is very crucial for HEP experiments to achieve their physics goals. As the tracking detector of BESIII experiment, the BESIII drift chamber has suffered from aging effects resulting in degraded tracking performance after operation for about 15 years. To preserve and enhance the tracking performance of BESIII, one of the proposals is to add one layer of thin CMOS pixel sensor in cylindrical shape based on the state-of-the-art stitching technology, between the beam pipe and the drift chamber. The improvement of tracking performance of BESIII with such an additional pixel detector compared to that with only the existing drift chamber is studied using the modern common tracking software ACTS, which provides a set of detector-agnostic and highly performant tracking algorithms that have demonstrated promising performance for a few high energy physics and nuclear physics experiments.
The Gas Electron Multiplier (GEM) detector is one of the advanced members of the Micro Pattern Gas Detector (MPGD) family, used in High Energy Physics (HEP) experiments as a tracking device due to its high rate handling capability and good spatial resolution. The uniformity in the performance of the detector is an essential criterion for any tracking device. The presence of the dielectric medium (Kapton) inside the active volume of the GEM chamber changes its behaviour when exposed to external irradiation. This phenomenon is known as the charging-up effect. In this article, the uniformity in terms of gain, energy resolution and count rate of a Single Mask (SM) triple GEM chamber of dimension 10 cm $\times$ 10 cm are reported for both the charged-up and uncharged GEM foils.
This Snowmass21 Contributed Paper encourages the Particle Physics community in fostering R&D in Superconducting Nb3Sn coated Copper RF Cavities instead of costly bulk Niobium. It describes the pressing need to devote effort in this direction, which would deliver higher gradient and higher temperature of operation and reduce the overall capital and operational costs of any future collider. It is unlikely that an ILC will be built in the next ten years with Nb as one of the main cost drivers of SRFs. This paper provides strong arguments on the benefits of using this time for R&D on producing Nb3Sn on inexpensive and thermally efficient metals such as Cu or bronze, while pursuing in parallel the novel U.S. concept of parallel-feed RF accelerator structures. A technology that synergistically uses both of these advanced tools would make an ILC or equivalent machines more affordable and more likely to be built. Such a successful enterprise would readily apply to other HEP accelerators, for instance a Muon Collider, and to accelerators beyond HEP. We present and assess current efforts in the U.S. on the novel concept of parallel-feed RF accelerator structures, and in the U.S. and abroad in producing Nb3Sn films on either Cu or bronze despite minimal funding.
The LUX-ZEPLIN experiment recently reported limits on WIMP-nucleus interactions from its initial science run, down to $9.2\times10^{-48}$ cm$^2$ for the spin-independent interaction of a 36 GeV/c$^2$ WIMP at 90% confidence level. In this paper, we present a comprehensive analysis of the backgrounds important for this result and for other upcoming physics analyses, including neutrinoless double-beta decay searches and effective field theory interpretations of LUX-ZEPLIN data. We confirm that the in-situ determinations of bulk and fixed radioactive backgrounds are consistent with expectations from the ex-situ assays. The observed background rate after WIMP search criteria were applied was $(6.3\pm0.5)\times10^{-5}$ events/keV$_{ee}$/kg/day in the low-energy region, approximately 60 times lower than the equivalent rate reported by the LUX experiment.
In this article, as an extension of our study on the angular distribution of the recoil flux of WIMP-scattered target nuclei, we demonstrate a possibility of determining the mass of incident halo WIMPs by using or combining "ridge-crater" structures of the angular recoil-energy spectra with different target nuclei observed in directional direct Dark Matter detection experiments. Our simulation results show that, for a WIMP mass of only a few tens GeV, the stereoscopic angular recoil-flux distributions of both of light and heavy target nuclei would have a (longitudinally) "ridge-like" structure. However, once the WIMP mass is as heavy as a few hundreds GeV, the angular recoil-flux distributions of heavy target nuclei would in contrast show a (latitudinally) "crater-like" structure.
Machine learning techniques are becoming an integral component of data analysis in High Energy Physics (HEP). These tools provide a significant improvement in sensitivity over traditional analyses by exploiting subtle patterns in high-dimensional feature spaces. These subtle patterns may not be well-modeled by the simulations used for training machine learning methods, resulting in an enhanced sensitivity to systematic uncertainties. Contrary to the traditional wisdom of constructing an analysis strategy that is invariant to systematic uncertainties, we study the use of a classifier that is fully aware of uncertainties and their corresponding nuisance parameters. We show that this dependence can actually enhance the sensitivity to parameters of interest. Studies are performed using a synthetic Gaussian dataset as well as a more realistic HEP dataset based on Higgs boson decays to tau leptons. For both cases, we show that the uncertainty aware approach can achieve a better sensitivity than alternative machine learning strategies.
One of the biggest challenges in the High-Luminosity LHC (HL- LHC) era will be the significantly increased data size to be recorded and analyzed from the collisions at the ATLAS and CMS experiments. ServiceX is a software R&D project in the area of Data Organization, Management and Access of the IRIS- HEP to investigate new computational models for the HL- LHC era. ServiceX is an experiment-agnostic service to enable on-demand data delivery specifically tailored for nearly-interactive vectorized analyses. It is capable of retrieving data from grid sites, on-the-fly data transformation, and delivering user-selected data in a variety of different formats. New features will be presented that make the service ready for public use. An ongoing effort to integrate ServiceX with a popular statistical analysis framework in ATLAS will be described with an emphasis of a practical implementation of ServiceX into the physics analysis pipeline.
Experimental High-Energy Physics (HEP), especially the Large Hadron Collider (LHC) programme at the European Organization for Nuclear Research (CERN), is one of the most computationally intensive activities in the world. This demand is set to increase significantly with the upcoming High-Luminosity LHC (HL-LHC), and even more in future machines, such as the Future Circular Collider (FCC). As a consequence, event reconstruction, and in particular jet clustering, is bound to become an even more daunting problem, thus challenging present day computing resources. In this work, we present the first digital quantum algorithm to tackle jet clustering, opening the way for digital quantum processors to address this challenging problem. Furthermore, we show that, at present and future collider energies, our algorithm has comparable, yet generally lower complexity relative to the classical state-of-the-art $k_t$ clustering algorithm.
The Drosophila MAP kinase DJNK is a homolog of the mammalian c-Jun amino-terminal kinase (JNK). Mutations in the DJNK gene correspond to the complementation group basket. DJNK is phosphorylated and activated by the Drosophila MAP kinase kinase HEP. Substrates of DJNK include the transcription factor DJun. DJNK participates in multiple physiological processes. Exposure to endotoxic lipopolysaccharide initiates an insect immune response and leads to DJNK activation. In addition, embryos lacking DJNK are defective in dorsal closure, a process in which the lateral epithelial cells migrate over the embryo and join at the dorsal midline. These data demonstrate that the DJNK signal transduction pathway mediates an immune response and morphogenesis in vivo.
In neutrino experiments, neutrino energy reconstruction is crucial because neutrino oscillations and differential cross-sections are functions of neutrino energy. It is also challenging due to the complexity in the detector response and kinematics of final state particles. We propose a regression Convolutional Neural Network (CNN) based method to reconstruct electron neutrino energy and electron energy in the NOvA neutrino experiment. We demonstrate that with raw detector pixel inputs, a regression CNN can reconstruct event energy even with complicated final states involving lepton and hadrons. Compared with kinematics-based energy reconstruction, this method shows a significantly better energy resolution. The reconstructed to true energy ratio shows comparable or less dependence on true energy, hadronic energy fractions, and interaction modes. The regression CNN also shows smaller systematic uncertainties from the simulation of neutrino interactions. The proposed energy estimator provides improvements of $16\%$ and $12\%$ in RMS for $ν_e$ CC and electron, respectively. This method can also be extended to solve other regression problems in HEP, taking over kinematics-based reconstruction tasks.