1,538 research outputs found

    First Search for the Associated Production of a Higgs Boson with a Single Top Quark

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    The production of the Higgs boson in association with a single top quark is sensitive to the relative sign of the coupling parameters describing its interaction with fermions and gauge bosons. The tHq production mode therefore provides an good handle on the Yukawa coupling Yt. The first searches for single-top + Higgs in the H>bbbar, gamma gamma, tau+tau- and W+W- decay channels are presented, using the full 8 TeV dataset recorded with the CMS detector. Special emphasis is put on the analyses' peculiarities and their dominating systematic uncertainties, and a combination of all individual channels is performed. The analyses are optimized for a scenario of Yt=-1, which is enhanced by a factor of 13 with respect to the Standard Model production rate. The observed combined upper exclusion limit is 2.8 times the cross section of this exotic scenario (2.0 expected).Comment: Preprint of the proceedings for the contribution to the LHCP2015 conference, St. Petersburg, Russi

    Search for Hbbˉ\text{H}\to \text{b}\bar{\text{b}} in association with single top quarks as a test of Higgs couplings

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    The associated production of Higgs boson and single top quark is of particular interest since it is senstive to the relative sign of the Higgs boson coupling to gauge bosons and the Yukawa coupling yy to fermions. The presented analysis is setting upper production limits on a model with yt=1y_\text{t}=-1, which has an enhanced cross section compared to the standard model expectation. For this it focusses on the Higgs boson decaying to a pair of b quarks and uses the full dataset of pppp collisions recorded with the CMS detector in 2012. It reports an upper limit on 7.57 times the expected cross section, with an expected sensitivity of 5.14. This translates into the exclusion of associated tHq production with yt=1y_\text{t}=-1-like characteristics with a cross section smaller than 1.77\,pb

    tWH associated production at the LHC

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    We study Higgs boson production in association with a top quark and a WW boson at the LHC. At NLO in QCD, tWHtWH interferes with ttˉHt\bar t H and a procedure to meaningfully separate the two processes needs to be employed. In order to define tWHtWH production for both total rates and differential distributions, we consider the diagram removal and diagram subtraction techniques that have been previously proposed for treating intermediate resonances at NLO, in particular in the context of tWtW production. These techniques feature approximations that need to be carefully taken into account when theoretical predictions are compared to experimental measurements. To this aim, we first critically revisit the tWtW process, for which an extensive literature exists and where an analogous interference with ttˉt \bar t production takes place. We then provide robust results for total and differential cross sections for tWtW and tWHtWH at 13 TeV, also matching short-distance events to a parton shower. We formulate a reliable prescription to estimate the theoretical uncertainties, including those associated to the very definition of the process at NLO. Finally, we study the sensitivity to a non-Standard-Model relative phase between the Higgs couplings to the top quark and to the WW boson in tWHtWH production.Comment: v3: expanded some discussions in the text, improved some plots (results unchanged

    Search for the associated production of a single top quark and a Higgs boson in the H to bbbar decay channel at 8 and 13 TeV with the CMS experiment

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    A search for the associated production of a single top quark and a Higgs boson is performed with 8 and 13 TeV data recorded with the CMS detector. Upper limits on the allowed cross sections are quoted

    Predicting Properties of Oxide Glasses Using Informed Neural Networks

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    Many modern-day applications require the development of new materials with specific properties. In particular, the design of new glass compositions is of great industrial interest. Current machine learning methods for learning the composition-property relationship of glasses promise to save on expensive trial-and-error approaches. Even though quite large datasets on the composition of glasses and their properties already exist (i.e., with more than 350,000 samples), they cover only a very small fraction of the space of all possible glass compositions. This limits the applicability of purely data-driven models for property prediction purposes and necessitates the development of models with high extrapolation power. In this paper, we propose a neural network model which incorporates prior scientific and expert knowledge in its learning pipeline. This informed learning approach leads to an improved extrapolation power compared to blind (uninformed) neural network models. To demonstrate this, we train our models to predict three different material properties, that is, the glass transition temperature, the Young's modulus (at room temperature), and the shear modulus of binary oxide glasses which do not contain sodium. As representatives for conventional blind neural network approaches we use five different feed-forward neural networks of varying widths and depths. For each property, we set up model ensembles of multiple trained models and show that, on average, our proposed informed model performs better in extrapolating the three properties of previously unseen sodium borate glass samples than all five conventional blind models.Comment: 25 page

    Triggering Dark Showers with Conditional Dual Auto-Encoders

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    Auto-encoders (AEs) have the potential to be effective and generic tools for new physics searches at colliders, requiring little to no model-dependent assumptions. New hypothetical physics signals can be considered anomalies that deviate from the well-known background processes generally expected to describe the whole dataset. We present a search formulated as an anomaly detection (AD) problem, using an AE to define a criterion to decide about the physics nature of an event. In this work, we perform an AD search for manifestations of a dark version of strong force using raw detector images, which are large and very sparse, without leveraging any physics-based pre-processing or assumption on the signals. We propose a dual-encoder design which can learn a compact latent space through conditioning. In the context of multiple AD metrics, we present a clear improvement over competitive baselines and prior approaches. It is the first time that an AE is shown to exhibit excellent discrimination against multiple dark shower models, illustrating the suitability of this method as a performant, model-independent algorithm to deploy, e.g., in the trigger stage of LHC experiments such as ATLAS and CMS.Comment: 25 pages, 7 figures, and 11 table

    Global DNA hypomethylation prevents consolidation of differentiation programs and allows reversion to the embryonic stem cell state.

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    DNA methylation patterns change dynamically during mammalian development and lineage specification, yet scarce information is available about how DNA methylation affects gene expression profiles upon differentiation. Here we determine genome-wide transcription profiles during undirected differentiation of severely hypomethylated (Dnmt1⁻/⁻) embryonic stem cells (ESCs) as well as ESCs completely devoid of DNA methylation (Dnmt1⁻/⁻;Dnmt3a⁻/⁻;Dnmt3b⁻/⁻ or TKO) and assay their potential to transit in and out of the ESC state. We find that the expression of only few genes mainly associated with germ line function and the X chromosome is affected in undifferentiated TKO ESCs. Upon initial differentiation as embryoid bodies (EBs) wild type, Dnmt1⁻/⁻ and TKO cells downregulate pluripotency associated genes and upregulate lineage specific genes, but their transcription profiles progressively diverge upon prolonged EB culture. While Oct4 protein levels are completely and homogeneously suppressed, transcription of Oct4 and Nanog is not completely silenced even at late stages in both Dnmt1⁻/⁻ and TKO EBs. Despite late wild type and Dnmt1⁻/⁻ EBs showing a much higher degree of concordant expression, after EB dissociation and replating under pluripotency promoting conditions both Dnmt1⁻/⁻ and TKO cells, but not wild type cells rapidly revert to expression profiles typical of undifferentiated ESCs. Thus, while DNA methylation seems not to be critical for initial activation of differentiation programs, it is crucial for permanent restriction of developmental fate during differentiation

    Magnetic microcalorimeter with paramagnetic temperature sensors and integrated dc-SQUID readout for high-resolution X-ray emission spectroscopy

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    We present two variants of a magnetic microcalorimeter with paramagnetic temperature sensors and integrated dc-SQUID readout for high-resolution X-ray emission spectroscopy. Each variant employs two overhanging gold absorbers with a sensitive area of 150μ\mum x 150μ\mum and a thickness of 3μ\mum, thus providing a quantum efficiency of 98% for photons up to 5keV and 50% for photons up to 10keV. The first variant turned out to be fully operational, but, at the same time, to suffer from Joule power dissipation of the Josephson junction shunt resistors, athermal phonon loss, and slew rate limitations of the overall setup. Overall, it only achieved an energy resolution ΔEFWHM=8.9eV\Delta E_\mathrm{FWHM} = 8.9eV. In the second variant, we introduced an innovative `tetrapod absorber geometry' as well as a membrane-technique for protecting the temperature sensors against the power dissipation of the shunt resistors. By this, the second variant achieves an outstanding energy resolution of ΔEFWHM=1.25(18)eV\Delta E_\mathrm{FWHM} =1.25(18)eV and hence provides, to our knowledge, the present best energy resolving power E/ΔEFWHME/\Delta E_\mathrm{FWHM} among all existing energy-dispersive detectors for soft and tender X-rays.Comment: submitted to Applied Physics Letter

    Pile-Up Mitigation using Attention

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    Particle production from secondary proton-proton collisions, commonly referred to as pile-up, impair the sensitivity of both new physics searches and precision measurements at LHC experiments. We propose a novel algorithm, PUMA, for identifying pile-up objects with the help of deep neural networks based on sparse transformers. These attention mechanisms were developed for natural language processing but have become popular in other applications. In a realistic detector simulation, our method outperforms classical benchmark algorithms for pile-up mitigation in key observables. It provides a perspective for mitigating the effects of pile-up in the high luminosity era of the LHC, where up to 200 proton-proton collisions are expected to occur simultaneously
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