Abstract: Measurements and searches performed with the ATLAS detector at the CERN LHC often involve signatures with one or more prompt leptons. Such analyses are subject to 'fake/non-prompt' lepton backgrounds, where either a hadron or a lepton from a hadron decay or an electron from a photon conversion satisfies the prompt-lepton selection criteria. These backgrounds often arise within a hadronic jet because of particle decays in the showering process, particle misidentification or particle interactions with the detector material. As it is challenging to model these processes with high accuracy in simulation, their estimation typically uses data-driven methods. Three methods for carrying out this estimation are described, along with their implementation in ATLAS and their performance.

Abstract: We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.

Abstract: We provide three Fortran programs which evaluate the QCD analytic (holomorphic) couplings A(v)(Q(2)) for complex or real squared momenta Q(2). These couplings are holomorphic analogs of the powers a(Q(2))(v) of the underlying perturbative QCD (pQCD) coupling a(Q(2)) equivalent to alpha(s)(Q(2))/pi, in three analytic QCD models (anQCD): Fractional Analytic Perturbation Theory (FAPT), Two-delta analytic QCD (2 delta anQCD), and Massive Perturbation Theory (MPT). The index v can be noninteger. The provided programs do basically the same job as the Mathematica package anQCD.m published by us previously (Ayala and Cvetic, 2015), but are now written in Fortran. Program summary Program title: AanQCDext Catalogue identifier: AEYKv10 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEYICv1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 12105 No. of bytes in distributed program, including test data, etc.: 98822 Distribution format: tar.gz Programming language: Fortran. Computer: Any work-station or PC where Fortran 95/200312008 (gfortran) is running. Operating system: Operating system Linux (Ubuntu and Scientific Linux), Windows (in all cases using gfortran). Classification: 11.1, 11.5. Nature of problem: Calculation of values of the running analytic couplings A(v)(Q(2); N-f) for general complex squared momenta Q(2) equivalent to -q(2), in three analytic QCD models, where A(v)(Q(2); N-f) is the analytic (holomorphic) analog of the power (alpha(s)(Q(2); N-f)/pi)(v). Here, A(v)(Q(2); N-f) is a holomorphic function in the Q(2) complex plane, with the exception of the negative semiaxis (-infinity, -M-thr(2)], reflecting the analyticity properties of the spacelike renormalization invariant quantities D(Q(2)) in QCD. In contrast, the perturbative QCD power (alpha(s)(Q(2); N-f)/pi)(v) has singularities even outside the negative semiaxis (Landau ghosts). The three considered models are: Analytic Perturbation theory (APT); Two-delta analytic QCD (2 delta anQCD); Massive Perturbation Theory (MPT). We refer to Ref. [1] for more details and literature. Solution method: The Fortran programs for FAPT and 2 delta anQCD models contain routines and functions needed to perform two-dimensional numerical integrations involving the spectral function, in order to evaluate A(v)(Q(2)) couplings. In MPT model, one-dimensional numerical integration involving A(1)(Q(2)) is sufficient to evaluate any A(v)(Q(2)) coupling. Restrictions: For unphysical choices of the input parameters the results are meaningless. When Q(2) is close to the cut region of the couplings (Q(2) real negative), the calculations can take more time and can have less precision. Running time: For evaluation of a set of about 10 related couplings, the times vary in the range t similar to 10(1)-10(2) s. MPT requires less time, t similar to 1-10(1) s. References: [1] C. Ayala and G. Cvetic, anQCD: a Mathematica package for calculations in general analytic QCD models, Comput. Phys. Commun. 190 (2015) 182.

Abstract: A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

Abstract: Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data.Methods. The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment.Results. We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.