Related papers: Stochastic Optimization for Collision Selection in…
To deepen the search for beyond the Standard Model physics, the Large Hadron Collider is pushing to higher and higher luminosity. At high luminosity, precision physics becomes increasingly difficult due to contamination from additional…
This review summarizes the program in the physics of the top quark being pursued at Fermilab's Tevatron proton-antiproton collider at a center of mass energy of 1.96 TeV. More than a decade after the discovery of the top quark at the two…
The so-called matrix-element method (MEM) has long been used successfully as a classification tool in particle physics searches. In the presence of invisible final state particles, the traditional MEM typically assigns probabilities to an…
Multi-object state estimation is a fundamental problem for robotic applications where a robot must interact with other moving objects. Typically, other objects' relevant state features are not directly observable, and must instead be…
This paper deals with shape optimization for elastic materials under stochastic loads. It transfers the paradigm of stochastic dominance, which allows for flexible risk aversion via comparison with benchmark random variables, from…
The mass of the top quark is a fundamental parameter of the standard model (SM) and has to be determined experimentally. In this talk, I present the most recent measurements of the top quark mass in $p\bar p$ collisions at $\sqrt…
We adapt and study a variance reduction approach for the homogenization of elliptic equations in divergence form. The approach, borrowed from atomistic simulations and solid-state science [von Pezold et al, Physical Review B 2010; Wei et…
A machine-learning-based framework for constructing generator-level observables optimized for parameter extraction in particle physics analyses is introduced, referred to as the Optimal Observable Machine (OOM). Unfoldable differential…
Several physical systems in condensed matter have been modeled approximating their constituent particles as hard objects. The hard spheres model has been indeed one of the cornerstones of the computational and theoretical description in…
A resonance peak in the invariant mass spectrum has been the main feature of a particle at collider experiments. However, broad resonances not exhibiting such a sharp peak are generically predicted in new physics models beyond the Standard…
An overview of the most recent results on top quark physics obtained using proton-proton collision data collected with the ATLAS and the CMS experiments at 7 TeV center-of-mass energy is given. Measurements for inclusive and differential…
An overview of recent searches with top quarks in the final state using up to 36 fb$^{-1}$ of pp collision data at $\sqrt{s}$ = 13 TeV collected with the ATLAS and CMS experiments at the LHC is presented. In particular, searches for heavy…
The physics impact of a precise determination of the top-quark mass, m_t, at the Linear Collider (LC) is discussed, and the results are compared with the prospective accuracy at the LHC. The importance of a precise knowledge of m_t for…
The top quark is the heaviest of the six quarks of the Standard Model. Precise knowledge of its mass is important for imposing constraints on a number of physics processes, including interactions of the as yet unobserved Higgs boson. The…
The top quark is the heaviest elementary particle observed to date. Its large mass of about 173 GeV/c^2 makes the top quark act differently than other elementary fermions, as it decays before it hadronises, passing its spin information on…
In almost every scientific field, an experiment involves collecting data and then analysing it. The analysis stage will often consist in trying to extract some physical parameter and estimating its uncertainty; this is known as Parameter…
The mass of the top quark is a fundamental parameter of the Standard Model and has to be determined experimentally. In these proceedings, I review recent direct measurements of the top quark mass in $p\bar p$ collisions at $\sqrt s=1.96$…
Analyses of collider data, often assisted by modern Machine Learning methods, condense a number of observables into a few powerful discriminants for the separation of the targeted signal process from the contributing backgrounds. These…
Optimization under uncertainty deals with the problem of optimizing stochastic cost functions given some partial information on their inputs. These problems are extremely difficult to solve and yet pervade all areas of technological and…
High-order clustering aims to identify heterogeneous substructures in multiway datasets that arise commonly in neuroimaging, genomics, social network studies, etc. The non-convex and discontinuous nature of this problem pose significant…