Secondary vertex finding in jets with neural networks

Jet classification is an important ingredient in measurements and searches for new physics at particle colliders, 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.

Efficiency Parameterization with Neural Networks

Multidimensional efficiency maps are commonly used in high-energy physics experiments to mitigate the limitations in the generation of large samples of simulated events. Binned efficiency maps are however strongly limited by statistics. We propose a neural network approach to learn ratios of local densities to estimate in an optimal fashion efficiencies as a function of a set of parameters. Graph neural network techniques are used to account for the high dimensional correlations between different physics objects in the event. We show in a specific toy model how this method is applicable to produce accurate multidimensional efficiency maps for heavy-flavor tagging classifiers in HEP experiments, including for processes on which it was not trained.

Jet charge in heavy-ion collisions

Jet quenching effects have been widely used to study the properties of strongly-interacting matter, quark-gluon plasma, in heavy-ion collisions. Flavor tagging in heavy-ion collisions plays an important role to reveal the medium parton showers for quark and gluon evolution. Combining with kinematic information, the average jet charge can be used to separate the contribution of different jet flavors, which is defined as the momentum- weighted sum of the charges of hadrons inside a given jet. Using soft-collinear effective theory with medium interactions, we investigate the factorization of the jet charge in QCD medium. We provide predictions for jet charge distributions and their modifications compared to the ones in proton-proton collisions.

The Belle II flavor tagger

Belle II is a particle-physics experiment at the intensity frontier focused on probing non Standard Model physics through precision measurements of quark-flavor and τ-lepton dynamics. Determining the flavor of neutral B mesons, i.e. their quark composition, is a crucial task which is addressed using flavor tagging algorithms. Due to the novel high-luminosity conditions and the increased beam backgrounds at Belle II, an improved flavor tagging algorithm had to be developed to ensure the success of the Belle II physics program. The new Belle II flavor tagger exploits the flavor-specific signatures of B 0 decays employing boosted decision trees and neural networks. It identifies B 0-decay products providing flavor-specific signatures and combines the information from all possible signatures into a final output. The algorithm has been validated by comparing its performance on simulated events with its performance on collision events collected by the predecessor experiment Belle. To explore the advantages of state-of-the-art deep-learning techniques, the Belle II collaboration developed a deep-learning-based flavor tagger. This algorithm tags the flavor of B 0 mesons without identifying flavor specific signatures using a deep-learning neural network. The validation on Belle data of this algorithm is currently ongoing.

Charm Physics Prospects at the Belle II Experiment

With a total integrated luminosity of 50 ab[Formula: see text] of data and improved performances at the Belle II detector, especially vertex resolution and particle identification, sensitivity estimations for [Formula: see text]-[Formula: see text] mixing, [Formula: see text] violation and time integrated [Formula: see text] asymmetries measurements are presented. Prospects on charm rare decays and (semi-)leptonic decays are discussed. Besides, a new [Formula: see text] flavor-tagging technique, ROE method, is introduced.

An asynchronous data-driven readout prototype for CEPC vertex detector

The Circular Electron Positron Collider (CEPC) is proposed as a Higgs boson and/or Z boson factory for high-precision measurements on the Higgs boson. The precision of secondary vertex impact parameter plays an important role in such measurements which typically rely on flavor-tagging. Thus silicon CMOS Pixel Sensors (CPS) are the most promising technology candidate for a CEPC vertex detector, which can most likely feature a high position resolution, a low power consumption and a fast readout simultaneously. For the R&D of the CEPC vertex detector, we have developed a prototype MIC4 in the Towerjazz 180 nm CMOS Image Sensor (CIS) process. We have proposed and implemented a new architecture of asynchronous zero-suppression data-driven readout inside the matrix combined with a binary front-end inside the pixel. The matrix contains 128 rows and 64 columns with a small pixel pitch of 25 [Formula: see text]m. The readout architecture has implemented the traditional OR-gate chain inside a super pixel combined with a priority arbiter tree between the super pixels, only reading out relevant pixels. The MIC4 architecture will be introduced in more detail in this paper. It will be taped out in May and will be characterized when the chip comes back.