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hep-aid: A Python Library for Sample Efficient Parameter Scans in Beyond the Standard Model Phenomenology

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper presents hep-aid, a modular Python library for machine-learning-guided, sample-efficient parameter scans in beyond-Standard-Model phenomenology, and reports that its b-CASTOR method reaches nearly 95% efficiency on a (B-L)SSM…

desk verdict A genuinely useful and well-documented software library, but the headline efficiency claim needs a random baseline and error bars before I'd believe it. read the letter →

arxiv 2412.17675 v1 pith:PG2WPZCT submitted 2024-12-23 hep-ph

Mauricio A. Diaz , Srinandan Dasmahapatra , Stefano Moretti This is my paper · ORCID
classification hep-ph
keywords hep-aidparameterscanactivesearchb-CASTORbeyondStandardModelHiggsmassmachinelearningsurrogate
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

hep-aid is a Python library that packages machine-learning-guided parameter scans for beyond-Standard-Model (BSM) phenomenology, with the goal of finding parameter regions that satisfy experimental constraints using as few expensive spectrum evaluations as possible. The paper argues that the active-search methods it implements, especially b-CASTOR, a batched variant of constraint active search, are more sample-efficient than a Metropolis-Hastings MCMC baseline. On a two-dimensional test function b-CASTOR shows the highest ratio of satisfactory points, and on an eight-dimensional (B-L)SSM scan targeting a 95 GeV Higgs-like state alongside the 125 GeV SM-like Higgs, it reaches nearly 95% efficiency with an initial dataset of 400 points. The library is structured so that new scan algorithms and new HEP software can be plugged in through configuration files.

What carries the argument

The load-bearing mechanism is the active-search loop. An Objective object wraps a black-box function $H(x)$, a set of constraints $\tau$, and an internal dataset; at each iteration a Gaussian-process surrogate is fitted to the normalised dataset, and the Expected Coverage Improvement (ECI) acquisition function scores candidate points by how much a hypersphere of radius $r$ placed there would expand coverage of the satisfactory region $S = \{x \mid H_i(x) \succeq \tau_i\}$. b-CASTOR is the batched version: it optimises ECI with the Tree-structured Parzen Estimator and then uses stochastic prioritisation to pick a batch of points each iteration. The library implements CAS, b-CASTOR, MLScan (an MLP surrogate with rejection sampling), and MCMC-MH as a comparison baseline, all sharing the same Objective, dataset, checkpoint, and metric machinery.

What would settle it

Run b-CASTOR and a uniform random sampler on the same (B-L)SSM objective, at the same evaluation budget and with the same initial dataset; if the active-search ratio of satisfactory to total points is not higher than random sampling's, the central efficiency claim fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that hep-aid's active-search methodologies make BSM parameter scans sample-efficient: instead of evaluating every candidate with the full HEP software stack, a Gaussian-process surrogate is fitted to collected points and an acquisition function decides which parameter vectors to evaluate next. For the (B-L)SSM case study, with the objective space reduced to the two lightest Higgs mass observables and the constraints set to $95 \pm 5$ GeV and $125 \pm 5$ GeV, b-CASTOR reports a satisfactory-to-total ratio $S_r \approx 0.95$ starting from 400 initial points. On the double-objective test function from [16], b-CASTOR outperforms MLScan and MCMC-MH on this efficiency metric, while MLScan shows broader exploration across the parameter space. The paper concludes that these results support the library as a versatile toolset for multi-objective searches for new physics.

Load-bearing premise

The broad efficiency conclusion rests on one two-dimensional test function and one eight-dimensional (B-L)SSM scan with a 5 GeV mass window and a 400-point initial dataset, so the reported hit rate may not transfer to other models, objectives, or baselines.

Editorial extensions

If this is right

  • If b-CASTOR's high hit rate holds across runs, BSM scans can concentrate expensive spectrum evaluations inside satisfactory regions instead of spreading the budget across the whole parameter box.
  • A new BSM model can be scanned by swapping the model files and configuration while reusing the same active-search policy and dataset machinery.
  • The library's automatic metric tracking makes it straightforward to compare CAS, b-CASTOR, MLScan, and MCMC-MH on the same objective in a few lines of code.
  • Checkpointing and compressed dataset export allow long scans to be resumed or shared, supporting reproducible phenomenological studies.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the efficiency metric $S_r$ depends on the width of the mass window and the size of the initial dataset, the ~95% figure is probably specific to the 5 GeV window used here rather than a fixed property of b-CASTOR.
  • A direct comparison against a uniform random sampler on the same (B-L)SSM objective would separate the contribution of the surrogate policy from the ease of the problem; the paper does not report such a baseline.
  • The same Objective-plus-surrogate-plus-acquisition structure could be adapted to other expensive black-box searches in high-energy physics, such as event-generator tuning or detector simulation parameters, with only configuration changes.
  • The ECI radius decay, currently a fixed schedule, could be tied to the surrogate's predictive uncertainty, which might make the scan adaptive in higher-dimensional spaces.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper presents hep-aid, a modular Python library for sample-efficient parameter scans in BSM phenomenology. It describes the library's two main modules — the hep module (interfacing SPheno, HiggsBounds, HiggsSignals, and MadGraph) and the search module (providing an Objective class and four parameter-scan methods: CAS, b-CASTOR, MLScan, and MCMC-MH) — along with configuration files, checkpointing, SLHA handling, and plotting utilities. The paper demonstrates the library on a two-dimensional double-objective test function proposed in the authors' earlier work and on an 8D (B-L)SSM scan targeting a ~95 GeV scalar together with the 125 GeV Higgs state. The central claim is that the included ML-based search methods 'enhance the efficiency of BSM studies', supported by an efficiency comparison among the methods and a reported 'nearly 95% efficiency' for b-CASTOR in the BSM example.

Significance. If the efficiency claim were properly supported, hep-aid would be a valuable community tool: it addresses a real need for modular, ML-based scan infrastructure that connects to standard spectrum generators, and it ships with several search algorithms, configuration-driven workflows, checkpointing, and dataset utilities. The paper is transparent about the library's design, provides code examples, and indicates availability of source code and reproducibility resources. However, the central performance claim currently rests on narrow, self-referential demonstrations that lack baselines and error bars, so the paper's significance hinges on whether the authors add an independent benchmark in a revision.

major comments (4)
  1. [Section 2.1.2, Figure 8] The comparison of b-CASTOR, MLScan, and MCMC-MH reports only the mean efficiency Sr over runs (five runs for b-CASTOR and MLScan, ten for MCMC-MH) and displays no error bars, quantiles, or individual run trajectories. The text itself notes that MCMC-MH achieved Sr=0 in some runs, which indicates substantial run-to-run variability, but the figure cannot convey this. More importantly, no uniform random-sampling or grid baseline is included; without a baseline, the reader cannot determine how much of Sr is attributable to the active search strategy rather than to the ease of the test problem or the choice of constraints.
  2. [Section 2.2, Eq. (3) and Figure 10] The claim that b-CASTOR achieves 'nearly 95% efficiency' in the (B-L)SSM scan is not a property of the algorithm alone: it depends on the initial dataset of 400 points, the user-defined mass window δm = 5 GeV, the total evaluation budget, and the number of repeated runs. The paper does not report the satisfactory fraction of the initial 400 random points, nor does it compare b-CASTOR against uniform random sampling with the same constraints and budget. Because Sr is the ratio of satisfactory to total sampled points, the reported 95% could be largely inherited from a favorable initial set; the claim that the method 'enhances' efficiency therefore needs control measurements to be supported.
  3. [Section 5 and Abstract] The conclusion states that 'All ML-based such approaches were also demonstrated to be superior to the more standard MCMC-HS one' and the abstract asserts that demonstrations 'show that hep-aid methodologies enhance the efficiency of BSM studies.' These generalisations are based on a two-dimensional test function and a single 8D BSM example, with the BSM example lacking multiple-seed variation and both lacking an independent baseline. The strength of the conclusion exceeds the evidence presented; it should be restricted to the specific test cases or supported by a broader benchmark suite.
  4. [Section 2.1.2, Section 2.2, and reference [16]] The evaluation is self-referential in a way that weakens external validity: the 2D test function, the (B-L)SSM case study, and the b-CASTOR algorithm all originate from the authors' earlier paper [16], and the comparison methods MLScan and MCMC-MH are used as implemented in the library itself. This is not a logical circularity, but it means the benchmark does not test the library against independently developed methods or problems. Adding at least one independent test function (e.g., from a public black-box optimisation benchmark) and one independent BSM model would substantially strengthen the claims of versatility and sample efficiency.
minor comments (5)
  1. [Section 3.3] The sentence 'However, in hep-aid implements its own SLHA, module.lass' appears to contain a typo; it should likely read 'hep-aid implements its own SLHA module, with the SLHA class.'
  2. [Figure 8] The axis label rendered as 'D∫⟩‡⌉' is corrupted; it presumably should be 'D_size' or 'Dataset size'.
  3. [Section 5] The phrase 'the more standard MCMC-HS one' uses an acronym inconsistent with the rest of the paper, where the method is defined as MCMC-MH.
  4. [Section 3.2] In the sentence 'Another notable library is [45]', the library is not named at the point of citation; naming it (xSLHA) immediately would improve readability.
  5. [Section 2.1.1] The installation instructions do not specify the tested Python version or the versions of key dependencies; stating the exact environment would aid reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the reported efficiencies are measured outputs, not consequences of the paper's definitions; self-referential benchmarking is an independence concern, not circularity.

full rationale

This paper is a software manual rather than a derivation. Its central claim that hep-aid methods enhance the efficiency of BSM parameter scans is an empirical claim supported by measured success rates, not a theorem derived from the paper's equations. Equations (4)-(8) define the satisfactory region, the sigmoid likelihood, and the MCMC acceptance rule, but none of them logically imply the observed Sr values: the 'nearly 95% efficiency' reported in Section 2.2 and Figure 10 is a run outcome, not an equation-level consequence. The use of the test function and the b-CASTOR algorithm from the same group's earlier paper [16] is self-referential benchmarking, and the absence of a random-sampling baseline or the satisfactory fraction of the initial 400-point dataset weakens the external validity of the efficiency comparison. However, this is an experimental-design limitation, not a circular reduction: the efficiency value is not defined in terms of itself, no fitted parameter is renamed as a prediction, no uniqueness theorem is imported, and no ansatz is smuggled in via citation. The library's HEP-stack integration with SPheno, HiggsBounds, HiggsSignals, and MadGraph is independently functional and does not depend on the performance claim. Therefore no specific circular step can be identified, and the appropriate circularity score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

No physically invented entities are introduced; hep-aid is software. The main assumptions are about surrogate model adequacy, benchmark representativeness, and the reliability of external HEP tools.

free parameters (5)
  • radius r (resolution parameter) = user-defined, typically 0 to 1 in normalised space
    CAS and b-CASTOR hyperparameter that sets the hypersphere coverage radius; the paper gives no fixed value.
  • initial dataset size = 400 points in BSM example
    User-chosen size of the initial dataset for b-CASTOR; affects the efficiency metric.
  • mass window delta_m = 5 GeV
    Constraint in Eq. (3) for the Higgs mass example; user chosen.
  • alpha (batch sampling) = 2
    Prioritisation degree in b-CASTOR rank-based sampling, set empirically.
  • epsilon (sigmoid likelihood) = not specified
    Smoothness parameter in the sigmoid likelihood of Eq. (7), used by MCMC-MH and MLScan.
assumptions (4)
  • domain assumption Gaussian process surrogate models provide adequate approximations of the BSM objective functions.
    The active search methods rely on GP predictions of each objective in Section 4.2.1; if the objective is highly discontinuous or noisy, the ECI policy may mislead.
  • ad hoc to paper The 2D double-objective test function from [16] is representative of BSM parameter scan problems.
    The test function is used to compare algorithms in Figure 8; no independent benchmark suite is used.
  • domain assumption The sigmoid-window likelihood in Eq. (6) defines a target distribution whose support corresponds to the satisfactory region.
    Used for MCMC-MH and MLScan; the shape of the sigmoid can bias sampling near constraint boundaries.
  • domain assumption The HEP-stack computations, SPheno, HiggsBounds, HiggsSignals, and MadGraph, are error-free and consistent.
    The BSM example treats these external tools as black boxes for computing masses and constraints; errors in the tools propagate to the scans.

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Cite this review

Pith. "Pith review of hep-aid: A Python Library for Sample Efficient Parameter Scans in Beyond the Standard Model Phenomenology." pith.science (2026). https://pith.science/paper/PG2WPZCT

@misc{pith2026241217675,
  author       = {Pith},
  title        = {Pith review of: hep-aid: A Python Library for Sample Efficient Parameter Scans in Beyond the Standard Model Phenomenology},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PG2WPZCT}},
  note         = {Machine review of arXiv:2412.17675}
}
read the original abstract

This paper presents hep-aid, a modular Python library conceived for utilising, implementing, and developing parameter scan algorithms. Originally devised for sample-efficient, multi-objective active search approaches in computationally expensive Beyond Standard Model (BSM) phenomenology, the library currently integrates three Machine Learning (ML)-based approaches: a Constraint Active Search (CAS) algorithm, a multi-objective Active Search (AS) method (called b-CASTOR), and a self-exploration method named Machine Learning Scan (MLScan). These approaches address the challenge of multi-objective optimisation in high-dimensional BSM scenarios by employing surrogate models and strategically exploring parameter spaces to identify regions that satisfy complex objectives with fewer evaluations. Additionally, a Markov-Chain Monte Carlo method using the Metropolis-Hastings algorithm (MCMC-MH) is implemented for method comparison. The library also includes a High Energy Physics (HEP) module based on SPheno as the spectrum calculator. However, the library modules and functionalities are designed to be easily extended and used also with other external software for phenomenology. This manual provides an introduction on how to use the main functionalities of hep-aid and describes the design and structure of the library. Demonstrations based on the aforementioned parameter scan methods show that hep-aid methodologies enhance the efficiency of BSM studies, offering a versatile toolset for complex, multi-objective searches for new physics in HEP contexts exploiting advanced ML-based approaches.

Figures

Figures reproduced from arXiv: 2412.17675 by the authors.

Figure 1
Figure 1. Diagram showing the general structure of the [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Example code demonstrating the initialisation of the HEP-stack with SPheno, HB, HS, and MG using a [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Commands to clone the repository and install the [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: Two-dimensional double-objective test function, where the objectives must satisfy the shaded areas. The [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Quick start guide illustrating the following: initialising a two-dimensional double objective test function, [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Code snippet that demonstrates the use of the [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Example code for the application of the generate meshgrid and reshape model output rou￾tines from the hepaid.search.objective.plot module, as well as the evaluation of the GP surrogate model b-CASTOR.model on the generated grid, illustrating the fitted model prediction…
Figure 8
Figure 8. Figure 8: Performance comparison of b-CASTOR, MLScan, and MCMC-MH for a two-dimensional, double [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: Code example demonstrating the definition of the objective function, [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: Corner plot visualisation of point distributions within and outside the [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]
Figure 11
Figure 11. Figure 11: Configuration file for the HEP-stack named [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 12
Figure 12. Figure 12: Using SPheno and HiggsSignals: The parameter heptool dir corresponds to the path to SPheno’s directory, output dir corresponds to the directory where the output will be stored, and model name is the name of the previously compiled model. The process is analogous for H…
Figure 13
Figure 13. Figure 13: Using the SLHA class for the default input SLHA file of the ( [PITH_FULL_IMAGE:figures/full_fig_p017_13.png]
Figure 14
Figure 14. Figure 14: Definition of the Egg Box model as a function, which serves as the objective function to initialise the [PITH_FULL_IMAGE:figures/full_fig_p018_14.png]
Figure 15
Figure 15. Figure 15: Configuration file for the b-CASTOR algorithm. The left side displays the general structure of a configu [PITH_FULL_IMAGE:figures/full_fig_p022_15.png]
Figure 16
Figure 16. Figure 16: A code example demonstrating the utilisation of the MLScan method, a neural network-based sampling [PITH_FULL_IMAGE:figures/full_fig_p024_16.png]
Figure 17
Figure 17. Figure 17: On the left, the predictions across the entire parameter space of the surrogate neural network model in the [PITH_FULL_IMAGE:figures/full_fig_p025_17.png]

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Forward citations

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Pith tools

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