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Adam Leinweber

Publications and source records attributed to Adam Leinweber.

3 recordsLinked to original sources

Simple, but not simplified: A new approach for optimising beyond-Standard Model physics searches at the Large Hadron Collider

Searches for beyond-Standard Model physics scenarios, such as supersymmetry (SUSY), at the Large Hadron Collider (LHC) are frequently optimised on simplified models. After assuming particular particle production and decay processes, analyses are optimised by tuning event selections on benchmark models generated in 2D planes of parameters, with all other parameters held fixed. Motivated by recent evidence that this removes sensitivity to a large volume of viable SUSY models, we propose an alternative approach based on dimensional reduction of global fit results. Starting from the results of a global fit of the 4D electroweak minimal supersymmetric standard model performed by the GAMBIT collaboration, we show how to define a 2D plane by using a variational autoencoder to map points in the original 4D parameter space to a 2D latent space. This allows for easy visualisation of the 4D global fit results, which generates insights into what we may be missing at the LHC. Furthermore, the invertible nature of the map to the 2D plane allows experimentalists to choose and simulate benchmark models in a 2D plane (as they could for a simplified model) whilst still accessing the full range of phenomenological scenarios identified by the global fit in the 4D model. We provide a demonstration of how our visualisation and benchmark model simulation process works, and develop an analysis that is able to exclude four benchmark models not excluded by the ATLAS and CMS collaborations in the set of results used for the global fit.

hep-ph

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.

hep-ph

Combining outlier analysis algorithms to identify new physics at the LHC

The lack of evidence for new physics at the Large Hadron Collider so far has prompted the development of model-independent search techniques. In this study, we compare the anomaly scores of a variety of anomaly detection techniques: an isolation forest, a Gaussian mixture model, a static autoencoder, and a $\beta$-variational autoencoder (VAE), where we define the reconstruction loss of the latter as a weighted combination of regression and classification terms. We apply these algorithms to the 4-vectors of simulated LHC data, but also investigate the performance when the non-VAE algorithms are applied to the latent space variables created by the VAE. In addition, we assess the performance when the anomaly scores of these algorithms are combined in various ways. Using supersymmetric benchmark points, we find that the logical AND combination of the anomaly scores yielded from algorithms trained in the latent space of the VAE is the most effective discriminator of all methods tested.

hep-ph