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Paper Citation Record · LEDGER

DLScanner: A parameter space scanner package assisted by deep learning methods

As of 11 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 3 inbound Pith citation observations for arXiv:2412.19675.

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

pith.paper-citation-record.v1
2412.19675 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:08:53.073885Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:21:25.337698Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T10:26:52.348390Z

Reference resolution

42 of 42 outbound references displayed

  • verified exact8
  • verified fuzzy6
  • unresolved28
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External citation measurements

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Outbound references

Observation 326f6a83-9c2e-44f9-8158-63a99a01ecb8 · outbound

This paper cites A Conceptual Introduction to Markov Chain Monte Carlo Methods.

DLScanner: A parameter space scanner package assisted by deep learning methods A Conceptual Introduction to Markov Chain Monte Carlo Methods

Reference 1

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Observation 6a8a7ad3-11d1-4523-bacf-f54fd337da68 · outbound

This paper cites An effective introduction to the Markov Chain Monte Carlo method.

DLScanner: A parameter space scanner package assisted by deep learning methods An effective introduction to the Markov Chain Monte Carlo method

Reference 2

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Observation c6aca303-0c65-4748-ba23-4a0aae8ea053 · outbound

This paper cites Robert and G.

DLScanner: A parameter space scanner package assisted by deep learning methods Robert and G

Reference 3

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Observation 8b7d3988-c7e0-47db-94f7-65afb5b1b379 · outbound

This paper cites Introduction to Markov Chain Monte Carlo Simulations and their Statistical Analysis.

DLScanner: A parameter space scanner package assisted by deep learning methods Introduction to Markov Chain Monte Carlo Simulations and their Statistical Analysis

Reference 4

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Observation bbe6afab-7e28-408d-8bc5-ade4cb78c39d · outbound

This paper cites MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics.

DLScanner: A parameter space scanner package assisted by deep learning methods MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics

Reference 5

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Observation 0c2f56ab-2e46-45a1-96ae-10f21ed8d478 · outbound

This paper cites Importance Nested Sampling and the MultiNest Algorithm.

DLScanner: A parameter space scanner package assisted by deep learning methods Importance Nested Sampling and the MultiNest Algorithm

Reference 6

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Observation dd3693f9-9cb8-4abd-9375-d7b663889494 · outbound

This paper cites Fittino, a program for determining MSSM parameters from collider observables using an iterative method.

DLScanner: A parameter space scanner package assisted by deep learning methods Fittino, a program for determining MSSM parameters from collider observables using an iterative method

Reference 7

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Observation b84927ac-d6c6-459b-948d-f23b5e229a0a · outbound

This paper cites Comparison of statistical sampling methods with ScannerBit, the GAMBIT scanning module.

DLScanner: A parameter space scanner package assisted by deep learning methods Comparison of statistical sampling methods with ScannerBit, the GAMBIT scanning module

Reference 8

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Observation 61075e77-0061-4620-9823-14aaac6ab107 · outbound

This paper cites BSMArt: simple and fast parameter space scans.

DLScanner: A parameter space scanner package assisted by deep learning methods BSMArt: simple and fast parameter space scans

Reference 9

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Observation b34cf32e-9f4c-4758-a718-eee308e0cbe0 · outbound

This paper cites EasyScan_HEP: a tool for connecting programs to scan the parameter space of physics models.

DLScanner: A parameter space scanner package assisted by deep learning methods EasyScan_HEP: a tool for connecting programs to scan the parameter space of physics models

Reference 10

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Observation e80eff83-2d6e-4a60-9b8e-3b96153a538a · outbound

This paper cites Cosmological parameters from CMB and other data: a Monte-Carlo approach.

DLScanner: A parameter space scanner package assisted by deep learning methods Cosmological parameters from CMB and other data: a Monte-Carlo approach

Reference 11

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Observation 9b3813c1-a2db-4bc7-9b80-9f68220205be · outbound

This paper cites A Markov Chain Monte Carlo Analysis of the CMSSM.

DLScanner: A parameter space scanner package assisted by deep learning methods A Markov Chain Monte Carlo Analysis of the CMSSM

Reference 12

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Observation 82b4e8d6-19f4-4903-acb7-3ee77f966125 · outbound

This paper cites Sampling using a `bank' of clues.

DLScanner: A parameter space scanner package assisted by deep learning methods Sampling using a `bank' of clues

Reference 13

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Observation fbad6d30-1516-4708-9b6c-2e2f1f1e3643 · outbound

This paper cites Profile likelihood maps of a 15-dimensional MSSM.

DLScanner: A parameter space scanner package assisted by deep learning methods Profile likelihood maps of a 15-dimensional MSSM

Reference 14

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Observation 1997a85f-202c-43c4-bcdf-4404dc3d09c4 · outbound

This paper cites Status of CMSSM in light of current LHC Run-2 and LUX data.

DLScanner: A parameter space scanner package assisted by deep learning methods Status of CMSSM in light of current LHC Run-2 and LUX data

Reference 15

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Observation 48079215-79ff-41d6-adae-c5f84849e641 · outbound

This paper cites Likelihood Analysis of the pMSSM11 in Light of LHC 13-TeV Data.

DLScanner: A parameter space scanner package assisted by deep learning methods Likelihood Analysis of the pMSSM11 in Light of LHC 13-TeV Data

Reference 16

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Observation 8b0c44a5-4427-4d8c-a8a6-39c7eaf78933 · outbound

This paper cites MontePython 3: boosted MCMC sampler and other features.

DLScanner: A parameter space scanner package assisted by deep learning methods MontePython 3: boosted MCMC sampler and other features

Reference 17

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Observation 1a558fde-0146-4669-9972-9c70762b84ef · outbound

This paper cites an unresolved cited work.

DLScanner: A parameter space scanner package assisted by deep learning methods Unresolved cited work

Reference 18

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Observation 01386b67-d532-4f75-8d97-00008dbd23aa · outbound

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

DLScanner: A parameter space scanner package assisted by deep learning methods hep-aid: A Python Library for Sample Efficient Parameter Scans in Beyond the Standard Model Phenomenology

Reference 19

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Observation bbe22992-d3f6-4035-8535-617c79ed32cf · outbound

This paper cites Active learning BSM parameter spaces.

DLScanner: A parameter space scanner package assisted by deep learning methods Active learning BSM parameter spaces

Reference 20

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Observation 549652df-8daa-422d-8f60-222a51753e0a · outbound

This paper cites Exploring supersymmetry with machine learning.

DLScanner: A parameter space scanner package assisted by deep learning methods Exploring supersymmetry with machine learning

Reference 21

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Observation e1188ac9-c902-4c04-affa-a29c134d8c59 · outbound

This paper cites Exploration of Parameter Spaces Assisted by Machine Learning.

DLScanner: A parameter space scanner package assisted by deep learning methods Exploration of Parameter Spaces Assisted by Machine Learning

Reference 22

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Observation cad67039-e427-4759-88a3-d4f513517fbc · outbound

This paper cites BAMBI: blind accelerated multimodal Bayesian inference.

DLScanner: A parameter space scanner package assisted by deep learning methods BAMBI: blind accelerated multimodal Bayesian inference

Reference 23

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Observation e1e6cd02-f8d5-4286-a958-6fe3d75dac4b · outbound

This paper cites xBIT: an easy to use scanning tool with machine learning abilities.

DLScanner: A parameter space scanner package assisted by deep learning methods xBIT: an easy to use scanning tool with machine learning abilities

Reference 24

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Observation 9c200a36-bec1-4cad-b8b4-57affb4e6e93 · outbound

This paper cites Constraining the Parameters of High-Dimensional Models with Active Learning.

DLScanner: A parameter space scanner package assisted by deep learning methods Constraining the Parameters of High-Dimensional Models with Active Learning

Reference 25

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Observation 6e8d381a-ab83-4a85-9022-35c6e768da1b · outbound

This paper cites Symmetries, Safety, and Self-Supervision.

DLScanner: A parameter space scanner package assisted by deep learning methods Symmetries, Safety, and Self-Supervision

Reference 26

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Observation 0f6d02fa-51c2-44aa-8b67-19d8198b51a2 · outbound

This paper cites Sharpening the $A\to Z^{(*)}h $ Signature of the Type-II 2HDM at the LHC through Advanced Machine Learning.

DLScanner: A parameter space scanner package assisted by deep learning methods Sharpening the $A\to Z^{(*)}h $ Signature of the Type-II 2HDM at the LHC through Advanced Machine Learning

Reference 27

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Observation 7bfadc95-583f-4557-b840-623a83a3daba · outbound

This paper cites Anomalies, Representations, and Self-Supervision.

DLScanner: A parameter space scanner package assisted by deep learning methods Anomalies, Representations, and Self-Supervision

Reference 28

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Observation 0502783a-77fa-4342-80c7-25c2b699e789 · outbound

This paper cites Quantum similarity learning for anomaly detection.

DLScanner: A parameter space scanner package assisted by deep learning methods Quantum similarity learning for anomaly detection

Reference 29

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Observation 796d73a9-b836-42b6-a3b2-2f39ea1d4413 · outbound

This paper cites Universal New Physics Latent Space.

DLScanner: A parameter space scanner package assisted by deep learning methods Universal New Physics Latent Space

Reference 30

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Observation 18de63a9-22f0-4d01-8c4e-b5e585de40e4 · outbound

This paper cites Lepage,A New Algorithm for Adaptive Multidimensional Integration, J.

DLScanner: A parameter space scanner package assisted by deep learning methods Lepage,A New Algorithm for Adaptive Multidimensional Integration, J

Reference 31

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Observation 6bc95412-eefd-4b09-9b1d-8cd47633b1a8 · outbound

This paper cites Lepage,VEGAS: An Adaptive Multidimensional Integration Program, CLNS-80/447 (1980).

DLScanner: A parameter space scanner package assisted by deep learning methods Lepage,VEGAS: An Adaptive Multidimensional Integration Program, CLNS-80/447 (1980)

Reference 32

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Observation 407a7eac-17c3-471d-8039-0fb07a2c26a3 · outbound

This paper cites Adaptive Multidimensional Integration: VEGAS Enhanced.

DLScanner: A parameter space scanner package assisted by deep learning methods Adaptive Multidimensional Integration: VEGAS Enhanced

Reference 33

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Observation 5ab5c9c8-e273-4fe2-948a-adaffad2c542 · outbound

This paper cites gplepage/vegas: vegas version 6.1.3.

DLScanner: A parameter space scanner package assisted by deep learning methods gplepage/vegas: vegas version 6.1.3

Reference 34

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Observation c6d5c6c4-35c0-4bcf-9dcc-e3112bd4b847 · outbound

This paper cites Install tensorflow with pip.

DLScanner: A parameter space scanner package assisted by deep learning methods Install tensorflow with pip

Reference 35

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Observation a2774522-fb33-42ff-a7be-ef073d892d5a · outbound

This paper cites Tested build configurations.

DLScanner: A parameter space scanner package assisted by deep learning methods Tested build configurations

Reference 36

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Observation e176d485-f649-46c7-9691-2a392131dbf3 · outbound

This paper cites DLScanner.

DLScanner: A parameter space scanner package assisted by deep learning methods DLScanner

Reference 37

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raw_fallback, observed 2026-08-11T00:08:53.601770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T00:08:53.052335Z digest=sha256:ac4dffa55e0d0ba69aae9513ac6ffb33abd98f2c3888b457763987ec0a5bc004

Observation 832114ff-e7ed-4359-90e5-4bb1237a022e · outbound

This paper cites Kingma and J.

DLScanner: A parameter space scanner package assisted by deep learning methods Kingma and J

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-11T00:08:53.056978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:08:53.056978Z digest=sha256:2197bd781e2ada2c215da00cfedc22deedbebbe154e2f2a61b01e97f92e283d5

Observation c76f49e5-7234-4069-8045-f5e328630c98 · outbound

This paper cites SPheno 3.1: extensions including flavour, CP-phases and models beyond the MSSM.

DLScanner: A parameter space scanner package assisted by deep learning methods SPheno 3.1: extensions including flavour, CP-phases and models beyond the MSSM

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-11T00:08:53.060646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:08:53.060646Z digest=sha256:1e7f288464c6485ce3558f9e9de04ddb812976dce3f31fc10c6be81bad27a0e2

Observation c6cf78c4-31c3-4c31-a307-c8e7e8c390f9 · outbound

This paper cites SPheno, a program for calculating supersymmetric spectra, SUSY particle decays and SUSY particle production at e+ e- colliders.

DLScanner: A parameter space scanner package assisted by deep learning methods SPheno, a program for calculating supersymmetric spectra, SUSY particle decays and SUSY particle production at e+ e- colliders

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-11T00:08:53.065441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:08:53.065441Z digest=sha256:da7fb9214a7aacc3d42c2df92861bba009384a2ccf1674221cfc0f6380269b5b

Observation 328c72eb-d8bf-47a3-9193-0cdfdd685363 · outbound

This paper cites micrOMEGAs 6.0: N-component dark matter.

DLScanner: A parameter space scanner package assisted by deep learning methods micrOMEGAs 6.0: N-component dark matter

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-11T00:08:53.069603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:08:53.069603Z digest=sha256:e646d7353360447012784017094a87c96092c3021c011d4d1871bd7103af3d55

Observation fe69aa75-e62e-4f55-9b2d-382e4acfde46 · outbound

This paper cites The Anatomy of Electro-Weak Symmetry Breaking. II: The Higgs bosons in the Minimal Supersymmetric Model.

DLScanner: A parameter space scanner package assisted by deep learning methods The Anatomy of Electro-Weak Symmetry Breaking. II: The Higgs bosons in the Minimal Supersymmetric Model

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-11T00:08:53.073885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:08:53.073885Z digest=sha256:c5e7b5799ebf13e99620a338a9e739e210c223c4afdd826c5d67998f8e1f20f0

Pith citing papers

Observation 683a368d-c4fb-4960-b671-292af275d010 · inbound

Normalizing Flow-Assisted Nested Sampling on Type-II Seesaw Model cites this paper.

Normalizing Flow-Assisted Nested Sampling on Type-II Seesaw Model DLScanner: A parameter space scanner package assisted by deep learning methods

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-10T13:21:25.337698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T13:21:25.337698Z digest=sha256:d7c6973ad707515ef9ce72a7dd8e64ea964ea4c1e1168b2ede53d48e18d4df06

Observation 83e98c0d-9452-4948-94bd-33dc5caf65fa · inbound

Symbolic Classification-Enabled LHC Limits Online BSM Global Fits cites this paper.

Symbolic Classification-Enabled LHC Limits Online BSM Global Fits DLScanner: A parameter space scanner package assisted by deep learning methods

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-22T05:04:37.131227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-22T05:03:33.071351Z digest=sha256:ee98841db800f9e6d5c4872733db336cd67e48af66077d49ec2ed3fc7c7c0505

Observation 7ad2e25e-1194-4078-828d-63007a6d825c · inbound

BSMArt 2: simpler and faster parameter space scans cites this paper.

BSMArt 2: simpler and faster parameter space scans DLScanner: A parameter space scanner package assisted by deep learning methods

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-07-02T10:26:52.349787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-28T05:02:44.747731Z digest=sha256:01bfa2c60db0fc4f3e802ce8e2368670ec2bce1b0b9d575729653dd316b88c80