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Application of Deep Learning in Top Pair and Single Top Quark Production at the Large Hadron Collider
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class="card"> <div class="card-body"><strong>Paper Count:</strong> 87354</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Application of Deep Learning in Top Pair and Single Top Quark Production at the Large Hadron Collider</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ijaz%20Ahmed">Ijaz Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Anwar%20Zada"> Anwar Zada</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Waqas"> Muhammad Waqas</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20U.%20Ashraf"> M. U. Ashraf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of performance in boosted top quark events is observed with our limited computing resources. We also compare modern machine learning approaches and perform a multivariate analysis of boosted top-pair as well as single top quark production through weak interaction at 鈭歴 = 14 TeV proton-proton Collider. The most relevant known background processes are incorporated. Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer Perceptron (MLP) the analysis is trained to observe the performance in comparison with the conventional cut based and count approach <iframe src="https://publications.waset.org/abstracts/156965.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=top%20tagger" title="top tagger">top tagger</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate" title=" multivariate"> multivariate</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=LHC" title=" LHC"> LHC</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20top" title=" single top"> single top</a> </p> <a href="https://publications.waset.org/abstracts/156965/application-of-deep-learning-in-top-pair-and-single-top-quark-production-at-the-large-hadron-collider" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156965.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">111</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> 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