Paper Citation Record · LEDGER

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum

As of 7 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2606.13527.

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

pith.paper-citation-record.v1
2606.13527 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T10:27:10.986171Z

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Reference resolution

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External citation measurements

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Outbound references

Observation 0bfbd8b5-5da1-4939-8f4d-3869c3ff8e47 · outbound

This paper cites TASI Lectures on Indirect Searches For Dark Matter.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum TASI Lectures on Indirect Searches For Dark Matter

Reference 1

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Observation cd8f5d8d-f7fe-4e27-8fa7-077d1a993e4b · outbound

This paper cites TASI lectures on dark matter models and direct detection.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum TASI lectures on dark matter models and direct detection

Reference 2

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Observation 358b2f2d-aa85-4c13-b404-710306a8306c · outbound

This paper cites Les Houches Lectures on Indirect Detection of Dark Matter.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Les Houches Lectures on Indirect Detection of Dark Matter

Reference 3

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Observation 9059fb82-da63-4b1a-8134-3b7c55810273 · outbound

This paper cites Report of the Topical Group on Particle Dark Matter for Snowmass 2021.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Report of the Topical Group on Particle Dark Matter for Snowmass 2021

Reference 4

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Observation c61cb450-88b7-497d-85a6-963791594bfa · outbound

This paper cites Dark Matter.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Dark Matter

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-07-03T16:28:38.615828Z

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Observation d3e03541-827d-4424-b89e-72f79c6493cd · outbound

This paper cites Dark Matter Candidates and Searches.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Dark Matter Candidates and Searches

Reference 6

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Observation c04b1db8-95e9-4329-b9e0-b6f9e87cb45f · outbound

This paper cites 2024 TASI Lectures: A Dark Matter Primer.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum 2024 TASI Lectures: A Dark Matter Primer

Reference 7

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Observation 0033b5fa-75c0-48ba-9bf4-b2f9ac9c8fa7 · outbound

This paper cites The First Three Seconds: a Review of Possible Expansion Histories of the Early Universe.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum The First Three Seconds: a Review of Possible Expansion Histories of the Early Universe

Reference 8

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Observation 8ee25484-f8d5-4945-a37d-6d56a2a2f4ec · outbound

This paper cites Snowmass2021 Cosmic Frontier White Paper: Dark Matter Physics from Halo Measurements.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Snowmass2021 Cosmic Frontier White Paper: Dark Matter Physics from Halo Measurements

Reference 9

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Observation 943c9e7b-b781-4a2a-9a67-a22d9865d700 · outbound

This paper cites Conversations and Deliberations: Non-Standard Cosmological Epochs and Expansion Histories.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Conversations and Deliberations: Non-Standard Cosmological Epochs and Expansion Histories

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-03T16:28:38.611055Z

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Observation 6e997c7d-b0fd-4cbd-b4a8-69df9708badc · outbound

This paper cites Deciphering the Archaeological Record: Cosmological Imprints of Non-Minimal Dark Sectors.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Deciphering the Archaeological Record: Cosmological Imprints of Non-Minimal Dark Sectors

Reference 11

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Observation d926d959-69a5-42f2-8efb-d7b94e8b089f · outbound

This paper cites Extracting Dark-Matter Velocities from Halo Masses: A Reconstruction Conjecture.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Extracting Dark-Matter Velocities from Halo Masses: A Reconstruction Conjecture

Reference 12

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Observation 174d30ab-328e-4a8b-a563-686d699934b2 · outbound

This paper cites Cowan, Conf.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Cowan, Conf

Reference 13

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Observation 471e81f0-80f6-4838-aedf-8ff3353131da · outbound

This paper cites Blobel, inPHYSTAT 2011(CERN, Geneva, 2011) pp.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Blobel, inPHYSTAT 2011(CERN, Geneva, 2011) pp

Reference 14

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Observation c6207dc1-7ac5-46c6-9bc8-c970fb61e21a · outbound

This paper cites Comparison of unfolding methods using RooFitUnfold.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Comparison of unfolding methods using RooFitUnfold

Reference 15

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Observation 2c06e8ae-dd20-4f5a-b4cb-3afd7ee6c9ac · outbound

This paper cites Canelli et al., (2025), arXiv:2507.09582 [hep-ph].

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Canelli et al., (2025), arXiv:2507.09582 [hep-ph]

Reference 16

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Observation 1e5bbbb2-3b5b-47f4-a2ec-d0210c6dbf7a · outbound

This paper cites The Cosmic Linear Anisotropy Solving System (CLASS) I: Overview.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum The Cosmic Linear Anisotropy Solving System (CLASS) I: Overview

Reference 17

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Observation 8bdc9ab1-f641-4f88-bbf8-dc65e6bbcafb · outbound

This paper cites The Cosmic Linear Anisotropy Solving System (CLASS) II: Approximation schemes.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum The Cosmic Linear Anisotropy Solving System (CLASS) II: Approximation schemes

Reference 18

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Observation 6e2c552f-6730-4c46-be48-47e8785da786 · outbound

This paper cites The Cosmic Linear Anisotropy Solving System (CLASS) III: Comparision with CAMB for LambdaCDM.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum The Cosmic Linear Anisotropy Solving System (CLASS) III: Comparision with CAMB for LambdaCDM

Reference 19

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Observation 377abb06-7eba-4462-89b7-f1dd6b5df3ed · outbound

This paper cites The Cosmic Linear Anisotropy Solving System (CLASS) IV: Efficient implementation of non-cold relics.

Reference 20

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Observation f940b6d6-f62a-4bad-9a0b-716eff8a29d4 · outbound

This paper cites Putney, D.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Putney, D

Reference 21

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Observation bc790b65-230d-4ec1-9897-a9c0bd82d385 · outbound

This paper cites Deep Potential: Recovering the gravitational potential and local pattern speed in the solar neighborhood with GDR3 using normalizing flows.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Deep Potential: Recovering the gravitational potential and local pattern speed in the solar neighborhood with GDR3 using normalizing flows

Reference 22

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Observation 79f44e3e-94ba-49df-99ec-15418a4c941c · outbound

This paper cites ClearPotential: Revealing Lo- cal Dark Matter in Three Dimensions,.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum ClearPotential: Revealing Lo- cal Dark Matter in Three Dimensions,

Reference 23

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Observation 55534498-6212-4cde-9f2a-ad4900a45395 · outbound

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Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Unresolved cited work

Reference 24

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Observation 7a7a141b-32a0-4c6f-aa6e-f4b00a7b1f14 · outbound

This paper cites Halo Formation in Warm Dark Matter Models.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Halo Formation in Warm Dark Matter Models

Reference 25

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Observation 5a84ff08-a4dc-4f5d-9649-408735b29163 · outbound

This paper cites Constraining Warm Dark Matter candidates including sterile neutrinos and light gravitinos with WMAP and the Lyman-alpha forest.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Constraining Warm Dark Matter candidates including sterile neutrinos and light gravitinos with WMAP and the Lyman-alpha forest

Reference 26

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Observation 56ccb2a7-bead-4962-9721-5b2a32ad5e90 · outbound

This paper cites Lyman-alpha constraints on warm and on warm-plus-cold dark matter models.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Lyman-alpha constraints on warm and on warm-plus-cold dark matter models

Reference 27

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Observation 5c4514b2-4cb8-4e38-bfbe-43615569be7b · outbound

This paper cites ETHOS - An Effective Theory of Structure Formation: From dark particle physics to the matter distribution of the Universe.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum ETHOS - An Effective Theory of Structure Formation: From dark particle physics to the matter distribution of the Universe

Reference 28

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Observation 37a0d78b-df92-4478-9406-1a0f72c58b4a · outbound

This paper cites ETHOS - An Effective Theory of Structure Formation: Dark matter physics as a possible explanation of the small-scale CDM problems.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum ETHOS - An Effective Theory of Structure Formation: Dark matter physics as a possible explanation of the small-scale CDM problems

Reference 29

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Observation 25e96a6d-016e-44a3-b14b-c38e78d5dbfe · outbound

This paper cites keV Sterile Neutrino Dark Matter from Singlet Scalar Decays: The Most General Case.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum keV Sterile Neutrino Dark Matter from Singlet Scalar Decays: The Most General Case

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-07-03T16:28:38.661867Z

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Observation c51adc8d-97bf-4b31-a56e-539e774ef446 · outbound

This paper cites "Non-cold" dark matter at small scales: a general approach.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum "Non-cold" dark matter at small scales: a general approach

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-07-03T16:28:38.639913Z

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Observation 8c365880-e901-4a2d-8efd-ad2a68c7d3f8 · outbound

This paper cites New Constraints on the free-streaming of warm dark matter from intermediate and small scale Lyman-$\alpha$ forest data.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum New Constraints on the free-streaming of warm dark matter from intermediate and small scale Lyman-$\alpha$ forest data

Reference 32

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Observation 2cad1388-8033-46bd-a67c-0b64f0125dd3 · outbound

This paper cites Novel constraints on non-cold (non-thermal) Dark Matter from Lyman-$\alpha$ forest data.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Novel constraints on non-cold (non-thermal) Dark Matter from Lyman-$\alpha$ forest data

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-07-03T16:28:38.605607Z

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Observation df7c82f4-5541-421d-82f6-daa4e0866f8e · outbound

This paper cites Constraining FIMP from the structure formation of the Universe: analytic mapping from $m_{\mathrm{WDM}}$.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Constraining FIMP from the structure formation of the Universe: analytic mapping from $m_{\mathrm{WDM}}$

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-03T16:28:38.579899Z

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Observation 37b47750-f981-4402-b8c7-aad0e16d7c49 · outbound

This paper cites Lower Mass Bounds on FIMP Dark Matter Produced via Freeze-In.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Lower Mass Bounds on FIMP Dark Matter Produced via Freeze-In

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-03T16:28:38.652457Z

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Observation d427222c-a436-4c39-b11c-eddaa6f75059 · outbound

This paper cites Evaluating Lyman-$\alpha$ Constraints for General Dark-Matter Velocity Distributions: Multiple Scales and Cautionary Tales.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Evaluating Lyman-$\alpha$ Constraints for General Dark-Matter Velocity Distributions: Multiple Scales and Cautionary Tales

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-07-03T16:28:38.654838Z

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Observation 7a0ab493-2aa6-4448-aeba-f4836514f9d7 · outbound

This paper cites Distinguishing thermal histories of dark matter from structure formation.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Distinguishing thermal histories of dark matter from structure formation

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-03T16:28:38.628307Z

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Observation daa4c53a-3459-4ca3-a7bd-193a6f6ebb94 · outbound

This paper cites D’Eramo, A.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum D’Eramo, A

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-03T16:28:38.634928Z

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Observation 43b4111e-b6eb-4bd1-a0ff-9f339f64a5f5 · outbound

This paper cites Seasons of Dark Matter Freeze-In Shaped by the Weather of the Early Universe.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Seasons of Dark Matter Freeze-In Shaped by the Weather of the Early Universe

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-07-03T16:28:38.602659Z

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Observation 41ae42ee-b74f-4c61-a36e-95ba26e64b50 · outbound

This paper cites Lyman-$\alpha$ Forest Constraint on Dark Matter from Dark Sector Decay.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Lyman-$\alpha$ Forest Constraint on Dark Matter from Dark Sector Decay

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-07-20T01:18:29.771816Z

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Observation ac670ae0-93b5-4876-a39e-0dd5b5c5fa7f · outbound

This paper cites Planck 2018 results. I. Overview and the cosmological legacy of Planck.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Planck 2018 results. I. Overview and the cosmological legacy of Planck

Reference 41

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Observation b851f7eb-b3cf-4509-ba46-f1a5d04cb2f6 · outbound

This paper cites Probing the Small-Scale Matter Power Spectrum with Large-Scale 21-cm Data.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Probing the Small-Scale Matter Power Spectrum with Large-Scale 21-cm Data

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-07-03T16:28:38.651298Z

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Observation 25ac6bce-9a6a-47c5-b2c9-2017a47bd5bc · outbound

This paper cites Decoupled Weight Decay Regularization.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Decoupled Weight Decay Regularization

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-07-03T16:28:38.653674Z

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Observation 956a9466-d29e-411f-88dc-fd9b76d906e6 · outbound

This paper cites Akiba, S.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Akiba, S

Reference 44

Resolution: unresolved; no resolver link, observed 2026-06-27T05:48:47.779139Z

Source-reported events for the cited work

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Observation e11ca828-fa94-44dc-abbb-28faf117d884 · outbound

This paper cites Planck 2018 results. VI. Cosmological parameters.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Planck 2018 results. VI. Cosmological parameters

Reference 45

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T16:28:38.600259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation da2a6620-ec2b-435c-90aa-32db7b4d73f6 · outbound

This paper cites Revisiting Dark Matter Freeze-in and Freeze-out through Phase-Space Distribution.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Revisiting Dark Matter Freeze-in and Freeze-out through Phase-Space Distribution

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-07-03T16:28:38.599810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 910e5324-ebd5-4d05-9ebe-f10819bf9fc1 · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum KAN: Kolmogorov-Arnold Networks

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-07-03T16:28:38.659539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 85835b01-7bab-48c4-9b47-75afc215df2b · outbound

This paper cites an unresolved cited work.

Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum Unresolved cited work

Reference 48

Resolution: unresolved; no resolver link, observed 2026-06-27T05:48:47.779139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation aaf21d6c-0c73-440f-beb4-960fb570b819 · inbound

KineticXGPU: A Tensorized Collision Operator for Dark-Sector Self-Scattering cites this paper.

KineticXGPU: A Tensorized Collision Operator for Dark-Sector Self-Scattering Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-07-02T10:36:51.990747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.