HEPP2020

Session

Session IV

30 Jan 2020, 14:00

University of Venda

Conveners

Session IV: Session IV

• Xifeng Ruan (University of the Witwatersrand (ZA))

40. BSM searches using SM measurements!

Deepak Kar (University of the Witwatersrand (ZA))

30/01/2020, 14:00

As no definite signs of new physics has been observed at the LHC data yet, alternate approaches have been proposed. These include looking at unusual topologies, and using existing measurements to constrain models (CONTUR). In tis overview, I will discuss some of the recent developments along these directions, covering jet substructure methods to identify semivisible jets, a realistic detector...

42. Implementing a robust anti-QCD tagger with mass de-correlated jet image data

Mr Kokotla Rapetsoa (University of Venda (ZA))

30/01/2020, 14:20

This project studies a robust anti-QCD tagger with mass de-correlating jet image data produced using the pre-processing method introduced in arXiv: 1903.02032. A semi-supervised (where data is only trained on background) learning anomaly detection approach using convolutional autoencoder neural networks is explored as an anti-QCD tagger in this study. Jet image data is used to train the...

8. Application of a novel Machine Learning approach for the search of heavy resonances with topological features at the LHC

Salah-Eddine Dahbi (University of the Witwatersrand (ZA))

30/01/2020, 14:35

We propose a new approach to search for new resonances beyond the Standard Model (SM) of particle physics in topological configurations using Machine Learning techniques. This involves a novel classification procedure based on a combination of weak-supervision and full-supervision in conjunction with Deep Neural Network algorithms. The performance of this strategy is evaluated on the...

33. Testing the search for new resonances in the di-photon channel with topological requirements on the production of the Standard Model Higgs boson at the LHC

Mr Nalamotse Joshua Choma (University of the Witwatersrand (ZA))

30/01/2020, 14:50

Unlike supervised learning which is known to assume a full knowledge of the underlying model, weak supervision allows with partial knowledge to extract new information from the data.
The objective of this study is to set up the search for heavy resonances at the electroweak scale with topological requirements. These resonances are expected to be produced with different production mechanisms....

14. An Alternative to Monte Carlo Generators with Deep Generative Models

Mr Thabang Lebese (University of the Witwatersrand (ZA))

30/01/2020, 15:05

What is typically referred to as the inverse problem in High Energy Physics (HEP) can be described as the use of data to extract key information to build new a theory. The search of new resonances beyond the Standard Model (SM) involves the use of different Machine Learning techniques. For this purpose, based on the recent and major successes in the field of deep learning, particularly Deep...

18. Applications of Weakly-Supervised Machine Learning Techniques in the Search for New Bosons Focusing on Dilepton Final States at the ATLAS Experiment.

Mr Benjamin Lieberman (University of the Witwatersrand (ZA))

30/01/2020, 15:20

In the search for new physics Beyond the Standard Model, MVA techniques are used to extract specific signal from Standard Model background processes. In this study weakly-supervised machine learning techniques are developed and evaluated using the ATLAS experiment, di-lepton (e±μ∓) final state data, in the H → Sh search. These weakly-supervised techniques use labelled background data to...

20. Searching for heavy scalar resonances in the LHC run 2 dataset in the $Z\gamma$ final states using machine learning techniques.

Ntsoko Phuti Rapheeha (University of the Witwatersrand (ZA))

30/01/2020, 16:05

Motivated by the statistically significant excesses in the multi-lepton final states compatible with physics at the electroweak scale, here we attempt a direct search for a heavy scalar resonance in the Z and photon system in the LHC Run 2 dataset. The study aims to extract the signal process using a machine learning algorithm.

39. Application of Machine Learning to satellite data

Mr Ayanda Thwala (University of Eswatini)

30/01/2020, 16:20

Satellite data enables the efficient mapping and monitoring of the earth’s resources, ecosystems, and events. Machine Learning can be applied to this data to predict weather conditions. Machine Learning techniques can be used to model and extract useful information out of a data stream. This helps governments and industries to share information, to make informed decisions, to act on time and...

34. Search for a resonance decaying into two photons in association with $b$ jets

Esra Mohammed Shrif (University of the Witwatersrand (ZA))

30/01/2020, 16:35

In this article we search for a heavy resonance decaying into two photons in association with $b$ jets. The search uses $139~\mbox{fb\(^{-1}\)}$ proton--proton collision data taken from the ATLAS detector at the centre-of mass energy of 13TeV during 2015 to 2018. Three models are tested in this final state. A Higgs boson like heavy scalar $X$ produced with top quarks, $b$ quarks or $Z$ boson...

26. Background determination of the control region of the $R\rightarrow SH\rightarrow 4\ell+E^{miss}_{T}$ signal at the ATLAS detector

Mr Abdualazem Fadol Mohammed (Chinese Academy of Sciences (CN))

30/01/2020, 16:50

The 4-lepton final state is a clean and important signal that is being studied at the ATLAS detector. In this study, we focus on four leptons originated from the $R\rightarrow SH\rightarrow 4\ell+E^{miss}_{T}$ signal. $R$ is a scalar boson produced via gluon--gluon fusion and decays to two lighter scalar bosons, $S$ and $H$. The $S$ decays to a pair of Standard Model of particle physics...