The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom GRU-based detector and developed a method for the detector parameters selection. Three different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets was intended for real-life experiments and model training. Several different setups of the developed anomaly detection system were evaluated and compared with state-of-the-art OC-SVM reference model operating on the same data. The OC-SVM model was equipped with a rich set of feature extractors accounting for a range of the input signal properties. It was determined in the course of the experiments that the detector, along with its supporting design methodology, reaches F1 equal or very close to 1 for almost all test sets. Due to the profile of the data, the best_length setup of the detector turned out to perform the best among all five tested configuration schemes of the detection system. The quantization parameters have the biggest impact on the overall performance of the detector with the best values of input/output grid equal to 16 and 8, respectively. The proposed solution of the detection significantly outperformed OC-SVM-based detector in most of the cases, with much more stable performance across all the datasets.Comment: Related to arXiv:1702.0083

Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets

The superconducting LHC magnets are coupled with an electronic monitoring system which records and analyses voltage time series reflecting their performance. A currently used system is based on a range of preprogrammed triggers which launches protection procedures when a misbehavior of the magnets is detected. All the procedures used in the protection equipment were designed and implemented according to known working scenarios of the system and are updated and monitored by human operators. This paper proposes a novel approach to monitoring and fault protection of the Large Hadron Collider (LHC) superconducting magnets which employs state-of-the-art Deep Learning algorithms. Consequently, the authors of the paper decided to examine the performance of LSTM recurrent neural networks for modeling of voltage time series of the magnets. In order to address this challenging task different network architectures and hyper-parameters were used to achieve the best possible performance of the solution. The regression results were measured in terms of RMSE for different number of future steps and history length taken into account for the prediction. The best result of RMSE=0.00104 was obtained for a network of 128 LSTM cells within the internal layer and 16 steps history buffer

LONG HORIZON ANOMALY PREDICTION IN MULTIVARIATE TIME SERIES WITH CAUSAL AUTOENCODERS

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Variational Autoencoders for New Physics Mining at the Large Hadron Collider

Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, the proposed procedure doesn't make specific assumptions on the nature of new physics. An event selection based on this algorithm would be complementary to classic LHC searches, typically based on model-dependent hypothesis testing. Such an algorithm would deliver a list of anomalous events, that the experimental collaborations could further scrutinize and even release as a catalog, similarly to what is typically done in other scientific domains. Event topologies repeating in this dataset could inspire new-physics model building and new experimental searches. Running in the trigger system of the LHC experiments, such an application could identify anomalous events that would be otherwise lost, extending the scientific reach of the LHC.Comment: 29 pages, 12 figures, 5 table

Systems and algorithms for low-latency event reconsturction for upgrades of the level-1 triger of the CMS experiment at CERN

With the increasing centre-of-mass energy and luminosity of the Large Hadron Collider (LHC), the Compact Muon Experiment (CMS) is undertaking upgrades to its triggering system in order to maintain its data-taking efficiency. In 2016, the Phase-1 upgrade to the CMS Level- 1 Trigger (L1T) was commissioned which required the development of tools for validation of changes to the trigger algorithm firmware and for ongoing monitoring of the trigger system during data-taking. A Phase-2 upgrade to the CMS L1T is currently underway, in preparation for the High-Luminosity upgrade of the LHC (HL-LHC). The HL-LHC environment is expected to be particularly challenging for the CMS L1T due to the increased number of simultaneous interactions per bunch crossing, known as pileup. In order to mitigate the effect of pileup, the CMS Phase-2 Outer Tracker is being upgraded with capabilities which will allow it to provide tracks to the L1T for the first time. A key to mitigating pileup is the ability to identify the location and decay products of the signal vertex in each event. For this purpose, two conventional algorithms have been investigated, with a baseline being proposed and demonstrated in FPGA hardware. To extend and complement the baseline vertexing algorithm, Machine Learning techniques were used to evaluate how different track parameters can be included in the vertex reconstruction process. This work culminated in the creation of a deep convolutional neural network, capable of both position reconstruction and association through the intermediate storage of tracks into a z histogram where the optimal weighting of each track can be learned. The position reconstruction part of this end-to-end model was implemented and when compared to the baseline algorithm, a 30% improvement on the vertex position resolution in tt̄ events was observed.Open Acces

Prototype of machine learning “as a service” for CMS physics in signal vs background discrimination

Big volumes of data are collected and analysed by LHC experiments at CERN. The success of this scientific challenges is ensured by a great amount of computing power and storage capacity, operated over high performance networks, in very complex LHC computing models on the LHC Computing Grid infrastructure. Now in Run-2 data taking, LHC has an ambitious and broad experimental programme for the coming decades: it includes large investments in detector hardware, and similarly it requires commensurate investment in the R&D in software and com- puting to acquire, manage, process, and analyse the shear amounts of data to be recorded in the High-Luminosity LHC (HL-LHC) era. The new rise of Artificial Intelligence - related to the current Big Data era, to the technological progress and to a bump in resources democratization and efficient allocation at affordable costs through cloud solutions - is posing new challenges but also offering extremely promising techniques, not only for the commercial world but also for scientific enterprises such as HEP experiments. Machine Learning and Deep Learning are rapidly evolving approaches to characterising and describing data with the potential to radically change how data is reduced and analysed, also at LHC. This thesis aims at contributing to the construction of a Machine Learning “as a service” solution for CMS Physics needs, namely an end-to-end data-service to serve Machine Learning trained model to the CMS software framework. To this ambitious goal, this thesis work contributes firstly with a proof of concept of a first prototype of such infrastructure, and secondly with a specific physics use-case: the Signal versus Background discrimination in the study of CMS all-hadronic top quark decays, done with scalable Machine Learning techniques

Inference Aware Neural Optimization for Top Pair Cross-Section Measurements with CMS Open Data

In recent years novel inference techniques have been developed based on the construction of summary statistics with neural networks by minimizing inference-motivated losses via automatic differentiation. The inference-aware summary statistics aim to be optimal with respect to the statistical inference goal of high energy physics analysis by accounting for the effects of nuisance parameters during the model training. One such technique is INFERNO (P. de Castro and T. Dorigo, Comp.\ Phys.\ Comm.\ 244 (2019) 170) which was shown on toy problems to outperform classical summary statistics for the problem of confidence interval estimation in the presence of nuisance parameters. In this thesis the algorithm is extended to common high energy physics problems based on a differentiable interpolation technique. In order to test and benchmark the algorithm in a real-world application, a complete, systematics-dominated analysis of the CMS experiment, "Measurement of the top-quark pair production cross section in the tau+jets channel in pp collisions at sqrt(s) = 7 TeV" (CMS Collaboration, The European Physical Journal C, 2013) is reproduced with CMS Open Data. The application of the INFERNO-powered neural network architecture to this analysis demonstrates the potential to reduce the impact of systematic uncertainties in real LHC analysis

CMS level-1 trigger muon momentum assignment with machine learning

With the advent of the High-Luminosity phase of the LHC (HL-LHC), the instantaneous luminosity of the Large Hadron Collider at CERN will increase up to 7,5 10^34 cm^-2s^-1. Therefore, new algorithmic techniques for data acquisition and processing will be necessary, in preparation for a high pile-up environment that would eventually make the current electronics and trigger devices obsolete. Nowadays, Machine Learning techniques represent a promising alternative to this problem, as they make possible the selection of multiple information - collected by the detector - and build from them different models, able to predict with a certain efficiency fundamental physical quantities, including the transverse momentum pT. The analysis presented in this Master Thesis consists in the production of such models - with data obtained through Monte Carlo simulations - capable of predicting the transverse momentum of muons crossing the Barrel region of the CMS muon chambers, and compare the results with the pT assigned by the current CMS Level 1 Barrel Muon Track Finder (BMTF) trigger system

Pre-Supernova Alert System for Super-Kamiokande with Gadolinium

The current phase of the Super-Kamiokande experiment, SK-Gd, is characterized by the addition of gadolinium sulfate to the water Cherenkov detector, which improves the detection capability of thermal neutrons. For low energy events, the main detection channel for electron anti-neutrinos is the Inverse Beta Decay interaction, which has, in its final state, a positron and a neutron. The neutron thermal capture by gadolinium emits an 8 MeV gamma-ray cascade, improving the identification of the products of this process. This improved identification reduces the background for low energy events, allowing the analysis of neutrinos with en- ergies below the usual Super-Kamiokande thresholds. One possible detection by SK-Gd is the neutrinos coming from pre-Supernova stars, which correspond to the last evolutionary state of massive stars before core-collapse Supernova. During this stage, pair annihilation and beta decay processes are the main cooling mechanisms of the massive stars, emitting high fluxes of electron anti-neutrinos. Their detection could provide an early warning for core-collapse Supernovae. The techniques for the development of the pre-Supernova alert system for SK-Gd are presented in this thesis