Abstract
The Phase-I trigger readout electronics upgrade of the ATLAS
Liquid Argon calorimeters enhances the
physics reach of the experiment during the upcoming operation at
increasing Large Hadron ...Collider luminosities.
The new system, installed during the second Large Hadron Collider Long Shutdown,
increases the trigger readout granularity by up to a factor of ten
as well as its precision and range.
Consequently, the background rejection at trigger level is improved
through enhanced filtering algorithms utilizing the additional information
for topological discrimination of electromagnetic and hadronic shower shapes.
This paper presents the final designs of the new electronic elements,
their custom electronic devices, the procedures
used to validate their proper functioning, and the performance achieved
during the commissioning of this system.
The Phase-I trigger readout electronics upgrade of the ATLAS Liquid Argon calorimeters enhances the physics reach of the experiment during the upcoming operation at increasing Large Hadron Collider ...luminosities. The new system, installed during the second Large Hadron Collider Long Shutdown, increases the trigger readout granularity by up to a factor of ten as well as its precision and range. Consequently, the background rejection at trigger level is improved through enhanced filtering algorithms utilizing the additional information for topological discrimination of electromagnetic and hadronic shower shapes. This paper presents the final designs of the new electronic elements, their custom electronic devices, the procedures used to validate their proper functioning, and the performance achieved during the commissioning of this system.
A
bstract
A Generative-Adversarial Network (GAN) based on convolutional neural networks is used to simulate the production of pairs of jets at the LHC. The GAN is trained on events generated using M
...ad
G
raph
5, P
ythia
8, and D
elphes
3 fast detector simulation. We demonstrate that a number of kinematic distributions both at Monte Carlo truth level and after the detector simulation can be reproduced by the generator network.
The code can be checked out or forked from the publicly accessible online repository
https://gitlab.cern.ch/disipio/DiJetGAN
.
This thesis presents measurement of the production of the Higgs boson in association with a Z boson in the WW∗ Higgs decay channel. In order to optimize sensitivity, this analysis focused on the ...leptonic final state which contains four charged leptons in addition to two neutrinos. The measurement was performed using 139fb−1 of data recorded by the ATLAS detector and obtained from proton-proton collisions of √s = 13TeV during Run-2 of the Large Hadron Collider. The signal process was measured with an excess of 4.5σ. The ratio of the observed cross section times branching ratio to the value predicted by the SM, assuming the existence of the Higgs boson with a mass of 125 GeV, was measured as 1.6±0.5, which indicates consistency between the observed data and the SM prediction. The cross section times branching ratio of the ZH → Z(WW∗) process was measured as 0.31+0.10 −0.09 pb. The uncertainties on this measurement are three times smaller than the uncertainties associated with the previous measurement of same process by the ATLAS experiment.
By emerging deep learning techniques in recent years, most fields from medical science to all engineering branches implemented these techniques in different ongoing challenges. The popularity of deep ...learning techniques in this research is due to their strength in finding a comparable solution with more analytical approaches using large data. Intelligent Transportation Systems is also one of the fields which flourished in this situation. In this thesis, I implemented different computer vision and deep learning models to enhance traffic safety.In the first chapter of this thesis, I surveyed recently published literature on different deep learning applications in Intelligent Transportation Systems and mentioned some of the gaps in the field which still have the potential for more focus and research.I proposed an augmented annotation pipeline inspired by imitation learning in the next chapter. In this pipeline, I proposed a pipeline that can save lots of time on annotating new data for training object detection models to detect traffic objects in naturalistic driving data and traffic surveillance data. By visualizing the model's performance at the end of each iteration, this iterative pipeline lets us recognize the model's weakness to detect particular objects in particular locations. This knowledge provides the annotators to focus more on those particular situations. This pipeline showed significant reductions in the number of false positives - negatives and an increase in the number of true positives after the fourth iteration of the augmented annotation process.Then in the next step, we proposed a fully automated pipeline to detect wrong-way driving on highways using Pan-tilt-zoom traffic cameras. A scalable solution is proposed in this pipeline by combining different multi-object detection and tracking models with a deep model. Contrary to existing solutions, which required exogenous specification of the camera as a separate parameter, in the suggested solution, camera orientation is considered a variable. Camera rotation detection performs by the model automatically. The model adopts new decision criteria accordingly by learning them from a neural network model. We showed the proposed solution could detect WWD with the precision of 0.99, where the recall is 0.97.Finally, in the next chapter, I proposed a deep model to predict cognition disability by observing the driver's driving behavior while driving. For this manner, a deep learning model with combination of CNN and RNN layers proposed. This model using a Convolutional Neural Network model extracted features from the video, and sets of Long-Short Term Memory cells have been implemented to analyze these features over time. This chapter discusses the results, performance, and limitations of this model. We showed that using this model we can predict driver’s cognition stat with over 70% accuracy.
The stability of a topical ointment primarily consisting of an emulsion of propylene glycol droplets dispersed in a continuous white petrolatum medium was studied using optical methods to monitor ...droplet size growth and phase separation when the ointment undergoes heating or fluid shear. To investigate the effects of shear, ointment at 32°C was sheared using a transparent narrow-gap temperature-controlled Couette flow apparatus operated under laminar flow conditions and that provided approximately uniform shear rates. Optical microscopy was used to obtain time-dependent in-situ propylene glycol droplet size distributions, while simultaneously a wide-field lens and camera were used to detect gross phase separation as the ointment was sheared. Microscopy was also used to observe and quantify ointment stability via analysis of droplet size evolution in the absence of fluid shear for a range of elevated temperatures. For quiescent ointment, it was observed that the dispersed propylene glycol droplets do not exhibit any appreciable growth over a period of two months and temperatures as high as 45°C. In contrast, fluid shear imposed at 32°C was observed to cause rapid growth of dispersed phase droplets and the onset of large phase separated regions on time scales ranging between a few minutes to approximately half an hour for fluid strain rates ranging between 50 s-1 and 5 s-1, respectively. Also in order to use the CFD technique to simulate ointment behavior in a different situation a rheological model is suggested due to non-Newtonian behavior of the ointment. Furthermore, a novel experimental method is used using a wide gap Couette reactor to predict the values of the properties of the mentioned model.
A Generative-Adversarial Network (GAN) based on convolutional neural networks is used to simulate the production of pairs of jets at the LHC. The GAN is trained on events generated using MadGraph5 + ...Pythia8, and Delphes3 fast detector simulation. We demonstrate that a number of kinematic distributions both at Monte Carlo truth level and after the detector simulation can be reproduced by the generator network with a very good level of agreement. The code can be checked out or forked from the publicly accessible online repository https://gitlab.cern.ch/disipio/DiJetGAN .
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