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


<frame>
<split>

<que>
Supervised
<list>
<e>Training given both the anomaly and the background events</e>
<e>Much easier to do</e>
<e>only able to find one specific anomaly</e>
</list>
</que>
<que>
Unsupervised
<list>
<e>Training only given background events</e>
<e>Able to find any anomaly</e>
<e>Used by QCDorWhat (arxiv 1808.08979) for unsupervised toptagging</e>
</list>
</que>

</split>

</frame>







<ignore>
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