Quantum anomaly detection in the latent space of proton collision events at the LHC

We propose a new strategy for anomaly detection at the LHC based on unsupervised quantum machine learning algorithms. To accommodate the constraints on the problem size dictated by the limitations of current quantum hardware we develop a classical convolutional autoencoder. The designed quantum anomaly detection models, namely an unsupervised kernel machine and two clustering algorithms, are trained to find new-physics events in the latent representation of LHC data produced by the autoencoder. The performance of the quantum algorithms is benchmarked against classical counterparts on different new-physics scenarios and its dependence on the dimensionality of the latent space and the size of the training dataset is studied. For kernel-based anomaly detection, we identify a regime where the quantum model significantly outperforms its classical counterpart. An instance of the kernel machine is implemented on a quantum computer to verify its suitability for available hardware. We demonstrate that the observed consistent performance advantage is related to the inherent quantum properties of the circuit used

Anomaly detection at LHC based on autoencoders

Potraga za novim pojavama u fizici koje su izvan Standardnog modela fizike jedan je od glavnih razloga za projektiranje i izradu Velikog Hadronskog Sudarača (engl. Large Hadron Collider) u CERN -u. Ovaj rad opisuje razvoj algoritama nenadziranog učenja za otkrivanje anomalija, osobito standardnih i varijacijskih autokodera, te strategije za njihovu primjenu u ograničenom okruženju sustava za filtriranje događaja koji djeluje u stvarnom vremenu u LHC eksperimentu. Takav model bio bi neovisan o prirodi anomalije, pa bi bio nepristran pri odabiru novih pojava u fizici. Dvije vrste autoenkodera s potpuno povezanom arhitekturom neuronske mreže uspoređuju se u smislu učinkovitosti otkrivanja anomalija i performansi u pogledu kašnjenja i korištenja resursa nakon što su raspoređene na primjeru programirljivih logičkih sklopova (engl. Field- programmable gate array) tipičnog sustava za odabir događaja na LHC-u. Također, pokazano je da model nenadziranog može učinkovito postići dobre performanse otkrivanja anomalija unutar strogo zadanih dopuštenih intervala kašnjenja i ograničenja resursa koje nameće FPGA okruženje.The search for new physics beyond the Standard Model is one of the main reasons for the creation of the Large Hadron Collider (LHC) at CERN. This thesis describes the development of unsupervised learning algorithms for anomaly detection, particu- larly standard and variational autoencoders, and the strategies for their deployment in the restricted environment of the online event filtering system at an LHC experiment. Such a model would be independent of the nature of the anomaly, thus being unbiased while selecting new physics signatures. Two types of autoencoders with fully con- nected neural network architecture are compared in terms of anomaly detection effi- ciency, and performance regarding latency and resource usage after being deployed on an example field-programmable gate array (FPGA) of a typical event selection system at the LHC. It is also demonstrated that the unsupervised learning model can effec- tively achieve good anomaly detection performance within strict latency and resource budgets imposed by the FPGA environment

Visualization of Social Network Page Users

Društvene mreže imaju važnu ulogu danas, kako osobnim korisnicima tako i poslovnim organizacijama. Među društvenim mrežama najutjecajnija je Facebook, što je dokazano brojnim analizama broja korisnika. U okviru ovog završnog rada istražene su metode dohvaćanja podataka s Facebook platforme te implementirana njihova vizualizacija u obliku mreže korisnika s dostupnim informacijama. Zbog boljeg prikaza mreže i uvida u podatke u mreži, omogućena je filtracija podataka zanimljivijih korisniku sustava. Filtracija je omogućena s obzirom na vrstu interakcije među korisnicima, broj korisnika koji su u interakciji s objavom, na vrijeme stvaranja objave i na vrstu objave. Također su prikazani primjeri mreža koje se mogu stvoriti koristeći sustav vizualizacije. Izrađeni ustav je moguće koristiti i s podacima drugih društvenih mreža uz male preinake.Social networks play an important role today, both for personal users and for business organizations. Among the social networks, Facebook is the most influential, which has been proven by many user analyzes. Within this report, methods for retrieving data from the Facebook platform have been explored and their visualization has been implemented in the form of a network of users with available information. For better display and understanding of the network, it is possible to filter the data which is more interesting to the user of the vizualization system. Filtration is enabled with regard to the type of interaction between users, the number of users interacting with the publication (post), the time of publication and the type of publication. This report includes also examples of networks that can be created using the visualization system. Developed visualization system can also be used with the data of other social networks with minor modifications

Anomaly detection at LHC based on autoencoders

Potraga za novim pojavama u fizici koje su izvan Standardnog modela fizike jedan je od glavnih razloga za projektiranje i izradu Velikog Hadronskog Sudarača (engl. Large Hadron Collider) u CERN -u. Ovaj rad opisuje razvoj algoritama nenadziranog učenja za otkrivanje anomalija, osobito standardnih i varijacijskih autokodera, te strategije za njihovu primjenu u ograničenom okruženju sustava za filtriranje događaja koji djeluje u stvarnom vremenu u LHC eksperimentu. Takav model bio bi neovisan o prirodi anomalije, pa bi bio nepristran pri odabiru novih pojava u fizici. Dvije vrste autoenkodera s potpuno povezanom arhitekturom neuronske mreže uspoređuju se u smislu učinkovitosti otkrivanja anomalija i performansi u pogledu kašnjenja i korištenja resursa nakon što su raspoređene na primjeru programirljivih logičkih sklopova (engl. Field- programmable gate array) tipičnog sustava za odabir događaja na LHC-u. Također, pokazano je da model nenadziranog može učinkovito postići dobre performanse otkrivanja anomalija unutar strogo zadanih dopuštenih intervala kašnjenja i ograničenja resursa koje nameće FPGA okruženje.The search for new physics beyond the Standard Model is one of the main reasons for the creation of the Large Hadron Collider (LHC) at CERN. This thesis describes the development of unsupervised learning algorithms for anomaly detection, particu- larly standard and variational autoencoders, and the strategies for their deployment in the restricted environment of the online event filtering system at an LHC experiment. Such a model would be independent of the nature of the anomaly, thus being unbiased while selecting new physics signatures. Two types of autoencoders with fully con- nected neural network architecture are compared in terms of anomaly detection effi- ciency, and performance regarding latency and resource usage after being deployed on an example field-programmable gate array (FPGA) of a typical event selection system at the LHC. It is also demonstrated that the unsupervised learning model can effec- tively achieve good anomaly detection performance within strict latency and resource budgets imposed by the FPGA environment

Anomaly detection at LHC based on autoencoders

Potraga za novim pojavama u fizici koje su izvan Standardnog modela fizike jedan je od glavnih razloga za projektiranje i izradu Velikog Hadronskog Sudarača (engl. Large Hadron Collider) u CERN -u. Ovaj rad opisuje razvoj algoritama nenadziranog učenja za otkrivanje anomalija, osobito standardnih i varijacijskih autokodera, te strategije za njihovu primjenu u ograničenom okruženju sustava za filtriranje događaja koji djeluje u stvarnom vremenu u LHC eksperimentu. Takav model bio bi neovisan o prirodi anomalije, pa bi bio nepristran pri odabiru novih pojava u fizici. Dvije vrste autoenkodera s potpuno povezanom arhitekturom neuronske mreže uspoređuju se u smislu učinkovitosti otkrivanja anomalija i performansi u pogledu kašnjenja i korištenja resursa nakon što su raspoređene na primjeru programirljivih logičkih sklopova (engl. Field- programmable gate array) tipičnog sustava za odabir događaja na LHC-u. Također, pokazano je da model nenadziranog može učinkovito postići dobre performanse otkrivanja anomalija unutar strogo zadanih dopuštenih intervala kašnjenja i ograničenja resursa koje nameće FPGA okruženje.The search for new physics beyond the Standard Model is one of the main reasons for the creation of the Large Hadron Collider (LHC) at CERN. This thesis describes the development of unsupervised learning algorithms for anomaly detection, particu- larly standard and variational autoencoders, and the strategies for their deployment in the restricted environment of the online event filtering system at an LHC experiment. Such a model would be independent of the nature of the anomaly, thus being unbiased while selecting new physics signatures. Two types of autoencoders with fully con- nected neural network architecture are compared in terms of anomaly detection effi- ciency, and performance regarding latency and resource usage after being deployed on an example field-programmable gate array (FPGA) of a typical event selection system at the LHC. It is also demonstrated that the unsupervised learning model can effec- tively achieve good anomaly detection performance within strict latency and resource budgets imposed by the FPGA environment

Visualization of Social Network Page Users

Društvene mreže imaju važnu ulogu danas, kako osobnim korisnicima tako i poslovnim organizacijama. Među društvenim mrežama najutjecajnija je Facebook, što je dokazano brojnim analizama broja korisnika. U okviru ovog završnog rada istražene su metode dohvaćanja podataka s Facebook platforme te implementirana njihova vizualizacija u obliku mreže korisnika s dostupnim informacijama. Zbog boljeg prikaza mreže i uvida u podatke u mreži, omogućena je filtracija podataka zanimljivijih korisniku sustava. Filtracija je omogućena s obzirom na vrstu interakcije među korisnicima, broj korisnika koji su u interakciji s objavom, na vrijeme stvaranja objave i na vrstu objave. Također su prikazani primjeri mreža koje se mogu stvoriti koristeći sustav vizualizacije. Izrađeni ustav je moguće koristiti i s podacima drugih društvenih mreža uz male preinake.Social networks play an important role today, both for personal users and for business organizations. Among the social networks, Facebook is the most influential, which has been proven by many user analyzes. Within this report, methods for retrieving data from the Facebook platform have been explored and their visualization has been implemented in the form of a network of users with available information. For better display and understanding of the network, it is possible to filter the data which is more interesting to the user of the vizualization system. Filtration is enabled with regard to the type of interaction between users, the number of users interacting with the publication (post), the time of publication and the type of publication. This report includes also examples of networks that can be created using the visualization system. Developed visualization system can also be used with the data of other social networks with minor modifications

Visualization of Social Network Page Users

Društvene mreže imaju važnu ulogu danas, kako osobnim korisnicima tako i poslovnim organizacijama. Među društvenim mrežama najutjecajnija je Facebook, što je dokazano brojnim analizama broja korisnika. U okviru ovog završnog rada istražene su metode dohvaćanja podataka s Facebook platforme te implementirana njihova vizualizacija u obliku mreže korisnika s dostupnim informacijama. Zbog boljeg prikaza mreže i uvida u podatke u mreži, omogućena je filtracija podataka zanimljivijih korisniku sustava. Filtracija je omogućena s obzirom na vrstu interakcije među korisnicima, broj korisnika koji su u interakciji s objavom, na vrijeme stvaranja objave i na vrstu objave. Također su prikazani primjeri mreža koje se mogu stvoriti koristeći sustav vizualizacije. Izrađeni ustav je moguće koristiti i s podacima drugih društvenih mreža uz male preinake.Social networks play an important role today, both for personal users and for business organizations. Among the social networks, Facebook is the most influential, which has been proven by many user analyzes. Within this report, methods for retrieving data from the Facebook platform have been explored and their visualization has been implemented in the form of a network of users with available information. For better display and understanding of the network, it is possible to filter the data which is more interesting to the user of the vizualization system. Filtration is enabled with regard to the type of interaction between users, the number of users interacting with the publication (post), the time of publication and the type of publication. This report includes also examples of networks that can be created using the visualization system. Developed visualization system can also be used with the data of other social networks with minor modifications

LHC physics dataset for unsupervised New Physics detection at 40 MHz

In the particle detectors at the Large Hadron Collider, hundreds of millions of proton-proton collisions are produced every second. If one could store the whole data stream produced in these collisions, tens of terabytes of data would be written to disk every second. The general-purpose experiments ATLAS and CMS reduce this overwhelming data volume to a sustainable level, by deciding in real-time whether each collision event should be kept for further analysis or be discarded. We introduce a dataset of proton collision events that emulates a typical data stream collected by such a real-time processing system, pre-filtered by requiring the presence of at least one electron or muon. This dataset could be used to develop novel event selection strategies and assess their sensitivity to new phenomena. In particular, we intend to stimulate a community-based effort towards the design of novel algorithms for performing unsupervised new physics detection, customized to fit the bandwidth, latency and computational resource constraints of the real-time event selection system of a typical particle detector.ISSN:2052-446

Unravelling physics beyond the standard model with classical and quantum anomaly detection

Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC). However, current experiments do not indicate clear signs of new physics that could guide the development of additional Beyond Standard Model (BSM) theories. Identifying signatures of new physics out of the enormous amount of data produced at the LHC falls into the class of anomaly detection and constitutes one of the greatest computational challenges. In this article, we propose a novel strategy to perform anomaly detection in a supervised learning setting, based on the artificial creation of anomalies through a random process. For the resulting supervised learning problem, we successfully apply classical and quantum support vector classifiers (CSVC and QSVC respectively) to identify the artificial anomalies among the SM events. Even more promising, we find that employing an SVC trained to identify the artificial anomalies, it is possible to identify realistic BSM events with high accuracy. In parallel, we also explore the potential of quantum algorithms for improving the classification accuracy and provide plausible conditions for the best exploitation of this novel computational paradigm

40 MHz Scouting with Deep Learning in CMS

A 40 MHz scouting system at CMS would provide fast and virtually unlimited statistics for detector diagnostics, alternative luminosity measurements and, in some cases, calibrations, and it has the potential to enable the study of otherwise inaccessible signatures, either too common to fit in the L1 accept budget, or with requirements which are orthogonal to ``mainstream'' physics, such as long-lived particles. Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw inputs. A series of studies on different aspects of LHC data processing have demonstrated the potential of deep learning for CERN applications. The usage of deep learning aims at improving physics performance and reducing execution time. This talk will present a deep learning approach to muon scouting in the Level-1 Trigger of the CMS detector. The idea is to utilise multilayered perceptrons to ``re-fit''' the Level-1 muon tracks, using fully reconstructed offline tracking parameters as the ground truth for neural network training. The network produces corrected helix parameters (transverse momentum, $\eta$ and $\phi$), with a precision that is greatly improved over the standard Level 1 reconstruction. The network is executed on an FPGA-based PCIe board produced by Micron Technology, the SB-852. It is implemented using the Micron Deep Learning Accelerator inference engine. The methodology for developing deep learning models will be presented, alongside the process of compiling the models for fast inference hardware. The metrics for evaluating performance and the achieved results will be discussed