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Tesse Tiemens

Publications and source records attributed to Tesse Tiemens.

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A platform for nuclear symmetry-violation searches with laser-coolable molecules carrying spinful nuclei

Cold heavy molecules are promising systems for exploring nuclear $\mathcal{P}$- and $\mathcal{CP}$-violating phenomena in search of new physics beyond the Standard Model. However, most proposed experimental strategies and their early realizations to date have been limited to proof-of-principle molecular species with effectively spin-zero nuclei that are not sensitive to nuclear symmetry-violating phenomena. Here, we introduce a comprehensive experimental toolbox that integrates cooling, trapping, coherent state manipulation, and a complete precision-measurement protocol that is applicable to molecules carrying relevant nuclear spins. Using ${}^{137}$Ba${}^{19}$F and nuclear-spin-dependent parity violation (NSD-PV) as representative species and benchmark application, respectively, our approach achieves a projected statistical sensitivity roughly two orders of magnitude beyond comparable molecular beams by combining techniques already demonstrated individually in current experiments. This level of precision could provide realistic experimental access not only to the enhanced NSD-PV signals arising from the heavy ${}^{137}$Ba nucleus within this molecule but also to the contributions from the lighter ${}^{19}$F nucleus, bringing direct benchmarks of nuclear \textit{ab initio} theory within reach. We further identify a candidate magic wavelength as a route to second-scale rotational coherence in future experiments. The techniques developed here can be transferred to measurements of nuclear Schiff and magnetic quadrupole moments in molecules containing deformed nuclei, establishing a general platform for laboratory searches for nuclear symmetry violations.

physics.atom-ph

Texture Recognition Using a Biologically Plausible Spiking Phase-Locked Loop Model for Spike Train Frequency Decomposition

In this paper, we present a novel spiking neural network model designed to perform frequency decomposition of spike trains. Our model emulates neural microcircuits theorized in the somatosensory cortex, rendering it a biologically plausible candidate for decoding the spike trains observed in tactile peripheral nerves. We demonstrate the capacity of simple neurons and synapses to replicate the phase-locked loop (PLL) and explore the emergent properties when considering multiple spiking phase-locked loops (sPLLs) with diverse oscillations. We illustrate how these sPLLs can decode textures using the spectral features elicited in peripheral nerves. Leveraging our model's frequency decomposition abilities, we improve state-of-the-art performances on a Multifrequency Spike Train (MST) dataset. This work offers valuable insights into neural processing and presents a practical framework for enhancing artificial neural network capabilities in complex pattern recognition tasks.

q-bio.NC