scholarly journals Unsupervised hadronic SUEP at the LHC

2021 ◽  
Vol 2021 (12) ◽  
Author(s):  
Jared Barron ◽  
David Curtin ◽  
Gregor Kasieczka ◽  
Tilman Plehn ◽  
Aris Spourdalakis
Keyword(s):  
Branching Ratios ◽  
Specific Model ◽  
Supervised Machine Learning ◽  
Particle Multiplicity ◽  
Detailed Knowledge ◽  
Charged Particle Multiplicity ◽  
Signal Features ◽  
The Matrix ◽  
Conformal Dynamics ◽  
Cent Level

Abstract Confining dark sectors with pseudo-conformal dynamics produce SUEPs, or Soft Unclustered Energy Patterns, at colliders: isotropic dark hadrons with soft and democratic energies. We target the experimental nightmare scenario, SUEPs in exotic Higgs decays, where all dark hadrons decay promptly to SM hadrons. First, we identify three promising observables: the charged particle multiplicity, the event ring isotropy, and the matrix of geometric distances between charged tracks. Their patterns can be exploited through a cut-and-count search, supervised machine learning, or an unsupervised autoencoder. We find that the HL-LHC will probe exotic Higgs branching ratios at the per-cent level, even without a detailed knowledge of the signal features. Our techniques can be applied to other SUEP searches, especially the unsupervised strategy, which is independent of overly specific model assumptions and the corresponding precision simulations.

scholarly journals Multiplicity dependence of (multi-)strange hadron production in proton-proton collisions at $$\sqrt{s}$$ = 13 TeV

2020 ◽  
Vol 80 (2) ◽  
Author(s):  
S. Acharya ◽  
◽  
D. Adamová ◽  
S. P. Adhya ◽  
A. Adler ◽  
...  
Keyword(s):  
Charged Particle ◽  
Charged Particles ◽  
General Purpose ◽  
Particle Multiplicity ◽  
Charged Particle Multiplicity ◽  
Production Rates ◽  
Proton Proton ◽  
Proton Collisions ◽  
Strange Hadron ◽  
Proton Proton Collisions

Abstract The production rates and the transverse momentum distribution of strange hadrons at mid-rapidity ($$\left| y\right| < 0.5$$y<0.5) are measured in proton-proton collisions at $$\sqrt{s}$$s = 13 TeV as a function of the charged particle multiplicity, using the ALICE detector at the LHC. The production rates of $$\mathrm{K}^{0}_{S}$$KS0, $$\Lambda $$Λ, $$\Xi $$Ξ, and $$\Omega $$Ω increase with the multiplicity faster than what is reported for inclusive charged particles. The increase is found to be more pronounced for hadrons with a larger strangeness content. Possible auto-correlations between the charged particles and the strange hadrons are evaluated by measuring the event-activity with charged particle multiplicity estimators covering different pseudorapidity regions. When comparing to lower energy results, the yields of strange hadrons are found to depend only on the mid-rapidity charged particle multiplicity. Several features of the data are reproduced qualitatively by general purpose QCD Monte Carlo models that take into account the effect of densely-packed QCD strings in high multiplicity collisions. However, none of the tested models reproduce the data quantitatively. This work corroborates and extends the ALICE findings on strangeness production in proton-proton collisions at 7 TeV.

scholarly journals Study of charged-particle multiplicity fluctuations in pp collisions with Monte Carlo event generators at the LHC

2020 ◽  
Vol 29 (09) ◽  
pp. 2050074
Author(s):  
E. Shokr ◽  
A. H. El-Farrash ◽  
A. De Roeck ◽  
M. A. Mahmoud
Keyword(s):  
Charged Particle ◽  
Particle Production ◽  
Local Density ◽  
Particle Density ◽  
Hadron Collider ◽  
Center Of Mass ◽  
Monte Carlo Event ◽  
Particle Multiplicity ◽  
Charged Particle Multiplicity ◽  
Proton Proton

Proton–Proton ([Formula: see text]) collisions at the Large Hadron Collider (LHC) are simulated in order to study events with a high local density of charged particles produced in narrow pseudorapidty windows of [Formula: see text] = 0.1, 0.2, and 0.5. The [Formula: see text] collisions are generated at center of mass energies of [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] TeV, i.e., the energies at which the LHC has operated so far, using PYTHIA and HERWIG event generators. We have also studied the average of the maximum charged-particle density versus the event multiplicity for all events, using the different pseudorapidity windows. This study prepares for the multi-particle production background expected in a future search for anomalous high-density multiplicity fluctuations using the LHC data.

Statistical vehicle classification methods derived from girder strains in bridges

2011 ◽  
Vol 38 (2) ◽  
pp. 200-209 ◽  
Author(s):  
Grant Rutherford ◽  
Dean K. McNeill
Keyword(s):  
Vehicle Classification ◽  
Strain Gauges ◽  
Detailed Knowledge ◽  
Vehicle Class ◽  
Filtering Algorithm ◽  
Vehicle Weight ◽  
Bridge Span ◽  
Signal Features ◽  
Manual Classification ◽  
Strain Signal

This paper investigates the use of a pre-existing network of resistive strain gauges located on the girders of a single bridge span to determine the classification and estimate the weight of vehicles traveling over that span. Vehicle events on the bridge are identified automatically by a measurement filtering algorithm. Manual classification labels are then applied to a subset of these events to investigate the strain signal features that distinguish various vehicle classes. Trends in these features over time are investigated, and an estimate of vehicle weight is obtained from these features without the need for detailed knowledge of the structure's composition. Additionally, a number of neural network configurations are tested on the problem of determining vehicle class from these features. Results are tested on data from both the summer and winter seasons. Finally, estimates of vehicle weight are improved by using the classification network to filter input events.

Modeling charged-particle multiplicity distributions at LHC

2020 ◽  
Vol 35 (36) ◽  
pp. 2050302
Author(s):  
Amr Radi
Keyword(s):  
High Energy Physics ◽  
Hadron Collider ◽  
Center Of Mass ◽  
Charged Particles ◽  
High Energy ◽  
Particle Multiplicity ◽  
Charged Particle Multiplicity ◽  
Proton Proton ◽  
Center Of Mass Energy ◽  
8 Tev

With many applications in high-energy physics, Deep Learning or Deep Neural Network (DNN) has become noticeable and practical in recent years. In this article, a new technique is presented for modeling the charged particles multiplicity distribution [Formula: see text] of Proton-Proton [Formula: see text] collisions using an efficient DNN model. The charged particles multiplicity n, the total center of mass energy [Formula: see text], and the pseudorapidity [Formula: see text] used as input in DNN model and the desired output is [Formula: see text]. DNN was trained to build a function, which studies the relationship between [Formula: see text]. The DNN model showed a high degree of consistency in matching the data distributions. The DNN model is used to predict with [Formula: see text] not included in the training set. The expected [Formula: see text] had effectively merged the experimental data and the values expected indicate a strong agreement with Large Hadron Collider (LHC) for ATLAS measurement at [Formula: see text], 7 and 8 TeV.

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