BEGIN:VCALENDAR VERSION:2.0 PRODID:-//linuxsoftware.nz//NONSGML Joyous v1.4//EN BEGIN:VEVENT SUMMARY:Search for hidden new physics signals at the Large Hadron Collider DTSTART:20240726T100000Z DTEND:20240726T120000Z DTSTAMP:20260928T103629Z UID:79544414-ed38-49ff-b454-5b648ac81160 SEQUENCE:1 CREATED:20240723T105036Z DESCRIPTION: The Standard Model (SM) of Particle Physics is currently the best theory to describe Nature at subatomic scales. However\, despite its remarkable agreement with the data collected so far at colliders\, the SM leaves several phenomena unexplained. Searches for new physics have been c arried out at the Large Hadron Collider (LHC) in the attempt to identify a more complete version of the SM\, but those efforts have not yet produced the desired result. We cannot rule out the possibility of new physics sea rches being missing signals whose experimental signatures are more elusive than expected. Thus\, in this thesis\, we study new physics signals that might be hidden in the LHC data\, as well as SM extensions where those sig nals are predicted. In particular\, we address multiboson production from a heavy Z ′ resonance in the framework of an U(1)′ extended next-to-mi nimal two-Higgs doublet model (UN2HDM). Small excesses around 95 GeV obser ved in many searches for a new scalar in three different decay channels\, γγ\, τ τ and bb\, motivate one further look into UN2HDMs. In this cont ext\, we check which anomalies can be explained by those models\, includin g scenarios where excesses in one or two decay channels turn out to be sta tistical fluctuations. The second goal of this thesis is to develop tools that can make new physics searches sensitive to a broader range of signals \, including those with non-conventional experimental signatures. To this end\, we introduce the concept of Mass Unspecific Supervised Tagging (MUST ) for the identification of multi-pronged jets. Using Machine Learning alg orithms\, namely Neural networks and Gradient Boosting\, we show that amon g other benefits\, jet tagging tools built upon MUST can discriminate many different types of multi-pronged jets in wide ranges of jet mass and tran sverse momentum. LAST-MODIFIED:20240723T105036Z LOCATION:Sala V0.15 (Piso 0 do Pavilhão de Civil) do IST URL:http://df.vps.tecnico.ulisboa.pt/en/events/search-for-hidden-new-physi cs-signals-at-the-large-hadron-collider/ X-ALT-DESC;FMTTYPE=text/html:


The Standard Model (SM) of Particle Physics is currently the best theory to describe Na ture at subatomic scales. However\, despite its remarkable agreement with the data collected so far at colliders\, the SM leaves several phenomena u nexplained.

Searches for new physics have been carried out at th e Large Hadron Collider (LHC) in the attempt to identify a more complete v ersion of the SM\, but those efforts have not yet produced the desired res ult. We cannot rule out the possibility of new physics searches being miss ing signals whose experimental signatures are more elusive than expected.< br/>
Thus\, in this thesis\, we study new physics signals that might be hidden in the LHC data\, as well as SM extensions where those signals a re predicted. In particular\, we address multiboson production from a heav y Z ′ resonance in the framework of an U(1)′ extended next-to-minimal two-Higgs doublet model (UN2HDM).

Small excesses around 95 GeV o bserved in many searches for a new scalar in three different decay channel s\, γγ\, τ τ and bb\, motivate one further look into UN2HDMs. In this context\, we check which anomalies can be explained by those models\, incl uding scenarios where excesses in one or two decay channels turn out to be statistical fluctuations.

The second goal of this thesis is to develop tools that can make new physics searches sensitive to a broader ra nge of signals\, including those with non-conventional experimental signat ures. To this end\, we introduce the concept of Mass Unspecific Supervised Tagging (MUST) for the identification of multi-pronged jets.

Us ing Machine Learning algorithms\, namely Neural networks and Gradient Boos ting\, we show that among other benefits\, jet tagging tools built upon MU ST can discriminate many different types of multi-pronged jets in wide ran ges of jet mass and transverse momentum.

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