17 research outputs found
Constraining the Parameters of High-Dimensional Models with Active Learning
Constraining the parameters of physical models with parameters is a
widespread problem in fields like particle physics and astronomy. The
generation of data to explore this parameter space often requires large amounts
of computational resources. The commonly used solution of reducing the number
of relevant physical parameters hampers the generality of the results. In this
paper we show that this problem can be alleviated by the use of active
learning. We illustrate this with examples from high energy physics, a field
where simulations are often expensive and parameter spaces are
high-dimensional. We show that the active learning techniques
query-by-committee and query-by-dropout-committee allow for the identification
of model points in interesting regions of high-dimensional parameter spaces
(e.g. around decision boundaries). This makes it possible to constrain model
parameters more efficiently than is currently done with the most common
sampling algorithms and to train better performing machine learning models on
the same amount of data. Code implementing the experiments in this paper can be
found on GitHub
Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
We explore the use of autoregressive flows, a type of generative model with
tractable likelihood, as a means of efficient generation of physical particle
collider events. The usual maximum likelihood loss function is supplemented by
an event weight, allowing for inference from event samples with variable, and
even negative event weights. To illustrate the efficacy of the model, we
perform experiments with leading-order top pair production events at an
electron collider with importance sampling weights, and with
next-to-leading-order top pair production events at the LHC that involve
negative weights.Comment: 26 pages, 7 figure
SPOT: Open Source framework for scientific data repository and interactive visualization
SPOT is an open source and free visual data analytics tool for
multi-dimensional data-sets. Its web-based interface allows a quick analysis of
complex data interactively. The operations on data such as aggregation and
filtering are implemented. The generated charts are responsive and OpenGL
supported. It follows FAIR principles to allow reuse and comparison of the
published data-sets. The software also support PostgreSQL database for
scalability
The BSM-AI project: SUSY-AI - Generalizing LHC limits on Supersymmetry with Machine Learning
A key research question at the Large Hadron Collider (LHC) is the test of
models of new physics. Testing if a particular parameter set of such a model is
excluded by LHC data is a challenge: It requires the time consuming generation
of scattering events, the simulation of the detector response, the event
reconstruction, cross section calculations and analysis code to test against
several hundred signal regions defined by the ATLAS and CMS experiment. In the
BSM-AI project we attack this challenge with a new approach. Machine learning
tools are thought to predict within a fraction of a millisecond if a model is
excluded or not directly from the model parameters. A first example is SUSY-AI,
trained on the phenomenological supersymmetric standard model (pMSSM). About
300,000 pMSSM model sets - each tested with 200 signal regions by ATLAS - have
been used to train and validate SUSY-AI. The code is currently able to
reproduce the ATLAS exclusion regions in 19 dimensions with an accuracy of at
least 93 percent. It has been validated further within the constrained MSSM and
a minimal natural supersymmetric model, again showing high accuracy. SUSY-AI
and its future BSM derivatives will help to solve the problem of recasting LHC
results for any model of new physics.
SUSY-AI can be downloaded at http://susyai.hepforge.org/. An on-line
interface to the program for quick testing purposes can be found at
http://www.susy-ai.org/
A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
Optimisation problems are ubiquitous in particle and astrophysics, and
involve locating the optimum of a complicated function of many parameters that
may be computationally expensive to evaluate. We describe a number of global
optimisation algorithms that are not yet widely used in particle astrophysics,
benchmark them against random sampling and existing techniques, and perform a
detailed comparison of their performance on a range of test functions. These
include four analytic test functions of varying dimensionality, and a realistic
example derived from a recent global fit of weak-scale supersymmetry. Although
the best algorithm to use depends on the function being investigated, we are
able to present general conclusions about the relative merits of random
sampling, Differential Evolution, Particle Swarm Optimisation, the Covariance
Matrix Adaptation Evolution Strategy, Bayesian Optimisation, Grey Wolf
Optimisation, and the PyGMO Artificial Bee Colony, Gaussian Particle Filter and
Adaptive Memory Programming for Global Optimisation algorithms
spot: Open Source framework for scientific data repository and interactive visualization
spot is an open source and free visual data analytics tool for multi-dimensional data-sets. Its web-based interface enables user to do a quick and interactive analysis of complex data. Various operations on data are implemented such as aggregation and filtering. The interface supports OpenGL acceleration, which makes the generated charts very responsive. In order to have scalability, the software also supports PostgreSQL as its database. It follows FAIR principles to allow reuse and comparison of the published data-sets. Keywords: Visualization, High-dimensional data, Theoretical models, Open data, FAIR, Particle physic
Machine Learning and LHC Event Generation
International audienceFirst-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem