Introduction to the Usage of Open Data from the Large Hadron Collider for Computer Scientists in the Context of Machine Learning

Matthias Schott; Timo Saala

REVIEW 4 major objections 5 minor 12 references

Introduction to the Usage of Open Data from the Large Hadron Collider for Computer Scientists in the Context of Machine Learning

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read CMS open data from the LHC is converted into documented pandas DataFrames for computer scientists.

desk verdict A genuinely useful resource paper: six CMS open datasets converted to documented per-event pandas DataFrames, but the validation and provenance details need tightening before it should be widely promoted. read the letter →

arxiv 2501.06896 v1 pith:OEKTIXT7 submitted 2025-01-12 cs.LG hep-exphysics.data-an

Timo Saala , Matthias Schott This is my paper · ORCID

classification cs.LGhep-exphysics.data-an

keywords LHCopendataCMSpandasFramesROOTmachinelearningparticlephysicsconversioninterdisciplinarycollaboration

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

This paper converts CMS open data from the Large Hadron Collider, originally stored in the ROOT format, into pandas DataFrames and publishes the result along with detailed documentation of every variable. The intent is to remove two barriers that keep computer scientists away from particle physics data: an unfamiliar file format and the need for a physics background to interpret the stored observables. The paper shows, through a worked example of a W-boson cross-section measurement and two sketched machine-learning tasks, that the converted data can support real analyses. If the conversion is faithful, the published DataFrames give the machine-learning community a direct, documented entry point to real collision data.

What carries the argument

The load-bearing mechanism is the conversion pipeline described in the appendix: a CMS analysis module filters the desired objects and roughly 100 variables per event out of each 2 GB ROOT file, writes them to a temporary ROOT tree, and a Python script converts the tree into pandas DataFrames, transposing the storage from per-variable to per-event orientation and saving the result as compressed feather files. The second half of the machinery is the paper's variable dictionary: a set of tables that maps every DataFrame column, such as vecMuon PT or nVertex, to its physical meaning, so the data can be used without a physics background.

What would settle it

Choose one of the published 10000-event DataFrames and re-extract the same events from the original ROOT files with a fresh reader, then compare the full set of retained variables element by element; any mismatch outside floating-point tolerance would show the conversion is not faithful. A less direct falsifier is to compute the dimuon invariant-mass peak from vecMuon PT, Eta, Phi in the Z-boson sample and check that it reproduces the known Z mass.

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Extended reading notes

Core claim

The central claim is that a large, useful portion of CMS open data from the 2011-2012 LHC run can be served to computer scientists as pandas DataFrames rather than ROOT trees. The authors implemented a pipeline in which a CMS analysis module filters roughly 100 of 121 event variables out of each 2 GB ROOT file, converts the values without alteration into a per-event tabular layout, and saves the result as compressed feather files that can be read directly with pandas. The released datasets cover simulated and real proton-proton collisions at 8 TeV, including a top-quark pair sample, W and Z boson samples, and muon-trigger data, each with 10000-event files. The paper presents the full variable dictionary, histograms of key distributions, and a step-by-step analysis estimating W-boson signal events to demonstrate that the DataFrames behave like the original physics data.

Load-bearing premise

The load-bearing premise is that the single-pass filtering step keeps every variable a machine-learning model could need and introduces no silent corruption of the values, so users can trust the released DataFrames without re-checking them against the original ROOT files.

Editorial extensions

If this is right

Machine-learning researchers can load and explore real LHC collision data with standard pandas tooling, without installing ROOT or learning its object model.
The per-event storage layout matches the way deep-learning data loaders typically consume tabular inputs, so the DataFrames can feed neural-network pipelines almost directly.
The documented variable tables give non-physicists enough vocabulary to design features and understand what each observable means physically.
The worked W-boson analysis shows the DataFrames support a conventional physics measurement, not just machine-learning toy tasks.
The pipeline is reusable: the same single-container recipe can be pointed at other ROOT-based CMS open data.

Reading between the lines

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

The same conversion approach could be applied to other ROOT-based open datasets beyond CMS, including ATLAS data, which would extend the bridge between high-energy-physics open data and the Python machine-learning ecosystem.
Because the DataFrames keep only about 100 of 121 variables, the dropped variables may matter for some analyses; a faithful machine-learning benchmark built on these files should state that the filtering step is part of the dataset definition.
The paper's variable tables could serve as the starting schema for a standardized interchange format for tabular high-energy-physics data, reducing the need for each new collaboration to re-derive the mapping.
The histogram comparisons in the paper could be turned into automated data-quality regression tests: any future change to the conversion pipeline should leave these distributions statistically unchanged.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The genuinely new thing here is an artifact: six curated CMS Open Data datasets converted to per-event pandas DataFrames with a documented variable dictionary, deposited on bonndata. The conversion itself is a known operation (uproot + pandas), but this particular resource did not exist before, and for computer scientists who bounce off ROOT it can lower the barrier to entry substantially. The primer sections are competent, and the W-boson mini-analysis in Section 5 is a useful sanity check that the DataFrames preserve enough information for a simple cross-section-style comparison with Monte Carlo.

What the paper does well: the variable tables in Section 4 are extensive and mostly clear; the data/MC agreement in Figure 22 gives real evidence of usability; the pipeline is on GitHub; the choice of feather with zstd compression is reasoned and benchmarked against alternatives. That is legitimate, reproducible groundwork.

Soft spots, in proportion: the validation is weaker than the central claim. Figures 15–22 are one-dimensional histograms. They cannot catch swapped entries between arrays of the same object, and they cannot detect a silent break in per-event object alignment. That is a real gap, though it is a validation gap, not evidence of actual corruption. More concrete: there are no checksums for the released feather files and no commit hash for the pipeline in reference [12], so the exact code that produced the DataFrames cannot be pinned down. The editorial errors are also real—the duplicate DYToMuMu row in Table 14 and the physically implausible identical means for tauons and jets in Tables 15/16 (78 per event) suggest the manuscript's self-checks are not yet reliable. The stress-test note also flags the invariant-mass definition in Section 2.4; I think that specific concern does not hold, since p1·p2 = m12^2 is the standard definition. So: fix the tables, add checksums and a commit hash, and ideally include an event-by-event comparison script for a few files.

Who this is for: computer scientists who want to try machine learning on LHC data without first learning ROOT. They will get real value from this resource. There is no new physics and no new ML method, but that is not the point of the paper. I would send it to peer review with a request for revision, and I would cite it if I were working with these datasets.

Referee Report

4 major / 5 minor

Summary. This paper presents a conversion of a subset of CMS LHC open data from ROOT TTrees to pandas DataFrames, released through the University of Bonn bonndata repository. The manuscript has three main parts: a pedagogical primer on Standard Model particles, LHC detectors, and kinematic variables; a detailed catalogue of the roughly 100 DataFrame variables organized by object type; and a description of the conversion pipeline (a CMSSW EDAnalyzer followed by uproot3/pandas transposition) together with file-format benchmarks. As a use case, the authors perform a simple W-boson selection and show data/Monte Carlo agreement. The central claim is that the released DataFrames are a faithful, usable entry point for machine-learning researchers without a particle-physics background.

Significance. If the DataFrames are faithful to the original CMS open data, this is a genuinely useful community resource: it lowers the entry barrier for computer scientists, provides a documented variable catalogue, and ships a reproducible pipeline skeleton on GitHub. The paper is honest about being a tutorial and data descriptor rather than a methods contribution, and the data/MC comparison in Section 5 is a reasonable first sanity check. The main value hinges on trust in the conversion, and that trust is not yet established by the evidence in the manuscript.

major comments (4)

[Appendix A.2, Figures 15-22] The central claim of a trustworthy conversion is not yet supported by the published validation. The pipeline in Appendix A.2 filters roughly 100 of 121 variables and transposes the data with uproot3/pandas, but the only validation consists of one-dimensional histograms (Figures 15-22). Such histograms are invariant under permutations of objects within an event and under swaps between variables of the same object (for example, exchanging the i-th entries of vecMuon_PT and vecMuon_Eta changes no histogram in Figures 16 and 21). I ask for an event-by-event comparison of the released DataFrames against the source ROOT files (or a documented subset), checksums for the released feather files, and a commit hash or version tag for the GitHub repository in reference [12] so that the exact pipeline that produced the data can be identified and re-run.
[Section 4.1, Tables 3, 15, and 16] The tau-lepton counts appear to be corrupted. In Table 3, the values of nTau are identical to nJets in all three example events (64/64, 98/98, 96/96), and in Tables 15 and 16 the mean "Tauon" value exactly equals the mean "Jets" value (78.2536 and 71.1567, respectively). Since roughly 78 reconstructed tau leptons per event is physically implausible, this strongly suggests that the tau variables were filled from the jet branch (or vice versa). The paper must clarify this and, if the released nTau/vecTau columns are wrong, regenerate and re-validate the affected datasets before the resource can be used.
[Section 2.4] The invariant-mass formula is stated incorrectly. The text defines the scalar product p1.p2 = E1*E2 - p_x1*p_x2 - p_y1*p_y2 - p_z1*p_z2 and equates it directly to (m12)^2, but the invariant mass of a two-particle system is obtained from the square of the summed four-vector, (p1+p2)^2 = m12^2, not from p1.p2. The subsequent discussion of adding the two muon four-vectors to obtain the Z-boson mass is conceptually correct, so the equation and the sentence "By taking the scalar product of this four-vector" need to be corrected to avoid teaching a wrong definition to the target audience.
[Table 14, Section 4.3] The dataset catalogue contains a duplicate entry: the DYToMuMu M-20 CT10 TuneZ2star v2 8TeV sample is listed twice with the same DOI (10.7483/OPENDATA.CMS.QGC3.PTZ9), so Table 14 lists six rows but only five distinct samples. Since the paper's deliverable is a documented set of public datasets, the catalogue must be corrected and each dataset should carry a stable record identifier or DOI so that users can cite and retrieve the exact version they used.

minor comments (5)

[Throughout] There are many typographical and formatting errors, including "T able" in several table captions, "Panda Data Frames" in the Appendix title, and "We are hope" in the Conclusion; please run a careful proofreading pass.
[Table 12] The caption of Table 12 says "Description of the variables associated to Monte Carlo Truth" but the table actually describes jet variables; the caption should be corrected.
[Section 4.1, Table 4] The first paragraph of Section 4.1 describes a Top-AntiTop semi-leptonic decay, but the caption of Table 4 refers to a "ZZ to 4mu decay"; the example and the caption should be aligned.
[Section A.2] The statement that the python step transforms the file "without altering or discarding any of the contained values" should be clarified to apply only to the ROOT-to-DataFrame step, because the EDAnalyzer already filters out roughly 20 of the 121 variables; as written, the two sentences can be read as claiming losslessness of the full pipeline.
[Section 2.1, Table 1] The text says "one GeV corresponds to 1.783 * 10^27 kg"; the exponent is missing a minus sign and should be 10^-27 kg.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper reports a data conversion with no fitted parameters or derived predictions, and the validation histograms do not function as inputs to the conversion.

full rationale

The paper's central deliverable is a set of pandas DataFrames converted from CMS Open Data ROOT files; the only 'derivation' is the pipeline in Appendix A.2, which filters about 100 variables through a CMSSW EDAnalyzer, writes a TTree, and transposes it with uproot3 and pandas. Nothing in the pipeline is fit to the validation histograms in Figures 15–22: those histograms are drawn from the converted DataFrames and serve as sanity checks, not as fitted inputs, so the conversion cannot reduce to them by construction. The benchmarking section A.3 chooses the feather/zstd file format based on measured read/write speed and disk usage; this is an engineering choice, not a prediction from first principles, and the benchmark data are the authors' own converted DataFrames, which is appropriate for format selection and does not assume the target result. References [1] and [12] are self-citations that point to the released data and pipeline code, but the paper's substantive claim—that the ROOT-to-DataFrame conversion was performed and documented—does not rest on an unverified theorem or on a fitted parameter. Concerns raised by the skeptical reviewer about per-object array alignment, missing event-by-event validation, duplicate rows in Table 14, and physically implausible means in Tables 15–16 are correctness and reproducibility risks, not circularity: the histograms could be wrong while the reasoning remains non-circular. There is no equation equated to its own input and no fitted quantity renamed as a prediction, so the appropriate circularity score is 0.

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

The paper makes no fitted parameters. It builds on external CMS open data, the Standard Model, and detector simulation. The assumptions are domain assumptions about physics correctness and software reliability, not circular inputs.

assumptions (4)

domain assumption The Standard Model of particle physics correctly describes the collisions.
The primer in Section 2 and the data/MC comparison in Section 5 assume Standard Model predictions are valid.
domain assumption The CMS detector simulation and reconstruction chain accurately model the recorded events.
Section 3 and the simple W analysis in Section 5 rely on MC samples representing the detector response.
ad hoc to paper The EDAnalyzer selection of roughly 100 variables retains all information needed for the intended ML tasks.
Appendix A.2 describes the filtering step; no validation against full object lists is provided.
ad hoc to paper The trigger flags and event weighting in the sample are correctly copied from the original CMS data.
Section 5 uses trigger booleans and MC weights; a misreading would distort the data/MC comparison.

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Cite this review

Pith. "Pith review of Introduction to the Usage of Open Data from the Large Hadron Collider for Computer Scientists in the Context of Machine Learning." pith.science (2026). https://pith.science/paper/OEKTIXT7

@misc{pith2026250106896,
  author       = {Pith},
  title        = {Pith review of: Introduction to the Usage of Open Data from the Large Hadron Collider for Computer Scientists in the Context of Machine Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OEKTIXT7}},
  note         = {Machine review of arXiv:2501.06896}
}

read the original abstract

Deep learning techniques have evolved rapidly in recent years, significantly impacting various scientific fields, including experimental particle physics. To effectively leverage the latest developments in computer science for particle physics, a strengthened collaboration between computer scientists and physicists is essential. As all machine learning techniques depend on the availability and comprehensibility of extensive data, clear data descriptions and commonly used data formats are prerequisites for successful collaboration. In this study, we converted open data from the Large Hadron Collider, recorded in the ROOT data format commonly used in high-energy physics, to pandas DataFrames, a well-known format in computer science. Additionally, we provide a brief introduction to the data's content and interpretation. This paper aims to serve as a starting point for future interdisciplinary collaborations between computer scientists and physicists, fostering closer ties and facilitating efficient knowledge exchange.

Figures

Figures reproduced from arXiv: 2501.06896 by the authors.

**Figure 1.** Schematic illustration of a tracking detector including three vertices and several charged particles that are measured by a pixel detector [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 3.** Feynmann diagram visualizing the qq¯ → Z → µ +µ − process. q q¯ q¯ g q [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 5.** Feynmann diagram visualizing the gg → g → tt¯→ W+W−b ¯b → e +νeb ¯bdu¯ process. The above discussion simplifies the interaction picture by assuming that only two fundamental particles within the colliding protons interact. However, a single collision between two protons often involves not just one pair of interacting quarks or gluons, but multiple simultaneous interactions among the quarks, antiquarks, and gluons in… view at source ↗

Figures from the paper (16 more)

**Figure 6.** Figure 6: Polar coordinates θ and ϕ represented in the transverse plane of the detector. 𝜙 x y [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 8.** Figure 8: Picture of the ATLAS Experiment at CERN [4] [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

**Figure 10.** Figure 10: Basic detector layout of an LHC detector with all its sub-detector systems (left) and basic particle identification (right). – Inner Detector (ID): The inner detector typically starts from 5 cm away from the collision point around the beam pipes and extends up to 50 c…

**Figure 11.** Figure 11: Schematic illustration of a tracking detector including three vertices and several charged particles that are measured by a pixel detector [PITH_FULL_IMAGE:figures/full_fig_p010_11.png]

**Figure 13.** Figure 13: Exemplary depiction of a jet reconstruction. This image was taken from a news article: [6], by the CMS Collaboration [PITH_FULL_IMAGE:figures/full_fig_p014_13.png]

**Figure 15.** Figure 15: Histograms visualizing the distributions of how many events contain which amount of PF objects (left), muons (middle), and electrons (right) over 10000 events. be always larger by 5% compared to reality, then one just rescales each reconstructed muon momentum in simu…

**Figure 16.** Figure 16: Histograms visualizing the distributions of how many muons (left) / electrons (right) have specific Transverse Momentum over 10000 events. 0 1 2 3 4 5 6 7 Number of Muons (nMuon) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Fraction of events Amount of Muons (nMuon) after 5 GeV pT…

**Figure 17.** Figure 17: Histograms visualizing the distributions of how many events contain which amount of muons (left) / electrons (right) over 10000 events, after applying a 5 GeV cutoff on the Transverse Momenta of the encountered objects. which contains all other sub-objects. Another fa…

**Figure 18.** Figure 18: Histogram visualizing the distributions of pT (left) η (middle) and ϕ (right) over 10000 events for PF objects. The length of the variables represented as arrays (prefixed with vec) depends on how many of the related object were encountered in the given event. In [PI…

**Figure 19.** Figure 19: Greyscale image of transverse momentum of a single events PF objects, ranging over η and ϕ. 4.2 Detailed Information In this section, a complete list of variables within the pandas DataFrames will be provided in tabular format, as well as a brief introduction into the…

**Figure 20.** Figure 20: Upper row: MC Truth pT Muon left, MC Reco pT muon Right. Lower row: MC Truth pT Neutrino left, ETMiss Reco Right. 5 A Simple Data Analysis In order to give a more concrete feeling of a typical LHC data analysis, we will discuss the cross-section measurements of W bos…

**Figure 21.** Figure 21: Comparison of pT Muon (top-left), ETMiss (top-right), Isolation (bottom-left), and η (bottom-right) for signal and background MC Processes. The area of all distributions are normalized to unity. Once the signal selection is defined, it is applied on all relevant MC si…

**Figure 22.** Figure 22: Top-left: Data/MC comparison for pT muon, top-right: Data/MC comparison for ETMiss, bottom-left: Data/MC comparison for Isolation, bottom-right: Data/MC comparison for η [PITH_FULL_IMAGE:figures/full_fig_p032_22.png]

**Figure 23.** Figure 23: Change of ordering of data from a per variable (red, V) basis, to a per event (blue, E) basis during the transformation of ROOT TTrees to pandas DataFrames. The resulting DataFrames are then saved to disk using, in our case, the feather file format. This format was ch…

**Figure 24.** Figure 24: Average memory usage of the given file format over four different datasets [PITH_FULL_IMAGE:figures/full_fig_p036_24.png]

**Figure 26.** Figure 26: Average memory usage of the given file format and compression method over four different datasets [PITH_FULL_IMAGE:figures/full_fig_p036_26.png]

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Reference graph

Works this paper leans on

12 extracted references · 9 canonical work pages

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

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