scholarly journals Searching for Supersymmetry at LHC Using the Complex-Network-Based Method of the Three-Dimensional Visibility-Graph

Physics ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 436-454
Author(s):  
Susmita Bhaduri ◽  
Anirban Bhaduri

For the last several decades, there has been tremendous interest in search for Supersymmetry (SUSY) in the area of high energy physics. At Large Hadron Collider (LHC), there have been continuous searches for SUSY for prompt and non-prompt, for particle R-parity conserving and R-parity violating generation and decays. The limits obtained from these experiments and analyses for detection of the signatures of supersymmetric particles (LSP), revealed greater possibilities of such experiments in the collider. However, these signatures are usually derived under the assumption of bit optimistic conditions of the decaying process of sparticles to the final states. Moreover, SUSY might have been in a disguised state at lower mass-scales as a result of difficult and challenging mass spectra and mixed modes of decays. In this investigation, a novel method of 3-dimensional (3D) Visibility-Graph Analysis is proposed. This is an extension of Visibility Graph analysis of data series to perform the scaling analysis for 3D space. The experimental data spaces analyzed are made up of the component-space (in the X,Y and Z coordinates) of transverse momentum (pT) values taken out from 4-momenta of the signatures of the final state of the pair of mega-jets extracted from the multiJet primary pp collision data from Run B of 2010 at 7 TeV which was used for the search of SUSY using razor filter. The symmetry scaling and the inherent scaling behavior, scale-freeness of multi-particle production process is studied in terms of 3D Power-of-Scale-freeness-of-Visibility-Graph (3D-PSVG) extracted from the 3D Visibility Graphs constructed out of the experimental data spaces. The signature of SUSY may be identified by analyzing the scaling behavior and long-range correlation inherent in the 3D space made up of signatures of final state of multi-particles produced in the pp collision at 7 TeV, for the analysis of SUSY, which the conventional method of analyzing the spectrum of invariant mass or pT may miss.

2016 ◽  
Vol 31 (27) ◽  
pp. 1650158 ◽  
Author(s):  
Susmita Bhaduri ◽  
Dipak Ghosh

There are numerous existing works on investigating the dynamics of particle production process in ultrarelativistic nuclear collision. In the past, fluctuation of spatial pattern has been analyzed in terms of the scaling behavior of voids. But analysis of the scaling behavior of the void in fractal scenario has not been explored yet. In this work, we have analyzed the fractality of void probability distribution with a completely different and rigorous method called visibility graph analysis, analyzing the void-data produced out of fluctuation of pions in [Formula: see text]S–AgBr interaction at 200 GeV in pseudo-rapidity [Formula: see text] and azimuthal angle [Formula: see text] space. The power of scale-freeness of visibility graph denoted by PSVG is a measure of fractality, which can be used as a quantitative parameter for the assessment of the state of chaotic system. As the behavior of particle production process depends on the target excitation, we can dwell down the void probability distribution in the event-wise fluctuation resulted out of the high energy interaction for different degree of target excitation, with respect to the fractal scenario and analyze the scaling behavior of the voids. From the analysis of the PSVG parameter, we have observed that scaling behavior of void probability distribution in multipion production changes with increasing target excitation. Since visibility graph method is a classic method of complex network analysis, has been applied over fractional Brownian motion (fBm) and fractional Gaussian noises (fGn) to measure the fractality and long-range dependence of a time series successfully, we can quantitatively confirm that fractal behavior of the void probability distribution in particle production process depends on the target excitation.


2021 ◽  
Vol 232 (3) ◽  
Author(s):  
Kamila Jessie Sammarro Silva ◽  
Larissa Lopes Lima ◽  
Gustavo Santos Nunes ◽  
Lyda Patricia Sabogal-Paz

2021 ◽  
Vol 9 ◽  
Author(s):  
Sumanta Kundu ◽  
Anca Opris ◽  
Yohei Yukutake ◽  
Takahiro Hatano

Recent observation studies have revealed that earthquakes are classified into several different categories. Each category might be characterized by the unique statistical feature in the time series, but the present understanding is still limited due to their non-linear and non-stationary nature. Here we utilize complex network theory to shed new light on the statistical properties of earthquake time series. We investigate two kinds of time series, which are magnitude and inter-event time (IET), for three different categories of earthquakes: regular earthquakes, earthquake swarms, and tectonic tremors. Following the criterion of visibility graph, earthquake time series are mapped into a complex network by considering each seismic event as a node and determining the links. As opposed to the current common belief, it is found that the magnitude time series are not statistically equivalent to random time series. The IET series exhibit correlations similar to fractional Brownian motion for all the categories of earthquakes. Furthermore, we show that the time series of three different categories of earthquakes can be distinguished by the topology of the associated visibility graph. Analysis on the assortativity coefficient also reveals that the swarms are more intermittent than the tremors.


2013 ◽  
Vol 102 (25) ◽  
pp. 253702 ◽  
Author(s):  
Sen Jiang ◽  
Chunhua Bian ◽  
Xinbao Ning ◽  
Qianli D. Y. Ma

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