In this work, we propose an approach for electromagnetic shower generation on a track level. Currently, Monte Carlo simulation occupies 50-70% of total computing resources that are used by physicists ...experiments worldwide. Thus, speedup of the simulation step allows to reduce simulation cost and accelerate synthetic experiments. In this paper, we suggest dividing the problem of shower generation into two separate issues: graph generation and tracks features generation. Both these problems can be efficiently solved with a cascade of deep autoregressive generative network and graph convolution network. The novelty of the proposed approach lies in the application of graph neural networks to the generation of a complex recursive physical process.
The outcome of a machine learning algorithm is a prediction model. Typically, these models are computationally expensive, where improving of the quality the prediction leads to a decrease in the ...inference speed. However it is not always tradeoff between quality and speed. In this paper we show it is possible to speed up the model by using additional memory without losing significat prediction quality for a novel boosted trees algorithm called CatBoost. The idea is to combine two approaches: training fewer trees and merging trees into a kind of hashmaps called DecisionTensors. The proposed method allows for pareto-optimal reduction of the computational complexity of the decision tree model with regard to the quality of the model. In the considered example the number of lookups was decreased from 5000 to only 6 (speedup factor of 1000) while AUC score of the model was reduced by less than 10−3.
It is quite common part of the data analysis in High Energy Physics to train a classifier for signal and background separation. In case the signal under investigation is a rare process, the signal ...sample is simulated and background sample is taken from the real data. Such setting create an unnecessary bias: the classifier might learn not the characteristic of the signal but the characteristic of the imperfect simulation. So the challenge is to train the classifier in such way that it picks up signal/background difference and doesnt overfit to the simulation-specific features. The suggested approach is based on cross-domain adaptation technique using neural networks with gradient reversal. The network architecture is a dense multi-branch structure. One branch is responsible for the signal/background discrimination, the second branch helps to avoid the overfitting on the Monte-Carlo training dataset. The tests showed that this architecture is a robust mechanism for choosing trade-offs between discrimination power and overfitting. So the resulting networks successfully distinguishes the signal from the background, but does not distinguish simulated events from the real ones. Moreover, such architecture could to be easily extended with more branches, and each one could be responsible for specific discrete and continuous domains. For example, the additional third network's branch could help to reduce the correlation between the classifier predictions and reconstructed mass of the decay, thereby making such approach highly viable for wide variety of physics searches. But such network's extensions weren't investigated during this work.
Daily operation of a large-scale experiment is a challenging task, particularly from perspectives of routine monitoring of quality for data being taken. We describe an approach that uses Machine ...Learning for the automated system to monitor data quality, which is based on partial use of data qualified manually by detector experts. The system automatically classifies marginal cases: both of good an bad data, and use human expert decision to classify remaining "grey area" cases. This study uses collision data collected by the CMS experiment at LHC in 2010. We demonstrate that proposed workflow is able to automatically process at least 20% of samples without noticeable degradation of the result.
We introduce a novel approach for the reconstruction of particle properties for the SHiP detector. The SHiP experiment significantly focuses on finding effects of dark matter particle interaction. A ...characteristic trace of such an interaction is an electromagnetic shower. Our algorithm aims to reconstruct the energy and origin of such showers using online Target Tracker subdetectors that do not suffer from pile-up. Thus, the online observation of the excess of events with proper energy can be a signal for a dark matter. Two different approaches were applied: classical, using Gaussian Mixtures and machine learning based on a convolutional neural network. We've refined the output of the previous step by clusterization techniques to improve transverse coordinate estimation. The obtained results are 25% for energy resolution, 0.8 cm for position resolution in the longitudinal direction and 1 mm in the transverse direction, without any usage of the emulsion.
We describe a fully GPU-based implementation of the first level trigger for the upgrade of the LHCb detector, due to start data taking in 2021. We demonstrate that our implementation, named Allen, ...can process the 40 Tbit/s data rate of the upgraded LHCb detector and perform a wide variety of pattern recognition tasks. These include finding the trajectories of charged particles, finding proton–proton collision points, identifying particles as hadrons or muons, and finding the displaced decay vertices of long-lived particles. We further demonstrate that Allen can be implemented in around 500 scientific or consumer GPU cards, that it is not I/O bound, and can be operated at the full LHC collision rate of 30 MHz. Allen is the first complete high-throughput GPU trigger proposed for a HEP experiment.
Traces of electromagnetic showers in the neutrino experiments may be considered as signals of dark matter particles. For example, SHiP experiment is going to use emulsion film detectors similar to ...the ones designed for OPERA experiment from dark matter search. The goal of this research is to develop an algorithm that can identify traces of electromagnetic showers in particle detectors, so it would be possible to analyse and compare various dark matter hypothesis. Both real data and signal simulation samples for this research come from OPERA experiment. Also we have used exploited algorithm for electromagnetic showers identification as a baseline. Although in this research we have used no hints about shower origin.
The paper considers the problem of velocity model acquisition for a complex media based on boundary measurements. The acoustic model is used to describe the media. We used an open-source dataset of ...velocity distributions to compare the presented results with the previous works directly. Forward modeling is performed using the grid-characteristic numerical method. The inverse problem is solved using deep convolutional neural networks. Modifications for a baseline UNet architecture are proposed to improve both structural similarity index measure and quantitative correspondence of the velocity profiles with the ground truth. We evaluate our enhancements and demonstrate the statistical significance of the results.
PDF
