Deep learning has led to several breakthroughs outside the field of high energy physics, yet in jet reconstruction for the CMS experiment at the CERN LHC it has not been used so far. This report ...shows results of applying deep learning strategies to jet reconstruction at the stage of identifying the original parton association of the jet (jet tagging), which is crucial for physics analyses at the LHC experiments. We introduce a custom deep neural network architecture for jet tagging. We compare the performance of this novel method with the other established approaches at CMS and show that the proposed strategy provides a significant improvement. The strategy provides the first multi-class classifier, instead of the few binary classifiers that previously were used, and thus yields more information and in a more convenient way. The performance results obtained with simulation imply a significant improvement for a large number of important physics analysis at the CMS experiment.
The positions of the silicon modules of the CMS tracker will be known to 0(100 μm) from survey measurements, mounting precision and the hardware alignment system. However, in order to fully exploit ...the capabilities of the tracker, these positions need to be known to a precision of a few μm. Only a track-based alignment procedure can reach this required precision. Such an alignment procedure is a major challenge given that about 50.000 geometry constants need to be measured. Making use of the novel χ2 minimization program Millepede II an alignment strategy has been developed in which all detector components are aligned simultaneously and all correlations between their position parameters taken into account. Tracks from different sources such as Z0 decays and cosmic ray muons, plus information about the mechanical structure of the tracker, and initial position uncertainties have been used as input for the alignment procedure. A proof of concept of this alignment strategy is demonstrated using simulated data.
Jet flavour classification is of paramount importance for a broad range of applications in modern-day high-energy-physics experiments, particularly at the LHC. In this paper we propose a novel ...architecture for this task that exploits modern deep learning techniques. This new model, called DeepJet, overcomes the limitations in input size that affected previous approaches. As a result, the heavy flavour classification performance improves, and the model is extended to also perform quark-gluon tagging.
We extend recent work (Brehmer, et. al., 2018) that use neural networks as surrogate models for likelihood-free inference. As in the previous work, we exploit the fact that the joint likelihood ratio ...and joint score, conditioned on both observed and latent variables, can often be extracted from an implicit generative model or simulator to augment the training data for these surrogate models. We show how this augmented training data can be used to provide a new cross-entropy estimator, which provides improved sample efficiency compared to previous loss functions exploiting this augmented training data.
An animalic note: The first total synthesis of the all-cis nupharamine 2, an alkaloid from beaver castoreum, is based on the stereoselective domino Mannich-Michael reaction of ...N-galactosylfurylaldimine to give 1 (Piv=pivaloyl), subsequent conjugate cuprate addition, and stereoselective protonation of the enolate. These reactions are all controlled by the carbohydrate. Protonation of the enolate after cleavage of the auxiliary leads to epimer 3.
Tierische Parfümalkaloide: Eine stereoselektive Mannich‐Michael‐Reaktion an N‐Galactosylfurylaldimin zu 1 (Piv = Pivaloyl), anschließende konjugierte Cuprataddition und stereoselektive Protonierung ...des gebildeten Enolats, alle kontrolliert vom Kohlenhydrat, ermöglichen erstmals die Totalsynthese des all‐cis‐Nupharamins 2 aus dem Castoreum. Enolatprotonierung nach Abspaltung des Kohlenhydrats führt alternativ zum Epimer 3.
Tierische Parfümalkaloide: Eine stereoselektive Mannich‐Michael‐Reaktion an N‐Galactosylfurylaldimin zu 1 (Piv = Pivaloyl), anschließende konjugierte Cuprataddition und stereoselektive Protonierung des gebildeten Enolats, alle kontrolliert vom Kohlenhydrat, ermöglichen erstmals die Totalsynthese des all‐cis‐Nupharamins 2 aus dem Castoreum. Enolatprotonierung nach Abspaltung des Kohlenhydrats führt alternativ zum Epimer 3.
Abstract
Tierische Parfümalkaloide
: Eine stereoselektive Mannich‐Michael‐Reaktion an
N
‐Galactosylfurylaldimin zu
1
(Piv = Pivaloyl), anschließende konjugierte Cuprataddition und stereoselektive ...Protonierung des gebildeten Enolats, alle kontrolliert vom Kohlenhydrat, ermöglichen erstmals die Totalsynthese des all‐
cis
‐Nupharamins
2
aus dem Castoreum. Enolatprotonierung nach Abspaltung des Kohlenhydrats führt alternativ zum Epimer
3
.
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