272 citations · 560 across the 10 of their papers we have counts for
11 papers
CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings
Tatiana Likhomanenko, Qiantong Xu, Gabriel Synnaeve +2
Without positional information, attention-based Transformer neural networks are permutation-invariant. Absolute or relative positional embeddings are the most popular ways to feed…
Physics case for an LHCb Upgrade II - Opportunities in flavour physics, and beyond, in the HL-LHC era
LHCb collaboration, I. Bediaga, M. Cruz Torres +828
The LHCb Upgrade II will fully exploit the flavour-physics opportunities of the HL-LHC, and study additional physics topics that take advantage of the forward acceptance of the LHC…
InfiniteBoost: building infinite ensembles with gradient descent
Alex Rogozhnikov, Tatiana Likhomanenko
In machine learning ensemble methods have demonstrated high accuracy for the variety of problems in different areas. Two notable ensemble methods widely used in practice are gradie…
Inclusive Flavour Tagging Algorithm
Tatiana Likhomanenko, Denis Derkach, Alex Rogozhnikov
Identifying the flavour of neutral mesons production is one of the most important components needed in the study of time-dependent violation. The harsh environment of the…
Discovery potential for directional Dark Matter detection with nuclear emulsions
N. Agafonova, A. Aleksandrov, A. Anokhina +53
Direct Dark Matter searches are nowadays one of the most fervid research topics with many experimental efforts devoted to the search for nuclear recoils induced by the scattering o…
The active muon shield in the SHiP experiment
SHiP collaboration, A. Akmete, A. Alexandrov +229
The SHiP experiment is designed to search for very weakly interacting particles beyond the Standard Model which are produced in a 400 GeV/c proton beam dump at the CERN SPS. An ess…
Reweighting with Boosted Decision Trees
A. Rogozhnikov
Machine learning tools are commonly used in modern high energy physics (HEP) experiments. Different models, such as boosted decision trees (BDT) and artificial neural networks (ANN…
LHCb Topological Trigger Reoptimization
Tatiana Likhomanenko, Philip Ilten, Egor Khairullin +3
The main b-physics trigger algorithm used by the LHCb experiment is the so-called topological trigger. The topological trigger selects vertices which are a) detached from the prima…
Reproducible Experiment Platform
Tatiana Likhomanenko, Alex Rogozhnikov, Alexander Baranov +2
Data analysis in fundamental sciences nowadays is an essential process that pushes frontiers of our knowledge and leads to new discoveries. At the same time we can see that complex…
A facility to Search for Hidden Particles (SHiP) at the CERN SPS
SHiP Collaboration, M. Anelli, S. Aoki +233
A new general purpose fixed target facility is proposed at the CERN SPS accelerator which is aimed at exploring the domain of hidden particles and make measurements with tau neutri…
New approaches for boosting to uniformity
Alex Rogozhnikov, Aleksandar Bukva, Vladimir Gligorov +2
The use of multivariate classifiers has become commonplace in particle physics. To enhance the performance, a series of classifiers is typically trained; this is a technique known…