Insights into ultrafast demagnetization in pseudo-gap half metals

Interest in femtosecond demagnetization experiments was sparked by Bigot's discovery in 1995. These experiments unveil the elementary mechanisms coupling the electrons' temperature to their spin order. Even though first quantitative models describing ultrafast demagnetization have just been published within the past year, new calculations also suggest alternative mechanisms. Simultaneously, the application of fast demagnetization experiments has been demonstrated to provide key insight into technologically important systems such as high spin polarization metals, and consequently there is broad interest in further understanding the physics of these phenomena. To gain new and relevant insights, we perform ultrafast optical pump-probe experiments to characterize the demagnetization processes of highly spin-polarized magnetic thin films on a femtosecond time scale. Previous studies have suggested shifting the Fermi energy into the center of the gap by tuning the number of electrons and thereby to study its influence on spin-flip processes. Here we show that choosing isoelectronic Heusler compounds (Co2MnSi, Co2MnGe and Co2FeAl) allows us to vary the degree of spin polarization between 60% and 86%. We explain this behavior by considering the robustness of the gap against structural disorder. Moreover, we observe that Co-Fe-based pseudo gap materials, such as partially ordered Co-Fe-Ge alloys and also the well-known Co-Fe-B alloys, can reach similar values of the spin polarization. By using the unique features of these metals we vary the number of possible spin-flip channels, which allows us to pinpoint and control the half metals electronic structure and its influence onto the elementary mechanisms of ultrafast demagnetization.Comment: 17 pages, 4 figures, plus Supplementary Informatio

FARCI: Fast and Robust Connectome Interference

The inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome. We demonstrated using in silico datasets from the Neural Connectomics Challenge (NCC) and those generated using the state-of-the-art simulator of Neural Anatomy and Optimal Microscopy (NAOMi) that FARCI provides an accurate connectome and its performance is robust to network sizes, missing neurons, and noise levels. Moreover, FARCI is computationally efficient and highly scalable to large networks. In comparison with the best performing connectome inference algorithm in the NCC, Generalized Transfer Entropy (GTE), and Fluorescence Single Neuron and Network Analysis Package (FluoroSNNAP), FARCI produces more accurate networks over different network sizes, while providing significantly better computational speed and scaling

Two-photon linewidth of light "stopping" via electromagnetically induced transparency

We analyze the two-photon linewidth of the recently proposed adiabatic transfer technique for ``stopping'' of light using electromagnetically induced transparency (EIT). We shown that a successful and reliable transfer of excitation from light to atoms and back can be achieved if the spectrum of the input probe pulse lies within the initial transparency window of EIT, and if the two-photon detuning $\delta$ is less than the collective coupling strength (collective vacuum Rabi-frequency) $g\sqrt{N}$ divided by $\sqrt{\gamma T}$, with $\gamma$ being the radiative decay rate, $N$ the effective number of atoms in the sample, and $T$ the pulse duration. Hence in an optically thick medium light ``storage'' and retrieval is possible with high fidelity even for systems with rather large two-photon detuning or inhomogeneous broadening.Comment: 2 figure