753 research outputs found

    Using Deep Neural Networks to Improve the Precision of Fast-Sampled Particle Timing Detectors

    Get PDF
    Measurements from particle timing detectors are often affected by the time walk effect caused by statistical fluctuations in the charge deposited by passing particles. The constant fraction discriminator (CFD) algorithm is frequently used to mitigate this effect both in test setups and in running experiments, such as the CMS-PPS system at the CERN’s LHC. The CFD is simple and effective but does not leverage all voltage samples in a time series. Its performance could be enhanced with deep neural networks, which are commonly used for time series analysis, including computing the particle arrival time. We evaluated various neural network architectures using data acquired at the test beam facility in the DESY-II synchrotron, where a precise MCP (MicroChannel Plate) detector was installed in addition to PPS diamond timing detectors. MCP measurements were used as a reference to train the networks and compare the results with the standard CFD method. Ultimately, we improved the timing precision by 8% to 23%, depending on the detector's readout channel. The best results were obtained using a UNet-based model, which outperformed classical convolutional networks and the multilayer perceptron

    Joseph the MoUSE : Mouse Ultrasonic Sound Explorer

    Get PDF
    Joseph the MoUSE — Mouse Ultrasonic Sound Explorer (MoUSE) software aims to address the issue of manual analysis of recordings from experiments on rodents by introducing automatic techniques for ultrasonic vocalization (USV) detection. It combines deep learning (DL) methods with classical pattern recognition and computer graphics algorithms. During development, we used a dataset that consisted of recordings from real-world experiments in the open field. Recordings like these pose obstacles to automatic USV detection, one of which is the noise produced by mice in the experimental area or in nearby cages. Therefore, additionally, we conducted research and implemented de-noising methods along with detection algorithms. The project includes Python packages with algorithms for sound noise removal and USV detection, and provides a user-friendly graphical interface

    Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at √s = 13 TeV

    Get PDF
    Abstract The parton-level top quark (t) forward-backward asymmetry and the anomalous chromoelectric (d̂ t) and chromomagnetic (Ό̂ t) moments have been measured using LHC pp collisions at a center-of-mass energy of 13 TeV, collected in the CMS detector in a data sample corresponding to an integrated luminosity of 35.9 fb−1. The linearized variable AFB(1) is used to approximate the asymmetry. Candidate t t ÂŻ events decaying to a muon or electron and jets in final states with low and high Lorentz boosts are selected and reconstructed using a fit of the kinematic distributions of the decay products to those expected for t t ÂŻ final states. The values found for the parameters are AFB(1)=0.048−0.087+0.095(stat)−0.029+0.020(syst),Ό̂t=−0.024−0.009+0.013(stat)−0.011+0.016(syst), and a limit is placed on the magnitude of | d̂ t| < 0.03 at 95% confidence level. [Figure not available: see fulltext.

    Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at root s=13 TeV