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The transverse momentum ($p_T$) spectra of charged hadrons in $p+p$, $p+Pb$ and $Pb+Pb$ collisions at $\sqrt {s_{NN}} = 5.02$ TeV are presented here within the rapidity range of $-2.5<y<2.0$. We study the medium effects, which is produced…

High Energy Physics - Phenomenology · Physics 2024-06-07 Kapil Saraswat , Prashanta Kumar Khandai , Deependra Singh Rawat , Venktesh Singh

The phenomenological approach in the framework of Quark-Gluon String Model (QGSM) has been applied to the description of transverse momentum spectra for the baryon production at colliders. The analysis of data on hyperon transverse momentum…

High Energy Physics - Phenomenology · Physics 2018-11-12 Olga I. Piskounova

The previously publicated analysis of transverse momentum spectra of $\Lambda^0$ hyperons from LHC experiments (ALICE, ATLAS, CMS)in the comparison with earlier experiments was reconsidered with correct spectra from STAR collaboration. The…

High Energy Physics - Phenomenology · Physics 2018-11-12 Olga Piskounova

Modified gravity theories generically predict a violation of Lorentz invariance, which may lead to a modified dispersion relation for propagating modes of gravitational waves. We construct a parametrized dispersion relation that can…

General Relativity and Quantum Cosmology · Physics 2014-12-02 Saeed Mirshekari , Nicolas Yunes , Clifford M. Will

I present some results about transverse momentum dependent distribution and fragmentation functions. Firstly I illustrate a simple model, with predictive power about the energy behavior, for T-odd, chiral odd functions. Moreover I propose a…

High Energy Physics - Phenomenology · Physics 2008-11-26 E. Di Salvo

In this work, we propose to model the interaction between visual and textual features for multi-modal neural machine translation (MMT) through a latent variable model. This latent variable can be seen as a multi-modal stochastic embedding…

Computation and Language · Computer Science 2019-05-17 Iacer Calixto , Miguel Rios , Wilker Aziz

The transverse momentum-dependent fragmentation functions (TMD FFs) of heavy (bottom and charm) quarks, which we recently introduced, are universal building blocks that enter predictions for a large number of observables involving…

High Energy Physics - Phenomenology · Physics 2025-03-04 Rebecca von Kuk , Johannes K. L. Michel , Zhiquan Sun

An inclusive search for supersymmetric processes that produce final states with jets and missing transverse energy is performed in pp collisions at a centre-of-mass energy of 8 TeV. The data sample corresponds to an integrated luminosity of…

High Energy Physics - Experiment · Physics 2013-10-30 CMS Collaboration

We investigate the use of jets to measure transverse momentum dependent distributions (TMDs). The example we use to present our framework is the dijet momentum decorrelation at lepton colliders. Translating this momentum decorrelation into…

High Energy Physics - Phenomenology · Physics 2018-10-24 Daniel Gutierrez-Reyes , Ignazio Scimemi , Wouter J. Waalewijn , Lorenzo Zoppi

The measurement of the transverse-momentum fraction ($z^{\rm ch}$) carried by prompt and non-prompt J/$\psi$ in charged-particle jets in proton--proton collisions with a center-of-mass energy $\sqrt{s}= 13$ TeV is reported by the ALICE…

High Energy Physics - Experiment · Physics 2026-02-27 ALICE Collaboration

Two related searches for phenomena beyond the standard model (BSM) are performed using events with hadronic jets and significant transverse momentum imbalance. The results are based on a sample of proton-proton collisions at a…

High Energy Physics - Experiment · Physics 2020-01-08 CMS Collaboration

This is a shortened, clarified, and mathematically more rigorous version of the original arXiv version. Its first four findings remain unchanged from the original: 1) measurement-to-track associations (MTAs) in multitarget tracking (MTT)…

Methodology · Statistics 2024-03-01 Ronald Mahler

Proximities are at the heart of almost all machine learning methods. If the input data are given as numerical vectors of equal lengths, euclidean distance, or a Hilbertian inner product is frequently used in modeling algorithms. In a more…

Machine Learning · Computer Science 2020-09-01 Maximilian Münch , Michiel Straat , Michael Biehl , Frank-Michael Schleif

A search for new physics is performed in multijet events with large missing transverse momentum produced in proton-proton collisions at sqrt(s) = 8 TeV using a data sample corresponding to an integrated luminosity of 19.5 inverse femtobarns…

High Energy Physics - Experiment · Physics 2014-07-08 CMS Collaboration

Tensor decomposition methods are popular tools for learning latent variables given only lower-order moments of the data. However, the standard assumption is that we have sufficient data to estimate these moments to high accuracy. In this…

Machine Learning · Statistics 2019-03-13 Omer Gottesman , Weiwei Pan , Finale Doshi-Velez

We review transverse momentum distributions of various identified charged particles stemming from high energy collisions fitted by various non-extensive distributions as well as by the usual Boltzmann-Gibbs statistics. We investigate the…

High Energy Physics - Phenomenology · Physics 2019-08-14 Keming Shen , Gergely Gábor Barnaföldi , Tamás Sándor Biró

Hadron colliders offer a unique opportunity to test perturbative QCD because, rather than producing events at a specific beam energy, the dynamics of the hard scattering is probed simultaneously at a wide range of momentum transfers. This…

High Energy Physics - Phenomenology · Physics 2008-11-26 W. T. Giele , E. W. N. Glover , J. Yu

The mass-constraining variable $M_2$, a $(1+3)$-dimensional natural successor of extremely popular $M_{T2}$, possesses an array of rich features having the ability to use on-shell mass constraints in semi-invisible production at a hadron…

High Energy Physics - Phenomenology · Physics 2016-02-02 Partha Konar , Abhaya Kumar Swain

Despite increasing focus on data publication and discovery in materials science and related fields, the global view of materials data is highly sparse. This sparsity encourages training models on the union of multiple datasets, but simple…

Machine Learning · Computer Science 2017-11-15 Maxwell L. Hutchinson , Erin Antono , Brenna M. Gibbons , Sean Paradiso , Julia Ling , Bryce Meredig

We review the concept of support vector machines (SVMs) and discuss examples of their use. One of the benefits of SVM algorithms, compared with neural networks and decision trees is that they can be less susceptible to over fitting than…

Data Analysis, Statistics and Probability · Physics 2016-12-21 A. Bethani , A. J. Bevan , J. Hays , T. J. Stevenson