Next-to-leading power threshold effects for inclusive and exclusive processes with final state jets

It is well known that cross-sections in perturbative QCD receive large corrections from soft and collinear radiation, which can be resummed to all orders in the coupling. Whether or not the universal properties of this radiation can be extended to next-to-leading power (NLP) in the threshold expansion has been the subject of much recent study. In particular, universal forms for next-to-leading order (NLO) cross-sections have been obtained for general colour-singlet production processes by considering only the emission of gluons. In this paper, we extend such formulae to processes containing final state jets, and show that the dominant NLP terms at NLO can be obtained using a similar prescription to the colour-singlet case. We furthermore consider the emission of soft quarks, which also leads to a class of universal NLP contributions at NLO. We illustrate our results using three different processes at NLO: deep-inelastic scattering, hadroproduction via electron-positron annihilation and prompt photon production

The role of soft quarks in next-to-leading power threshold effects

Cross sections in perturbative QCD are plagued by large corrections from soft and collinear radiation. The most singular terms are known to be universal, which allows their resummation to all orders in the coupling. In our work, we have examined the structure of the next-to-singular contributions, which can originate from the emission of both soft quarks and gluons. We show that we can derive a next-to-soft amplitude for both types of emissions. The numerical impact of these contributions on the transverse momentum distribution of the single-photon production process are also discussed

Analyzing mechanisms of action of antimicrobial peptides on bacterial membranes requires multiple complimentary assays and different bacterial strains

Antimicrobial peptides (AMPs) commonly target bacterial membranes and show broad-spectrum activity against microorganisms. In this research we used three AMPs (nisin, epilancin 15×, [R4L10]-teixobactin) and tested their membrane effects towards three strains (Staphylococcus simulans, Micrococcus flavus, Bacillus megaterium) in relation with their antibacterial activity. We describe fluorescence and luminescence-based assays to measure effects on membrane potential, intracellular pH, membrane permeabilization and intracellular ATP levels. The results show that our control peptide, nisin, performed mostly as expected in view of its targeted pore-forming activity, with fast killing kinetics that coincided with severe membrane permeabilization in all three strains. However, the mechanisms of action of both Epilancin 15× as well as [R4L10]-teixobactin appeared to depend strongly on the bacterium tested. In certain specific combinations of assay, peptide and bacterium, deviations from the general picture were observed. This was even the case for nisin, indicating the importance of using multiple assays and bacteria for mode of action studies to be able to draw proper conclusions on the mode of action of AMPs

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 β-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 super- symmetric 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.Melissa van Beekveld, Sascha Caron, Luc Hendriks, Paul Jackson, Adam Leinweber, Sydney Otten ... et al

A New Era in the Quest for Dark Matter

There is a growing sense of `crisis' in the dark matter community, due to the absence of evidence for the most popular candidates such as weakly interacting massive particles, axions, and sterile neutrinos, despite the enormous effort that has gone into searching for these particles. Here, we discuss what we have learned about the nature of dark matter from past experiments, and the implications for planned dark matter searches in the next decade. We argue that diversifying the experimental effort, incorporating astronomical surveys and gravitational wave observations, is our best hope to make progress on the dark matter problem.Comment: Published in Nature, online on 04 Oct 2018. 13 pages, 1 figur

Benchmark data and model independent event classification for the large hadron collider

We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb−1 of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge

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