Search for a Heavy Resonance Decaying into a Z Boson and a Higgs Boson in Proton-Proton Collisions at 13 TeV

Core Concepts

This research paper presents a novel analysis of data from the CMS experiment at CERN, demonstrating improved sensitivity in the search for heavy resonances decaying into a Z boson and a Higgs boson, with implications for beyond-the-Standard Model physics.

Abstract

• Bibliographic Information: CMS Collaboration. (2024). Search for a heavy resonance decaying into a Z and a Higgs boson in events with an energetic jet and two electrons, two muons, or missing transverse momentum in proton-proton collisions at $\sqrt{s}$ = 13 TeV [Preprint]. CERN.

• Research Objective: The paper aims to identify a potential heavy resonance, a particle predicted by theories beyond the Standard Model, by analyzing its decay products: a Z boson and a Higgs boson.

• Methodology: The researchers utilized data collected by the CMS detector at the Large Hadron Collider (LHC) from proton-proton collisions at 13 TeV. They focused on events where the Z boson decays into pairs of electrons, muons, or neutrinos, and the Higgs boson decays hadronically, identified as a single large-radius jet. The team employed advanced machine learning techniques, particularly the ParticleNet-MD algorithm, to analyze the jet substructure and flavor content, distinguishing between signal and background processes.

• Key Findings: While no significant excess of events indicating a new heavy resonance was observed, the study significantly improved the sensitivity of the search compared to previous analyses. This improvement stems from the novel application of machine learning for identifying Higgs boson decays, particularly those with less than two b-tagged subjets.

• Main Conclusions: The research sets new exclusion limits on the mass of the hypothetical heavy resonance and its couplings to fermions and bosons within the framework of the heavy vector triplet model. The absence of a signal in this data reinforces the constraints on beyond-the-Standard Model theories.

• Significance: This research pushes the boundaries in the search for new particles and contributes to our understanding of fundamental physics. The innovative use of machine learning algorithms for analyzing complex particle decays paves the way for future discoveries at the LHC and beyond.

• Limitations and Future Research: The study is limited by the available data statistics. Future research with larger datasets from the High-Luminosity LHC will further probe higher mass ranges and potentially uncover weaker signals. Continued development and refinement of analysis techniques, particularly those involving machine learning, will be crucial for maximizing the physics potential of future collider experiments.

Stats

The analysis uses data from proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb−1.
The study focuses on resonance masses between 1.4 and 5 TeV.
The PARTICLENET-MD algorithm is used for heavy-flavor jet identification, with a signal region defined by a score greater than 0.95.
Approximately 60% of the selected signal events consist of H →bb decays, despite the rejection of jets with two b-tagged subjets.
The analysis selection increases the fraction of signal events from H →cc and qqqq decays by factors of five and three, respectively, compared to the Standard Model branching fractions.
The neutrino channel exhibits the highest sensitivity across the investigated mass range due to a larger branching fraction and higher selection efficiency.
The observed 95% confidence level limits are consistent with the background-only hypothesis within approximately one standard deviation across the entire mass range.
The analysis demonstrates improved sensitivity compared to previous CMS studies, with up to a 60% improvement in expected sensitivity on the product of production cross section and branching fraction.
The new techniques employed in this analysis result in a significantly enhanced sensitivity for high resonance masses in the ZH channel.

Quotes

"This article presents a new search for a heavy resonance decaying into a Z and an H boson, where the H boson decays hadronically and the Z boson decays into a pair of oppositely charged leptons, e+e−or µ+µ−, or into neutrinos."
"Advanced jet identification algorithms using machine-learning techniques are used to exploit the H boson decays to quarks that have not been generally targeted by previous searches."
"The analysis presented in this article demonstrates a significant improvement in the sensitivity for the category with fewer than two identified b quarks."
"This novel technique enables a generalized approach to the tagging of hadronic final states, remaining largely independent of specific branching fractions, which allows for a more model-independent search."
"A significantly improved sensitivity for high resonance masses in the ZH channel is provided by the new techniques employed by this analysis."

Key Insights Distilled From

Search for a heavy resonance decaying into a Z and a Higgs boson in events with an energetic jet and two electrons, two muons, or missing transverse momentum in proton-proton collisions at $\sqrt{s}$ = 13 TeV

by CMS Collabor... at arxiv.org 11-04-2024

https://arxiv.org/pdf/2411.00202.pdf

Deeper Inquiries

How might the use of quantum computing techniques potentially enhance the analysis of particle physics data and the search for new particles beyond the Standard Model?

Quantum computing has the potential to revolutionize particle physics data analysis and the search for new particles like the heavy resonance described in the paper. Here's how:

Enhanced Simulation: Quantum computers excel at simulating quantum systems, which is precisely what particle collisions are. They could provide more accurate and efficient simulations of particle interactions compared to classical computing methods, leading to better background estimations and signal modeling. This is particularly relevant for complex final states like the H → qqqq decay, which is challenging to simulate classically.
Improved Optimization: Searching for rare events within massive datasets is a computationally demanding optimization problem. Quantum algorithms, such as Grover's algorithm, offer the potential for speedups in searching and classifying events, enabling physicists to explore larger parameter spaces and potentially uncover subtle signals of new physics.
Advanced Machine Learning: Quantum machine learning algorithms are being developed that could enhance the performance of algorithms like the ParticleNet-MD tagger used in the study. These quantum-enhanced algorithms could lead to better jet identification, quark flavor tagging, and ultimately, improved sensitivity in distinguishing signal events from background noise.
Handling Larger Datasets: The High-Luminosity LHC will produce data at an unprecedented rate. Quantum computing could provide the necessary computational power to process and analyze these massive datasets, enabling physicists to probe even rarer processes and potentially discover new particles with weaker signals.
It's important to note that quantum computing for particle physics is still in its early stages. Building powerful and stable quantum computers is an ongoing challenge. However, the potential benefits for advancing our understanding of fundamental physics are significant, making it an active area of research and development.

Could there be alternative theoretical frameworks beyond the heavy vector triplet model that could explain the observed data, and how would searches for those differ?

Yes, the heavy vector triplet (HVT) model is just one of many theoretical frameworks that extend beyond the Standard Model and predict new resonances decaying to ZH. Here are a few alternatives and how searches might differ:

Two-Higgs-Doublet Models (2HDMs): These models introduce an additional Higgs doublet, leading to a richer Higgs sector with charged and neutral Higgs bosons. Searches would focus on different final states, potentially involving decays of these additional Higgs bosons. The mass range and production cross-sections would also differ, requiring tailored search strategies.
Supersymmetry (SUSY): SUSY predicts a partner particle for each SM particle. Some SUSY models predict heavy neutralinos (Z') that could decay to a Z boson and a heavier Higgs boson. Searches would need to account for additional particles produced in the decay chain and potentially different decay channels for the Higgs boson.
Extra Dimensions: Models with extra spatial dimensions can also accommodate heavy resonances. The properties of these resonances, such as their couplings and decay modes, would depend on the specific model. Searches might involve looking for characteristic signatures related to the extra dimensions, such as graviton production.
The search strategies for these alternative models would differ in several ways:

Final States: Different models predict different decay products, requiring searches to target specific final states and optimize their analyses accordingly.
Mass Range: The predicted mass range for the new particles varies between models, influencing the energy scales and kinematic regions explored in the searches.
Production Mechanisms: The dominant production modes for the new particles can differ, requiring different trigger strategies and background estimations.
The paper's focus on the HVT model is motivated by its simplicity and ability to encompass a range of BSM theories. However, exploring alternative models is crucial to ensure a comprehensive search for new physics.

What are the broader implications for cosmology and our understanding of the early universe if a heavy resonance of this nature were to be discovered in future experiments?

The discovery of a heavy resonance decaying to ZH would have profound implications for cosmology and our understanding of the early universe:

Electroweak Symmetry Breaking: The Higgs mechanism, responsible for giving particles mass, is still not fully understood. A heavy resonance coupled to the Higgs boson could provide clues about the dynamics of electroweak symmetry breaking and potentially point towards new physics at higher energy scales.
Dark Matter: The nature of dark matter, which makes up a significant portion of the universe's mass-energy content, remains a mystery. Some theoretical models connect heavy resonances to dark matter candidates. A discovery could provide indirect evidence for the existence of dark matter particles and offer insights into their properties and interactions.
Inflation: The theory of inflation posits a period of rapid expansion in the very early universe. Some inflationary models involve fields that could manifest as heavy resonances at later times. Observing such a resonance could provide support for inflationary scenarios and constrain the models describing this early phase of the universe.
Baryogenesis: The observed asymmetry between matter and antimatter in the universe is not fully explained by the Standard Model. New physics, potentially involving heavy resonances, could have played a role in generating this asymmetry during the early universe.
Furthermore, the discovery of a heavy resonance could:

Motivate New Theoretical Frameworks: It would necessitate extensions to the Standard Model and could inspire the development of new theoretical frameworks to explain the properties and interactions of the observed particle.
Guide Future Experiments: The discovery would provide a clear target for future experiments, focusing efforts on precisely measuring its properties and exploring its connections to other fundamental particles and forces.
In conclusion, while the search for a heavy resonance decaying to ZH has not yet yielded a discovery, the potential implications for cosmology and our understanding of the universe are significant. It remains a compelling avenue for exploring physics beyond the Standard Model and unraveling the mysteries of the early universe.