Failure of Effective Potential Approach: Nucleus-Electron Entanglement in the He-Ion

Entanglement may be considered a resource for quantum-information processing, as the origin of robust and universal equilibrium behaviour, but also as a limit to the validity of an effective potential approach, in which the influence of certain interacting subsystems is treated as a potential. Here we show that a closed three particle (two protons, one electron) model of a He-ion featuring realistic size, interactions and energy scales of electron and nucleus, respectively, exhibits different types of dynamics depending on the initial state: For some cases the traditional approach, in which the nucleus only appears as the center of a Coulomb potential, is valid, in others this approach fails due to entanglement arising on a short time-scale. Eventually the system can even show signatures of thermodynamical behaviour, i.e. the electron may relax to a maximum local entropy state which is, to some extent, independent of the details of the initial state.Comment: Submitted to Europhysics Letter

Detector development for the CRESST experiment

Recently low-mass dark matter direct searches have been hindered by a low energy background, drastically reducing the physics reach of the experiments. In the CRESST-III experiment, this signal is characterised by a significant increase of events below 200 eV. As the origin of this background is still unknown, it became necessary to develop new detector designs to reach a better understanding of the observations. Within the CRESST collaboration, three new different detector layouts have been developed and they are presented in this contribution.Comment: 8 pages, 4 figure

Optimal operation of cryogenic calorimeters through deep reinforcement learning

Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to light dark matter-nucleus scattering in current direct detection dark matter searches. In such devices, the temperature of the thermometer and the bias current in its readout circuit need careful optimization to achieve optimal detector performance. This task is not trivial and is typically done manually by an expert. In our work, we automated the procedure with reinforcement learning in two settings. First, we trained on a simulation of the response of three CRESST detectors used as a virtual reinforcement learning environment. Second, we trained live on the same detectors operated in the CRESST underground setup. In both cases, we were able to optimize a standard detector as fast and with comparable results as human experts. Our method enables the tuning of large-scale cryogenic detector setups with minimal manual interventions.Comment: 23 pages, 14 figures, 2 table

Targeting poly(ADP-ribose) polymerase activity for cancer therapy

Poly(ADP-ribosyl)ation is a ubiquitous protein modification found in mammalian cells that modulates many cellular responses, including DNA repair. The poly(ADP-ribose) polymerase (PARP) family catalyze the formation and addition onto proteins of negatively charged ADP-ribose polymers synthesized from NAD+. The absence of PARP-1 and PARP-2, both of which are activated by DNA damage, results in hypersensitivity to ionizing radiation and alkylating agents. PARP inhibitors that compete with NAD+ at the enzyme’s activity site are effective chemo- and radiopotentiation agents and, in BRCA-deficient tumors, can be used as single-agent therapies acting through the principle of synthetic lethality. Through extensive drug-development programs, third-generation inhibitors have now entered clinical trials and are showing great promise. However, both PARP-1 and PARP-2 are not only involved in DNA repair but also in transcription regulation, chromatin modification, and cellular homeostasis. The impact on these processes of PARP inhibition on long-term therapeutic responses needs to be investigated