A neural network z-vertex trigger for Belle II

We present the concept of a track trigger for the Belle II experiment, based on a neural network approach, that is able to reconstruct the z (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger will thus be able to suppress a large fraction of the dominating background from events outside of the interaction region. The trigger uses the drift time information of the hits from the Central Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal angle as well as in transverse momentum (sectors), and estimates the z-vertex without explicit track reconstruction. The preprocessing for the track trigger is based on the track information provided by the standard CDC trigger. It takes input from the 2D ($r - \varphi$) track finder, adds information from the stereo wires of the CDC, and finds the appropriate sectors in the CDC for each track in a given event. Within each sector, the z-vertex of the associated track is estimated by a specialized neural network, with a continuous output corresponding to the scaled z-vertex. The input values for the neural network are calculated from the wire hits of the CDC.Comment: Proceedings of the 16th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT), Preprint, reviewed version (only minor corrections

Data-Driven Sliding Bearing Temperature Model for Condition Monitoring in Internal Combustion Engines

Condition monitoring of components in internal combustion engines is an essential tool for increasing engine durability and avoiding critical engine operation. If lubrication at the crankshaft main bearings is insufficient, metal-to-metal contacts become likely and thus wear can occur. Bearing temperature measurements with thermocouples serve as a reliable, fast responding, individual bearing-oriented method that is comparatively simple to apply. In combination with a corresponding reference model, such measurements could serve to monitor the bearing condition. Based on experimental data from an MAN D2676 LF51 heavy-duty diesel engine, the derivation of a data-driven model for the crankshaft main bearing temperatures under steady-state engine operation is discussed. A total of 313 temperature measurements per bearing are available for this task. Readily accessible engine operating data that represent the corresponding engine operating points serve as model inputs. Different machine learning methods are thoroughly tested in terms of their prediction error with the help of a repeated nested cross-validation. The methods include different linear regression approaches (i.e., with and without lasso regularization), gradient boosting regression and support vector regression. As the results show, support vector regression is best suited for the problem. In the final evaluation on unseen test data, this method yields a prediction error of less than 0.4 °C (root mean squared error). Considering the temperature range from approximately 76 °C to 112 °C, the results demonstrate that it is possible to reliably predict the bearing temperatures with the chosen approach. Therefore, the combination of a data-driven bearing temperature model and thermocouple-based temperature measurements forms a powerful tool for monitoring the condition of sliding bearings in internal combustion engines

Online Estimation of Particle Track Parameters based on Neural Networks for the Belle II Trigger System

The Belle II particle accelerator experiment is experiencing substantial background from outside of the interaction point. To avoid taking data representing this background, track parameters are estimated within the pipelined and dead time-free level 1 trigger system of the experiment and used to suppress such events. The estimation of a particle track\u27s origin with respect to the z-Axis, which is along the beamline, is performed by the neural z-Vertex trigger. This system is estimating the origin or z-Vertex using a trained multilayer perceptron, leveraging the advantages of training to current circumstances of operation. In order fulfil the requirements set by the overall trigger system it has to provide the estimation within an overall latency of 5 us while matching a refresh rate of up to 31.75 for new track estimations. The focus of this contribution is this system\u27 current status. For this both implementation and integration into the level 1 trigger will be presented, supported by first data taken during operation as well as figures of merit such as latency and resource consumption. In addition its upgrade plan for the near future will be presented. The center of these is a Hough based track finding approach that uses Bayes theorem for training the weighting of track candidates. Characteristics of this system\u27s current prototypical implementation on FPGAs as well as present plants towards integration for future operation will be presented

Machine Learning for Nondestructive Wear Assessment in Large Internal Combustion Engines

Digitalization offers a large number of promising tools for large internal combustion engines such as condition monitoring or condition-based maintenance. This includes the status evaluation of key engine components such as cylinder liners, whose inner surfaces are subject to constant wear due to their movement relative to the pistons. Existing state-of-the-art methods for quantifying wear require disassembly and cutting of the examined liner followed by a high-resolution microscopic surface depth measurement that quantitatively evaluates wear based on bearing load curves (also known as Abbott-Firestone curves). Such reference methods are destructive, time-consuming and costly. The goal of the research presented here is to develop nondestructive yet reliable methods for quantifying the surface condition. A deep-learning framework is proposed that allows computation of the bearing load curves from reflection RGB images of the liner surface that can be collected with a wide variety of simple imaging devices, without the need to remove and destroy the investigated liner. For this purpose, a convolutional neural network is trained to predict the bearing load curve of the corresponding depth profile from the collected RGB images, which in turn can be used for further wear evaluation. Training of the network is performed using a custom-built database containing depth profiles and reflection images of liner surfaces of large gas engines. The results of the proposed method are visually examined and quantified considering several probabilistic distance metrics and comparison of roughness indicators between ground truth and model predictions. The observed success of the proposed method suggests its great potential for quantitative wear assessment on engines during service directly on site

Comunicar ciencia en México: Tendencias y narrativas

Para que sea útil, el conocimiento debe fluir desde las aulas, los laboratorios o la mente de los investigadores y creadores hacia el público en general y al especializado. Tarea primordial de la comunicación de la ciencia es tender los puentes que generen los diálogos y una retroalimentación enriquecedora entre ambos polos, y construir así una cultura que incorpore de una manera crítica y efectiva el conocimiento científico a la práctica cotidiana y al quehacer colectivo en aras de un desarrollo más armónico de la sociedad y con el entorno. Bajo esta perspectiva, en esta obra se presentan diversos trabajos que muestran desde las tendencias de la investigación académica internacional en la comunicación pública de la ciencia hasta la utilización de los periódicos, la televisión, los blogs u otros medios para vincular a los científicos y el público, y trasformar las relaciones entre ellos en beneficio común, ya sea a través del análisis y difusión de problemas médicos, como el Sida, o socioambientales, como la contaminación del agua, hasta el utilizar los principios del branding (construcción de marca) para una mejor difusión del conocimiento científico. Una obra de interés para todas aquellas personas involucradas en la generación y divulgación de la ciencia y la tecnología.ITESO, A.C

A time resolved study of injection backgrounds during the first commissioning phase of SuperKEKB

We report on measurements of beam backgrounds during the first commissioning phase of the SuperKEKB collider in 2016, performed with the plastic scintillator and silicon photomultiplier-based CLAWS detector system. The sub-nanosecond time resolution and single particle detection capability of the sensors allow bunch-by-bunch measurements, enable CLAWS to perform a novel time resolved analysis of beam backgrounds, and make the system uniquely suited for the study of injection backgrounds. We present measurements of various aspects of regular beam background and injection backgrounds which include time structure and decay behavior of injection backgrounds, hit-energy spectra and overall background rates. These measurements show that the elevated background rates following an injection generally last for several milliseconds, with the majority of the background particles typically observed within the first 500 us. The injection backgrounds exhibit pronounced patterns in time, connected to betatron and synchrotron oscillations in the accelerator rings. The frequencies of these patterns are determined from detector data.Comment: 19 pages, 12 figures, published in EPJ

First-level trigger systems for LHC experiments

We propose to carry out a broad-based programme of R&D on level-1 trigger systems for LHC experiments. We will consider the overall level-1 which coordinates different subtriggers and which interacts with the front end electronics and with the level-2 system. Careful attention will be paid to systems aspects and problems of synchronization within the pipelined processor system. Trigger algorithms for selecting events with high-pt electrons, photons, muons, jets and large missing Et will be evaluated by physics simulation studies. We will study possible implementations of such trigger algorithms in fast electronics by making conceptual design studies and using behavioural simulation models. For critical areas more detailed design studies will be made, and prototypes of some key elements will be constructed and tested. The proposed R&D project builds on existing studies and will complement other R&D projects already funded by the DRDC