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Found 13 papers, 5 papers with code
The Landscape of Unfolding with Machine Learning
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions.
Anomalies, Representations, and Self-Supervision
We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021.
Generative Networks for Precision Enthusiasts
Generative networks are opening new avenues in fast event generation for the LHC.
Shared Data and Algorithms for Deep Learning in Fundamental Physics
We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle physics, and hadron- and nuclear physics - for supervised machine learning studies.
Better Latent Spaces for Better Autoencoders
In particular, the Dirichlet setup solves the problem and improves both the performance and the interpretability of the networks.
How to GAN Higher Jet Resolution
QCD-jets at the LHC are described by simple physics principles.
Super-Resolution
High Energy Physics - Phenomenology
GANplifying Event Samples
A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample.
Light Dark Matter Annihilation and Scattering in LHC Detectors
We systematically study models with light scalar and pseudoscalar dark matter candidates and their potential signals at the LHC.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Deep-learning Top Taggers or The End of QCD?
Machine learning based on convolutional neural networks can be used to study jet images from the LHC.
High Energy Physics - Phenomenology
Resonance Searches with an Updated Top Tagger
The performance of top taggers, for example in resonance searches, can be significantly enhanced through an increased set of variables, with a special focus on final-state radiation.
High Energy Physics - Phenomenology
Squark and gluino production cross sections in pp collisions at $\sqrt{s}$ = 13, 14, 33 and 100 TeV
We present state-of-the-art cross section predictions for the production of supersymmetric squarks and gluinos at the upcoming LHC run with a centre-of-mass energy of $\sqrt{s} = 13$ and $14$ TeV, and at potential future $pp$ colliders operating at $\sqrt{s} = 33$ and $100$ TeV.
High Energy Physics - Phenomenology
Supersymmetry production cross sections in pp collisions at sqrt{s} = 7 TeV
This document emerged from work that started in January 2012 as a joint effort by the ATLAS, CMS and LPCC supersymmetry (SUSY) working groups to compile state-of-the-art cross section predictions for SUSY particle production at the LHC.
High Energy Physics - Phenomenology
Stop Reconstruction with Tagged Tops
Using a Standard-Model top tagger on fully hadronic top decays we can not only extract the stop signal but also measure the top momentum.
High Energy Physics - Phenomenology High Energy Physics - Experiment
