Reconstructing the invisible with matrix elements

We propose a fully flexible method to perform an hypothesis test between signal and background based on the Matrix Element Method in the presence of multiple invisible particles. The proposed method performs a mapping of the measured final state onto its minimal hypersurface of degrees of freedom for a given process and then maximises the matrix element on this hypersurface separately for signal and background. To show how performant the method is in separating signal from background, we apply it to the prominent partly invisible decay of a Higgs boson into a muon-antimuon pair and two muon-neutrinos via two W bosons.Comment: 4 page

Top-antitop cross section measurement as a function of the jet multiplicity in the final state and beyond the Standard Model top-antitop resonances search at the ATLAS detector at CERN

The top quark is the heaviest particle in the Standard Model, with a strong coupling to the Higgs boson. It is often seen as a window to new physics, therefore understanding its production is a key ingredient for testing the Standard Model or physics Beyond the Standard Model. In this document, the production cross section of top-antitop pairs in its semileptonic decay channel is measured as a function of the jet multiplicity in the ATLAS experiment, using proton-proton collisions at the center-of-mass energy of sqrt(s) = 7 TeV. The top-antitop production with extra jets is the main background for many analyses, including the top-antitop-Higgs production studies. The analysis performed is extended in a search for Beyond the Standard Model physics which predicts a resonance decaying in a top-antitop pair, using ATLAS data at center-of-mass energy of sqrt(s) = 7 TeV. The latter analysis is repeated for ATLAS data collected with sqrt(s) = 8 TeV. Performance studies of b-tagging algorithms in the ATLAS Trigger System are also presented

Higgs Self-Coupling Measurements at a 100 TeV Hadron Collider

An important physics goal of a possible next-generation high-energy hadron collider will be precision characterisation of the Higgs sector and electroweak symmetry breaking. A crucial part of understanding the nature of electroweak symmetry breaking is measuring the Higgs self-interactions. We study dihiggs production in proton-proton collisions at 100 TeV centre of mass energy in order to estimate the sensitivity such a machine would have to variations in the trilinear Higgs coupling around the Standard Model expectation. We focus on the bb¯¯γγbb¯γγ final state, including possible enhancements in sensitivity by exploiting dihiggs recoils against a hard jet. We find that it should be possible to measure the trilinear self-coupling with 40% accuracy given 3/ab and 12% with 30/ab of data

Jet substructure and H/V/top-tagging

I was not sent the abstract originally sent for the talk

Search for resonances decaying into tt, ttˉt\bar{t} or tbtb with the ATLAS detector

A search for new resonances that decay into top quark pairs and new resonances that decay into a top quark and a b quark are reported. The search is performed with the ATLAS experiment at the LHC using proton-proton collision data collected at a centre-of-mass energy of $\sqrt{s}=13$ TeV. Both the lepton plus jets channel in the top pair search and the all hadronic channel are explored in both searches. In the top pair search, the invariant mass spectrum of top quark pairs is examined for local excesses or deficits that are inconsistent with the Standard Model prediction. In the top-bottom search, the invariant mass spectrum of the $tb$ final state is reconstructed and compared to the Standard Model prediction

Validação do sistema de filtragem online de um detector de partículas de altas energias

O Centro Europeu de Pesquisa Nuclear (CERN) tem seu foco no acelerador de partículas Large Hadron Collider(LHC). Seu objetivo é colidir feixes de partículas em altas energias. Vários detectores estarão prontos para medir as características das partículas após a colisão, que serão usadas para verificar a validade das novas teorias·de física de partículas.O ATLAS, maior destes detectores, entrará em operação em breve e deve ter todos seus subsistemas operando perfeitamente para os ambiciosos objetivos dos físicos que analisarão a informação obtida. O Sistema de Filtragem de Alto Nível do ATLAS foi desenvolvido em software para rejeitar informação irrelevante, excluindo o ruído da eletrônica e acontecimentos físicos já bem conhecidos. O presente trabalho tem como objetivo validar o funcionamento do Sistema de Filtragem de Alto Nível do ATLAS utilizando um procedimento sistemático. Tanto a verificação do funcionamento correto do software, quanto da performance física esperada são realizadas a cada nova versão disponível do sistema. Um sistema semi-automático de controle de qualidade também é implementado, utilizando métodos estatísticos para comparar os resultados da última versão do sistema com versões anteriores. O sistema desenvolvido envia mensagens eletrônicas mostrando a discrepância entre os resultados obtidos

Searching for processes with invisible particles using a matrix element-based method

We propose a fully flexible method to discriminate between signal and background using a matrix element-based method in the presence of multiple invisible particles. The proposed method performs a mapping of the measured final state onto an observable which improves the separation between signal and background using their matrix elements. To show how performant this generic method is in separating signal from background, we apply it to the prominent partly invisible decay of a Higgs boson into a muon–antimuon pair and two muon-neutrinos via two W bosons

Application of self-supervised approaches to the classification of X-ray diffraction spectra during phase transitions

Spectroscopy and X-ray diffraction techniques encode ample information on investigated samples. The ability of rapidly and accurately extracting these enhances the means to steer the experiment, as well as the understanding of the underlying processes governing the experiment. It improves the efficiency of the experiment, and maximizes the scientific outcome. To address this, we introduce and validate three frameworks based on self-supervised learning which are capable of classifying 1D spectral curves using data transformations preserving the scientific content and only a small amount of data labeled by domain experts. In particular, in this work we focus on the identification of phase transitions in samples investigated by x-ray powder diffraction. We demonstrate that the three frameworks, based either on relational reasoning, contrastive learning, or a combination of the two, are capable of accurately identifying phase transitions. Furthermore, we discuss in detail the selection of data augmentation techniques, crucial to ensure that scientifically meaningful information is retained