Fabrication of a Polyimide Film Pneumatic Actuator by Molding and Welding Processes

The bellows pneumatic actuator, which is made by folding a non-stretch film, has been proposed for various applications because it is easy to fabricate and is extremely thin and light. However, it has subpar durability performance, especially in the folded part of the film. In this study, we propose an actuator with a pod structure that possesses high design flexibility and is free from folding. A method of molding a pod structure on a polyimide film was established and a pneumatic actuator was successfully fabricated by using PI films. Two types of PI film pneumatic actuators with the same curvature, bellows type, and pod type were fabricated. Both were confirmed to have equivalent output characteristics. The bending angle and generated torque of the pod-structure actuator were 34 degrees and 3.3 mNm, respectively. In addition, the pod structure has approximately twice the durability of the bellows structure. By using the fabrication method proposed in this paper, it is possible to realize an air chamber (i.e., an actuator) that has both high durability and bending motion

Application of Particle Transformer to quark flavor tagging in the ILC project

International Linear Collider (ILC) is a next-generation e+e− linear collider to explore Beyond-Standard-Models by precise measurements of Higgs bosons. Jet flavor tagging plays a vital role in the ILC project by identification of H → bb¯, cc¯, gg, ss¯ to measure Higgs coupling constants and of HH → bb¯bb¯ and bb¯WW which are the main channels to measure the Higgs self-coupling constant. Jet flavor tagging relies on a large amount of jet information such as particle momenta, energies, and impact parameters, obtained from trajectories of particles within a jet. Since jet flavor tagging is a classification task based on massive amounts of information, machine learning techniques have been utilized for faster and more efficient analysis for the last several decades. Particle Transformer (ParT) is a machine learning model based on Transformer architecture developed for jet analysis, including jet flavor tagging. In this study, we apply ParT to ILD full simulation data to improve the efficiency of jet flavor tagging. Our research focused on evaluating the performance of ParT compared to that of the previously used flavor tagging software, LCFIPlus. We will also report the status of the performance of strange tagging on ILD full simulation dataset using ParT, which can be applied to the analysis of Higgs-strange coupling

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements