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.

Secondary Vertex Reconstruction

The chapter reviews methods for the search for secondary vertices. Four types of secondary vertices are discussed in detail: decays of short-lived particles, decays of long-lived particles, photon conversions, and hadronic interactions in the detector material.

B-TAGGING AND SEARCHES FOR NEW PHYSICS BEYOND THE STANDARD MODEL

The article is devoted to the searches for new particles predicted by physics beyond the Standard Model through the b-tagging algorithm. The dependence of b-tagging efficiency on the jet identification, impact parameter identification, secondary vertex identification, kinematic cuts is studied with the help of computer programs Pythia 8.2 and Fastjet 3.3.0. The selection criteria for kinematic parameters, their ratios for an optimal result on the reconstruction of the vertices of heavy particles are found.

Correlation of Heavy and Light Flavors in Simulations

The ALICE experiment at the Large Hadron Collider (LHC) ring is designed to study the strongly interacting matter at extreme energy densities created in high-energy heavy-ion collisions. In this paper we investigate correlations of heavy and light flavors in simulations at LHC energies at mid-rapidity, with the primary purpose of proposing experimental applications of these methods. Our studies have shown that investigating the correlation images can aid the experimental separation of heavy quarks and help understanding the physics that create them. The shape of the correlation peaks can be used to separate the electrons stemming from b quarks. This could be a method of identification that, combined with identification in silicon vertex detectors, may provide much better sample purity for examining the secondary vertex shift. Based on a correlation picture it is also possible to distinguish between prompt and late contributions to D meson yields.

A track finding algorithm for the inner tracking system of MPD/NICA

An inner tracking system (ITS) based on silicon pixel sensors is currently considered as one of the possible MPD upgrade steps. The main purpose of the new detector is to provide a better precision of the primary and secondary vertex reconstruction and improve track reconstruction in MPD in the region close to the interaction point. To study the ITS performance a new track finding algorithm was developed, which better takes into account the new system’s advantages. In this paper the new algorithm is described and first results obtained on simulated data are presented.