Shih-Chieh Hsu
We report searches for standard model (SM) Higgs production decaying to WW(*) and continuum ZZ production in the two charged lepton and two neutrino final states. The data were collected with the CDF II detector at the Fermilab Tevatron and correspond to an integrated luminosity of 1.1^fb-1. In order to separate the processes contributing to the final state, event probabilities calculated using the leading order differential cross-sections were used to construct a likelihood ratio discriminant. The observed (median expected) 95% C.L. upper limit for sigma(H->WW^(*)) with 160 GeV/c^2 mass hypothesis is 1.3 (1.8) pb which corresponds to 3.4 (4.8) times the SM prediction at next-to-next-to-leading logarithmic level (NNLL) calculation. The significance of the observed ZZ signal is 1.9 sigma and the 95% C.L. upper limit is 3.4 pb which is consistent with the next-to-leading order (NLO) calculation of 1.4+/-0.1 pb.
Shih-Chieh Hsu
A measurement of the ZZ production cross section in proton-proton collisions at sqrt{s} = 7 TeV using data collected by the ATLAS experiment at the LHC is presented. In a data sample corresponding to an integrated luminosity of 1.02fb-1, 12 events containing two Z boson candidates decaying to electrons and/or muons were observed. The expected background contribution is 0.3^{+0.9}_{-0.3} (stat) ^{+0.4}_{-0.3} (syst) events. The total cross section for on-shell ZZ production has been determined to be σ_{ZZ}_{tot}= 8.4^{+2.7}_{-2.3}(stat) ^{+0.4}_{-0.7}(syst)\pm 0.3 (lumi) pb$ and is compatible with the Standard Model expectation of 6.5^{+0.3}_{-0.2} pb calculated at the next-to-leading order in QCD. Limits on anomalous neutral triple gauge boson couplings are derived.
Zhen Wang, Xuliang Zhu, Elham E Khoda, Shih-Chieh Hsu, Nikolaos Konstantinidis, Ke Li, Shu Li, Michael J. Ramsey-Musolf, Yanda Wu, Yuwen E. Zhang
A strong first-order electroweak phase transition (EWPT) can be induced by light new physics weakly coupled to the Higgs. This study focuses on a scenario in which the first-order EWPT is driven by a light scalar $s$ with a mass between 15-60 GeV. A search for exotic decays of the Higgs boson into a pair of spin-zero particles, $h \to ss$, where the $s$-boson decays into $b$-quarks promptly is presented. The search is performed in events where the Higgs boson is produced in association with a $Z$ boson, giving rise to a signature of two charged leptons (electrons or muons) and multiple jets from $b$-quark decays. The analysis is considering a scenario of analysing 5000 fb$^{-1}$ $e^+ e^-$ collision data at $\sqrt{s} = 240 $ GeV from the Circular Electron Positron Collider (CEPC). This study with $4b$ final state conclusively tests the expected sensitivity of probing the light scalars in the CEPC experiment. The sensitivity reach is significantly larger than that can be achieved at the LHC.
Alex Schuy, Lukas Heinrich, Kyle Cranmer, Shih-Chieh Hsu
RECAST is an analysis reinterpretation framework; since analyses are often sensitive to a range of models, RECAST can be used to constrain the plethora of theoretical models without the significant investment required for a new analysis. However, experiment-specific full simulation is still computationally expensive. Thus, to facilitate rapid exploration, RECAST has been extended to truth-level reinterpretations, interfacing with existing systems such as RIVET.
Xiaocong Ai, Shih-Chieh Hsu, Ke Li, Chih-Ting Lu
Many extensions of the standard model (SM) predict the existence of axion-like particles and/or dark Higgs in the sub-GeV scale. Two new sub-GeV particles, a scalar and a pseudoscalar, produced through the Higgs boson exotic decays, are investigated. The decay signatures of these two new particles with highly collimated photons in the final states are discriminated from the ones of SM backgrounds using the Convolutional Neural Networks and Boosted Decision Trees techniques. The sensitivities of searching for such new physics signatures at the Large Hadron Collider are obtained.
Taylor Faucett, Shih-Chieh Hsu, Daniel Whiteson
We train a network to identify jets with fractional dark decay (semi-visible jets) using the pattern of their low-level jet constituents, and explore the nature of the information used by the network by mapping it to a space of jet substructure observables. Semi-visible jets arise from dark matter particles which decay into a mixture of dark sector (invisible) and Standard Model (visible) particles. Such objects are challenging to identify due to the complex nature of jets and the alignment of the momentum imbalance from the dark particles with the jet axis, but such jets do not yet benefit from the construction of dedicated theoretically-motivated jet substructure observables. A deep network operating on jet constituents is used as a probe of the available information and indicates that classification power not captured by current high-level observables arises primarily from low-$p_\textrm{T}$ jet constituents.
Kingman Cheung, Yi-Lun Chung, Shih-Chieh Hsu
Higgs boson pair production is well known to probe the structure of the electroweak symmetry breaking sector. We illustrate using the gluon-fusion process $pp \to H \to h h \to (b\bar b) (b\bar b)$ in the framework of two-Higgs-doublet models and how the machine learning approach (three-stream convolutional neural network) can substantially improve the signal-background discrimination and thus improves the sensitivity coverage of the relevant parameter space. We show that such $gg \to hh \to b \bar b b\bar b$ process can further probe the currently allowed parameter space by HiggsSignals and HiggsBounds at the HL-LHC. The results for Types I to IV are shown.
Haoran Zhao, Andrew Naylor, Shih-Chieh Hsu, Paolo Calafiura, Steven Farrell, Yongbing Feng, Philip Coleman Harris, Elham E Khoda, William Patrick Mccormack, Dylan Sheldon Rankin, Xiangyang Ju
Recent studies have shown promising results for track finding in dense environments using Graph Neural Network (GNN)-based algorithms. However, GNN-based track finding is computationally slow on CPUs, necessitating the use of coprocessors to accelerate the inference time. Additionally, the large input graph size demands a large device memory for efficient computation, a requirement not met by all computing facilities used for particle physics experiments, particularly those lacking advanced GPUs. Furthermore, deploying the GNN-based track-finding algorithm in a production environment requires the installation of all dependent software packages, exclusively utilized by this algorithm. These computing challenges must be addressed for the successful implementation of GNN-based track-finding algorithm into production settings. In response, we introduce a ``GNN-based tracking as a service'' approach, incorporating a custom backend within the NVIDIA Triton inference server to facilitate GNN-based tracking. This paper presents the performance of this approach using the Perlmutter supercomputer at NERSC.
FASER Collaboration, Henso Abreu, Elham Amin Mansour, Claire Antel, Akitaka Ariga, Tomoko Ariga, Florian Bernlochner, Tobias Boeckh, Jamie Boyd, Lydia Brenner, Franck Cadoux, David W. Casper, Charlotte Cavanagh, Xin Chen, Andrea Coccaro, Olivier Crespo-Lopez, Stephane Debieux, Monica D'Onofrio, Liam Dougherty, Candan Dozen, Abdallah Ezzat, Yannick Favre, Deion Fellers, Jonathan L. Feng, Didier Ferrere, Edward Karl Galantay, Jonathan Gall, Enrico Gamberini, Stephen Gibson, Sergio Gonzalez-Sevilla, Carl Gwilliam, Daiki Hayakawa, Shih-Chieh Hsu, Zhen Hu, Giuseppe Iacobucci, Tomohiro Inada, Sune Jakobsen, Eliott Johnson, Enrique Kajomovitz, Hiroaki Kawahara, Felix Kling, Umut Kose, Rafaella Kotitsa, Jesse Krusse, Susanne Kuehn, Helena Lefebvre, Lorne Levinson, Ke Li, Jinfeng Liu, Chiara Magliocca, Fulvio Martinelli, Josh McFayden, Sam Meehan, Matteo Milanesio, Manato Miura, Dimitar Mladenov, Theo Moretti, Magdalena Munker, Mitsuhiro Nakamura, Toshiyuki Nakano, Marzio Nessi, Friedemann Neuhaus, Laurie Nevay, John Osborne, Hidetoshi Otono, Carlo Pandini, Hao Pang, Lorenzo Paolozzi, Brian Petersen, Francesco Pietropaolo, Markus Prim, Michaela Queitsch-Maitland, Filippo Resnati, Chiara Rizzi, Hiroki Rokujo, Elisa Ruiz-Choliz, Jakob Salfeld-Nebgen, Francisco Sanchez Galan, Osamu Sato, Paola Scampoli, Kristof Schmieden, Matthias Schott, Anna Sfyrla, Savannah Shively, Roland Sipos, John Spencer, Yosuke Takubo, Noshin Tarannum, Ondrej Theiner, Pierre Thonet, Eric Torrence, Serhan Tufanli, Camille Vendeuvre, Benedikt Vormwald, Di Wang, Stefano Zambito, Gang Zhang
FASER, the ForwArd Search ExpeRiment, is an experiment dedicated to searching for light, extremely weakly-interacting particles at CERN's Large Hadron Collider (LHC). Such particles may be produced in the very forward direction of the LHC's high-energy collisions and then decay to visible particles inside the FASER detector, which is placed 480 m downstream of the ATLAS interaction point, aligned with the beam collisions axis. FASER also includes a sub-detector, FASER$ν$, designed to detect neutrinos produced in the LHC collisions and to study their properties. In this paper, each component of the FASER detector is described in detail, as well as the installation of the experiment system and its commissioning using cosmic-rays collected in September 2021 and during the LHC pilot beam test carried out in October 2021. FASER will start taking LHC collision data in 2022, and will run throughout LHC Run 3.
FASER Collaboration, Roshan Mammen Abraham, John Anders, Claire Antel, Akitaka Ariga, Tomoko Ariga, Jeremy Atkinson, Florian U. Bernlochner, Tobias Boeckh, Jamie Boyd, Lydia Brenner, Angela Burger, Franck Cadoux, Roberto Cardella, David W. Casper, Charlotte Cavanagh, Xin Chen, Andrea Coccaro, Stephane Débieux, Monica D'Onofrio, Ansh Desai, Sergey Dmitrievsky, Sinead Eley, Yannick Favre, Deion Fellers, Jonathan L. Feng, Carlo Alberto Fenoglio, Didier Ferrere, Max Fieg, Wissal Filali, Stephen Gibson, Sergio Gonzalez-Sevilla, Yuri Gornushkin, Carl Gwilliam, Daiki Hayakawa, Shih-Chieh Hsu, Zhen Hu, Giuseppe Iacobucci, Tomohiro Inada, Luca Iodice, Sune Jakobsen, Hans Joos, Enrique Kajomovitz, Hiroaki Kawahara, Alex Keyken, Felix Kling, Daniela Köck, Pantelis Kontaxakis, Umut Kose, Rafaella Kotitsa, Susanne Kuehn, Thanushan Kugathasan, Helena Lefebvre, Lorne Levinson, Ke Li, Jinfeng Liu, Margaret S. Lutz, Jack MacDonald, Chiara Magliocca, Fulvio Martinelli, Lawson McCoy, Josh McFayden, Andrea Pizarro Medina, Matteo Milanesio, Théo Moretti, Magdalena Munker, Mitsuhiro Nakamura, Toshiyuki Nakano, Friedemann Neuhaus, Laurie Nevay, Ken Ohashi, Hidetoshi Otono, Hao Pang, Lorenzo Paolozzi, Brian Petersen, Markus Prim, Michaela Queitsch-Maitland, Hiroki Rokujo, Elisa Ruiz-Choliz, André Rubbia, Jorge Sabater-Iglesias, Osamu Sato, Paola Scampoli, Kristof Schmieden, Matthias Schott, Anna Sfyrla, Mansoora Shamim, Savannah Shively, Yosuke Takubo, Noshin Tarannum, Ondrej Theiner, Eric Torrence, Svetlana Vasina, Benedikt Vormwald, Di Wang, Yuxiao Wang, Eli Welch, Samuel Zahorec, Stefano Zambito, Shunliang Zhang
The Forward Search Experiment (FASER) at CERN's Large Hadron Collider (LHC) has recently directly detected the first collider neutrinos. Neutrinos play an important role in all FASER analyses, either as signal or background, and it is therefore essential to understand the neutrino event rates. In this study, we update previous simulations and present prescriptions for theoretical predictions of neutrino fluxes and cross sections, together with their associated uncertainties. With these results, we discuss the potential for possible measurements that could be carried out in the coming years with the FASER neutrino data to be collected in LHC Run 3 and Run 4.
Qibin Liu, Chase Shimmin, Xiulong Liu, Eli Shlizerman, Shu Li, Shih-Chieh Hsu
We introduce a novel machine learning method developed for the fast simulation of calorimeter detector response, adapting vector-quantized variational autoencoder (VQ-VAE). Our model adopts a two-stage generation strategy: initially compressing geometry-aware calorimeter data into a discrete latent space, followed by the application of a sequence model to learn and generate the latent tokens. Extensive experimentation on the Calo-challenge dataset underscores the efficiency of our approach, showcasing a remarkable improvement in the generation speed compared with conventional method by a factor of 2000. Remarkably, our model achieves the generation of calorimeter showers within milliseconds. Furthermore, comprehensive quantitative evaluations across various metrics are performed to validate physics performance of generation.
Farah Fahim, Benjamin Hawks, Christian Herwig, James Hirschauer, Sergo Jindariani, Nhan Tran, Luca P. Carloni, Giuseppe Di Guglielmo, Philip Harris, Jeffrey Krupa, Dylan Rankin, Manuel Blanco Valentin, Josiah Hester, Yingyi Luo, John Mamish, Seda Orgrenci-Memik, Thea Aarrestad, Hamza Javed, Vladimir Loncar, Maurizio Pierini, Adrian Alan Pol, Sioni Summers, Javier Duarte, Scott Hauck, Shih-Chieh Hsu, Jennifer Ngadiuba, Mia Liu, Duc Hoang, Edward Kreinar, Zhenbin Wu
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. In scientific domains, real-time near-sensor processing can drastically improve experimental design and accelerate scientific discoveries. To support domain scientists, we have developed hls4ml, an open-source software-hardware codesign workflow to interpret and translate machine learning algorithms for implementation with both FPGA and ASIC technologies. We expand on previous hls4ml work by extending capabilities and techniques towards low-power implementations and increased usability: new Python APIs, quantization-aware pruning, end-to-end FPGA workflows, long pipeline kernels for low power, and new device backends include an ASIC workflow. Taken together, these and continued efforts in hls4ml will arm a new generation of domain scientists with accessible, efficient, and powerful tools for machine-learning-accelerated discovery.
Jan-Frederik Schulte, Benjamin Ramhorst, Chang Sun, Jovan Mitrevski, Nicolò Ghielmetti, Enrico Lupi, Dimitrios Danopoulos, Vladimir Loncar, Javier Duarte, David Burnette, Lauri Laatu, Stylianos Tzelepis, Konstantinos Axiotis, Quentin Berthet, Haoyan Wang, Paul White, Suleyman Demirsoy, Marco Colombo, Thea Aarrestad, Sioni Summers, Maurizio Pierini, Giuseppe Di Guglielmo, Jennifer Ngadiuba, Javier Campos, Ben Hawks, Abhijith Gandrakota, Farah Fahim, Nhan Tran, George Constantinides, Zhiqiang Que, Wayne Luk, Alexander Tapper, Duc Hoang, Noah Paladino, Philip Harris, Bo-Cheng Lai, Manuel Valentin, Ryan Forelli, Seda Ogrenci, Lino Gerlach, Rian Flynn, Mia Liu, Daniel Diaz, Elham Khoda, Melissa Quinnan, Russell Solares, Santosh Parajuli, Mark Neubauer, Christian Herwig, Ho Fung Tsoi, Dylan Rankin, Shih-Chieh Hsu, Scott Hauck
We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). With its flexible and modular design, hls4ml supports a large number of deep learning frameworks and can target HLS compilers from several vendors, including Vitis HLS, Intel oneAPI and Catapult HLS. Together with a wider eco-system for software-hardware co-design, hls4ml has enabled the acceleration of ML inference in a wide range of commercial and scientific applications where low latency, resource usage, and power consumption are critical. In this paper, we describe the structure and functionality of the hls4ml platform. The overarching design considerations for the generated HLS code are discussed, together with selected performance results.
Lisa Benato, Wahid Bhimji, Paolo Calafiura, Ragansu Chakkappai, Po-Wen Chang, Yuan-Tang Chou, Sascha Diefenbacher, Jordan Dudley, Ibrahim Elsharkawy, Steven Farrell, Aishik Ghosh, Cristina Giordano, Isabelle Guyon, Chris Harris, Yota Hashizume, Shih-Chieh Hsu, Elham E. Khoda, Claudius Krause, Ang Li, Benjamin Nachman, Peter Nugent, David Rousseau, Robert Schoefbeck, Maryam Shooshtari, Dennis Schwarz, Benjamin Thorne, Ihsan Ullah, Daohan Wang, Yulei Zhang
The FAIR Universe HiggsML Uncertainty Challenge focused on measuring the physical properties of elementary particles with imperfect simulators. Participants were required to compute and report confidence intervals for a parameter of interest regarding the Higgs boson while accounting for various systematic (epistemic) uncertainties. The dataset is a tabular dataset of 28 features and 280 million instances. Each instance represents a simulated proton-proton collision as observed at CERN's Large Hadron Collider in Geneva, Switzerland. The features of these simulations were chosen to capture key characteristics of different types of particles. These include primary attributes, such as the energy and three-dimensional momentum of the particles, as well as derived attributes, which are calculated from the primary ones using domain-specific knowledge. Additionally, a label feature designates each instance's type of proton-proton collision, distinguishing the Higgs boson events of interest from three background sources. As outlined in this paper, the permanent release of the dataset allows long-term benchmarking of new techniques. The leading submissions, including Contrastive Normalising Flows and Density Ratios estimation through classification, are described. Our challenge has brought together the physics and machine learning communities to advance our understanding and methodologies in handling systematic uncertainties within AI techniques.
FASER Collaboration, Henso Abreu, Claire Antel, Akitaka Ariga, Tomoko Ariga, Jamie Boyd, Franck Cadoux, David W. Casper, Xin Chen, Andrea Coccaro, Candan Dozen, Peter B. Denton, Yannick Favre, Jonathan L. Feng, Didier Ferrere, Iftah Galon, Stephen Gibson, Sergio Gonzalez-Sevilla, Shih-Chieh Hsu, Zhen Hu, Giuseppe Iacobucci, Sune Jakobsen, Roland Jansky, Enrique Kajomovitz, Felix Kling, Susanne Kuehn, Lorne Levinson, Congqiao Li, 1 Josh McFayden, Sam Meehan, Friedemann Neuhaus, Hidetoshi Otono, Brian Petersen, Helena Pikhartova, Michaela Queitsch-Maitland, Osamu Sato, Kristof Schmieden, Matthias Schott, Anna Sfyrla, Savannah Shively, Jordan Smolinsky, Aaron M. Soffa, Yosuke Takubo, Eric Torrence, Sebastian Trojanowski, Callum Wilkinson, Dengfeng Zhang, Gang Zhang
Neutrinos are copiously produced at particle colliders, but no collider neutrino has ever been detected. Colliders, and particularly hadron colliders, produce both neutrinos and anti-neutrinos of all flavors at very high energies, and they are therefore highly complementary to those from other sources. FASER, the recently approved Forward Search Experiment at the Large Hadron Collider, is ideally located to provide the first detection and study of collider neutrinos. We investigate the prospects for neutrino studies of a proposed component of FASER, FASER$ν$, a 25cm x 25cm x 1.35m emulsion detector to be placed directly in front of the FASER spectrometer in tunnel TI12. FASER$ν$ consists of 1000 layers of emulsion films interleaved with 1-mm-thick tungsten plates, with a total tungsten target mass of 1.2 tons. We estimate the neutrino fluxes and interaction rates at FASER$ν$, describe the FASER$ν$ detector, and analyze the characteristics of the signals and primary backgrounds. For an integrated luminosity of 150 fb$^{-1}$ to be collected during Run 3 of the 14 TeV Large Hadron Collider from 2021-23, and assuming standard model cross sections, approximately 1300 electron neutrinos, 20,000 muon neutrinos, and 20 tau neutrinos will interact in FASER$ν$, with mean energies of 600 GeV to 1 TeV, depending on the flavor. With such rates and energies, FASER will measure neutrino cross sections at energies where they are currently unconstrained, will bound models of forward particle production, and could open a new window on physics beyond the standard model.
Xin Chen, Yue Xu, Yongcheng Wu, Yu-Ping Kuang, Qing Wang, Hang Chen, Shih-Chieh Hsu, Zhen Hu, Congqiao Li
A generic heavy Higgs has both dim-4 and effective dim-6 interactions with the Standard Model (SM) particles. The former has been the focus of LHC searches in all major Higgs production channels, just as the SM one, but with negative results so far. If the heavy Higgs is connected with Beyond Standard Model (BSM) physics at a few TeV scale, its dim-6 operators will play a very important role - they significantly enhance the Higgs momentum, and reduce the SM background in a special phase space corner to a level such that a heavy Higgs emerges, which is not possible with dim-4 operators only. We focus on the associated VH production channel, where the effect of dim-6 operators is the largest and the SM background is the lowest. Main search regions for this type of signal are identified, and substructure variables of boosted jets are employed to enhance the signal from backgrounds. The parameter space of these operators are scanned over, and expected exclusion regions with 300 fb$^{-1}$ and 3 ab$^{-1}$ LHC data are shown, if no BSM is present. The strategy given in this paper will shed light on a heavy Higgs which may be otherwise hiding in the present and future LHC data.
Michael James Fenton, Alexander Shmakov, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi
Top quarks, produced in large numbers at the Large Hadron Collider, have a complex detector signature and require special reconstruction techniques. The most common decay mode, the "all-jet" channel, results in a 6-jet final state which is particularly difficult to reconstruct in $pp$ collisions due to the large number of permutations possible. We present a novel approach to this class of problem, based on neural networks using a generalized attention mechanism, that we call Symmetry Preserving Attention Networks (SPA-Net). We train one such network to identify the decay products of each top quark unambiguously and without combinatorial explosion as an example of the power of this technique.This approach significantly outperforms existing state-of-the-art methods, correctly assigning all jets in $93.0%$ of $6$-jet, $87.8%$ of $7$-jet, and $82.6%$ of $\geq 8$-jet events respectively.
Alessandro Pappalardo, Yaman Umuroglu, Michaela Blott, Jovan Mitrevski, Ben Hawks, Nhan Tran, Vladimir Loncar, Sioni Summers, Hendrik Borras, Jules Muhizi, Matthew Trahms, Shih-Chieh Hsu, Scott Hauck, Javier Duarte
We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. We first introduce support for low precision quantization in existing ONNX-based quantization formats by leveraging integer clipping, resulting in two new backward-compatible variants: the quantized operator format with clipping and quantize-clip-dequantize (QCDQ) format. We then introduce a novel higher-level ONNX format called quantized ONNX (QONNX) that introduces three new operators -- Quant, BipolarQuant, and Trunc -- in order to represent uniform quantization. By keeping the QONNX IR high-level and flexible, we enable targeting a wider variety of platforms. We also present utilities for working with QONNX, as well as examples of its usage in the FINN and hls4ml toolchains. Finally, we introduce the QONNX model zoo to share low-precision quantized neural networks.
Brad Abbott, Aram Apyan, Bianca Azartash-Namin, Veena Balakrishnan, Jeffrey Berryhill, Shih-Chieh Hsu, Sergo Jindariani, Mayuri Kawale, Elham E Khoda, Ryan Parsons, Alexander Schuy, Michael Strauss, John Stupak, Connor Waits
The prospects of searches for anomalous production of hadronically decaying weak boson pairs at proposed high-energy muon colliders are reported. Muon-muon collision events are simulated at $\sqrt{s}=6$, 10, and 30 TeV, corresponding to an integrated luminosity of $4$, $10$, and $10$ ab$^{-1}$, respectively. Simulated $μμ\rightarrow\mathrm{W}\mathrm{W}+νν/μμ$ events are used to set expected constraints on the structure of quartic vector boson interactions in the framework of a dimension-8 effective field theory. Similarly, $μμ\rightarrow\mathrm{W}\mathrm{W}/\mathrm{Z}\mathrm{Z}+νν$ events are used to report constraints on the product of the cross section and branching fraction for vector boson fusion production of a heavy neutral Higgs boson decaying to weak boson pairs. These results are interpreted in the context of the Georgi-Machacek model.
Kingman Cheung, Yi-Lun Chung, Shih-Chieh Hsu, Benjamin Nachman
The modeling of jet substructure significantly differs between Parton Shower Monte Carlo (PSMC) programs. Despite this, we observe that machine learning classifiers trained on different PSMCs learn nearly the same function. This means that when these classifiers are applied to the same PSMC for testing, they result in nearly the same performance. This classifier universality indicates that a machine learning model trained on one simulation and tested on another simulation (or data) will likely be optimal. Our observations are based on detailed studies of shallow and deep neural networks applied to simulated Lorentz boosted Higgs jet tagging at the LHC.