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<div class="elementToProof">Hi GAPS,</div>
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<div class="elementToProof">We are happy to invite you to the latest talk in our Early Career Scientist seminar series! Today's talk will take place at 1 PM in the MOB auditorium for those of you onsite (light snacks will be provided) and on Zoom for those
that aren't (information listed below). The seminar is structured as a 40 minute talk followed by 15 minutes for you to ask any and all questions you might have for the speaker.</div>
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<div class="elementToProof">These seminars are initially focusing on machine learning and its connections with the physics we do here at TRIUMF. Today, we are happy to invite Dr. Oz Amram. Currently, Oz is a postdoctoral researcher at Fermilab and a member
of the CMS experiment,<span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif;"> having
</span><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">received his PhD titled "Searching for anomalies in proton-proton collisions at the LHC" from Johns Hopkins
in 2022. His research focuses on novel applications of machine learning in particle physics, with an aim to improve the discovery potential of the LHC to new physics.</span></div>
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More information about the talk and zoom coordinates can be found below. See you later today!</div>
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Your GAPS, on behalf of the Early Career Talks organizing committee</div>
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Speaker: Dr. Oz Amram (remote)</div>
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Title: Treasure hunting without a map: First anomaly detection results from CMS</div>
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Abstract: Despite hundreds of searches, no clear evidence of beyond standard model particles have been found at the LHC so far. However, there still remains a huge landscape of possible signals hiding in LHC data, which is very difficult to fully cover with
traditional strategies. Recently, a new class of ‘anomaly detection’ techniques have been developed which aim to cover as much of this landscape as possible so that potential discoveries are not missed. These techniques leverage novel data-driven machine learning
methods to minimize the trade-off between sensitivity and model independence. In this talk, I will present results from the first application of anomaly detection in CMS : a search for resonances decaying to two jets with 'anomalous' substructure. I will also
discuss future prospects for these techniques more broadly, and how they may play a key role in a future, robust LHC search program.</div>
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Zoom: <span style="color: rgb(0, 134, 240);"><a href="https://ubc.zoom.us/j/69004309966?pwd=3HTwZ1gHElqbnNkkWPjbXCaAawsdax.1" id="LPlnk" class="ms-outlook-linkify OWAAutoLink" title="https://ubc.zoom.us/j/69004309966?pwd=3HTwZ1gHElqbnNkkWPjbXCaAawsdax.1" style="color: rgb(0, 134, 240);" data-auth="NotApplicable" data-ogsc="rgb(0, 134, 240)" data-linkindex="0" data-outlook-id="e01e19b6-5d97-43c1-86ff-8efa0a83bd03">https://ubc.zoom.us/j/69004309966?pwd=3HTwZ1gHElqbnNkkWPjbXCaAawsdax.1</a></span></div>
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Meeting ID: 690 0430 9966</div>
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Passcode: 723743</div>
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<span style="background-color: rgb(255, 255, 255);"><i>TRIUMF is located on the traditional, ancestral, and unceded territory of the xʷməθkʷəy̓əm (Musqueam) people, who for millennia have passed on their culture, history, and traditions from one generation
to the next on this site.</i></span></div>
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