Search

This paper presents a comprehensive analysis of the double-differential $p_T$ distributions of charged particles in twelve distinct pseudorapidity regions of equal width in $pp$ collisions at center-of-mass energies of 0.9, 2.36, and 7 TeV. Utilizing the modified Tsallis function with mean transverse flow velocity, our study demonstrates a very good agreement between experimental data and the model employed. The fit quality is consistently high across all $p_T$ ranges, as assessed by Data/Fit panels accompanying each plot. Extracted parameters, including kinetic freeze-out temperature ($T_0$), transverse flow velocity ($\beta_T$), non-extensivity parameter ($q$) and mean transverse momentum $\langle p_T \rangle$ dependencies are shown on pseudorapidity ($\eta$) and collision energy ($\sqrt{s}$). $T_0$, $\beta_T$ and $\langle p_T \rangle$ exhibit a decreasing trend with increasing $\eta$ due to lower in energy transfer along high $\eta$ regions, while they show a heightened sensitivity to $\sqrt{s}$. $q$ increases with $\eta$, indicating a closer thermal equilibrium in mid-$\eta$ particles. The paper also explores correlations among these parameters, emphasizing relationships between $T_0$, $\beta_T$, $q$ and $\langle p_T \rangle$. Our study provides valuable insights into the thermal and dynamic characteristics of high-energy proton-proton collisions, contributing to the broader understanding of the bulk properties of nuclear matter produced in these interactions., Comment: 7 pages, 3 figures

This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the problem as a Markov decision process and use reinforcement learning algorithms to find the most likely failure scenarios. This paper presents Monte Carlo Tree Search (MCTS) and Deep Reinforcement Learning (DRL) solutions that can scale to large environments. We show that DRL can find more likely failure scenarios than MCTS with fewer calls to the simulator. A simulation scenario involving a vehicle approaching a crosswalk is used to validate the framework. Our proposed approach is very general and can be easily applied to other scenarios given the appropriate models of the vehicle and the environment.

We report the first experimental demonstration of frequency-locking of an extended-cavity quantum-cascade-laser (EC-QCL) to a near-infrared frequency comb. The locking scheme is applied to carry out absolute spectroscopy of N2O lines near 7.87 {\mu}m with an accuracy of ~60 kHz. Thanks to a single mode operation over more than 100 cm^{-1}, the comb-locked EC-QCL shows great potential for the accurate retrieval of line center frequencies in a spectral region that is currently outside the reach of broadly tunable cw sources, either based on difference frequency generation or optical parametric oscillation. The approach described here can be straightforwardly extended up to 12 {\mu}m, which is the current wavelength limit for commercial cw EC-QCLs., Comment: 4 pages, 3 figures