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M. Petzold

Publications and source records attributed to M. Petzold.

2 recordsLinked to original sources

Characterizing the Zeeman slowing force for $^{40}$Ca$^{19}$F molecules

In this paper we investigate the feasibility of Zeeman slowing calcium monofluoride (CaF) molecules originating from a cryogenic buffer gas cell. We measure the $A^2\Pi_{1/2} (v=0, J=\frac{1}{2}) - X^2\Sigma_{1/2} (v=0, N=1)$ hyperfine spectrum of CaF in the Paschen-Back regime and find excellent agreement with theory. We then investigate the scattering rate of the molecules in a molecular Zeeman slower by illuminating them with light from a 10mW broad repumper and a 10mW multi-frequency slowing laser. By comparing our results to theory we can calculate the photon scattering rate at higher powers, leading to a force profile for Zeeman slowing. We show results from a simple 1D simulation demonstrating that this force is both strong and narrow enough to lead to significant compression, and slowing of the molecules to trappable velocities.

physics.atom-ph

In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals

Pathogen outbreaks (i.e., outbreaks of bacteria and viruses) in hospitals can cause high mortality rates and increase costs for hospitals significantly. An outbreak is generally noticed when the number of infected patients rises above an endemic level or the usual prevalence of a pathogen in a defined population. Reconstructing transmission pathways back to the source of an outbreak -- the patient zero or index patient -- requires the analysis of microbiological data and patient contacts. This is often manually completed by infection control experts. We present a novel visual analytics approach to support the analysis of transmission pathways, patient contacts, the progression of the outbreak, and patient timelines during hospitalization. Infection control experts applied our solution to a real outbreak of Klebsiella pneumoniae in a large German hospital. Using our system, our experts were able to scale the analysis of transmission pathways to longer time intervals (i.e., several years of data instead of days) and across a larger number of wards. Also, the system is able to reduce the analysis time from days to hours. In our final study, feedback from twenty-five experts from seven German hospitals provides evidence that our solution brings significant benefits for analyzing outbreaks. It is also applicable to COVID-19 hospital-associated transmissions.

cs.SI