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Khosa, C. K., Sanz, V., & Soughton, M. (2021). Using machine learning to disentangle LHC signatures of Dark Matter candidates. SciPost Phys., 10(6), 151–26pp.
Abstract: We study the prospects of characterising Dark Matter at colliders using Machine Learning (ML) techniques. We focus on the monojet and missing transverse energy (MET) channel and propose a set of benchmark models for the study: a typical WIMP Dark Matter candidate in the form of a SUSY neutralino, a pseudo-Goldstone impostor in the shape of an Axion-Like Particle, and a light Dark Matter impostor whose interactions are mediated by a heavy particle. All these benchmarks are tensioned against each other, and against the main SM background (Z+jets). Our analysis uses both the leading-order kinematic features as well as the information of an additional hard jet. We explore different representations of the data, from a simple event data sample with values of kinematic variables fed into a Logistic Regression algorithm or a Fully Connected Neural Network, to a transformation of the data into images related to probability distributions, fed to Deep and Convolutional Neural Networks. We also study the robustness of our method against including detector effects, dropping kinematic variables, or changing the number of events per image. In the case of signals with more combinatorial possibilities (events with more than one hard jet), the most crucial data features are selected by performing a Principal Component Analysis. We compare the performance of all these methods, and find that using the 2D images of the combined information of multiple events significantly improves the discrimination performance.
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Khosa, C. K., & Sanz, V. (2023). Anomaly Awareness. SciPost Phys., 15(2), 053–24pp.
Abstract: We present a new algorithm for anomaly detection called Anomaly Awareness. The algorithm learns about normal events while being made aware of the anomalies through a modification of the cost function. We show how this method works in different Particle Physics situations and in standard Computer Vision tasks. For example, we apply the method to images from a Fat Jet topology generated by Standard Model Top and QCD events, and test it against an array of new physics scenarios, including Higgs production with EFT effects and resonances decaying into two, three or four subjets. We find that the algorithm is effective identifying anomalies not seen before, and becomes robust as we make it aware of a varied-enough set of anomalies.
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Khosa, C. K., & Sanz, V. (2022). On the Impact of the LHC Run 2 Data on General Composite Higgs Scenarios. Adv. High. Energy Phys., 2022, 8970837–13pp.
Abstract: We study the impact of Run 2 LHC data on general composite Higgs scenarios, where nonlinear effects, mixing with additional scalars, and new fermionic degrees of freedom could simultaneously contribute to the modification of Higgs properties. We obtain new experimental limits on the scale of compositeness, the mixing with singlets and doublets with the Higgs, and the mass and mixing angle of top-partners. We also show that for scenarios where new fermionic degrees of freedom are involved in electroweak symmetry breaking, there is an interesting interplay among Higgs coupling measurements, boosted Higgs properties, SMEFT global analyses, and direct searches for single and double production of vector-like quarks.
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Khosa, C. K., Mars, L., Richards, J., & Sanz, V. (2020). Convolutional neural networks for direct detection of dark matter. J. Phys. G, 47(9), 095201–20pp.
Abstract: The XENON1T experiment uses a time projection chamber (TPC) with liquid xenon to search for weakly interacting massive particles (WIMPs), a proposed dark matter particle, via direct detection. As this experiment relies on capturing rare events, the focus is on achieving a high recall of WIMP events. Hence the ability to distinguish between WIMP and the background is extremely important. To accomplish this, we suggest using convolutional neural networks (CNNs); a machine learning procedure mainly used in image recognition tasks. To explore this technique we use XENON collaboration open-source software to simulate the TPC graphical output of dark matter signals and main backgrounds. A CNN turns out to be a suitable tool for this purpose, as it can identify features in the images that differentiate the two types of events without the need to manipulate or remove data in order to focus on a particular region of the detector. We find that the CNN can distinguish between the dominant background events (ER) and 500 GeV WIMP events with a recall of 93.4%, precision of 81.2% and an accuracy of 87.2%.
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Khatun, A., Chatterjee, S. S., Thakore, T., & Agarwalla, S. K. (2020). Enhancing sensitivity to non-standard neutrino interactions at INO combining muon and hadron information. Eur. Phys. J. C, 80(6), 533–17pp.
Abstract: In this paper, we explore the impact of flavor violating neutral current non-standard interaction (NSI) parameter epsilon(mu tau) in the oscillation of atmospheric neutrinos and antineutrinos separately using the 50 kt magnetized ICAL detector at INO. We find that due to non-zero epsilon(mu tau), nu(mu) -> nu(mu) and (nu) over bar (mu) -> (nu) over bar (mu) transition probabilities get modified substantially at higher energies and longer baselines, where vacuum oscillation dominates. We demonstrate for the first time that by adding the hadron energy information along with the muon energy and muon direction in each event, the sensitivity of ICAL to the NSI parameter epsilon(mu tau) can be enhanced significantly. The most optimistic bound on epsilon(mu tau) that we obtain is – 0.01 < epsilon(mu tau) < 0.01 at 90% C.L. using 500 kt.yr exposure and considering E-mu, cos theta(mu), and E-had' as observables in their ranges of [1, 21] GeV, [- 1, 1], and [0, 25] GeV, respectively. We discuss for the first time the importance of the charge identification capability of the ICAL detector to have better constraints on epsilon(mu t). We also study the impact of non-zero epsilon(mu tau) on mass hierarchy determination and precision measurement of oscillation parameters.
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Khachatryan, M. et al, Coloma, P. (2021). Electron-beam energy reconstruction for neutrino oscillation measurements. Nature, 599(7886), 565–570.
Abstract: Neutrinos exist in one of three types or 'flavours'-electron, muon and tau neutrinos-and oscillate from one flavour to another when propagating through space. This phenomena is one of the few that cannot be described using the standard model of particle physics (reviewed in ref. (1)), and so its experimental study can provide new insight into the nature of our Universe (reviewed in ref. (2)). Neutrinos oscillate as a function of their propagation distance (L) divided by their energy (E). Therefore, experiments extract oscillation parameters by measuring their energy distribution at different locations. As accelerator-based oscillation experiments cannot directly measure E, the interpretation of these experiments relies heavily on phenomenological models of neutrino-nucleus interactions to infer E. Here we exploit the similarity of electron-nucleus and neutrino-nucleus interactions, and use electron scattering data with known beam energies to test energy reconstruction methods and interaction models. We find that even in simple interactions where no pions are detected, only a small fraction of events reconstruct to the correct incident energy. More importantly, widely used interaction models reproduce the reconstructed energy distribution only qualitatively and the quality of the reproduction varies strongly with beam energy. This shows both the need and the pathway to improve current models to meet the requirements of next-generation, high-precision experiments such as Hyper-Kamiokande (Japan)(3) and DUNE (USA)(4). Electron scattering measurements are shown to reproduce only qualitatively state-of-the-art lepton-nucleus energy reconstruction models, indicating that improvements to these particle-interaction models are required to ensure the accuracy of future high-precision neutrino oscillation experiments.
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AGATA Collaboration(Kaya, L. et al), & Gadea, A. (2019). Isomer spectroscopy in Ba-133 and high-spin structure of Ba-134. Phys. Rev. C, 100(2), 024323–18pp.
Abstract: The transitional nuclei Ba-134 and Ba-133 are investigated after multinucleon transfer employing the high-resolution Advanced GAmma Tracking Array coupled to the magnetic spectrometer PRISMA at the Laboratori Nazionali di Legnaro, Italy, and after fusion-evaporation reaction at the FN tandem accelerator of the University of Cologne, Germany. The J(pi) = 19/2(+) state at 1942 keV in Ba-133 is identified as an isomer with a half-life of 66.6(20) ns corresponding to a B(E1) value of 7.7(4) x 10(-6) e(2) fm(2) for the J(pi) = 19/2(+) to J(pi) = 19/2(-) transition. The level scheme of Ba-134 above the J(pi) = 10(+) isomer is extended to approximately 6 MeV. A pronounced backbending is observed at h omega = 0.38 MeV along the positive-parity yrast band. The results are compared to the high-spin systematics of the Z = 56 isotopes. Large-scale shell-model calculations employing the GCN50:82, SN100PN, SNV, PQM130, Realistic SM, and EPQQM interactions reproduce the experimental findings and elucidate the structure of the high-spin states. The shell-model calculations employing the GCN50:82 and PQM130 interactions reproduce alignment properties and provide detailed insight into the microscopic origin of this phenomenon in transitional Ba-134.
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Kaur, D., Khan Chowdhury, N. R., & Rahaman, U. (2024). Effect of non-unitary mixing on the mass hierarchy and CP violation determination at the Protvino to ORCA experiment. Eur. Phys. J. C, 84(2), 118–18pp.
Abstract: In this paper, we have estimated the neutrino mass ordering and the CP violation sensitivity of the proposed Protvino to ORCA (P2O) experiment after 6 years of data-taking. Both unitary and non-unitary 3x3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3\times 3$$\end{document} neutrino mass mixing have been considered in the simulations. A forecast analysis deriving possible future constraints on non-unitary parameters at P2O have been performed.
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Kasieczka, G. et al, & Sanz, V. (2021). The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics. Rep. Prog. Phys., 84(12), 124201–64pp.
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.
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Karuseichyk, I., Sorelli, G., Walschaers, M., Treps, N., & Gessner, M. (2022). Resolving mutually-coherent point sources of light with arbitrary statistics. Phys. Rev. Res., 4(4), 043010–11pp.
Abstract: We analyze the problem of resolving two mutually coherent point sources with arbitrary quantum statistics, mutual phase, and relative and absolute intensity. We use a sensitivity measure based on the method of moments and compare direct imaging with spatial-mode demultiplexing (SPADE), analytically proving advantage of the latter. We show that the moment-based sensitivity of SPADE saturates the quantum Fisher information for all known cases, even for non-Gaussian states of the sources.
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