The widths of quarkonia in quark gluon plasma

Recent lattice calculations showed that the quarkonia will survive beyond the phase transition temperature, and will dissolve at different temperatures depending on the type of the quarkonium. In this work, we calculate the thermal width of the quarkonium at finite temperature before it dissolves into open heavy quarks. The input of the calculation are the parton quarkonium dissociation cross section to NLO in QCD, the quarkonium wave function in a temperature-dependent potential from lattice QCD, and a thermal distribution of partons with thermal masses. We find that for the J/psi, the total thermal width above 1.4 Tc becomes larger than 100 to 250 MeV, depending on the effective thermal masses of the quark and gluon, which we take between 400 to 600 MeV. Such a width corresponds to an effective dissociation cross section by gluons between 1.5 to 3.5 mb and by quarks 1 to 2 mb at 1.4 Tc. However, at similar temperatures, we find a much smaller thermal width and effective cross section for the upsilon.Comment: 7 pages, 13 figures, 2 tables, version to be published in Phys. Rev.

Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents

Deep reinforcement learning (RL) has achieved remarkable success in solving complex tasks through its integration with deep neural networks (DNNs) as function approximators. However, the reliance on DNNs has introduced a new challenge called primacy bias, whereby these function approximators tend to prioritize early experiences, leading to overfitting. To mitigate this primacy bias, a reset method has been proposed, which performs periodic resets of a portion or the entirety of a deep RL agent while preserving the replay buffer. However, the use of the reset method can result in performance collapses after executing the reset, which can be detrimental from the perspective of safe RL and regret minimization. In this paper, we propose a new reset-based method that leverages deep ensemble learning to address the limitations of the vanilla reset method and enhance sample efficiency. The proposed method is evaluated through various experiments including those in the domain of safe RL. Numerical results show its effectiveness in high sample efficiency and safety considerations.Comment: NeurIPS 2023 camera-read

J/psi hadron interaction in vacuum and in QGP

Motivated by the recent lattice data that $J/\psi$ will survive up to 1.6$T_c$, we calculate the thermal width of $J/\psi$ at finite temperature in perturbative QCD. The inputs of the calculation are the parton quarkonium dissociation cross sections at the NLO in QCD, which were previously obtained by Song and Lee, and a gaussian charmonium wave function, whose size were fitted to an estimate by Wong by solving the schrodinger equation for charmonium in a potential extracted from the lattice at finite temperature. We find that the total thermal width above 1.4$T_c$ becomes larger than 100 to 200 MeV, depending on the effective thermal masses of the quark and gluon, which we take it to vary from 600 to 400 MeV.Comment: 4 pages, Talk at Quark Matter 200

Novel Synthesis and High Pressure Behavior of Na0.3CoO2 x 1.3 H2O and Related Phases

We have prepared powder samples of NaxCoO2 x yH2O using a new synthesis route. Superconductivity was observed in Na0.3CoO2 x 1.3H2O between 4 and 5K as indicated by the magnetic susceptibility. The bulk compressibilities of Na0.3CoO2 x 1.3H2O, Na0.3CoO2 x 0.6H2O and Na0.3CoO2 were determined using a diamond anvil cell and synchrotron powder diffraction. Chemical changes occurring under pressure when using different pressure transmitting media are discussed and further transport measurements are advocated.Comment: 7 pages, 4 figures, PRrapid submitte

The thermal width of heavy quarkonia moving in quark gluon plasma

The velocity dependence of the thermal width of heavy quarkonia traveling with respect to the quark gluon plasma is calculated up to the NLO in perturbative QCD. At the LO, the width decreases with increasing speed, whereas at the NLO it increases with a magnitude approximately proportional to the expectation value of the relative velocity between the quarkonium and a parton in thermal equilibrium. Such an asymptotic behavior is due to the NLO dissociation cross section converging to a nonvanishing value in the high energy limit.Comment: 8 pages, 4 figures, references addes. version to be published in Phys. Lett.

Numerical Validation of Two-Parameter Weibull Model for Assessing Failure Fatigue Lives of Laminated Cementitious Composites—Comparative Assessment of Modeling Approaches

In this paper, comparative assessment of failure fatigue lives of thin laminated cementitious composites (LCCs) modeled by two modeling approaches—double-parameter Weibull distribution model and triple-parameter distribution model—was carried out. LCCs were fabricated of ordinary Portland cement (OPC), fly ash cenosphere (FAC), quartz sand, and reinforcing meshes and fibers. The failure fatigue life assessment at various probabilities by the two-parameter model was based on numerical calculations whereas the three-parameter model was applied by an open source program—ProFatigue®. Respective parameters, shape and scale parameters in the two-parameter Weibull distribution model while shape, scale, and location parameters in three-parameter model were determined, and the corresponding probabilistic fatigue lives at various failure probabilities were calculated. It is concluded that the two-parameter model is more accurate in probabilistic fatigue life assessment of double-layer mesh-reinforced LCCs, whereas for single-layer reinforced LCCs, both models could be used at a fair confidence level

Optimization of Steam-Curing Regime for Recycled Aggregate Concrete Incorporating High Early Strength Cement—A Parametric Study

This paper investigates the properties of steam cured recycled aggregate concrete (RAC), in an attempt to determine the optimum conditions of the steam-curing cycle for RAC, and incorporating high early strength cement (HESC). Varying conditions of steam curing were employed. The steam-curing cycle was set based on the peak temperature, and the duration for which the peak temperature was maintained. Three peak temperatures were used for steam curing, 50 °C, 60 °C, and 70 °C, maintained for up to two hours. The compressive strength results indicated that the steam-curing cycle employing the peak temperature of 50 °C maintained for one hour with a total duration of four hours was the optimum for strength development, both at the early and late stages of hydration. Determining the optimum steam-curing temperature and duration will help reduce the associated curing cost, thus further economizing the production cost of recycled aggregate concrete

A VCO-Based 2nd-Order ???2????????? Modulator for Small-Size High Energy-Efficient Current Sensing Front-End

In this letter, a 2nd-order ?? 2-??\?? modulator consisting of a voltage-controlled-oscillator-based quantizer (VCOQ) and a current digital-to-analog converter (DAC) with a pulse width modulator (PWM) is presented for the precise acquisition of a wide-range photocurrent in an area-and energy-efficient form factor. The proposed ?? 2-modulation realized by the 2nd-order infinite impulse response (IIR) filter on the feedback significantly attenuates the magnitude of input signals, enhancing the DR and linearity. Moreover, an additional differentiator followed by the VCOQ features the negative feedback loop in the 2nd-order ??\?? modulator, improving noise shaping with no additional current DAC noise. In addition, a 1-bit PWM current DAC substituting the multibit current DAC is devised to mitigate the noise from the current DAC, realizing the high resolution of 1 pA with 500-Hz bandwidth. The prototype chip fabricated in a 110-nm CMOS occupies 0.0308 mm2 and achieves Walden FoM of 4.15 pJ/conv

Stop-loss adjusted labels for machine learning-based trading of risky assets

Since the rise of ML/AI, many researchers and practitioners have been trying to predict future stock price movements. In actual implementations, however, stop-loss is widely adopted to manage risks, which sells an asset if its price goes below a predetermined level. Hence, some buy signals from prediction models could be wasted if stop-loss is triggered. In this study, we propose a stop-loss adjusted labeling scheme to reduce the discrepancy between prediction and decision making. It can be easily incorporated to any ML/AI prediction models. Experimental results on U.S. futures and cryptocurrencies show that this simple tweak significantly reduces risk