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Register a new userTwo is not always better than one: Single Top Quarks and Dark Matter
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Dark matter interacting with the Standard Model fermions through new scalars or pseudoscalars with flavour-diagonal couplings proportional to fermion mass are well motivated theoretically, and provide a useful phenomenological model with which to interpret experimental results. Two modes of dark matter production from these models have been considered in the existing literature: pairs of dark matter produced through top quark loops with an associated monojet in the event, and pair production of dark matter with pairs of heavy flavoured quarks (tops or bottoms). In this paper, we demonstrate that a third, previously overlooked channel yields a non-negligible contribution to LHC dark matter searches in these models. In spite of a generally lower production cross section at LHC when compared to the associated top-pair channel, non-flavour violating single top quark processes are kinematically favored and can significantly increase the sensitivity to these models. Including dark matter production in association with a single top quark through scalar or pseudoscalar mediators, the exclusion limit set by the LHC searches for dark matter can be improved by $30$--$90%$, depending on the mass assumed for the mediator particle.
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Recent work has argued that neural networks can be understood theoretically by taking the number of channels to infinity, at which point the outputs become Gaussian process (GP) distributed. However, we note that infinite Bayesian neural networks lack a key facet of the behaviour of real neural networks: the fixed kernel, determined only by network hyperparameters, implies that they cannot do any form of representation learning. The lack of representation or equivalently kernel learning leads to less flexibility and hence worse performance, giving a potential explanation for the inferior performance of infinite networks observed in the literature (e.g. Novak et al. 2019). We give analytic results characterising the prior over representations and representation learning in finite deep linear networks. We show empirically that the representations in SOTA architectures such as ResNets trained with SGD are much closer to those suggested by our deep linear results than by the corresponding infinite network. This motivates the introduction of a new class of network: infinite networks with bottlenecks, which inherit the theoretical tractability of infinite networks while at the same time allowing representation learning.
Models incorporating flavoured dark matter provide an elegant solution to the dark matter problem, evading the tight LHC and direct direction constraints on simple WIMP models. In Dark Minimal Flavour Violation, a simple framework of flavoured dark matter with new sources of flavour violation, the constraints from thermal freeze-out, direct detection experiments, and flavour physics create well-defined benchmark scenarios for these models. We study the LHC phenomenology of four such scenarios, focusing on final states where a single top quark is produced accompanied by no jets, one jet from the fragmentation of light quarks or a $b$-tagged jet. For each of these signatures we develop a realistic LHC analysis, and we show that the proposed analyses would increase the parameter space coverage for the four benchmarks, compared to existing flavour-conserving LHC analyses. Finally we show the projected discovery potential of the considered signatures for the full LHC statistics at 14 TeV, and for the High Luminosity LHC.
We show how single top production at the LHC can be used to discover (and characterize the couplings of) B quarks, which are an essential part of many natural models of new physics beyond the Standard Model. We present the B effective model and concentrate on resonant production via a colored anomalous magnetic moment. Generally, Bs preferentially decay into a single top quark produced in association with a W boson; thus, this production process makes associated single top production essential to B searches at the LHC. We demonstrate the background processes are manageable and the signal cross section is sufficient to yield a large signal significance even during the 7 TeV LHC run. Specifically, we show that B masses of 700 GeV or more can be probed. Moreover, if a B is found, then the chirality of its coupling can be determined. Finally, we present signal cross sections for several different LHC energies.
Two chaotic systems which interact by mutually exchanging a signal built from their delayed internal variables, can synchronize. A third unit may be able to record and to manipulate the exchanged signal. Can the third unit synchronize to the common chaotic trajectory, as well? If all parameters of the system are public, a proof is given that the recording system can synchronize as well. However, if the two interacting systems use private commutative filters to generate the exchanged signal, a driven system cannot synchronize. It is shown that with dynamic private filters the chaotic trajectory even cannot be calculated. Hence two way (interaction) is more than one way (drive). The implication of this general result to secret communication with chaos synchronization is discussed.
We present a comprehensive analysis of the loop induced top quark FCNC signals at the LHC within one class of the simplified model. The loop level FCNC interactions are well motivated to avoid the hierarchy of the top quark couplings from the new physics and standard model. Such a theory will posit a Majorana dark matter candidate and could be tested through dark matter relic density, direct detection experiments (the scattering between dark matter and heavy nuclei), and the collider signals at the LHC. We find that the spin-independent (SI) scattering between Majorana dark matter and nuclei will vanish at the leading order, while the next-to-leading order correction to the SI scattering becomes significance to constrain the parameter space of the model. A detailed comparison from direct detection experiments and LHC searches is also discussed and both of them are very important to full constrain the model.
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