90 research outputs found
Simultaneous CTEQ-TEA extraction of PDFs and SMEFT parameters from jet and data
Recasting phenomenological Lagrangians in terms of SM effective field theory
(SMEFT) provides a valuable means of connecting potential BSM physics at
momenta well above the electroweak scale to experimental signatures at lower
energies. In this work we jointly fit the Wilson coefficients of SMEFT
operators as well as the PDFs in an extension of the CT18 global analysis
framework, obtaining self-consistent constraints to possible BSM physics
effects. Global fits are boosted with machine-learning techniques in the form
of neural networks to ensure efficient scans of the full PDF+SMEFT parameter
space. We focus on several operators relevant for top-quark pair and jet
production at hadron colliders and obtain constraints on the Wilson
coefficients with Lagrange Multiplier scans. We find mild correlations between
the extracted Wilson coefficients, PDFs, and other QCD parameters, and see
indications that these correlations may become more prominent in future
analyses based on data of higher precision. This work serves as a new platform
for joint analyses of SM and BSM physics based on the CTEQ-TEA framework.Comment: 39 pages, 18 figure
Sampling Plans for Control-Inspection Schemes Under Independent and Dependent Sampling Designs With Applications to Photovoltaics
The evaluation of produced items at the time of delivery is, in practice,
usually amended by at least one inspection at later time points. We extend the
methodology of acceptance sampling for variables for arbitrary unknown
distributions when additional sampling infor- mation is available to such
settings. Based on appropriate approximations of the operating characteristic,
we derive new acceptance sampling plans that control the overall operating
characteristic. The results cover the case of independent sampling as well as
the case of dependent sampling. In particular, we study a modified panel
sampling design and the case of spatial batch sampling. The latter is advisable
in photovoltaic field monitoring studies, since it allows to detect and analyze
local clusters of degraded or damaged modules. Some finite sample properties
are examined by a simulation study, focusing on the accuracy of estimation
Modelling and control of a marine current turbine-driven doubly fed induction generator
0,2An cross-ring cleavage as a general diagnostic tool for glycan assignment in glycoconjugate mixtures
Glycomics using mass spectrometry
Mass spectrometry plays an increasingly important role in structural glycomics. This review provides an overview on currently used mass spectrometric approaches such as the characterization of glycans, the analysis of glycopeptides obtained by proteolytic cleavage of proteins and the analysis of glycosphingolipids. The given examples are demonstrating the application of mass spectrometry to study glycosylation changes associated with congenital disorders of glycosylation, lysosomal storage diseases, autoimmune diseases and cancer
Evaluation of Random cDNA Clones as Probes for Human Restriction Fragment Length Polymorphisms
Peer Reviewe
DeepKnit: Learning-based generation of machine knitting code
Modern knitting machines allow the manufacturing of various textile products with complex surface structures and patterns. However, programming these machines requires expert knowledge due to constraints of the process and the programming language. We present a long short-term memory (LSTM) based deep learning model that generates low-level code of novel knitting patterns based on high-level style specifications. To be processable by our model, we describe knitting instructions as one-dimensional sequences of tokens, which diverts from image-based approaches reported in previous research. We integrate our model into a design tool, that allows to assemble the atomic patterns to bigger swatches or garments. To evaluate our approach quantitatively, we formalize the requirements for patterns to be syntactically correct and valid to manufacture. Although our generated patterns look more random and seem to resemble less to human patterns, our evaluation shows that their knittability is orders of magnitudes better than randomly generated patterns
Simultaneous CTEQ-TEA extraction of PDFs and SMEFT parameters from jet and t t ¯ data
Abstract Recasting phenomenological Lagrangians in terms of SM effective field theory (SMEFT) provides a valuable means of connecting potential BSM physics at momenta well above the electroweak scale to experimental signatures at lower energies. In this work we jointly fit the Wilson coefficients of SMEFT operators as well as the PDFs in an extension of the CT18 global analysis framework, obtaining self-consistent constraints to possible BSM physics effects. Global fits are boosted with machine-learning techniques in the form of neural networks to ensure efficient scans of the full PDF+SMEFT parameter space. We focus on several operators relevant for top-quark pair and jet production at hadron colliders and obtain constraints on the Wilson coefficients with Lagrange Multiplier scans. We find mild correlations between the extracted Wilson coefficients, PDFs, and other QCD parameters, and see indications that these correlations may become more prominent in future analyses based on data of higher precision. This work serves as a new platform for joint analyses of SM and BSM physics based on the CTEQ-TEA framework
Automatic processing of clinical aphasia data collected during diagnosis sessions: challenges and prospects
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