Boosted Analysis of Higgs Pair Production in the bbτ + τ − Lephad Final State

This dissertation presents the development of a boosted analysis in the searchfor the resonant production of a new heavy scalar X decaying to two Higgs bosons, which is predicted by some Beyond the Standard Model theories. The bbτ + τ − semi-hadronic decay channel of the Higgs bosons is considered. The analysis is developed using Monte Carlo simulated data and validated with 0.11 fb−1 of proton-proton collisions at √s = 13 TeV from the ATLAS detector at the Large Hadron Collider (LHC). Scalar X masses of 1, 1.6, and 2 TeV are considered and expected limits of 5.29 × 103 , 23.42, and 18.60 fb, respectively, are placed on the pp → X → HH cross section at 95% confidence level. These results are compared to existing resolved and boosted ATLAS bbτ + τ − analyses. A new method for di-τ identification and a kinematic neural network for event selection are also described. This dissertation contains previously published and unpublished material

Observation of γγ → ττ in proton-proton collisions and limits on the anomalous electromagnetic moments of the τ lepton

The production of a pair of τ leptons via photon–photon fusion, γγ → ττ, is observed for the f irst time in proton–proton collisions, with a significance of 5.3 standard deviations. This observation is based on a data set recorded with the CMS detector at the LHC at a center-of-mass energy of 13 TeV and corresponding to an integrated luminosity of 138 fb−1. Events with a pair of τ leptons produced via photon–photon fusion are selected by requiring them to be back-to-back in the azimuthal direction and to have a minimum number of charged hadrons associated with their production vertex. The τ leptons are reconstructed in their leptonic and hadronic decay modes. The measured fiducial cross section of γγ → ττ is σfid obs = 12.4+3.8 −3.1 fb. Constraints are set on the contributions to the anomalous magnetic moment (aτ) and electric dipole moments (dτ) of the τ lepton originating from potential effects of new physics on the γττ vertex: aτ = 0.0009+0.0032 −0.0031 and |dτ| < 2.9×10−17ecm (95% confidence level), consistent with the standard model

Recent Advances in Machine Learning for Physics Analysis at ATLAS

This presentation is for the talk titled "Recent Advances in Machine Learning for Physics Analysis at ATLAS" to be presented at CIPANP 2025

ADFilter—A Web Tool for New Physics Searches with Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks

A web-based tool called ADFilter (short for Anomaly Detection Filter) was developed to process collision events using autoencoders based on a deep unsupervised neural network. The autoencoders are trained on a small fraction of either collision data or Standard Model (SM) Monte Carlo simulations. The tool calculates loss distributions for input events, helping to determine the degree to which the events can be considered anomalous with respect to the SM events used for training. Therefore, it can be used for new physics searches in collider experiments. Real-life examples are provided to demonstrate how the tool can be used to reinterpret existing results from the Large Hadron Collider (LHC), with the goal of significantly improving exclusion limits. This tool is expected to mitigate the “reproducibility crisis” associated with various machine learning techniques, as it can incorporate machine learning approaches from third-party publications, making them accessible to the general public

Engineering Cell Type Specific Delivery Vectors for Noninvasive Modulation of Brain Circuits and Behaviors

Within neuroscience the modulation and monitoring of specific circuits is achieved using a combination of transgenic animals and direct injection of viral vectors. To provide an alternative to invasive brain injection and increase transduction coverage and efficiency, we previously engineered adeno-associated viruses (AAVs) that efficiently cross the BBB in adult mice (Deverman et al, 2016; Chan et al, 2017). When delivering genes systemically, however, it is currently not possible to achieve a high degree of specificity for defined neuronal cell types in specific brain regions without the use of transgenic approaches. Here, we attempt to address this limitation by selectively engineering capsids with regional and cell-type specificity using CREATE, the Cre recombination-dependent AAV targeted evolution (Deverman et al, 2016) methodology that recovers capsids that transduce predefined Cre+ target cell populations