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Outline: Understanding gluon PDFs using photon+jet cross section measurement from the CMS pp data; Heavy flavor “tagging”/classification using Machine Learning tools at PHENIX; Unfolding development for top quark pair spin correlation and polarization at the CMS; Other contributions to the CMS and PHENIX; and, the author's interests in the EIC physics.
The Standard Model (SM) of particle physics has been very successful in describing the fundamental forces, the properties of elementary particles, and predicting particles prior to their discovery, for example the top quark and the Higgs boson. Yet the SM does not explain all phenomena, and discoveries of physics processes beyond the SM will allow progress in our understanding of the fundamental nature of matter and forces. The search for rare processes is one of the paths to look for new physics. Two of these searches are for muon to electron conversion and the decay $\text{K}_\text{L}\rightarrow \pi^0 \nu \bar{\nu}$. Our main effort was in the Mu2e experiment under construction at Fermilab, which has the goal of searching for the neutrinoless conversion of a muon into an electron at a single event sensitivity of 2.87 x 10-17 and, in the process, provide evidence of physics processes beyond the Standard Model. We developed the firmware to be used in the Mu2e tracker readout, assisted in the construction and operation of the Mu2e Vertical Slice Test, and provided input to studies of cosmic event reconstructions in Mu2e using simulated data. Under this reward we also concluded our effort in two KOTO experiment in J-PARC, Japan and set a limit on the rate of the rare decay, $\text{K}_\text{L}\rightarrow \pi^0 \nu \bar{\nu}$ using the data collected in the 2016-2018 period. We also maintained and operated the DAQ system used for data collection thru 2021.
Our group pioneers the use of Quantum Machine Learning (QML) on High Energy Physics analysis at LHC. We have successfully employed several QML classification algorithms in the ttH (Higgs production in association with a top quark pair) and Higgs to two muons (Higgs coupling to second generation fermions), two recent LHC flagship physics analysis, on gate-model quantum computer simulators and hardware. The simulation studies have been performed with the IBM Quantum Framework, Google Tensorflow Quantum Framework, and Amazon Braket Framework, and we have achieved good classification performance that is similar to the performances of the classical machine learning methods currently used in LHC physics analyses, classical SVM, classical BDT, and classical deep neural network for example. We have also performed our studies using IBM superconducting quantum computer hardware and the performance is promising and is approaching the performance from IBM quantum simulators. Moreover, we extend our studies to other QML areas such as quantum anomaly detection and quantum generative adversarial, and some preliminary results have been obtained. Also, we have overcome the challenges of intensive computing resources in the cases of large qubits (25 qubits or more) and large numbers of events using NVIDIA cuQuantum with NERSC Perlmutter HPC. Our studies give an example that Quantum Machine Learning performs as well as its classical counterpart for realistic High Energy Physics analysis datasets. Furthermore, our result on noisy quantum hardware provides important validation for the result on noiseless quantum simulators.
The Large hadron Collider at CERN and four scientific collaborations: Alice, ATLAS, CMS, LHCb are in operation since September 2008. The accelerator delivered and the experiments processed trillions of events of proton-proton and heavy ions collisions and provided useful data for thousands of physics analyses and publications. The jewel of the published data is a discovery in 2012 of the long anticipated Higgs boson. During the operation time the regular acquisition of data was interleaved with two periods of maintenance, Long-shutdown-1 (2013-2015) and Long-shutdown-2 (2018-2022), which resulted in several improvements to the machine and the experiments. The CMS collaboration recorded to date two large physics datasets of pp collision data. The first dataset, called Run 2, was recorded between 2015 and 2018 with 140fb-1 of integrated luminosity taken at 13TeV of center of mass energy. The second dataset, called Run 3, has just started in 2022 and it has been recording pp collision data at 13.6 TeV of center of mass energy, with 80 fb-1 of data collected till the end of July 2023.For my presentation I selected the most recent results produced by CMS from Run2 and Run 3 which are covering the topics of Higgs boson properties, searches for new particles and additional scalars, top quark production and properties, and I am discussing other interesting models e.g. using SM particles as portals to the Dark matter.
This talk reviews a recently introduced Monte-Carlo algorithm for the matching of four- and five-flavor scheme calculations in the simulation of top-quark pair production in association with b-jets.
Fully hadronic \(t\bar{t}\) reconstruction requires assigning reconstructed jets to the two top-quark decay branches, \(t\bar{t}\to(Wb)(Wb)\to(qqb)(qqb)\). This is a combinatorially large, symmetry-rich, and frequently underconstrained set-assignment problem due to detector effects and partial reconstructability. SPANet provides a strong symmetry-preserving baseline for this task, but its standard inference eagerly commits to a single hypothesis and does not explicitly treat branch-level reconstructability decisions. We present a two-stage extension of the SPANet pipeline for fully hadronic \(t\bar{t}\) events: (i) SPANet is modified into a proposal model that generates a shortlist of candidate jet assignments; (ii) a custom dual-head set transformer is trained to re-rank these candidates and identify reconstructible branches. Our extension gives modest improvements in candidate-selection efficiency, with SPANet achieving \(69.9\%\) and our extension achieving \(71.1\%\). Finally, an oracle study of the generated shortlist shows a substantial upper bound of \(88.5\%\) on the evaluated fully reconstructable subset, suggesting that the shortlist contains significant residual information that is not fully exploited.
One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using machine learning algorithms greatly enhances our ability to achieve this objective. With the progress of quantum technologies, quantum machine learning could become a powerful tool for data analysis in high energy physics. In this study, using IBM gate-model quantum computing systems, we employ the quantum variational classifier method and the quantum kernel estimator method in two recent LHC flagship physics analyses: $t\bar{t}H$ (Higgs boson production in association with a top quark pair) and $H\rightarrow\mu\mu$ (Higgs boson decays to two muons). We have obtained early results with 10 qubits on the IBM quantum simulator and the IBM quantum hardware. On the quantum simulator, the quantum machine learning methods perform similarly to classical algorithms such as SVM (support vector machine) and BDT (boosted decision tree), which are often employed in LHC physics analyses. On the quantum hardware, the quantum machine learning methods have shown promising discrimination power, comparable to that on the quantum simulator. This study demonstrates that quantum machine learning has the ability to differentiate between signal and background in realistic physics datasets.
Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics.We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3).We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed.Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.
The SM effective field theory (SMEFT) provides a model-independent and systematically improvable framework for new physics searches. In this talk, we outline our approach of simultaneously fitting SMEFT parameters and Probability Density Functions (PDFs) in an extension of the CT18 global analysis framework. To enhance the efficiency of our global fitting and Lagrange multiplier scans, we leverage machine-learning techniques. We focus on several representative operators relevant to top-quark pair production and jet production. Through this approach, we establish self-consistent limitations on the associated Wilson coefficients, and explore the correlations between these Wilson coefficients and the PDFs.
This thesis presents a search for a heavy pseudoscalar particle, A, which decays into a lighterscalar, H, and a Z boson within the Two-Higgs-Doublet Model (2HDM). We focus on Zboson decays to leptons, with H decays into a t t ̄ pair. The search uses the Asimov dataset–a theoretical dataset generated to predict the expected results for discovery and exclusion based on conditions from proton-proton collisions at the LHC, atintegrated luminosity of 138f b−1, as recorded by the CMS detector. The analysis focuses on events with three charged leptons, two of which come from the Z boson decay and one from a top quark decay. The expected results suggest the search has high sensitivity, showing discovery potential across much of the 2HDM Type-II parameter space, with possible deviations from the Standard Model background exceeding 5σ significance. The search sets upper limits on the cross-section times branching ratio, assuming narrow particle widths, for A masses up to 2100 GeV and H masses up to 1900 GeV. The results are interpreted in the 2HDM Type-II framework to exclude certain regions of parameter space at the 95% confidence level as a function of particle masses and variables such as tanβ and cos(β−α). The findings highlight the potential reach implications of future search in this parameter region.
We continue our examination of prospects for the discovery of heavy Higgs bosons of natural SUSY (natSUSY) models at the high luminosity LHC (HL-LHC), this time focusing on charged Higgs bosons. In natSUSY, higgsinos are expected at the few hundred GeV scale whilst electroweak gauginos inhabit the TeV scale and the heavy Higgs bosons, H, A and H± could range up tens of TeV without jeopardizing naturalness. For TeV-scale heavy SUSY Higgs bosons H, A and H±, as currently required by LHC searches, SUSY decays into gaugino plus higgsino can dominate H± decays provided these decays are kinematically accessible. The visible decay products of higgsinos are soft making them largely invisible, whilst the gauginos decay to W, Z or h plus missing transverse energy (ET). Charged Higgs bosons are dominantly produced at LHC14 via the parton subprocess, gb→H±t. In this paper, we examine the viability of observing signatures from H±→τν, H±→tb and H±→W,Z,h+ET events produced in association with a top quark at the HL-LHC over large Standard Model (SM) backgrounds from (mainly) tt¯, tt¯V and tt¯h production (where V=W,Z). We find that the greatest reach is found via the SM H±(→τν)+t channel with a subdominant contribution from the H±(→tb)+t channel. Unlike for neutral Higgs searches, the SUSY decay modes appear to be unimportant for H± searches at the HL-LHC. We delineate regions of the mA vs. tanβ plane, mostly around mA∼ 1–2 TeV, where signals from charged Higgs bosons would serve to confirm signals of a heavy, neutral Higgs boson at the 5σ level or, alternatively, to exclude heavy Higgs bosons at the 95% confidence level at the high luminosity LHC.
We apply the recently developed concept of the nucleon energy–energy correlator (NEEC) for the gluon sector to investigate the long-range azimuthal angular correlations in proton–proton collisions at the Large Hadron Collider. The spinning gluon in these collisions will introduce substantial nonzero cos(2Φ) asymmetries in both Higgs boson and top quark pair productions, where Φ is the azimuthal angle between the forward and backward energy correlators in the NEEC observables. The genesis of the cos(2Φ) correlation lies in the intricate quantum entanglement. Owing to the substantial cos(2Φ) effect, the NEEC observable in Higgs boson and $t\bar{t}$ production emerges as a pivotal avenue for delving into quantum entanglement and scrutinizing the Bell inequality at high-energy colliders.
We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in particle physics. These datasets encompass a diverse range of jet sources, including quark/gluon jets, jets from the decay of W bosons, top quarks, and heavy new-physics particles. The datasets provide detailed substructure information, including jet kinematics, constituent kinematics, and track displacement details, enabling a wide range of applications in jet tagging, anomaly detection, and generative modelling.
We examine the dynamics of the electroweak phase transition in the early Universe. For Higgs masses in the range 46 less than or = M sub H less than or = 150 GeV and top quark masses less than 200 GeV, regions of symmetric and asymmetric vacuum coexist to below the critical temperature, with thermal equilibrium between the two phases maintained by fluctuations of both phases. We propose that the transition to the asymmetric vacuum is completed by percolation of these subcritical fluctuations. Our results are relevant to scenarios of baryogenesis that invoke a weakly first-order phase transition at the electroweak scale.
We examine the dynamics of the electroweak phase transition in the early Universe. For Higgs masses in the range 46 less than or = M sub H less than or = 150 GeV and top quark masses less than 200 GeV, regions of symmetric and asymmetric vacuum coexist to below the critical temperature, with thermal equilibrium between the two phases maintained by fluctuations of both phases. We propose that the transition to the asymmetric vacuum is completed by percolation of these subcritical fluctuations. Our results are relevant to scenarios of baryogenesis that invoke a weakly first-order phase transition at the electroweak scale.
Alvin Tollestrup was a physicist and engineer of uncommon insight, intuition, and ingenuity who was revered in life for his essential leadership in the development of superconducting magnets for high-energy accelerators. He exhibited a matchless mastery of what he called freshman physics — though his mastery hardly ended there — and an uncanny gift for joining careful measurements with transparent calculations to reveal the essence of a subject. To watch Alvin at work was to learn how a true scientist formulates and solves a problem. He had other gifts: a nose for significant problems in both science and technology, the ability to draw talented people to an important cause, and a mentor's instinct for spotting gifted junior colleagues and helping them to mature as engineers and scientists. He was also good company! Alvin brought his keen vision about what constitutes important science, along with powerful ambition for the institution and for fundamental science, to considerat ions about Fermilab's future. His questions were uniformly probing, his opinions unfailingly interesting, and his appetite for work consistently inspiring. Among Alvin's many contributions to experimental particle physics — in a career that spanned seven decades — the rich achievements of the Collider Detector at Fermilab Collaboration, of which he was a founder and an enduring leader, stand out. The discovery of the top quark in 1995 earned worldwide acclaim. He reaped individual accolades as well, including election to the National Academy of Sciences. The National Medal of Technology, conferred in 1989, recognized Alvin’s leading role in developing superconducting magnets for Fermilab's Tevatron. Universities Research Association created the Tollestrup Award for Postdoctoral Research, commemorating Alvin's avuncular support for young scientists. This volume celebrates Alvin Tollestrup’s career and the rich, fulfilling life he led beyond the laboratory. Contributions from colleagues and family illuminate many facets of this remarka! ble figure. May old friends savor the memory of the Alvin they knew, while discovering less-familiar parts of his history; and may readers who did not have the opportunity to know Alvin firsthand find inspiration in his exemplary life in science.
Scalar bosons composed of a pair of chiral fermions in a non-confining potential have an effective Yukawa coupling, $g$, to free external chiral fermions. At large distance a Feynman loop of external fermions generates a scale invariant potential, $V_{loop}\propto -g^2/{\rho^{2}}$, for separation $\rho$, which acts on valence fermions. This generally forces the $s$-wave ground state to deform to a static, zero mass, configuration, and for slowly running, perturbative $g$, a large external "shroud" wave function forms. This is related to old results of Landau and Lifshitz in quantum mechanics. The massless composite scalar boson ground state is then an extended object. Infra-red effects can generate a small mass for the system. This points to a perturbative BEH-boson composed of top and anti-top quarks and a novel dynamical mechanism for spontaneous electroweak symmetry breaking.
In the path towards a muon collider with center of mass energy of 10 TeV or more, a stage at 3 TeV emerges as an appealing option. Reviewing the physics potential of such muon collider is the main purpose of this document. In order to outline the progression of the physics performances across the stages, a few sensitivity projections for higher energy are also presented. There are many opportunities for probing new physics at a 3 TeV muon collider. Some of them are in common with the extensively documented physics case of the CLIC 3 TeV energy stage, and include measuring the Higgs trilinear coupling and testing the possible composite nature of the Higgs boson and of the top quark at the 20 TeV scale. Other opportunities are unique of a 3 TeV muon collider, and stem from the fact that muons are collided rather than electrons. This is exemplified by studying the potential to explore the microscopic origin of the current $g$-2 and $B$-physics anomalies, which are both related with muons.