⚛️ phenomenology

TRSMScans

This paper introduces TRSMScans, a new automated scan tool based on ScannerS that calculates maximal cross-section predictions for new physics scenarios involving additional scalar states, with a specific demonstration of its application to the Two-Real-Singlet Model (TRSM) and comparisons to LHC data.

Original authors: Tania Robens, Roxana Rodriguez, Manuel Samaniego, Jason Veatch

Published 2026-07-14

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Tania Robens, Roxana Rodriguez, Manuel Samaniego, Jason Veatch

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the universe as a giant, cosmic video game. For years, players have been playing with the "Standard Model" character, but physicists suspect there are hidden levels and secret bosses lurking in the code—specifically, new, invisible particles called "scalars." The Large Hadron Collider (LHC) is the ultimate gaming console smashing particles together to find these secrets. But here's the problem: the game has so many hidden settings (parameters) that trying to find the perfect combination to reveal a new boss is like trying to find a single specific grain of sand on a beach by picking up one grain at a time.

Enter TRSMScans, a new digital tool created by Tania Robens, Roxana Rodriguez, Manuel Samaniego, and Jason Veatch. Think of this tool as a super-smart, automated drone that doesn't just pick up sand grains randomly; it uses a "zoom" lens to intelligently hunt down the most exciting spots on the beach.

The Problem: The Infinite Sandcastle

In the world of new physics, specifically a model called the Two-Real-Singlet Model (TRSM), there are 7 free parameters (like volume knobs on a mixing board) that determine how these new particles behave. Two of these knobs are the masses of the new particles, and the other five are mixing angles and energy levels (called vacuum expectation values).

Experimental scientists at the LHC usually fix the masses (the "mass points") and look for signals. But for every single mass they pick, the other 5 knobs can be turned to any setting within a huge range. If you try to test every possible combination of those 5 knobs by just guessing, you'd need to sample a volume so vast that you'd never find the "maximum" signal before the heat death of the universe. The paper argues that this random guessing is inefficient and often misses the critical areas where new physics might be hiding.

The Solution: The "Zoom" Drone

The authors built TRSMScans to solve this. Instead of randomly throwing darts at a board, the tool uses a clever "zoom" strategy.

The Prescan: First, the drone takes a quick, rough look at the whole beach to see where the interesting "bumps" in the data are.
The Zoom: It then splits the beach into smaller, manageable patches. Inside each patch, it sends out a swarm of smaller drones that focus intensely on the highest points.
The Convergence: These drones keep narrowing their focus, "zooming in" on the local maximums (the highest peaks of probability) until they find the absolute highest point allowed by the rules of physics.

This process is powered by existing tools called ScannerS and HiggsTools, which act as the drone's rulebook. They check every potential setting to make sure it doesn't break the laws of the universe (like ensuring the math stays stable) and that it hasn't already been ruled out by previous experiments.

The Rules of the Game

The tool is very strict about what it accepts. It filters out any "sand grain" that:

Has been ruled out by previous LHC experiments (using a tool called HiggsTools).
Has a particle that is too "fuzzy" (if a particle's width is more than 15% of its mass, the tool rejects it).

The goal is to find the maximum cross-section times branching ratio (a fancy way of saying "the highest possible chance of creating and seeing this new particle"). The tool calculates this for specific decay modes, such as a heavy particle turning into two lighter ones, which then decay into things like bottom quarks and tau leptons (written as $b\bar{b}\tau^+\tau^-$ ).

What They Found (and Didn't Find)

The authors tested this tool on the Two-Real-Singlet Model (TRSM). They didn't discover a new particle; instead, they created a map of where new particles could exist without contradicting what we already know.

They compared their "maximum possible rates" against real data from the CMS and ATLAS experiments at the LHC.

The Result: They generated plots (like Figures 4 and 5 in the paper) showing the "mass plane" (a map of the heavy particle mass $m_X$ vs. the lighter particle mass $m_S$ ).
The Insight: In some areas of the map, the experimental limits (the "no-go zones" set by the LHC) are lower than the maximum rates their tool predicts. This means the LHC is sensitive enough to rule out those specific settings of the TRSM model. In other words, if the universe were playing the TRSM model, those specific settings are now "game over."

The Verdict

The paper does not claim to have found new physics. It explicitly states that this is a tool designed to make the search for new physics more efficient. It argues against the idea that random sampling is sufficient for finding the best possible signals in complex models.

The authors are confident that their "zoom" algorithm works well for the TRSM model, as shown in their simulations where the drones successfully converged on maximum values. However, they note that the tool is currently optimized for this specific model and that other optimization algorithms are still under development. They suggest that in the future, the tool could be expanded to handle other models and become even faster, but for now, it is a specialized, highly effective scanner for one specific type of cosmic mystery.

In short, TRSMScans is a smart, automated guide that helps physicists stop guessing and start hunting, ensuring that when they look for new particles, they are looking in the right places with the sharpest eyes possible.

Technical Summary of TRSMScans

Problem Statement
Experimental collaborations at the Large Hadron Collider (LHC) are actively searching for Beyond the Standard Model (BSM) physics, particularly in extended scalar sectors. While many searches are conducted in a model-independent manner—setting upper limits on production cross-sections times branching ratios ( $\sigma \times \text{BR}$ )—comparing these results to specific BSM models is essential for determining sensitivity and constraining parameter spaces. However, extended scalar models often contain multiple free parameters (e.g., mixing angles, vacuum expectation values, and masses). Conventional experimental presentations often fix all but two parameters, potentially missing critical regions of the parameter space where the predicted rates are maximal. Analytically finding the maximum rates in such high-dimensional spaces is infeasible, and random or systematic sampling becomes computationally prohibitive as the number of free parameters increases, requiring an increasing number of points to converge on a maximum with reasonable confidence.

Methodology
To address this challenge, the authors introduce TRSMScans, a new software tool designed to automatically calculate maximal cross-section predictions for new physics scenarios involving additional scalar states. The tool is built upon Python and serves as an intelligent wrapper for two publicly available codes: ScannerS and HiggsTools (which utilize HiggsBounds and HiggsSignals).

The core methodology involves an iterative optimization algorithm, currently implemented as a "zoom" strategy:

Pre-scan: An initial random sampling of the full free parameter space is performed to estimate the topology of allowed regions. This step depends only on scalar masses and is independent of specific production/decay modes.
Filtering: Sampled points are validated against theoretical constraints (boundedness from below, perturbativity, electroweak precision observables) via ScannerS and against direct search constraints via HiggsTools. Additional filters exclude points where scalar relative widths exceed configurable limits (default 15%).
Optimization: Based on the pre-scan, a kernel density estimation splits the parameter volume into smaller volumes containing local "bumps" in the target quantity ( $x_b = \sigma \times \text{BR}$ ). Separate optimizers are launched for each volume.
Convergence: Each optimizer iteratively samples a decreasing volume with increasing point density. The process utilizes parallel processing (via Python's multiprocessing library) to improve efficiency.
Stopping Conditions: The optimization terminates when local stopping conditions (sufficient increase in local maximum) or global conditions (comparability to the highest global maximum found) are met. An adaptive precision strategy can also be employed, adjusting strictness based on the ratio of the current maximum to a target experimental limit.

Key Contributions

Automation of Maximal Rate Calculation: TRSMScans efficiently finds the maximum predicted rates for specific collider signatures with minimal human intervention, scanning over all free parameters for fixed mass points.
Modular Architecture: While currently optimized for the Two-Real-Singlet Model (TRSM), the tool is designed to be extendable to any model supported by ScannerS and HiggsTools.
Comprehensive Workflow: The package includes scripts for installation, running optimizations (single mass points or mass lists), parallelization (including HTCondor batch support), and plotting results (evolution of points and combination plots).
Integration with Experimental Data: The tool allows for the direct comparison of maximal theoretical rates against experimental limits (expected and observed) from LHC collaborations, facilitating the identification of excluded parameter regions.

Results and Applications
The paper demonstrates the tool's performance using the TRSM, which introduces two additional real scalars to the Standard Model potential. The model features seven free parameters (three masses, three mixing angles, and one vacuum expectation value), reduced to five free parameters when scalar masses are fixed.

Performance: Visualizations (Figures 1–3) show the "zoom" optimization process converging on local and global maxima for a specific mass point ( $m_X = 1000$ GeV, $m_S = 300$ GeV) in the $SH \to b\bar{b}\tau^+\tau^-$ channel. The plots illustrate how optimizers concentrate around maxima and iteratively refine the search.
Experimental Comparison: The authors apply the tool to recent LHC results. Figure 4 displays the maximal allowed rates in the $m_X$ - $m_S$ plane compared to CMS search limits for $SH \to b\bar{b}\tau^+\tau^-$ . Figure 5 presents a similar comparison for the ATLAS search for $SH \to b\bar{b}\gamma\gamma$ . These plots identify regions where experimental limits fall below the maximal theoretical rates, indicating sensitivity to the TRSM.

Significance and Claims
The authors position TRSMScans as a practical solution to the "fundamental challenge" of sampling $N$ -dimensional parameter spaces to find global maxima. By automating the process and utilizing intelligent algorithms rather than brute-force sampling, the tool enables a more thorough exploration of BSM parameter spaces than traditional fixed-parameter scans. The paper claims that while the current version is optimized for the TRSM, the underlying structure allows for extension to any model available within the ScannerS framework. The tool is presented as a step toward more efficient interpretation of LHC data in the context of specific extended scalar models, with future development planned to include more sophisticated optimization strategies and computational improvements.

The Problem: The Infinite Sandcastle

The Solution: The "Zoom" Drone

The Rules of the Game

What They Found (and Didn't Find)

The Verdict

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