83 lines
1.8 KiB
Plaintext
83 lines
1.8 KiB
Plaintext
<subsection Top Tagging>
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%new physics at the lhc
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%introduce toptagging on this slide
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<frame title="Top Tagging">
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<split>
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<que>
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<list>
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<e>Classify events into those that origin from a top quark, and those by other qcd particles</e>
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<e>to do this, use either calorimeter like images or 4-vectors</e>
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</list>
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</que>
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<que>
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<i f="xoneqcd.png" f2="xonetop.png" wmode="False" wid="0.8"></i>
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</que>
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</split>
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</frame>
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<frame title="Finding new Physics at the LHC">
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<list>
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<e>classical approach:</e>
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<l2st>
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<e>first build a theory (for example super symmetry)</e>
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<e>make predictions</e>
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<e>test them</e>
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</l2st>
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<e>not very effective in the last time</e>
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<e>so try using unsupervised algorithms to find 'weird' stuff</e>
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<e>these algorithm are tested quite well using top tagging since</e>
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<l2st>
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<e>the top quark was only discovered 1995, so before this, tops actually were 'weird'</e>
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<e>the top quark has a quite low cross section (about #1# top event for each #10# million collisions)</e>
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</l2st>
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</list>
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</frame>
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<frame>
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%slide to show the history of toptagging
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<split>
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<que>
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<list>
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<e>classically you use smart physics to differentiate them (arXiv:1806.01263)</e>
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<e>but then there were deep learning approaches (arXiv:1704.02124) which do this a bit better</e>
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<e>today even better using a fancy graph neuronal network (ParticleNet,arXiv:1902.08570)</e>
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</list>
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</que>
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<que>
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<i f="ttot"></i>
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</que>
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</split>
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</frame>
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<frame>
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<split>
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<que>
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Supervised
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<list>
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<e>Training given both the anomaly and the background events</e>
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<e>Much easier to do</e>
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<e>only able to find one specific anomaly</e>
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</list>
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</que>
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<que>
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Unsupervised
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<list>
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<e>Training only given background events</e>
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<e>Able to find any anomaly</e>
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<e>Used by QCDorWhat (arxiv 1808.08979) for unsupervised toptagging</e>
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</list>
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</que>
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</split>
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</frame>
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<ignore>
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</ignore>
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