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Found 16 papers, 8 papers with code
The LHCb ultra-fast simulation option, Lamarr: design and validation
In this context, we propose Lamarr, a Gaudi-based framework designed to offer the fastest solution for the simulation of the LHCb detector.
Symbolic expression generation via Variational Auto-Encoder
There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature.
Understanding of the properties of neural network approaches for transient light curve approximations
Modern-day time-domain photometric surveys collect a lot of observations of various astronomical objects and the coming era of large-scale surveys will provide even more information on their properties.
Latent Neural Stochastic Differential Equations for Change Point Detection
Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process.
Supernova Light Curves Approximation based on Neural Network Models
Normalizing Flows exceeds Gaussian processes in terms of approximation quality as well.
Towards Reliable Neural Generative Modeling of Detectors
The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced.
Online Neural Networks for Change-Point Detection
Moments when a time series changes its behaviour are called change points.
Photometric Data-driven Classification of Type Ia Supernovae in the Open Supernova Catalog
We propose a novel approach for a machine-learning-based detection of the type Ia supernovae using photometric information.
Variational Dropout Sparsification for Particle Identification speed-up
Accurate particle identification (PID) is one of the most important aspects of the LHCb experiment.
NFAD: Fixing anomaly detection using normalizing flows
Most of the conventional approaches to anomaly detection, such as one-class SVM and Robust Auto-Encoder, are one-class classification methods, i. e. focus on separating normal data from the rest of the space.
$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets
We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples.
Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks
The increasing luminosities of future Large Hadron Collider runs and next generation of collider experiments will require an unprecedented amount of simulated events to be produced.
Cherenkov Detectors Fast Simulation Using Neural Networks
We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details.
Towards automation of data quality system for CERN CMS experiment
Daily operation of a large-scale experiment is a challenging task, particularly from perspectives of routine monitoring of quality for data being taken.
Numerical optimization for Artificial Retina Algorithm
High-energy physics experiments rely on reconstruction of the trajectories of particles produced at the interaction point.
Inclusive Flavour Tagging Algorithm
The proposed inclusive flavour-tagging algorithm is applicable to tag the flavour of $B$ mesons in any proton-proton experiment.
