Data Science and Machine Learning in Education

The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research. Moreover, exploiting symmetries inherent in physics data have inspired physics-informed ML as a vibrant sub-field of computer science research. HEP researchers benefit greatly from materials widely available materials for use in education, training and workforce development. They are also contributing to these materials and providing software to DS/ML-related fields. Increasingly, physics departments are offering courses at the intersection of DS, ML and physics, often using curricula developed by HEP researchers and involving open software and data used in HEP. In this white paper, we explore synergies between HEP research and DS/ML education, discuss opportunities and challenges at this intersection, and propose community activities that will be mutually beneficial.Comment: Contribution to Snowmass 202

Report of the Topical Group on Electroweak Precision Physics and Constraining New Physics for Snowmass 2021

The precise measurement of physics observables and the test of their consistency within the standard model (SM) are an invaluable approach, complemented by direct searches for new particles, to determine the existence of physics beyond the standard model (BSM). Studies of massive electroweak gauge bosons (W and Z bosons) are a promising target for indirect BSM searches, since the interactions of photons and gluons are strongly constrained by the unbroken gauge symmetries. They can be divided into two categories: (a) Fermion scattering processes mediated by s- or t-channel W/Z bosons, also known as electroweak precision measurements; and (b) multi-boson processes, which include production of two or more vector bosons in fermion-antifermion annihilation, as well as vector boson scattering (VBS) processes. The latter categories can test modifications of gauge-boson self-interactions, and the sensitivity is typically improved with increased collision energy. This report evaluates the achievable precision of a range of future experiments, which depend on the statistics of the collected data sample, the experimental and theoretical systematic uncertainties, and their correlations. In addition it presents a combined interpretation of these results, together with similar studies in the Higgs and top sector, in the Standard Model effective field theory (SMEFT) framework. This framework provides a model-independent prescription to put generic constraints on new physics and to study and combine large sets of experimental observables, assuming that the new physics scales are significantly higher than the EW scale.Comment: 55 pages; Report of the EF04 topical group for Snowmass 202

Mixture Density Networks for tracking in dense environments on ATLAS

The high collision energy and luminosity of the LHC allow to study jets and hadronically-decaying tau leptons at extreme energies with the ATLAS detector. These signatures lead to topologies with charged particles with an angular separation smaller than the size of the ATLAS Inner Detector sensitive elements and consequently to a reduced track reconstruction efficiency. In order to regain part of the track reconstruction efficiency loss, a neural network (NN) based approach was adopted in the ATLAS pixel detector in 2011 for estimating particle hit multiplicity, hit positions and associated uncertainties. Currently used algorithms and their performance in ATLAS will be summarized in the talk. An alternative algorithm based on Mixture Density Network (MDN) is currently being studied and the initial performance is promising. An overview of MDN algorithm and its performance will be highlighted in the talk. Comparisons will also be made with the currently used NNs in ATLAS tracking

Searches for new phenomena in final states with 3rd3^{\text{rd}} generation quarks using the ATLAS detector

Many theories beyond the Standard Model predict new phenomena, such as $Z’$ and vector-like quarks, in final states containing bottom or top quarks. Such final states offer great potential to reduce the Standard Model background, although with significant challenges in reconstructing and identifying the decay products and modelling the remaining background. The recent 13 TeV pp results, along with the associated improvements in identification techniques, will be reported

Pixel cluster splitting with Mixture Density Network

The high energy and luminosity of the LHC allows to study jets and hadronically decaying tau leptons at extreme energies with the ATLAS tracking detector. These topologies lead to charged particles with an angular separation smaller than the size of the ATLAS Inner Detector sensitive elements and consequently to a reduced track reconstruction efficiency. In order to regain part of the track reconstruction efficiency loss, a neural network (NN) based approach was adopted in the ATLAS pixel detector in 2011 for estimating particle hit multiplicity, hit positions and associated uncertainties. Currently used algorithms in ATLAS will be briefly summarized. An alternative algorithm based on Mixture Density Network (MDN) is currently being studied and the initial performance is promising. As MDN can provide an estimate of position and uncertainty at the same time, the execution can be faster compared to current ATLAS NNs. Overview of MDN algorithm and its performance will be highlighted in the poster. At the same time comparison will be made with the currently used NNs in ATLAS tracking

Searches for new high-mass resonances in top-antitop and di-electron final states using the ATLAS detector

The standard model of particle physics (SM) describes all the fundamental particles and their interactions. It is a very successful theory; however, many experimental observations - such as the origin of neutrino mass, particle origin of dark matter, e.t.c. - are either not consistent or not explained by the SM. So, it is inevitable that there has to be a (physics) model beyond the SM which will consistently explain all these observations, not covered by the SM. Several of such extensions predict new heavy particles that can interact with SM particles. This dissertation presents searches for the resonant production of such high-mass particles in dielectron and top-antitop final states. These searches use proton-proton collision data at the center-of-mass energy of 13 TeV collected by the ATLAS detector at the Large Hadron Collider (LHC) between 2015 and 2018. Electrons are stable and easy to reconstruct, but top-quarks decay instantaneously. Two dominant top-decay final states, all-hadronic and semi-leptonic, are studied in this dissertation. The combined mass distributions of all the final-state particles are used to perform model-dependent and model-independent statistical searches. No evidence for the existence of new particles is found in any of the explored final states. Hence, upper limits on production cross-section times branching ratio and lower limits on the mass of heavy Z' particles, predicted by the BSM models, are placed at a 95% confidence level. The dilepton resonance search excludes Z' boson below 3.6 TeV. The resonance search in the boosted all-hadronic top-antitop final state excludes Z' bosons with a mass lower than 4.1 TeV. Whereas in the semi-leptonic search, the same signal is expected to be excluded up to 3.6 TeV. The dissertation also presents a new algorithm for splitting the merged charge clusters in the ATLAS pixel detector, based on a Mixture Density Network (MDN). The performance of this new algorithm is found to be better than the existing algorithm. As a result, the MDN-based algorithm is expected to be used as a default algorithm in ATLAS during the next data collection period, which will start in 2022.Science, Faculty ofPhysics and Astronomy, Department ofGraduat

ATLAS pixel cluster splitting using Mixture Density Networks

The high energy and luminosity of the LHC allows to study jets and hadronically decaying tau leptons at extreme energies with the ATLAS tracking detector. These topologies lead to charged particles with an angular separation smaller than the size of the ATLAS Inner Detector sensitive elements and consequently to a reduced track reconstruction efficiency. In order to regain part of the track reconstruction efficiency loss, a neural network (NN) based approach was adopted in the ATLAS pixel detector in 2011 for estimating particle hit multiplicity, hit positions and associated uncertainties. Currently used algorithms in ATLAS will be briefly summarized. An alternative algorithm based on Mixture Density Network (MDN) is currently being studied and the initial performance is promising. As MDN can provide an estimate of position and uncertainty at the same time, the execution can be faster compared to current ATLAS NNs. Overview of MDN algorithm and its performance will be highlighted in the poster. At the same time comparison will be made with the currently used NNs in ATLAS tracking

Searches for new phenomena in final states with 3rd3^{\mathrm{rd}} generation quarks using the ATLAS detector

Many theories beyond the Standard Model predict new phenomena, such as $Z'$ and vector-like quarks, in final states containing bottom- or top-quarks. It is challenging to reconstruct and identify the decay products and model the major backgrounds. Nevertheless, such final states offer great potential to reduce the Standard Model backgrounds due to their characteristic decay signature. The latest search in the third-generation quark final states using the full Run-2 proton-proton collision data collected by the ATLAS experiment are presented. Particularly, the recent results of di-bjet and top-antitop resonance searches and dark matter produced in association with a top-quark are discussed in this proceedings. The associated improvements in $b$-quark and top-quark identification techniques are also highlighted

Search for new physics using unsupervised machine learning for anomaly detection in s=13\sqrt{s} = 13 TeV pppp collisions recorded by the ATLAS detector at the LHC

Searches for new resonances in two-body invariant masses are performed using an unsupervised anomaly detection technique in events produced in collisions at a center-of-mass energy of 13 TeV recorded by the ATLAS detector at the LHC. An autoencoder network is trained with 1% randomly selected collision events and anomalous regions are then defined which contain events with high reconstruction losses. Studies are conducted in data containing at least one isolated lepton. Nine invariant masses () are inspected which contain pairs of one jet (-jet) and one lepton (, ), photon, or a second jet (-jet). No significant deviation from the background-only hypothesis is observed after applying the event-based anomaly detection technique. The obtained model-independent limits are shown to have a strong potential to exclude generic heavy states with complex decays

Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml

Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environments has, however, been limited as a result of the difficulties of implementing recurrent architectures on field-programmable gate arrays (FPGAs). In this paper we present an implementation of two types of recurrent neural network layers—long short-term memory and gated recurrent unit—within the hls4ml framework. We demonstrate that our implementation is capable of producing effective designs for both small and large models, and can be customized to meet specific design requirements for inference latencies and FPGA resources. We show the performance and synthesized designs for multiple neural networks, many of which are trained specifically for jet identification tasks at the CERN Large Hadron Collider