35 research outputs found
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
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Factorization and Precision Calculations in Particle Physics
We state and prove to all orders in perturbation theory a factorization theorem in Quantum Chromodynamics that concisely describes the separation of the physics associated with jet formation from that associated with the hard-scattering in high-energy particle collisions. We show how the factorization theorem, which provides an equality between amplitudes in gauge theories, can be readily applied to precision calculations of cross-sections. In the resulting factorized cross sections, the components relevant to jet production are universal and perturbatively calculable. Their renormalization group evolution can be used to sum large logarithms of scale ratios to all orders in perturbation theory, thus enabling quantitive predictions in the regime of disparate scales relevant to many important collider-physics observables. As an application, we calculate the observable 2-subjettiness at next-to-next-to-next-to-leading-logarithmic order for the decay of boosted heavy color-singlet particles such as Electroweak bosons. Our calculation is the first analytic calculation of a jet substructure observable.Physic