340 research outputs found
Searches for Charged Higgs Bosons in ATLAS
No full textThe charged Higgs boson is predicted by many extended Higgs scenarios, such as models containing Higgs triplets and Two-Higgs-Doublet Models (2HDM). The observation of charged Higgs bosons, , would indicate physics beyond the Standard Model (SM). Both analyses presented here consider the type-II 2HDM, which is also the Higgs sector of the Minimal Supersymmetric Standard Model (MSSM). For charged Higgs boson masses, , smaller than the top quark mass, , the dominant production mode at the LHC for is through top quark decay via . The dominant source of top quarks at the LHC is production
The euroSAMPL1 p K a blind prediction and reproducible research data management challenge
No full textThe euroSAMPL1 p K a prediction challenge combined performance measurements of computational methods with peer-evaluated adherence to research data management FAIR principles.The development and testing of methods in computational chemistry for the prediction of physicochemical properties is by now a mature form of scientific research, with a number of different methods ranging from molecular mechanics simulations, over quantum calculations, to empirical and machine learning models. Blind prediction challenges for these properties are regularly organized to allow researchers from academia and industry to test their methods in a fair and unbiased manner. At the same time, research data management (RDM) is still not utilized as extensively as it could be in the development and application of such models, especially in academia. In particular, the FAIR standards (Findable, Accessible, Interoperable, Reusable) can serve as guidelines for good RDM, but many models, the data used to train them, and the data they generate fall short of one, or multiple, of these standards. The goal of the first euroSAMPL p K a blind prediction challenge was to promote and help develop good RDM standards for computational chemistry. To achieve this, the challenge was designed to rank not just the predictive performance of the models but also evaluate the adherence to the FAIR principles by cross-evaluation of the participants themselves. We here present the analysis of the blind prediction quality by their statistical metrics as well as of the cross-evaluation by a newly defined “FAIRscore”. The results suggest that multiple methods can predict the p K a to within chemical accuracy, but also that “consensus” predictions constructed from multiple, independent methods may outperform each individual prediction. Furthermore, the state of research data management in the field of computational chemistry is discussed, and suggestions for future improvements developed.The euroSAMPL1 p K a prediction challenge combined performance measurements of computational methods with peer-evaluated adherence to research data management FAIR principles.The development and testing of methods in computational chemistry for the prediction of physicochemical properties is by now a mature form of scientific research, with a number of different methods ranging from molecular mechanics simulations, over quantum calculations, to empirical and machine learning models. Blind prediction challenges for these properties are regularly organized to allow researchers from academia and industry to test their methods in a fair and unbiased manner. At the same time, research data management (RDM) is still not utilized as extensively as it could be in the development and application of such models, especially in academia. In particular, the FAIR standards (Findable, Accessible, Interoperable, Reusable) can serve as guidelines for good RDM, but many models, the data used to train them, and the data they generate fall short of one, or multiple, of these standards. The goal of the first euroSAMPL p K a blind prediction challenge was to promote and help develop good RDM standards for computational chemistry. To achieve this, the challenge was designed to rank not just the predictive performance of the models but also evaluate the adherence to the FAIR principles by cross-evaluation of the participants themselves. We here present the analysis of the blind prediction quality by their statistical metrics as well as of the cross-evaluation by a newly defined “FAIRscore”. The results suggest that multiple methods can predict the p K a to within chemical accuracy, but also that “consensus” predictions constructed from multiple, independent methods may outperform each individual prediction. Furthermore, the state of research data management in the field of computational chemistry is discussed, and suggestions for future improvements developed.Deutsche Forschungsgemeinschaft https://doi.org/10.13039/50110000165
Triggering on hadronic Tau Decays in ATLAS: Algorithms and Performance
No full textHadronic tau decays play a crucial role in taking Standard Model measurements as well as in the search for physics beyond the Standard Model. However, hadronic tau decays are difficult to identify and trigger on due to their resemblance to QCD jets. Given the large production cross section of QCD processes, designing and operating a trigger system with the capability to efficiently select hadronic tau decays, while maintaining the rate within the bandwidth limits, is a difficult challenge. This contribution will summarize the algorithms and performance of the ATLAS tau trigger system during the 2011 data taking period. The use of resources and implementation of trigger algorithms in the ATLAS trigger architecture will be shown in detail. Moreover, comparisons of data and simulation results, studies of the correlation of the variable definitions at different trigger stages as well as efficiency versus rate analyses are the key elements to describe the performance of the tau trigger Finally, in light of the vast statistics collected in 2011, future prospects for triggering on hadronic tau decays in this exciting new period of increased instantaneous luminosity will be presented
Search for H± → τ±(had)+ν in ATLAS
No full textDecays of top quarks in 4.6 fb of proton-proton collisions data at TeV collected by the ATLAS experiment at the Large Hadron Collider at CERN were selected and analyzed to search for signatures of charged Higgs bosons with a hadronically decaying lepton in the final state. The data are consistent with the expected background from Standard Model processes. Assuming a branching ratio of the charged Higgs boson to a lepton and a neutrino of 100% and combining with the leptonically decaying analysis upper limits on the branching ratio of top quark decays to a quark and a charged Higgs boson between 5% and 1% for charged Higgs boson masses ranging from 90 GeV to 160 GeV, respectively are obtained. In the context of the scenario of the MSSM, above 12-26, as well as between 1 and 2-6, can be excluded for charged Higgs boson masses between 90 GeV and 150 GeV
Search for H+->tau(had)+nu in ATLAS
No full textThis talk shall summarize the search efforts of the ATLAS collaboration for the (light) charged Higgs bosons decaying into hadronically decaying tau leptons and with a special focus on the presentation the new results which were won by utilizing the an ratio method approach on the so called tau+lepton final state. These new results are then combined with the existing ones from the other final states yielding improved sensitivity. As no excess is observed, limits are set on the branching ratio of top quarks decaying to charged Higgs bosons
Blind, one-eyed, or eagle-eyed? p<i>K</i> <sub>a</sub> calculations during blind predictions with staphylococcal nuclease
No full textPhysicochemical property prediction for small molecules using integral equation-based solvation models
No full textIn this thesis the accurate prediction of physicochemical properties of small, pharmaceutically relevant compounds is investigated. To predict condensed phase properties such as hydration free energies, acid dissociation constants (pKa), and distribution and partition coefficients (log D and log P, respectively) it is necessary to accurately describe the solute, the solute-solvent interactions, and the solvent-response to the solute’s presence. When this is achieved, the Gibbs energies of the molecules in solution can be used to calculate macroscopic physicochemical properties.
The embedded cluster reference interaction site model (EC-RISM) makes it possible to combine a quantum chemical description of the solute with an accurate solvent response via the three-dimensional reference interaction site model (3D RISM). This is ideal for calculating physicochemical properties of small molecules, because EC RISM yields both the electronic energy of the solvent-polarized wave function, as well as the excess chemical potential of the molecule in solution, the sum of which can be defined as the Gibbs energy of the molecule in solution.
The development of solvent susceptibilities for the non-aqueous solvents cyclohexane and n octanol is reported, as well as the challenges and implications of including water saturation for organic solvents. The solvent susceptibilities are used to train partial molar volume corrections to correct for the error inherent in the calculation of the 3D RISM excess chemical potential using reference data from the Minnesota solvation database (MNSOL). Additionally, a method to calculate accurate pKa values is presented and the formal equivalence of a microstate transition and a partition function approach is briefly summarized. The performance of the models is benchmarked by participation in the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges. First, the SAMPL5 challenge, where cyclohexane-water distribution coefficients log D7.4 had to be calculated. In subsequent challenges the task was split into determining aqueous pKa values during the SAMPL6 challenge and octanol-water partition coefficients log P of a subset of these compounds for SAMPL6 part II. Over the course of these challenges a number of key improvements were made to the EC RISM model, often directly as a result of inconsistencies or performance issues during one of the SAMPL challenges. Finally, an extension of the partial molar volume correction to extreme conditions such as high pressure is reported
Identification of lysosomotropism using explainable machine learning and morphological profiling cell painting data
No full text<jats:p>Explainable ML was used to identify important chemical structural properties that contribute to lysosomotropism.</jats:p>
LigpKa – a database of pKa values for small molecule ligands designed for the use in structure-based pKa calculations
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