Graph Neural Networks in Particle Physics

Particle physics is a branch of science aiming at discovering the fundamental laws of matter and forces. Graph neural networks are trainable functions which operate on graphs—sets of elements and their pairwise relations—and are a central method within the broader field of geometric deep learning. They are very expressive and have demonstrated superior performance to other classical deep learning approaches in a variety of domains. The data in particle physics are often represented by sets and graphs and as such, graph neural networks offer key advantages. Here we review various applications of graph neural networks in particle physics, including different graph constructions, model architectures and learning objectives, as well as key open problems in particle physics for which graph neural networks are promising

Graph Neural Networks in Particle Physics

Particle physics is a branch of science aiming at discovering the fundamental laws of matter and forces. Graph neural networks are trainable functions which operate on graphs -- sets of elements and their pairwise relations -- and are a central method within the broader field of geometric deep learning. They are very expressive and have demonstrated superior performance to other classical deep learning approaches in a variety of domains. The data in particle physics are often represented by sets and graphs and as such, graph neural networks offer key advantages. Here we review various applications of graph neural networks in particle physics, including different graph constructions, model architectures and learning objectives, as well as key open problems in particle physics for which graph neural networks are promising.Comment: 29 pages, 11 figures, submitted to Machine Learning: Science and Technology, Focus on Machine Learning for Fundamental Physics collectio

Secondary Vertex Finding in Jets with Neural Networks

Jet classification is an important ingredient in measurements and searches for new physics at particle coliders, and secondary vertex reconstruction is a key intermediate step in building powerful jet classifiers. We use a neural network to perform vertex finding inside jets in order to improve the classification performance, with a focus on separation of bottom vs. charm flavor tagging. We implement a novel, universal set-to-graph model, which takes into account information from all tracks in a jet to determine if pairs of tracks originated from a common vertex. We explore different performance metrics and find our method to outperform traditional approaches in accurate secondary vertex reconstruction. We also find that improved vertex finding leads to a significant improvement in jet classification performance

Reconstructing particles in jets using set transformer and hypergraph prediction networks

The task of reconstructing particles from low-level detector response data to predict the set of final state particles in collision events represents a set-to-set prediction task requiring the use of multiple features and their correlations in the input data. We deploy three separate set-to-set neural network architectures to reconstruct particles in events containing a single jet in a fully-simulated calorimeter. Performance is evaluated in terms of particle reconstruction quality, properties regression, and jet-level metrics. The results demonstrate that such a high dimensional end-to-end approach succeeds in surpassing basic parametric approaches in disentangling individual neutral particles inside of jets and optimizing the use of complementary detector information. In particular, the performance comparison favors a novel architecture based on learning hypergraph structure, HGPflow, which benefits from a physically-interpretable approach to particle reconstruction.Comment: 17 pages, 21 figure

Epitope mapping using combinatorial phage-display libraries: a graph-based algorithm

A phage-display library of random peptides is a combinatorial experimental technique that can be harnessed for studying antibody–antigen interactions. In this technique, a phage peptide library is scanned against an antibody molecule to obtain a set of peptides that are bound by the antibody with high affinity. This set of peptides is regarded as mimicking the genuine epitope of the antibody's interacting antigen and can be used to define it. Here we present PepSurf, an algorithm for mapping a set of affinity-selected peptides onto the solved structure of the antigen. The problem of epitope mapping is converted into the task of aligning a set of query peptides to a graph representing the surface of the antigen. The best match of each peptide is found by aligning it against virtually all possible paths in the graph. Following a clustering step, which combines the most significant matches, a predicted epitope is inferred. We show that PepSurf accurately predicts the epitope in four cases for which the epitope is known from a solved antibody–antigen co-crystal complex. We further examine the capabilities of PepSurf for predicting other types of protein–protein interfaces. The performance of PepSurf is compared to other available epitope mapping programs

Pediatric colloid cysts: a multinational, multicenter study. An IFNE-ISPN-ESPN collaboration

OBJECTIVE Colloid cysts (CCs) are rare at all ages, and particularly among children. The current literature on pediatric CC is limited, and often included in mixed adult/pediatric series. The goal of this multinational, multicenter study was to combine forces among centers and investigate the clinical course of pediatric CCs. METHODS A multinational, multicenter retrospective study was performed to attain a large sample size, focusing on CC diagnosis in patients younger than 18 years of age. Collected data included clinical presentation, radiological characteristics, treatment, and outcome. RESULTS One hundred thirty-four children with CCs were included. Patient age at diagnosis ranged from 2.4 to 18 years (mean 12.8 ± 3.4 years, median 13.2 years, interquartile range 10.3–15.4 years; 22% were \u3c 10 years of age). Twenty-two cases (16%) were diagnosed incidentally, including 48% of those younger than 10 years of age. Most of the other patients had symptoms related to increased intracranial pressure and hydrocephalus. The average follow-up duration for the entire group was 49.5 ± 45.8 months. Fifty-nine patients were initially followed, of whom 28 were eventually operated on at a mean of 19 ± 32 months later due to cyst growth, increasing hydrocephalus, and/or new symptoms. There was a clear correlation between larger cysts and symptomatology, acuteness of symptoms, hydrocephalus, and need for surgery. Older age was also associated with the need for surgery. One hundred three children (77%) underwent cyst resection, 60% using a purely endoscopic approach. There was 1 death related to acute hydrocephalus at presentation. Ten percent of operated patients had some form of complication, and 7.7% of operated cases required a shunt at some point during follow-up. Functional outcome was good; however, the need for immediate surgery was associated with educational limitations. Twenty operated cases (20%) experienced a recurrence of their CC at a mean of 38 ± 46 months after the primary surgery. The CC recurrence rate was 24% following endoscopic resection and 15% following open resections (p = 0.28). CONCLUSIONS CCs may present in all pediatric age groups, although most that are symptomatic present after the age of 10 years. Incidentally discovered cysts should be closely followed, as many may grow, leading to hydrocephalus and other new symptoms. Presentation of CC may be acute and may cause life-threatening conditions related to hydrocephalus, necessitating urgent treatment. The outcome of treated children with CCs is favorable

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Measurement of the W-boson mass in pp collisions at √s=7 TeV with the ATLAS detector

A measurement of the mass of the W boson is presented based on proton–proton collision data recorded in 2011 at a centre-of-mass energy of 7 TeV with the ATLAS detector at the LHC, and corresponding to 4.6 fb−1 of integrated luminosity. The selected data sample consists of 7.8×106 candidates in the W→μν channel and 5.9×106 candidates in the W→eν channel. The W-boson mass is obtained from template fits to the reconstructed distributions of the charged lepton transverse momentum and of the W boson transverse mass in the electron and muon decay channels, yielding mW=80370±7 (stat.)±11(exp. syst.) ±14(mod. syst.) MeV =80370±19MeV, where the first uncertainty is statistical, the second corresponds to the experimental systematic uncertainty, and the third to the physics-modelling systematic uncertainty. A measurement of the mass difference between the W+ and W−bosons yields mW+−mW−=−29±28 MeV