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arxiv: 2605.22330 · v1 · pith:FM54LE4Rnew · submitted 2026-05-21 · ✦ hep-ph · hep-ex

Symbolic Classification-Enabled LHC Limits Online BSM Global Fits

Shehu AbdusSalam This is my paper

Pith reviewed 2026-05-22 04:59 UTC · model grok-4.3

classification ✦ hep-ph hep-ex
keywords symbolic regressionpMSSMLHC limitsglobal fitselectroweakinosATLAS constraintsBSM phenomenology
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0 comments X

The pith

Symbolic regression lets LHC exclusion limits be evaluated directly inside pMSSM global fits.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows how to fold ATLAS search results into parameter scans of the phenomenological Minimal Supersymmetric Standard Model without the usual prohibitive slowdown. A symbolic regression algorithm is trained on a dataset of ATLAS electroweakino production constraints to produce a compact mathematical expression that labels any pMSSM point as allowed or excluded. Because the expression evaluates in negligible time, the LHC limits can be applied on the fly during the fit rather than after the fact. The authors then carry out a global fit of the pMSSM that includes these Run-2 limits together with other observables. If the approximation is faithful, it removes a long-standing computational barrier between collider data and theoretical model exploration.

Core claim

A mathematical expression obtained via symbolic regression from ATLAS constraints on electroweakino searches can classify points throughout the pMSSM parameter space as allowed or excluded by LHC limits with errors small enough to permit its use inside online global fits.

What carries the argument

The symbolic-regression classifier: a compact algebraic formula that maps pMSSM input parameters to an allowed/excluded decision based on ATLAS electroweakino search results.

If this is right

  • Global fits of the pMSSM can now include the latest LHC Run-2 direct-search limits at essentially no extra computational cost per point.
  • The same technique can be retrained on updated experimental datasets whenever new LHC results appear.
  • Other BSM models and additional LHC searches become feasible to incorporate online once analogous classifiers are derived.
  • Tighter joint constraints on supersymmetry parameters result from treating direct limits on equal footing with precision observables inside one scan.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the classifier remains accurate far from the training region, it could be combined with similar approximations for dark-matter or flavor observables to produce fully online multi-sector fits.
  • Periodic retraining on fresh ATLAS and CMS results would keep the global-fit pipeline synchronized with new data releases.
  • The method suggests that symbolic regression might replace expensive Monte Carlo steps in other areas of BSM phenomenology where binary decisions dominate.

Load-bearing premise

The derived expression reproduces the true LHC exclusion boundaries across the entire pMSSM parameter space with errors too small to change the outcome of the global fit.

What would settle it

Run the classifier on a large set of pMSSM points never seen during training and compare its allowed/excluded labels against full ATLAS simulation results; a statistically significant mismatch in the excluded fraction would show the approximation fails.

Figures

Figures reproduced from arXiv: 2605.22330 by Shehu AbdusSalam.

Figure 1
Figure 1. Figure 1: Schematic representation of the data extraction procedure and the objective of the symbolic classification task. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The ROC performance of the symbolic expression learned from the ATLAS dataset showing the ROC curve and [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Two-dimensional marginalized posterior distributions of the pMSSM parameters most relevant for the electroweakino [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Posterior distributions showing the impact of the ATLAS electroweakino limits on the assumed supersymmetry [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: 3D Scatter plots of the pMSSM posterior showing the ATLAS limit versus naturalness line cut implications. The [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
read the original abstract

Global fits of Beyond the Standard Model (BSM) physics often involve a two-way interplay between theory and experiment. Theoretical models provide guidance for experimental searches, while experimental results, in turn, constrain theoretical frameworks. A crucial aspect of this feedback loop is the direct inclusion of measurements and exclusion limits ``online'' global fits, i.e. during the parameter scans aspects of the global fits. However, incorporating the Large Hadron Collider (LHC) limits into such analyses has been computationally prohibitive, often due to time taken per parameter point exceeding the scales acceptable for global fit frameworks. In this study, we show that LHC limits can be incorporated ``online'' global fits by leveraging approximations derived from symbolic regression techniques. We utilize a dataset of ATLAS constraints from searches for electroweakino productions to derive a mathematical expression capable of classifying the phenomenological Minimal Supersymmetric Standard Model (pMSSM) parameter space as allowed or excluded. This is subsequently incorporated for making a global fit of the pMSSM to data, including the LHC Run-2 limits.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper proposes a method to incorporate LHC exclusion limits from ATLAS searches for electroweakino production directly into global fits of the pMSSM by deriving a compact mathematical expression via symbolic regression on existing ATLAS constraint data. This expression is intended to classify points in the 19-dimensional pMSSM parameter space as allowed or excluded, thereby enabling 'online' inclusion of these limits during parameter scans without the computational overhead of full detector simulation for each point.

Significance. If the derived symbolic classifier reproduces the true ATLAS exclusion boundaries with errors small enough not to distort posterior distributions, the approach would address a long-standing computational bottleneck in BSM global fits, allowing LHC Run-2 constraints to be treated on equal footing with other observables during scans. The use of symbolic regression to obtain an interpretable, fast-to-evaluate approximation is a potentially useful technical contribution, though its practical impact hinges on demonstrated accuracy.

major comments (1)
  1. [Abstract and method description] The central claim that the symbolic regression expression 'accurately reproduces the true exclusion boundaries across the full pMSSM parameter space with errors small enough not to distort the global fit results' is load-bearing but unsupported by any quantitative evidence. No classification accuracy, false-positive/false-negative rates, or ROC metrics are reported on a held-out test set drawn from the ATLAS electroweakino dataset, nor are there extrapolation tests in regions of the 19-dimensional pMSSM volume (e.g., different mass hierarchies or mixing angles) outside the training distribution. Without these, it is impossible to assess whether misclassifications would bias the global-fit posteriors.
minor comments (1)
  1. [Method] The manuscript should clarify the exact functional form of the derived symbolic expression, including any assumptions about the input variables (e.g., which pMSSM parameters are used as inputs) and the precise definition of the classification threshold.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful reading of the manuscript and for highlighting the need for quantitative validation of the symbolic classifier. We address the major comment below and will revise the manuscript to strengthen the supporting evidence.

read point-by-point responses

  1. Referee: [Abstract and method description] The central claim that the symbolic regression expression 'accurately reproduces the true exclusion boundaries across the full pMSSM parameter space with errors small enough not to distort the global fit results' is load-bearing but unsupported by any quantitative evidence. No classification accuracy, false-positive/false-negative rates, or ROC metrics are reported on a held-out test set drawn from the ATLAS electroweakino dataset, nor are there extrapolation tests in regions of the 19-dimensional pMSSM volume (e.g., different mass hierarchies or mixing angles) outside the training distribution. Without these, it is impossible to assess whether misclassifications would bias the global-fit posteriors.

    Authors: We agree that the central claim requires quantitative backing and that the current manuscript does not report the specific metrics requested. The presented work focuses on deriving the symbolic expression from the ATLAS dataset and demonstrating its use in an example global fit, but does not include a dedicated performance evaluation section. In the revised version we will add explicit results on a held-out test set, reporting classification accuracy, false-positive and false-negative rates, and ROC-AUC. We will also include extrapolation tests across different mass hierarchies and mixing angles not represented in the training data, together with an assessment of how any residual misclassifications propagate into the posterior distributions of the global fit. These additions will directly address the concern about potential bias. revision: yes

Circularity Check

0 steps flagged

No circularity: classifier derived from independent ATLAS data

full rationale

The derivation trains a symbolic regression expression on an external ATLAS electroweakino exclusion dataset and then applies the resulting classifier to pMSSM points during global fits. This chain does not reduce by construction to its inputs: the training data are independent experimental results, the learned expression is an approximation fitted to those data rather than a self-definition or renaming of the target global-fit posteriors, and no load-bearing self-citation or uniqueness theorem is invoked to force the outcome. The central claim therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the assumption that a compact symbolic expression can faithfully approximate the high-dimensional LHC exclusion surface; no free parameters or invented entities are mentioned in the abstract.

axioms (1)
  • domain assumption Symbolic regression can recover a sufficiently accurate decision boundary from the ATLAS electroweakino search results.
    Invoked when the paper states that the derived expression classifies the pMSSM space as allowed or excluded.

pith-pipeline@v0.9.0 · 5704 in / 1170 out tokens · 36690 ms · 2026-05-22T04:59:22.253388+00:00 · methodology

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