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Petr Bouř

Publications and source records attributed to Petr Bouř.

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Raman and Terahertz Spectroscopy of Low-Frequency Chiral Phonons in Amino Acids

Chiral phonons are mirror-symmetric vibrations that correspond to twisting and rotational motions of atoms. In chiral biomolecules, they correspond to low-energy terahertz (THz)-range vibrations of the molecular segments involving dozens of atoms whose energies are sensitive to the chirality of the molecules and local atomic geometries. Here we present spectral signatures of chiral phonons in circularly polarized low-frequency Raman and Raman optical activity (ROA) spectra from crystals of several amino acids in different enantiomeric forms. Along with complementary THz circular dichroism (TCD) measurements, our ROA data reveal two sets of bisignate peaks in valine, alanine, tyrosine and proline between 1 and 4.5 THz that are more intense than the ROA peaks in the fingerprint region. Density functional theory (DFT) calculations on L-alanine attribute these modes to twisting and shearing molecular motions. The strong agreement between the ROA and TCD data demonstrates the power of these complementary vibrational spectroscopy techniques to identify chiral phonons in biomolecules, and offers new insights into their vibrational properties and interactions with circularly polarized light.

cond-mat.mtrl-sci

New Statistical Techniques in the Measurement of the inclusive Top Pair Production Cross Section

We present several different types of multivariate statistical techniques used in the measurement of the inclusive top pair production cross section in $p \bar{p}$-collisions at $\sqrt{s} = 1.96 \text{TeV}$ employing the full RunII data ($9.7\textrm{ fb}^{-1}$) collected with the D0 detector at the Fermilab Tevatron Collider. We consider the final state of the top quark pair decays containing one electron or muon and at least two jets. We proceed various statistical homogeneity tests such as Anderson - Darling, Kolmogorov - Smirnov, and $φ$-divergences tests to determine, which variables have good data-MC agreement, as well as a good separation power. We adjusted all tests for using weighted empirical distribution functions. Further we separate $t\bar{t}$ signal from the background by the application of Generalized Linear Models, Gaussian Mixture Models, Neural Networks with Switching Units and confront them with familiar methods from ROOT TMVA package such as Boosted Decision Trees, and Multi-layer Perceptron. We compare results by area under receiver operating characteristic curve and verify the quality of the discrimination from all methods.

hep-ex