754 research outputs found
About the rapidity and helicity distributions of the W bosons produced at LHC
bosons are produced at LHC from a forward-backward symmetric initial
state. Their decay to a charged lepton and a neutrino has a strong spin
analysing power. The combination of these effects results in characteristic
distributions of the pseudorapidity of the leptons decaying from and
of different helicity. This observation may open the possibility to
measure precisely the and rapidity distributions for the two
transverse polarisation states of bosons produced at small transverse
momentum.Comment: 8 pages, 5 figure
Study the effect of beam energy spread and detector resolution on the search for Higgs boson decays to invisible particles at a future ee circular collider
We study the expected sensitivity to measure the branching ratio of Higgs
boson decays to invisible particles at a future circular \epem collider
(FCC-ee) in the process with ( or
) using an integrated luminosity of 3.5 ab at a center-of-mass
energy GeV. The impact of the energy spread of the FCC-ee beam
and of the resolution in the reconstruction of the leptons is discussed. %Two
different detector concepts are considered: a detector corresponding to the CMS
reconstruction performances and the expected design of the ILC detector. The
minimum branching ratio for a observation after 3.5ab of data
taking is . The branching ratio exclusion limit at
95\% CL is .Comment: 17 pages, submitted to EPJ
Variational Autoencoders for New Physics Mining at the Large Hadron Collider
Using variational autoencoders trained on known physics processes, we develop
a one-sided threshold test to isolate previously unseen processes as outlier
events. Since the autoencoder training does not depend on any specific new
physics signature, the proposed procedure doesn't make specific assumptions on
the nature of new physics. An event selection based on this algorithm would be
complementary to classic LHC searches, typically based on model-dependent
hypothesis testing. Such an algorithm would deliver a list of anomalous events,
that the experimental collaborations could further scrutinize and even release
as a catalog, similarly to what is typically done in other scientific domains.
Event topologies repeating in this dataset could inspire new-physics model
building and new experimental searches. Running in the trigger system of the
LHC experiments, such an application could identify anomalous events that would
be otherwise lost, extending the scientific reach of the LHC.Comment: 29 pages, 12 figures, 5 table
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the
problem of detecting new physics processes in proton-proton collisions at the
Large Hadron Collider. Anomaly detection based on ALAD matches performances
reached by Variational Autoencoders, with a substantial improvement in some
cases. Training the ALAD algorithm on 4.4 fb-1 of 8 TeV CMS Open Data, we show
how a data-driven anomaly detection and characterization would work in real
life, re-discovering the top quark by identifying the main features of the
t-tbar experimental signature at the LHC.Comment: 16 pages, 9 figure
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning
We present a fast simulation application based on a Deep Neural Network,
designed to create large analysis-specific datasets. Taking as an example the
generation of W+jet events produced in sqrt(s)= 13 TeV proton-proton
collisions, we train a neural network to model detector resolution effects as a
transfer function acting on an analysis-specific set of relevant features,
computed at generation level, i.e., in absence of detector effects. Based on
this model, we propose a novel fast-simulation workflow that starts from a
large amount of generator-level events to deliver large analysis-specific
samples. The adoption of this approach would result in about an
order-of-magnitude reduction in computing and storage requirements for the
collision simulation workflow. This strategy could help the high energy physics
community to face the computing challenges of the future High-Luminosity LHC.Comment: 15 pages, 12 figure
Variational Autoencoders for New Physics Mining at the Large Hadron Collider
Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, the proposed procedure doesnât make specific assumptions on the nature of new physics. An event selection based on this algorithm would be complementary to classic LHC searches, typically based on model-dependent hypothesis testing. Such an algorithm would deliver a list of anomalous events, that the experimental collaborations could further scrutinize and even release as a catalog, similarly to what is typically done in other scientific domains. Event topologies repeating in this dataset could inspire new-physics model building and new experimental searches. Running in the trigger system of the LHC experiments, such an application could identify anomalous events that would be otherwise lost, extending the scientific reach of the LHC
New Physics Agnostic Selections For New Physics Searches
We discuss a model-independent strategy for boosting new physics searches with the help of an unsupervised anomaly detection algorithm. Prior to a search, each input event is preprocessed by the algorithm - a variational autoencoder (VAE). Based on the loss assigned to each event, input data can be split into a background control sample and a signal enriched sample. Following this strategy, one can enhance the sensitivity to new physics with no assumption on the underlying new physics signature. Our results show that a typical BSM search on the signal enriched group is more sensitive than an equivalent search on the original dataset
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