Related papers: Preserving physically important variables in optim…
The inclusion of higher-dimensional gauge invariant operators induces new Lorentz structures in Higgs couplings with electroweak gauge boson pairs. This in principle affects the kinematics of Higgs production and decay, thereby modifying…
We consider non-Standard Model physics effects using an effective lagrangian parameterization. We determine the operators whose effects are most significant and extract the sensitivity to the scale of new physics generated by the existing…
A search is presented for the production of the Standard Model Higgs boson in association with a high-energy photon. With a focus on the vector-boson fusion process and the dominant Higgs boson decay into $b$-quark pairs, the search…
We introduce a data-driven method for learning the equations of motion of mechanical systems directly from position measurements, without requiring access to velocity data. This is particularly relevant in system identification tasks where…
Dataset distillation aims to create a compact dataset that retains essential information while maintaining model performance. Diffusion models (DMs) have shown promise for this task but struggle in low images-per-class (IPC) settings, where…
A measurement is performed of Higgs bosons produced with high transverse momentum ($p_\mathrm{T}$) via vector boson or gluon fusion in proton-proton collisions. The result is based on a data set with a center-of-mass energy of 13 TeV…
We propose a novel strategy for disentangling proton collisions at hadron colliders such as the LHC that considerably improves over the current state of the art. Employing a metric inspired by optimal transport problems as the cost function…
We present a framework for discriminative sequence classification where the learner works directly in the high dimensional predictor space of all subsequences in the training set. This is possible by employing a new coordinate-descent…
We describe a concrete, predictive incarnation of the general paradigm of a composite Higgs boson, which provides a valid alternative to the standard holographic models in five space-time dimensions. Differently from the latter, our model…
Anomaly detection is a key application of machine learning, but is generally focused on the detection of outlying samples in the low probability density regions of data. Here we instead present and motivate a method for unsupervised…
We study Higgs boson production via Double Pomeron Exchange allowing for the presence of Pomeron remnants. We estimate the number of events produced at the LHC collider, as a function of the Higgs boson mass and its decay channel. The model…
The quadratic divergences of the Higgs mass may be cancelled either accidentally or by the exchange of some new particles. Alternatively its impact on naturalness may be weakened by raising the Higgs mass, which requires changing the…
We investigate a method of model-agnostic anomaly detection through studying jets, collimated sprays of particles produced in high-energy collisions. We train a transformer neural network to encode simulated QCD "event space" dijets into a…
We propose a method to organize experimental data from particle collision experiments in a general format which can enable a simple visualisation and effective classification of collision data using machine learning techniques. The method…
We present a novel multivariate classification technique based on Genetic Programming. The technique is distinct from Genetic Algorithms and offers several advantages compared to Neural Networks and Support Vector Machines. The technique…
We deploy an advanced Machine Learning (ML) environment, leveraging a multi-scale cross-attention encoder for event classification, towards the identification of the $gg\to H\to hh\to b\bar b b\bar b$ process at the High Luminosity Large…
The proposed $e^+e^-$ collider offers an ideal environment for precise estimation of Higgs boson properties which are of utmost importance to validate the Standard Model of particle physics. We investigate $hVV$ couplings, where $V\in…
Prospective searches about Higgs physics and beyond the Standard Model are presented for the CMS and ATLAS experiments. Possible excesses of events in real data could be an indication of the existence of new particles, even with few hundred…
We use the noncommutative Higgs effective standard model to make a phenomenological prediction for the transverse momentum distribution of the Higgs boson produced in association with a jet at hadron colliders. We calculate at leading order…
A new type of dependent thinning for point processes in continuous space is proposed, which leverages the advantages of determinantal point processes defined on finite spaces and, as such, is particularly amenable to statistical, numerical,…