728 research outputs found
Reducing the Top Quark Mass Uncertainty with Jet Grooming
The measurement of the top quark mass has large systematic uncertainties
coming from the Monte Carlo simulations that are used to match theory and
experiment. We explore how much that uncertainty can be reduced by using jet
grooming procedures. We estimate the inherent ambiguity in what is meant by
Monte Carlo mass to be around 530 MeV without any corrections. This uncertainty
can be reduced by 60% to 200 MeV by calibrating to the W mass and a further 33%
to 140 MeV by applying soft-drop jet grooming (or by 20% more to 170 MeV with
trimming). At e+e- colliders, the associated uncertainty is around 110 MeV,
reducing to 50 MeV after calibrating to the W mass. By analyzing the tuning
parameters, we conclude that the importance of jet grooming after calibrating
to the W mass is to reduce sensitivity to the underlying event.Comment: 21 pages, 7 figure
JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
In applications of machine learning to particle physics, a persistent
challenge is how to go beyond discrimination to learn about the underlying
physics. To this end, a powerful tool would be a framework for unsupervised
learning, where the machine learns the intricate high-dimensional contours of
the data upon which it is trained, without reference to pre-established labels.
In order to approach such a complex task, an unsupervised network must be
structured intelligently, based on a qualitative understanding of the data. In
this paper, we scaffold the neural network's architecture around a
leading-order model of the physics underlying the data. In addition to making
unsupervised learning tractable, this design actually alleviates existing
tensions between performance and interpretability. We call the framework
JUNIPR: "Jets from UNsupervised Interpretable PRobabilistic models". In this
approach, the set of particle momenta composing a jet are clustered into a
binary tree that the neural network examines sequentially. Training is
unsupervised and unrestricted: the network could decide that the data bears
little correspondence to the chosen tree structure. However, when there is a
correspondence, the network's output along the tree has a direct physical
interpretation. JUNIPR models can perform discrimination tasks, through the
statistically optimal likelihood-ratio test, and they permit visualizations of
discrimination power at each branching in a jet's tree. Additionally, JUNIPR
models provide a probability distribution from which events can be drawn,
providing a data-driven Monte Carlo generator. As a third application, JUNIPR
models can reweight events from one (e.g. simulated) data set to agree with
distributions from another (e.g. experimental) data set.Comment: 37 pages, 24 figure
Neural Networks for Full Phase-space Reweighting and Parameter Tuning
Precise scientific analysis in collider-based particle physics is possible
because of complex simulations that connect fundamental theories to observable
quantities. The significant computational cost of these programs limits the
scope, precision, and accuracy of Standard Model measurements and searches for
new phenomena. We therefore introduce Deep neural networks using Classification
for Tuning and Reweighting (DCTR), a neural network-based approach to reweight
and fit simulations using all kinematic and flavor information -- the full
phase space. DCTR can perform tasks that are currently not possible with
existing methods, such as estimating non-perturbative fragmentation
uncertainties. The core idea behind the new approach is to exploit powerful
high-dimensional classifiers to reweight phase space as well as to identify the
best parameters for describing data. Numerical examples from
demonstrate the fidelity of these methods for
simulation parameters that have a big and broad impact on phase space as well
as those that have a minimal and/or localized impact. The high fidelity of the
full phase-space reweighting enables a new paradigm for simulations, parameter
tuning, and model systematic uncertainties across particle physics and possibly
beyond.Comment: 7 pages, 3 figures; v2 has updated citations and clarifications; v3
has a new appendix with an alternative fitting metho
OmniFold: A Method to Simultaneously Unfold All Observables
Collider data must be corrected for detector effects ("unfolded") to be
compared with many theoretical calculations and measurements from other
experiments. Unfolding is traditionally done for individual, binned observables
without including all information relevant for characterizing the detector
response. We introduce OmniFold, an unfolding method that iteratively reweights
a simulated dataset, using machine learning to capitalize on all available
information. Our approach is unbinned, works for arbitrarily high-dimensional
data, and naturally incorporates information from the full phase space. We
illustrate this technique on a realistic jet substructure example from the
Large Hadron Collider and compare it to standard binned unfolding methods. This
new paradigm enables the simultaneous measurement of all observables, including
those not yet invented at the time of the analysis.Comment: 8 pages, 3 figures, 1 table, 1 poem; v2: updated to approximate PRL
versio
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