Off diagonal charged scalar couplings with the Z boson: Zee-type models as an example

Models with scalar doublets and charged scalar singlets have the interesting property that they have couplings between one Z boson and two charged scalars of different masses. This property is often ignored in phenomenological analysis, as it is absent from models with only extra scalar doublets. We explore this issue in detail, considering $$h \rightarrow Z \gamma $$ h → Z γ , $$B \rightarrow X_s \gamma $$ B → X s γ , and the decay of a heavy charged scalar into a lighter one and a Z boson. We propose that the latter be actively searched for at the LHC, using the scalar sector of the Zee-type models as a prototype and proposing benchmark points which obey all current experimental data, and could be within reach of the LHC.

Using delayed decoupling to attenuate residual signals in editing filters

Isotope filtering methods are instrumental in biomolecular nuclear magnetic resonance (NMR) studies as they isolate signals of chemical moieties of interest within complex molecular assemblies. However, isotope filters suppress undesired signals of isotopically enriched molecules through scalar couplings, and variations in scalar couplings lead to imperfect suppressions, as occurs for aliphatic and aromatic moieties in proteins. Here, we show that signals that have escaped traditional filters can be attenuated with mitigated sensitivity losses for the desired signals of unlabeled moieties. The method uses a shared evolution between the detection and preceding preparation period to establish non-observable antiphase coherences and eliminates them through composite pulse decoupling. We demonstrate the method by isolating signals of an unlabeled post-translational modification tethered to an isotopically enriched protein.

Theoretical constraints on scalar parameters in the compact 341 model

Theoretical constraints on the scalar potential of the compact 341 model with three quadruplets scalar fields are discussed. It is shown that in order to ensure the good behavior of the potential and the viability of the model, the criteria, such as copositivity, minimization, perturbative unitarity, perturbativity of the scalar couplings and no ghost scalar bosons (scalar bosons masses positivity), are imposed and bounds on the scalar couplings are obtained. Moreover, the existence of the Landau pole in the model imposes stringent limits.

Virtual Homonuclear Decoupling in Direct Detection NMR Experiments using Deep Neural Networks

Nuclear magnetic resonance (NMR) experiments are frequently complicated by the presence of homonuclear scalar couplings. For the growing body of biomolecular 13C-detected methods, one-bond 13C-13C couplings significantly reduce sensitivity and resolution. The solution to this problem has typically been to record in-phase and anti-phase (IPAP) or spin state selective excitation (S3E) spectra and take linear combinations to yield singlet resolved resonances. This however, results in a doubling of the effective phase cycle and requires additional delays and pulses to create the necessary magnetisation. Here, we propose an alternative method of virtual decoupling using deep neural networks. This methodology requires only the in-phase spectra, halving the experimental time and, by decoupling signals, gives a significant boost in resolution while concomitantly doubling sensitivity relative to the in-phase spectrum. We successfully apply this methodology to virtually decouple in-phase CON (13CO-15N) protein NMR spectra and 13C-13C correlation spectra of protein side chains. <br>

Virtual Homonuclear Decoupling in Direct Detection NMR Experiments using Deep Neural Networks

Nuclear magnetic resonance (NMR) experiments are frequently complicated by the presence of homonuclear scalar couplings. For the growing body of biomolecular 13C-detected methods, one-bond 13C-13C couplings significantly reduce sensitivity and resolution. The solution to this problem has typically been to record in-phase and anti-phase (IPAP) or spin state selective excitation (S3E) spectra and take linear combinations to yield singlet resolved resonances. This however, results in a doubling of the effective phase cycle and requires additional delays and pulses to create the necessary magnetisation. Here, we propose an alternative method of virtual decoupling using deep neural networks. This methodology requires only the in-phase spectra, halving the experimental time and, by decoupling signals, gives a significant boost in resolution while concomitantly doubling sensitivity relative to the in-phase spectrum. We successfully apply this methodology to virtually decouple in-phase CON (13CO-15N) protein NMR spectra and 13C-13C correlation spectra of protein side chains. <br>

Using delayed decoupling to attenuate residual signals in editing filters

Isotope filtering methods are instrumental in biomolecular nuclear magnetic resonance (NMR) studies as they isolate signals of chemical moieties of interest within complex molecular assemblies. However, isotope filters suppress undesired signals of isotopically enriched molecules through scalar couplings, and variations in scalar couplings lead to imperfect suppressions, as occurs for aliphatic and aromatic moieties in proteins. Here, we show that signals that have escaped traditional filters can be attenuated without sensitivity losses for the desired signals of unlabeled moieties. The method uses a shared evolution between the detection and preceding preparation period to establish non-observable antiphase coherences and eliminates them through composite pulse decoupling. We demonstrate the method by isolating signals of an unlabeled post-translational modification tethered to an isotopically enriched protein.