Related papers: CNN-Based Online Trigger for QGP Event Selection
The performed systematic meta-analysis of the quality of data description (QDD) of existing event generators of nucleus-nucleus collisions allows us to extract a very important physical information. Our meta-analysis is dealing with the…
Biomedical events describe complex interactions between various biomedical entities. Event trigger is a word or a phrase which typically signifies the occurrence of an event. Event trigger identification is an important first step in all…
By representing each collider event as a point cloud, we adopt the Graphic Convolutional Network (GCN) with focal loss to reconstruct the Higgs jet in it. This method provides higher Higgs tagging efficiency and better reconstruction…
An important aspect of the study of Quark-Gluon Plasma (QGP) in ultra-relativistic collisions of heavy ions is the ability to identify, in experimental data, a subset of the jets that were strongly modified by the interaction with the QGP.…
Future collider experiments require unprecedented precision in measurements of Higgs, electroweak, and flavour observables, placing stringent demands on event reconstruction. The achievable precision on Higgs couplings scales directly with…
It is the purpose of this note to point out that the CMS observation is in line with previous observations in particle physics at large transverse momenta and/or high multiplicities at lower energies, which were interpreted as possible…
There seems to be a general consensus now that a first glimpse of a QGP-like effect has become visible in the beautiful NA50 data on J/\psi production and the `anomalous supression' phenomenon. On the other hand, it is still widely believed…
In the effort to obtain a precise measurement of leptonic CP-violation with the ESS$\nu$SB experiment, accurate and fast reconstruction of detector events plays a pivotal role. In this work, we examine the possibility of replacing the…
High energy nuclear collisions manifest a variety of interesting phenomena over a broad range of energy scales. Many of these phenomena are related to the formation of a hot and dense state of deconfined quarks and gluons known as the quark…
Experimental particle physics demands a sophisticated trigger and acquisition system capable to efficiently retain the collisions of interest for further investigation. Heterogeneous computing with the employment of FPGA cards may emerge as…
Quark-Gluon Plasma (QGP), a QCD state of matter created in ultra-relativistic heavy-ion collisions, has remarkable properties, including, for example, a low shear viscosity over entropy ratio. By detecting the collection of low-momentum…
In the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods.…
Generative sequence models have shown strong results in recommendation. Applying them to search ranking is more challenging. Search behavior is inherently query-driven. Each query switch introduces a sharp topic shift in the user's…
A recent letter published in the journal Nature reports observation at the relativistic heavy ion collider (RHIC) of quark-gluon plasma (QGP) formation in small asymmetric collision systems denoted as $p$-Au, $d$-Au and $^3$He-Au. The…
A key question for machine learning approaches in particle physics is how to best represent and learn from collider events. As an event is intrinsically a variable-length unordered set of particles, we build upon recent machine learning…
Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in both training and inference. In contrast to the previous…
This paper presents studies on application of convolutional neural network (CNN) to GEANT4 optical simulation data generated with a scintillator detector subdivided into 1 cubic cm, which is designed for the long-baseline neutrino…
Based on the jet image approach, which treats the energy deposition in each calorimeter cell as the pixel intensity, the Convolutional neural network (CNN) method has been found to achieve a sizable improvement in jet tagging compared to…
The growing luminosity frontier at the Large Hadron Collider is challenging the reconstruction and analysis of particle collision events. Increased particle multiplicities are straining latency and storage requirements at the data…
We apply dynamical string models of heavy-ions collisions at high energies to the analysis of event-by-event fluctuations. Main attention is devoted to a new variable proposed to study "equilibration" in heavy-ions collisions. Recent…