arXiv subjects

Johannes Heinrich

Publications and source records attributed to Johannes Heinrich.

3 recordsLinked to original sources

arXiv2020

Trapping, cooling, and photodissociation analysis of state-selected H$\_2^+$ ions produced by (3+1) multiphoton ionization

We report on the production of cold, state-selected H$_2^+$ molecular ions in a linear RF trap. The ions are produced by (3+1) resonance-enhanced multi-photon ionisation (REMPI) of H$_2$, and sympathetically cooled by laser-cooled Be$^+$ ions. After demonstrating and characterizing the REMPI process, we use photodissociation by a deep UV laser at 213~nm to verify the high vibrational purity of the produced H$_2^+$ ion samples. Moreover, the large difference between the photodissociation efficiencies of ions created in the $v=0$ and $v=1$ levels provides a way to detect a $v=0 \to 1$ transition. These results pave the way towards high-resolution vibrational spectroscopy of H$_2^+$ for fundamental metrology applications.

physics.atom-ph↗

arXiv2017

$\bar{\textrm{H}}^{+}$ Sympathetic Cooling Simulations with a Variable Time Step

In this paper we present a new variable time step criterion for the velocity-Verlet algorithm allowing to correctly simulate the dynamics of charged particles exchanging energy via Coulomb collisions while minimising simulation time. We present physical arguments supporting the use of the criterion along with numerical results proving its validity. We numerically show that $\bar{\textrm{H}}^{+}$ ions with 18 meV initial energy can be captured and sympathetically cooled by a Coulomb crystal of $\textrm{Be}^{+}$ and $\textrm{HD}^{+}$ in less than 10 ms, an important result for the GBAR project.

physics.comp-ph↗

arXiv2016

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

Many real-world applications can be described as large-scale games of imperfect information. To deal with these challenging domains, prior work has focused on computing Nash equilibria in a handcrafted abstraction of the domain. In this paper we introduce the first scalable end-to-end approach to learning approximate Nash equilibria without prior domain knowledge. Our method combines fictitious self-play with deep reinforcement learning. When applied to Leduc poker, Neural Fictitious Self-Play (NFSP) approached a Nash equilibrium, whereas common reinforcement learning methods diverged. In Limit Texas Holdem, a poker game of real-world scale, NFSP learnt a strategy that approached the performance of state-of-the-art, superhuman algorithms based on significant domain expertise.

cs.LG↗