Paper Citation Record · LEDGER

A simulation-based training framework for machine-learning applications in ARPES

As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2508.15983.

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

pith.paper-citation-record.v1
2508.15983 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:40:16.940945Z

measured 33 of 33 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-11T02:36:19.020830Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T03:10:53.983692Z

Reference resolution

32 of 32 outbound references displayed

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External citation measurements

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Outbound references

Observation 4839a883-1658-47b6-a6de-be0e14b60c39 · outbound

This paper cites an unresolved cited work.

A simulation-based training framework for machine-learning applications in ARPES Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:40:38.037043Z

Source-reported events for the cited work

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Observation 7669619c-ce67-49ed-90e1-bf1080a8f30b · outbound

This paper cites an unresolved cited work.

A simulation-based training framework for machine-learning applications in ARPES Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-05T17:40:38.026871Z

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Observation 34b26fdb-789f-4647-b5e7-32370382c11a · outbound

This paper cites use-or-regenerate.

A simulation-based training framework for machine-learning applications in ARPES use-or-regenerate

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:38.016453Z

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Observation a3d3c70c-7174-4bb5-bbf1-3eb290ee0e8f · outbound

This paper cites 6j, while the MLS gives a score of 1.

A simulation-based training framework for machine-learning applications in ARPES 6j, while the MLS gives a score of 1

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:38.005258Z

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Observation 0a9846a7-268d-4229-a87a-218973743603 · outbound

This paper cites Probing the electronic structure of complex systems by ARPES.

A simulation-based training framework for machine-learning applications in ARPES Probing the electronic structure of complex systems by ARPES

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.995146Z

Source-reported events for the cited work

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Observation 7dc05af0-b430-42e1-9fb5-76e4d50123e5 · outbound

This paper cites Angle- resolved photoemission studies of quantum materials.Re- views of Modern Physics , 93(2):25006, 2021.

A simulation-based training framework for machine-learning applications in ARPES Angle- resolved photoemission studies of quantum materials.Re- views of Modern Physics , 93(2):25006, 2021

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.984965Z

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Observation d85a8227-1878-47c3-b843-2375d4fcfac2 · outbound

This paper cites Time-resolved arpes studies of quantum materials.

A simulation-based training framework for machine-learning applications in ARPES Time-resolved arpes studies of quantum materials

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.974958Z

Source-reported events for the cited work

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Observation 97cdf650-7a26-406c-bd57-fa33f44a323c · outbound

This paper cites Advancing time-and angle-resolved photoemission spec- troscopy: The role of ultrafast laser development.Physics Reports, 1036:1–47, 2023.

A simulation-based training framework for machine-learning applications in ARPES Advancing time-and angle-resolved photoemission spec- troscopy: The role of ultrafast laser development.Physics Reports, 1036:1–47, 2023

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.964903Z

Source-reported events for the cited work

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Observation 84e9350a-63e1-4c5d-b89f-3b91de0fb40f · outbound

This paper cites A perspective on the application of spatially resolved arpes for 2d materials.

A simulation-based training framework for machine-learning applications in ARPES A perspective on the application of spatially resolved arpes for 2d materials

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.954755Z

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 0aec7bca-4026-49f2-ae2e-32224259fe3e · outbound

This paper cites Recent trends in spin-resolved photoelec- tron spectroscopy.

A simulation-based training framework for machine-learning applications in ARPES Recent trends in spin-resolved photoelec- tron spectroscopy

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.944599Z

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 b1988362-dcfd-4d6c-9817-d78d7807525c · outbound

This paper cites Revealing hidden orbital pseudospin texture with time- reversal dichroism in photoelectron angular distributions.

A simulation-based training framework for machine-learning applications in ARPES Revealing hidden orbital pseudospin texture with time- reversal dichroism in photoelectron angular distributions

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.935076Z

Source-reported events for the cited work

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Observation 8c58a60f-4208-40d2-8e77-73ee60884b02 · outbound

This paper cites Machine learning and the physical sciences.

A simulation-based training framework for machine-learning applications in ARPES Machine learning and the physical sciences

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.925903Z

Source-reported events for the cited work

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Observation a68060fb-7d42-480b-88a9-9f0a9fa1e74e · outbound

This paper cites Artificial-intelligence- driven scanning probe microscopy.

A simulation-based training framework for machine-learning applications in ARPES Artificial-intelligence- driven scanning probe microscopy

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.916265Z

Source-reported events for the cited work

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Observation a1f05955-4859-49c8-a958-676c6af7f59f · outbound

This paper cites Artificial intelligence driven exper- iments at user facilities.

A simulation-based training framework for machine-learning applications in ARPES Artificial intelligence driven exper- iments at user facilities

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.901254Z

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 2cc082c5-6b98-44ca-8ca2-322cc062ad5c · outbound

This paper cites Super resolution convolutional neu- ral network for feature extraction in spectroscopic data.

A simulation-based training framework for machine-learning applications in ARPES Super resolution convolutional neu- ral network for feature extraction in spectroscopic data

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.887715Z

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 ac7fab0c-3098-4eb1-979d-b2f08b72ea99 · outbound

This paper cites Deep learning-based statistical noise reduction for mul- 9 tidimensional spectral data.

A simulation-based training framework for machine-learning applications in ARPES Deep learning-based statistical noise reduction for mul- 9 tidimensional spectral data

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.875623Z

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 22fed723-b664-431c-99be-2d955e3e72ff · outbound

This paper cites Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks.

A simulation-based training framework for machine-learning applications in ARPES Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.862595Z

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 bda06090-598a-4f43-b687-91301b91e693 · outbound

This paper cites Hidden self-energies as origin of cuprate superconductivity revealed by machine learning.

A simulation-based training framework for machine-learning applications in ARPES Hidden self-energies as origin of cuprate superconductivity revealed by machine learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.850780Z

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 3183ea40-7895-4306-8585-b0a0503a70cc · outbound

This paper cites Machine learning the spectral function of a hole in a quantum antiferromagnet.

A simulation-based training framework for machine-learning applications in ARPES Machine learning the spectral function of a hole in a quantum antiferromagnet

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.839524Z

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 8b1110c8-77f6-4d46-9709-5d530b88154f · outbound

This paper cites Machine-learning- assisted acceleration on high-symmetry materials search: Space group predictions from band structures.

A simulation-based training framework for machine-learning applications in ARPES Machine-learning- assisted acceleration on high-symmetry materials search: Space group predictions from band structures

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.829061Z

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 7f73b409-3de0-4a63-805a-bedbd528d6a4 · outbound

This paper cites A machine learning route between band mapping and band structure.

A simulation-based training framework for machine-learning applications in ARPES A machine learning route between band mapping and band structure

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.817448Z

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 ee1d385b-0d42-42f3-87ab-6d32ad25bbb4 · outbound

This paper cites A survey on deep learning tools dealing with data scarcity: defini- tions, challenges, solutions, tips, and applications.

A simulation-based training framework for machine-learning applications in ARPES A survey on deep learning tools dealing with data scarcity: defini- tions, challenges, solutions, tips, and applications

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.806811Z

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 359bd436-2fab-44a6-b5cb-6cf275142d65 · outbound

This paper cites Demystifying quantum materials with deep learning and angle-resolved photoemission spectroscopy.

A simulation-based training framework for machine-learning applications in ARPES Demystifying quantum materials with deep learning and angle-resolved photoemission spectroscopy

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.796634Z

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 4265dbe8-6f97-4f58-86e5-033bf2a41e23 · outbound

This paper cites Detect- ing thermodynamic phase transition via explainable ma- chine learning of photoemission spectroscopy.

A simulation-based training framework for machine-learning applications in ARPES Detect- ing thermodynamic phase transition via explainable ma- chine learning of photoemission spectroscopy

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.785946Z

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 09db72bb-e1e9-43c8-a7b3-47bb08b70e35 · outbound

This paper cites aurelia: An ARPES data simulator for training machine learning models.

A simulation-based training framework for machine-learning applications in ARPES aurelia: An ARPES data simulator for training machine learning models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.640379Z

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 f622a39d-a533-4e7d-bc7e-337d34907106 · outbound

This paper cites J Jones, and Andrea Damascelli.

A simulation-based training framework for machine-learning applications in ARPES J Jones, and Andrea Damascelli

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.515098Z

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 8c0b10f5-f4ca-4280-976c-f1dc608ffb98 · outbound

This paper cites Computational framework chinook for angle-resolved photoemission spectroscopy.npj Quan- tum Materials , 4(1):54, 2019.

A simulation-based training framework for machine-learning applications in ARPES Computational framework chinook for angle-resolved photoemission spectroscopy.npj Quan- tum Materials , 4(1):54, 2019

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.368215Z

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 033ab8c8-f767-4fe6-ba80-e9c3ae2dd25b · outbound

This paper cites Interpretation of the shirley background in x-ray photoelectron spectroscopy analysis.

A simulation-based training framework for machine-learning applications in ARPES Interpretation of the shirley background in x-ray photoelectron spectroscopy analysis

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:37.271255Z

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 6637f614-d777-42da-96c5-b7974c0799b7 · outbound

This paper cites Autonomous micro- focus angle-resolved photoemission spectroscopy.

A simulation-based training framework for machine-learning applications in ARPES Autonomous micro- focus angle-resolved photoemission spectroscopy

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:17.897877Z

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 a131421a-f28a-4a4d-adfb-3096c61448a3 · outbound

This paper cites Transfer learning application of self-supervised learning in arpes.

A simulation-based training framework for machine-learning applications in ARPES Transfer learning application of self-supervised learning in arpes

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:17.464602Z

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 4b54650f-1fd3-4283-b457-f7231d45c133 · outbound

This paper cites Marigold: Efficient k-means clustering in high dimen- sions.

A simulation-based training framework for machine-learning applications in ARPES Marigold: Efficient k-means clustering in high dimen- sions

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:17.304329Z

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 02f5137d-3ce5-4454-b1f1-8716342e244d · outbound

This paper cites Machine-learning approach to understanding ultrafast carrier dynamics in the three- dimensional brillouin zone of ptbi 2.

A simulation-based training framework for machine-learning applications in ARPES Machine-learning approach to understanding ultrafast carrier dynamics in the three- dimensional brillouin zone of ptbi 2

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:40:17.147851Z

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.

Pith citing papers

Observation 8c6aaab7-cabc-4f77-ab97-ed59d93e8ece · inbound

Probabilistic denoising for reliable signal extraction in spectroscopy cites this paper.

Probabilistic denoising for reliable signal extraction in spectroscopy A simulation-based training framework for machine-learning applications in ARPES

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:10:53.985383Z

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.