Fri·Nov 25, 2022Friday·November 25, 2022

1. 01*

   Quarkonia production and elliptic flow in small systems measured with ALICE

   Maurice Coquet (for the ALICE collaboration)🇫🇷

   The production of quarkonia in hadronic collisions provides a unique testing ground for understanding quantum chromodynamics (QCD) since it involves both the perturbative and non-perturbative regimes of this theory. Given that a satisfactory description of quarkonia production has not yet been achieved, new measurements that can provide new insights, helping to constrain models, are needed. The ALICE apparatus allows to measure inclusive J/$\psi$ production, as well as to separate prompt charmonia from those originating from b-hadron decays. The study of the azimuthal correlation of the emitted particles, e.g. via the measurement of the elliptic flow ($v_2$), in high multiplicity proton-proton (pp) collisions can probe collective behaviour in small systems. In this contribution, we present new measurements of the inclusive, prompt and non-prompt J/$\psi$ production in pp collisions at different collision energies, together with the J/$\psi$ $v_2$ in high multiplicity pp collisions at $\sqrt{s}$=13 TeV.

   nucl-exPoS(2022)·0 citations
2. 02*

   Transverse momentum decorrelation of the flow vector in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV

   Emil Gorm Nielsen🇩🇰 · You Zhou🇩🇰

   The individual studies of the anisotropic flow vector, flow angle and flow magnitude fluctuations with multi-particle correlations provide insight into the initial conditions and properties of the quark-gluon plasma (QGP) created in high-energy heavy-ion collisions. Recent measurements of these fluctuations have been available and the comparison to hydrodynamic models shows unique sensitivities to the initial conditions of the system, but also a puzzling dependence on the specific shear viscosity $\eta /s$. In this paper, a systematic study with A Multi-Phase Transport (AMPT) model using different tunings of the initial conditions, partonic cross section and hadronic interactions investigates the $p_{\mathrm{T}}$-dependent flow vector, flow angle and flow magnitude fluctuations. It is found that the transport model reasonably describes the flow vector, flow angle and flow magnitude fluctuations observed in data and that the fluctuations are driven by fluctuations in the initial state. The comparison of data and model presented in this paper enables further constraints on the initial conditions of the heavy-ion collisions.

   nucl-exnucl-thEPJC(2023)·12 citations
3. 03*

   Production of muons from heavy-flavour hadron decays in heavy-ion collisions with ALICE at the LHC

   Bharati Naik (for the ALICE Collaboration)🇿🇦

   Measurements of the production of muons from heavy-flavour hadron decays at forward rapidity ($2.5<y<4.0$) in Pb--Pb collisions at $\sqrt{s_{\mathrm{NN}}}=2.76$ and 5.02 TeV with the ALICE detector are presented along with the $R_{\mathrm{AA}}$ measurements in different centrality intervals as a function of transverse momentum, $p_{\mathrm{T}}$. Results of the measured $R_{\mathrm{AA}}$ at both energies are shown and are compared to the different model predictions.

   nucl-exJ.Phys.Conf.Ser.(2023)·0 citations
4. 04*

   Deep Machine Learning for the PANDA Software Trigger

   P. Jiang🇩🇪 · K. Götzen🇩🇪 · R. Kliemt🇩🇪 · F. Nerling🇩🇪 · K. Peters🇩🇪

   Deep machine learning methods have been studied for the software trigger of the future PANDA experiment at FAIR, using Monte Carlo simulated data from the GEANT-based detector simulation framework PandaRoot. Ten physics channels that cover the main physics topics, including electromagnetic, exotic, charmonium, open charm, and baryonic reaction channels, have been investigated at four different anti-proton beam momenta. Binary and multi-class classification together with seven different network architectures have been studied. Finally a residual convolutional neural network with four residual blocks in a binary classification scheme has been chosen due to its extendability, performance and stability. The presented study represents a feasibility study of a completely software-based trigger system. Compared to a conventional selection method, the deep machine learning approach achieved a significant efficiency gain of up to 200\%, while keeping the background reduction factor at the required level of 1/1000. Furthermore, it is shown that the use of additional input variables can improve the data quality for subsequent analysis. This study shows that the PANDA software trigger can benefit greatly from the deep machine learning methods.

   ↳ physics.ins-dethep-exnucl-exEPJC(2023)·2 citations

* Reconstructed cohort: no mailing for this day survives in the archive. Papers are grouped by their submission times and arXiv's announcement cut-off, assuming announcement without delay; positions follow identifier order. Validated at ~91% exact-day agreement against the archived era.