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Register a new userPulling Out All the Tops with Computer Vision and Deep Learning
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We apply computer vision with deep learning -- in the form of a convolutional neural network (CNN) -- to build a highly effective boosted top tagger. Previous work (the DeepTop tagger of Kasieczka et al) has shown that a CNN-based top tagger can achieve comparable performance to state-of-the-art conventional top taggers based on high-level inputs. Here, we introduce a number of improvements to the DeepTop tagger, including architecture, training, image preprocessing, sample size and color pixels. Our final CNN top tagger outperforms BDTs based on high-level inputs by a factor of $sim 2$--3 or more in background rejection, over a wide range of tagging efficiencies and fiducial jet selections. As reference points, we achieve a QCD background rejection factor of 500 (60) at 50% top tagging efficiency for fully-merged (non-merged) top jets with $p_T$ in the 800--900 GeV (350--450 GeV) range. Our CNN can also be straightforwardly extended to the classification of other types of jets, and the lessons learned here may be useful to others designing their own deep NNs for LHC applications.
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The LHC search strategies for leptoquarks that couple dominantly to a top quark are different than for the ones that couple mostly to the light quarks. We consider charge $1/3$ ($phi_1$) and $5/3$ ($phi_5$) scalar leptoquarks that can decay to a top quark and a charged lepton ($tell$) giving rise to a resonance system of a boosted top and a high-$p_{rm T}$ lepton. We introduce simple phenomenological models suitable for bottom-up studies and explicitly map them to all possible scalar leptoquark models within the Buchm{u}ller-R{u}ckl-Wyler classifications that can have the desired decays. We study pair and single productions of these leptoquarks. Contrary to the common perception, we find that the single production of top-philic leptoquarks $phi = {phi_1,phi_5}$ in association with a lepton and jets could be significant for order one $phi tell$ coupling in certain scenarios. We propose a strategy of selecting events with at least one hadronic-top and two high-$p_{rm T}$ same flavour opposite sign leptons. This captures events from both pair and single productions. Our strategy can significantly enhance the LHC discovery potential especially in the high-mass region where single productions become more prominent. Our estimation shows that a scalar leptoquark as heavy as $sim1.7$ TeV can be discovered at the $14$ TeV LHC with 3 ab$^{-1}$ of integrated luminosity in the $tellell+X$ channel for $100%$ branching ratio in the $phito tell $ decay mode. However, in some scenarios, the discovery reach can increase beyond $2$ TeV even though the branching ratio comes down to about $50%$.
We explore the application of computer vision and machine learning (ML) techniques to predict material properties (e.g. compressive strength) based on SEM images. We show that its possible to train ML models to predict materials performance based on SEM images alone, demonstrating this capability on the real-world problem of predicting uniaxially compressed peak stress of consolidated molecular solids samples. Our image-based ML approach reduces mean absolute percent error (MAPE) by an average of 24% over baselines representative of the current state-of-the-practice (i.e., domain-experts analysis and correlation). We compared two complementary approaches to this problem: (1) a traditional ML approach, random forest (RF), using state-of-the-art computer vision features and (2) an end-to-end deep learning (DL) approach, where features are learned automatically from raw images. We demonstrate the complementarity of these approaches, showing that RF performs best in the small data regime in which many real-world scientific applications reside (up to 24% lower RMSE than DL), whereas DL outpaces RF in the big data regime, where abundant training samples are available (up to 24% lower RMSE than RF). Finally, we demonstrate that models trained using machine learning techniques are capable of discovering and utilizing informative crystal attributes previously underutilized by domain experts.
Jet tagging has become an essential tool for new physics searches at the high-energy frontier. For jets that contain energetic charged leptons we introduce Feature Extended Supervised Tagging (FEST) which, in addition to jet substructure, considers the features of the charged lepton within the jet. With this method we build dedicated taggers to discriminate among boosted $H to ell u q bar q$, $t to ell u b$, and QCD jets (with $ell$ an electron or muon). The taggers have an impressive performance, allowing for overall light jet rejection factors of $10^4-10^5$, for top quark / Higgs boson efficiencies of $0.5$. The taggers are also excellent in the discrimination of Higgs bosons from top quarks and vice versa, for example rejecting top quarks by factors of $100-300$ for Higgs boson efficiencies of $0.5$. We demonstrate the potential of these taggers to improve the sensitivity to new physics by using as example a search for a new $Z$ boson decaying into $Z H$, in the fully-hadronic final state.
Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research.
We present a first attempt to design a quantum circuit for the determination of the parton content of the proton through the estimation of parton distribution functions (PDFs), in the context of high energy physics (HEP). The growing interest in quantum computing and the recent developments of new algorithms and quantum hardware devices motivates the study of methodologies applied to HEP. In this work we identify architectures of variational quantum circuits suitable for PDFs representation (qPDFs). We show experiments about the deployment of qPDFs on real quantum devices, taking into consideration current experimental limitations. Finally, we perform a global qPDF determination from collider data using quantum computer simulation on classical hardware and we compare the obtained partons and related phenomenological predictions involving hadronic processes to modern PDFs.
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