scholarly journals Artificial proto-modelling: building precursors of a next standard model from simplified model results

2021 ◽  
Vol 2021 (3) ◽  
Author(s):  
Wolfgang Waltenberger ◽  
André Lessa ◽  
Sabine Kraml
Keyword(s):  
Random Walk ◽  
Standard Model ◽  
New Physics ◽  
Experimental Results ◽  
Simplified Model ◽  
Software Framework ◽  
Combinatorial Algorithm ◽  
Random Walk Algorithm ◽  
Walk Algorithm ◽  
Novel Algorithm

Abstract We present a novel algorithm to identify potential dispersed signals of new physics in the slew of published LHC results. It employs a random walk algorithm to introduce sets of new particles, dubbed “proto-models”, which are tested against simplified-model results from ATLAS and CMS (exploiting the SModelS software framework). A combinatorial algorithm identifies the set of analyses and/or signal regions that maximally violates the SM hypothesis, while remaining compatible with the entirety of LHC constraints in our database. Demonstrating our method by running over the experimental results in the SModelS database, we find as currently best-performing proto-model a top partner, a light-flavor quark partner, and a lightest neutral new particle with masses of the order of 1.2 TeV, 700 GeV and 160 GeV, respectively. The corresponding global p-value for the SM hypothesis is pglobal≈ 0.19; by construction no look-elsewhere effect applies.

Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm

2014 ◽  
Vol 38 (8) ◽  
pp. 753-763 ◽  
Author(s):  
D.P. Onoma ◽  
S. Ruan ◽  
S. Thureau ◽  
L. Nkhali ◽  
R. Modzelewski ◽  
...  
Keyword(s):  
Random Walk ◽  
Fdg Pet ◽  
Random Walk Algorithm ◽  
Locally Adaptive ◽  
Walk Algorithm

AN IMPROVED RANDOM WALK SEGMENTATION ON THE LUNG NODULES

2013 ◽  
Vol 06 (06) ◽  
pp. 1350043 ◽  
Author(s):  
LI GUO ◽  
YUNTING ZHANG ◽  
ZEWEI ZHANG ◽  
DONGYUE LI ◽  
YING LI
Keyword(s):  
Random Walk ◽  
Lung Nodule ◽  
Texture Feature ◽  
Ellipse Fitting ◽  
Lung Nodule Detection ◽  
Random Walk Algorithm ◽  
Fitting Algorithm ◽  
Nodule Detection ◽  
Walk Algorithm ◽  
Automatic Technique

In this paper, we proposed a semi-automatic technique with a marker indicating the target to locate and segment nodules. For the lung nodule detection, we develop a Gabor texture feature by FCM (Fuzzy C Means) segmentation. Given a marker indicating a rough location of the nodules, a decision process is followed by applying an ellipse fitting algorithm. From the ellipse mask, the foreground and background seeds for the random walk segmentation can be automatically obtained. Finally, the edge of the nodules is obtained by the random walk algorithm. The feasibility and effectiveness of the proposed method are evaluated with the various types of the nodules to identify the edges, so that it can be used to locate the nodule edge and its growth rate.

Study of the semileptonic Bc → ηcτ−v̄τ decay in W′ model

2021 ◽  
Author(s):  
S. Mahata ◽  
P. Maji ◽  
S. Biswas ◽  
S. Sahoo
Keyword(s):  
Standard Model ◽  
Branching Ratio ◽  
New Physics ◽  
Experimental Results ◽  
Lepton Flavor ◽  
The Standard Model ◽  
Text Model

Recently, many discrepancies between the Standard Model (SM) predictions and experimental results have been found in [Formula: see text] quark transitions. Motivated by these discrepancies, we investigated the semileptonic [Formula: see text] decay in [Formula: see text] model. In this paper, we have estimated different decay observables such as branching ratio, lepton flavor universality (LFU) ratio [Formula: see text] and forward–backward asymmetry in the SM as well as in the [Formula: see text] model. In [Formula: see text] model, we find significant deviations from the SM for the observables except for the forward–backward asymmetry. This deviation gives us a possible indication of new physics (NP).

Stochastic Learning for SAT- Encoded Graph Coloring Problems

2010 ◽  
Vol 1 (3) ◽  
pp. 1-19 ◽  
Author(s):  
Noureddine Bouhmala ◽  
Ole-Christoffer Granmo
Keyword(s):  
Random Walk ◽  
Graph Coloring ◽  
Learning Automata ◽  
Frequency Assignment ◽  
Random Walk Algorithm ◽  
Stochastic Learning ◽  
Graph Coloring Problem ◽  
Sat Encoding ◽  
Test Sets ◽  
Walk Algorithm

The graph coloring problem (GCP) is a widely studied combinatorial optimization problem due to its numerous applications in many areas, including time tabling, frequency assignment, and register allocation. The need for more efficient algorithms has led to the development of several GC solvers. In this paper, the authors introduce a team of Finite Learning Automata, combined with the random walk algorithm, using Boolean satisfiability encoding for the GCP. The authors present an experimental analysis of the new algorithm’s performance compared to the random walk technique, using a benchmark set containing SAT-encoding graph coloring test sets.

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