Related papers: Deep-learned Top Tagging with a Lorentz Layer
Classification of jets as originating from light-flavor or heavy-flavor quarks is an important task for inferring the nature of particles produced in high-energy collisions. The large and variable dimensionality of the data provided by the…
We study the tagging of Higgs exotic decay signals using different types of deep neural networks (DNNs), focusing on the $W^\pm h$ associated production channel followed by Higgs decaying into $n$ $b$-quarks with $n=4$, 6 and 8. All the…
In top quark production, the polarization of top quarks, decided by the chiral structure of couplings, is likely to be modified in the presence of any new physics contribution to the production. Hence the same is a good discriminator for…
In the context of prospective studies for searches of new physics at the LHC Run 3, this paper investigates the relevance of using top quarks produced from new long-lived particles, and detected in the tracker volume of the ATLAS and CMS…
Identifying jets originating from bottom quarks is vital in collider experiments for new physics searches. This paper proposes a novel approach based on Retentive Networks (RetNet) for b-jet tagging using low-level features of jet…
Learning to Rank is the problem involved with ranking a sequence of documents based on their relevance to a given query. Deep Q-Learning has been shown to be a useful method for training an agent in sequential decision making. In this…
The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the…
Machine-learning assisted jet substructure tagging techniques have the potential to significantly improve searches for new particles and Standard Model measurements in hadronic final states. Techniques with simple analytic forms are…
Machine learning methods incorporating deep neural networks have been the subject of recent proposals for new hadronic resonance taggers. These methods require training on a dataset produced by an event generator where the true class labels…
We present a new technique for distinguishing the hadronic decays of boosted heavy particles from QCD backgrounds based on wavelet transforms. As an initial exploration, we illustrate the technique in the particular case of hadronic $W$…
Using unfolded top-quark decay data we can measure the top quark mass, as well as search for unexpected kinematic effects. We present a new generative unfolding method for the two tasks and show how they both benefit from unbinned,…
Despite their increasing popularity and success in a variety of supervised learning problems, deep neural networks are extremely hard to interpret and debug: Given and already trained Deep Neural Net, and a set of test inputs, how can we…
High-dimensional data in the form of tensors are challenging for kernel classification methods. To both reduce the computational complexity and extract informative features, kernels based on low-rank tensor decompositions have been…
We explicitly study how jet substructure taggers act on a set of signal and background events. We focus on two-pronged hadronic decay of a boosted Z boson. The background to this process comes from QCD jets with masses of the order of m_Z.…
A method of reconstruction of the top quarks produced in the process E+E- -> t\bar{t} -> 6 jets at a Linear Collider (LC) is proposed. The approach does not involve a kinematic fit, as well as assumptions on the invariant masses of the…
We apply advanced machine learning techniques to two challenging jet classification problems at the LHC. The first is strange-quark tagging, in particular distinguishing strange-quark jets from down-quark jets. The second, which we term…
We introduce a novel approach to jet tagging and classification through the use of techniques inspired by computer vision. Drawing parallels to the problem of facial recognition in images, we define a jet-image using calorimeter towers as…
The Future Circular Hadron Collider (FCC-hh) will probe unprecedented energy regimes, enabling direct searches for new elementary particles at a scale of tens of TeV. FCC-hh is currently in the planning stage, and one of its primary physics…
The top quark is expected to be a probe to new physics beyond the standard model. Thanks to the large number of top quarks produced at the Tevatron and the LHC, various properties of the top quark can now be measured accurately. An…
We describe a new deep learning architecture for learning to rank question answer pairs. Our approach extends the long short-term memory (LSTM) network with holographic composition to model the relationship between question and answer…