Paper Citation Record · LEDGER

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC

As of 18 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 2 inbound Pith citation observations for arXiv:2506.08826.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.08826 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:10:08.225125Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:48:54.891642Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-08T15:28:56.702365Z

Reference resolution

32 of 32 outbound references displayed

verified exact5
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External citation measurements

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Outbound references

Observation 873252cc-f1f6-4e79-95d2-bccb06524d51 · outbound

This paper cites Search for High Mass Top Quark Production in p anti-p Collisions at S**(1/2) = 1.8 TeV.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Search for High Mass Top Quark Production in p anti-p Collisions at S**(1/2) = 1.8 TeV

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:10:08.830731Z

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Observation 5eccbd29-0177-474a-9c16-45d44c7a9416 · outbound

This paper cites Data analysis in high energy physics: a practical guide to statistical methods.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Data analysis in high energy physics: a practical guide to statistical methods

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.050828Z

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Observation 6fe724c1-d8be-4ba7-860c-1f5f7f913693 · outbound

This paper cites Background estimation with the ABCD method featuring the TRooFit toolkit.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Background estimation with the ABCD method featuring the TRooFit toolkit

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:10:08.914643Z

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Observation 21ce5714-86e3-460e-a300-07830aae1592 · outbound

This paper cites ABCDisCo: Automating the ABCD Method with Machine Learning.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC ABCDisCo: Automating the ABCD Method with Machine Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.061258Z

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Observation 744459cb-7f23-4201-92a9-28ff626099fa · outbound

This paper cites Measuring and testing dependence by correlation of distances.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Measuring and testing dependence by correlation of distances

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.067579Z

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Observation 40948730-49c5-44b0-aba5-8c773b0f1026 · outbound

This paper cites The CMS experiment at the CERN LHC.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC The CMS experiment at the CERN LHC

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.072994Z

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Observation ef0dea6c-d9d5-4ee3-9ebd-d7a8d4b4a816 · outbound

This paper cites LHC Machine.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC LHC Machine

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.078477Z

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Observation 0fe5fbb8-3d02-4c80-9586-8a901bd24ef7 · outbound

This paper cites Search for top squarks in final states with many light-flavor jets and 0, 1, or 2 charged leptons in proton-proton collisions at √s=13 TeV.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Search for top squarks in final states with many light-flavor jets and 0, 1, or 2 charged leptons in proton-proton collisions at √s=13 TeV

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:10:08.898570Z

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Observation fd42e890-2d37-46de-95eb-383dd6df7eaa · outbound

This paper cites Constrained differential optimization.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Constrained differential optimization

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:10:08.881536Z

Source-reported events for the cited work

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Observation 3589d3de-573e-435a-bcc2-7c29afbc98ae · outbound

This paper cites Note on regression and inheritance in the case of two parents.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Note on regression and inheritance in the case of two parents

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:10:08.865391Z

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Observation 94ecf6cb-dabb-4c22-b0eb-039103503fe5 · outbound

This paper cites Visualizing the Pareto frontier.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Visualizing the Pareto frontier

Reference 11

Resolution: verified exact; doi, observed 2026-08-07T05:10:08.409208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e84827c2-1d93-432b-897b-35279eff740d · outbound

This paper cites Stealth Supersymmetry.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Stealth Supersymmetry

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:10:08.774782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7f1fe3b5-d765-4032-9fed-88ad8cbdb0a6 · outbound

This paper cites A Stealth Supersymmetry Sampler.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC A Stealth Supersymmetry Sampler

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:10:08.753095Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 481befb9-9be7-4943-951c-7024dadf3281 · outbound

This paper cites Stealth Supersymmetry Simplified.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Stealth Supersymmetry Simplified

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:10:08.730940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d730f070-03fb-4d9e-9f4c-ac08ec112a95 · outbound

This paper cites Search for top squarks in final states with two top quarks and several light-flavor jets in proton-proton collisions at $\sqrt{s} =$ 13 TeV.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Search for top squarks in final states with two top quarks and several light-flavor jets in proton-proton collisions at $\sqrt{s} =$ 13 TeV

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.126697Z

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Observation 96b3d74f-318d-4225-bd6e-5a47e9e2a151 · outbound

This paper cites Chollet et al., “Keras”, 2015.https://keras.io.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Chollet et al., “Keras”, 2015.https://keras.io

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:10:08.847406Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aa02f009-a2ef-4285-a467-db2766b38517 · outbound

This paper cites TensorFlow: A system for large-scale machine learning.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC TensorFlow: A system for large-scale machine learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.138713Z

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Observation e4fddb1f-b122-4308-abbf-72ed2e92e5e6 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.144866Z

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Observation ce830f58-565f-4e3c-bed0-06d1ba8e974c · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Adam: A Method for Stochastic Optimization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.150897Z

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Observation 216c0228-6d77-477b-8b36-974372b147c0 · outbound

This paper cites A New Method for Combining NLO QCD with Shower Monte Carlo Algorithms.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC A New Method for Combining NLO QCD with Shower Monte Carlo Algorithms

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.156018Z

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Observation b08b29b0-1a98-4bae-8ff2-959a1f3a6ef6 · outbound

This paper cites Matching NLO QCD computations with Parton Shower simulations: the POWHEG method.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Matching NLO QCD computations with Parton Shower simulations: the POWHEG method

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.162282Z

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Observation 4c316d31-ffff-4301-a172-cb6d71bfa364 · outbound

This paper cites A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.168071Z

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Observation 3013f4bd-cfe5-4eef-ac72-f8d51da228cf · outbound

This paper cites A Positive-Weight Next-to-Leading-Order Monte Carlo for Heavy Flavour Hadroproduction.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC A Positive-Weight Next-to-Leading-Order Monte Carlo for Heavy Flavour Hadroproduction

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.173191Z

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Observation c3d696f6-e149-4330-913c-925fc4fca02f · outbound

This paper cites Top++: a program for the calculation of the top-pair cross-section at hadron colliders.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Top++: a program for the calculation of the top-pair cross-section at hadron colliders

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.178172Z

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Observation 8d908cef-5948-4249-8d87-865312b44395 · outbound

This paper cites The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.183089Z

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Observation 06cfac79-735e-4de2-9762-b02c8afc230c · outbound

This paper cites Squark and gluino production cross sections in pp collisions at $\sqrt{s}$ = 13, 14, 33 and 100 TeV.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Squark and gluino production cross sections in pp collisions at $\sqrt{s}$ = 13, 14, 33 and 100 TeV

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.189861Z

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Observation 73906fcf-0ac0-48cf-beb5-2e83f464f108 · outbound

This paper cites NNLL-fast: predictions for coloured supersymmetric particle production at the LHC with threshold and Coulomb resummation.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC NNLL-fast: predictions for coloured supersymmetric particle production at the LHC with threshold and Coulomb resummation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.195443Z

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Observation 3fca7983-c2e0-4efd-9a7c-149112f0ecd2 · outbound

This paper cites An Introduction to PYTHIA 8.2.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC An Introduction to PYTHIA 8.2

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.201214Z

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Observation d56d22b0-9f9f-418f-9b9e-94459bd18a74 · outbound

This paper cites Parton distributions from high-precision collider data.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Parton distributions from high-precision collider data

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.208636Z

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Observation adcf1332-596f-42dc-b7b3-544c7a3c391d · outbound

This paper cites Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.214593Z

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Observation 8dd35076-0da7-4066-bd7c-56fb860ee810 · outbound

This paper cites GEANT4—a simulation toolkit.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC GEANT4—a simulation toolkit

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.219762Z

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Observation e3e7a955-bd32-4dcd-a030-9c51d2fd8b11 · outbound

This paper cites Improved Extrapolation Methods of Data-driven Background Estimation in High-Energy Physics.

Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC Improved Extrapolation Methods of Data-driven Background Estimation in High-Energy Physics

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T05:10:08.225125Z

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Pith citing papers

Observation dc8fa1ee-17b1-448e-b88a-19b20e498def · inbound

A Numerical Rosenblatt Method for Forced Variable Independence cites this paper.

A Numerical Rosenblatt Method for Forced Variable Independence Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T15:48:54.891642Z

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Observation d5667c1b-206e-4110-a4f5-a6098ee31ddc · inbound

Search for resonant production of lepton-enriched semivisible jets in proton-proton collisions at $\sqrt{s}$ = 13 TeV cites this paper.

Search for resonant production of lepton-enriched semivisible jets in proton-proton collisions at $\sqrt{s}$ = 13 TeV Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-08T15:28:56.707902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.