Paper Citation Record · LEDGER

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments

As of 13 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2604.21869.

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

pith.paper-citation-record.v1
2604.21869 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-09T21:32:28.022724Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

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External citation measurements

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Outbound references

Observation 5a5f886b-860b-4165-9dac-cc8c54c2710a · outbound

This paper cites Acero, C.A.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Acero, C.A

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:09.718936Z

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Observation 130934d2-b58c-4183-9ace-b45c90ad40d5 · outbound

This paper cites Bisset, B.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Bisset, B

Reference 2

Resolution: verified exact; doi, observed 2026-05-09T21:33:27.932524Z

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Observation 84c50723-acca-403a-86df-85ac1886a896 · outbound

This paper cites Dark matter signals from timing spectra at neutrino experiments.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Dark matter signals from timing spectra at neutrino experiments

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:04.996859Z

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Observation fb908781-6266-497a-9c9c-4e11c16ba6fb · outbound

This paper cites Searching for Dark Matter Signals in Timing Spectra at Neutrino Experiments.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Searching for Dark Matter Signals in Timing Spectra at Neutrino Experiments

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:04.978338Z

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Observation 7e4bf2ff-dc51-4c9d-a376-6272b1ad4194 · outbound

This paper cites A Search for Electron Neutrino Transitions to Sterile States in the BEST Experiment.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments A Search for Electron Neutrino Transitions to Sterile States in the BEST Experiment

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:04.967710Z

Source-reported events for the cited work

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Observation 9bbc501f-937d-40e8-956b-5c9f60c8ed2d · outbound

This paper cites Library Event Matching event classification algorithm for electron neutrino interactions in the NOvA detectors.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Library Event Matching event classification algorithm for electron neutrino interactions in the NOvA detectors

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:28:16.650215Z

Source-reported events for the cited work

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Observation 269dad77-5715-4d09-b908-42d4553719b9 · outbound

This paper cites A Convolutional Neural Network Neutrino Event Classifier.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments A Convolutional Neural Network Neutrino Event Classifier

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:05.154416Z

Source-reported events for the cited work

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Observation 8a14a95e-503c-4deb-8919-4b11b1037a01 · outbound

This paper cites Improved Energy Reconstruction in NOvA with Regression Convolutional Neural Networks.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Improved Energy Reconstruction in NOvA with Regression Convolutional Neural Networks

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:05.086354Z

Source-reported events for the cited work

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Observation 540806c6-08fb-47c5-868d-dfd384d35477 · outbound

This paper cites Villarreal, J.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Villarreal, J

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:09.679143Z

Source-reported events for the cited work

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Observation cdbc2185-4aff-4b58-a19a-70aacb999f8c · outbound

This paper cites A Review on Machine Learning for Neutrino Experiments.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments A Review on Machine Learning for Neutrino Experiments

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:05.031341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0df5622f-d435-4cd4-98d9-41b76b945bf9 · outbound

This paper cites Deep-Learned Event Variables for Collider Phenomenology.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Deep-Learned Event Variables for Collider Phenomenology

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:05.098190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b4f2226b-1586-4f60-a863-be854dd0f6c5 · outbound

This paper cites Kinematic Variables and Feature Engineering for Particle Phenomenology.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Kinematic Variables and Feature Engineering for Particle Phenomenology

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:05.070404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 23445d29-b134-41a3-a5c7-5d90697ed167 · outbound

This paper cites The FERMIACC: Agents for Particle Theory.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments The FERMIACC: Agents for Particle Theory

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:05.051686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2eaf5d1b-30a9-4d12-bfc3-91c586eb93a7 · outbound

This paper cites AI Agents Can Already Autonomously Perform Experimental High Energy Physics.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments AI Agents Can Already Autonomously Perform Experimental High Energy Physics

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-23T03:12:46.910862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f9a88a68-9214-4af7-b51e-492a1771a30f · outbound

This paper cites Probing the dark sector with accelerators: New opportunities!.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Probing the dark sector with accelerators: New opportunities!

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:09.688143Z

Source-reported events for the cited work

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Observation deb8b32b-be9b-4837-8a74-46d6c3aeb2c8 · outbound

This paper cites Freedman,Coherent Neutrino Nucleus Scattering as a Probe of the Weak Neutral Current,Phys.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Freedman,Coherent Neutrino Nucleus Scattering as a Probe of the Weak Neutral Current,Phys

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:09.674980Z

Source-reported events for the cited work

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Observation 7ad5399d-af54-4d08-8d9e-52a9ff203090 · outbound

This paper cites Prospects for measuring coherent neutrino-nucleus elastic scattering at a stopped-pion neutrino source.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Prospects for measuring coherent neutrino-nucleus elastic scattering at a stopped-pion neutrino source

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:05.121633Z

Source-reported events for the cited work

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Observation 94248e28-1e21-47e7-a7cd-3457f9e3a771 · outbound

This paper cites From eV to EeV: Neutrino Cross Sections Across Energy Scales.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments From eV to EeV: Neutrino Cross Sections Across Energy Scales

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:05.174417Z

Source-reported events for the cited work

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Observation 6574d58e-2bca-488e-8ed7-d5ff0a8b1389 · outbound

This paper cites Helm,Inelastic and Elastic Scattering of 187-Mev Electrons from Selected Even-Even Nuclei,Phys.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Helm,Inelastic and Elastic Scattering of 187-Mev Electrons from Selected Even-Even Nuclei,Phys

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:09.691999Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 69c4556d-4ade-42cf-966f-c885ae03a14a · outbound

This paper cites MINER Reactor Based Search for Axion-Like Particles Using Sapphire (Al2O3) Detectors.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments MINER Reactor Based Search for Axion-Like Particles Using Sapphire (Al2O3) Detectors

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:05.007910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3053516d-6236-419f-bd84-073404c68c09 · outbound

This paper cites Large-mass single-electron-resolution detector for dark matter and neutrino elastic interaction searches.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Large-mass single-electron-resolution detector for dark matter and neutrino elastic interaction searches

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:05.165358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6fca71d8-7a7c-4561-ad71-5016865bb931 · outbound

This paper cites Sensitivity to oscillation with a sterile fourth generation neutrino from ultra-low threshold neutrino-nucleus coherent scattering.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Sensitivity to oscillation with a sterile fourth generation neutrino from ultra-low threshold neutrino-nucleus coherent scattering

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:05.192354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ff1a0c59-b2ea-4332-b6dc-9ee021afc356 · outbound

This paper cites Raftery,Bayesian model selection in social research,Sociological Methodology25(1995) 111.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Raftery,Bayesian model selection in social research,Sociological Methodology25(1995) 111

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T15:53:09.683778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ed23bfa8-1518-4fa9-80e9-a3d4d320fdf4 · outbound

This paper cites Multimodal nested sampling: an efficient and robust alternative to MCMC methods for astronomical data analysis.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Multimodal nested sampling: an efficient and robust alternative to MCMC methods for astronomical data analysis

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:05.183223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 00e4fa3c-8165-45d2-af05-142c47753746 · outbound

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

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:05.137178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c7e5ccae-63c9-430b-a574-33dd9a8aba63 · outbound

This paper cites Importance Nested Sampling and the MultiNest Algorithm.

Analytical and Machine Learning Methods for Model Discernment at CE$\nu$NS Experiments Importance Nested Sampling and the MultiNest Algorithm

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:39:48.763594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

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