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Paper Citation Record · LEDGER

Anomaly detection with spiking neural networks for LHC physics

As of 18 August 2026, this Paper Citation Record lists 91 of 91 outbound references and 0 inbound Pith citation observations for arXiv:2508.00063.

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

pith.paper-citation-record.v1
2508.00063 v1

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measured 91 of 91 reference resolution

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91 of 91 outbound references displayed

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Outbound references

Observation 1ec5a79e-008b-490f-a80d-b12730f40d55 · outbound

This paper cites DESI 2024 VII: Cosmological Constraints from the Full-Shape Modeling of Clustering Measurements.

Anomaly detection with spiking neural networks for LHC physics DESI 2024 VII: Cosmological Constraints from the Full-Shape Modeling of Clustering Measurements

Reference 1

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Observation de26100e-6348-40d9-8949-2454c0239587 · outbound

This paper cites Besuner, A.

Anomaly detection with spiking neural networks for LHC physics Besuner, A

Reference 2

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Observation 857c369e-eea2-4457-bac7-22a24fe96ea0 · outbound

This paper cites Djorgovski and M.

Anomaly detection with spiking neural networks for LHC physics Djorgovski and M

Reference 3

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Observation 996efa04-22a9-4db8-8ea3-0e96f9e42974 · outbound

This paper cites Dressler, D.

Anomaly detection with spiking neural networks for LHC physics Dressler, D

Reference 4

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Observation 7e9748eb-0ce1-4401-9681-f651da7ac882 · outbound

This paper cites Tully and J.R.

Anomaly detection with spiking neural networks for LHC physics Tully and J.R

Reference 5

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Observation 65525e6b-648a-4bda-86df-83ec5f46253e · outbound

This paper cites Phillips,The absolute magnitudes of Type IA supernovae, ApJ413(1993) L105.

Anomaly detection with spiking neural networks for LHC physics Phillips,The absolute magnitudes of Type IA supernovae, ApJ413(1993) L105

Reference 6

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Observation ef1c6e27-cc9e-404c-9df3-c8beba604bf6 · outbound

This paper cites The Redshift-Space Momentum Power Spectrum I: Optimal Estimation From Peculiar Velocity Surveys.

Anomaly detection with spiking neural networks for LHC physics The Redshift-Space Momentum Power Spectrum I: Optimal Estimation From Peculiar Velocity Surveys

Reference 7

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Observation 827077bf-2876-4964-848a-9fcfa8297ead · outbound

This paper cites Cross-correlating radial peculiar velocities and CMB lensing convergence.

Anomaly detection with spiking neural networks for LHC physics Cross-correlating radial peculiar velocities and CMB lensing convergence

Reference 8

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Observation 86b42d25-6e97-4742-96d4-ae763ae5cbae · outbound

This paper cites Prospects for galaxy-mass relations from the 6dF Galaxy Survey.

Anomaly detection with spiking neural networks for LHC physics Prospects for galaxy-mass relations from the 6dF Galaxy Survey

Reference 9

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Observation 4832aeff-9408-4ee2-99c7-5e414f1c1143 · outbound

This paper cites Are peculiar velocity surveys competitive as a cosmological probe?.

Anomaly detection with spiking neural networks for LHC physics Are peculiar velocity surveys competitive as a cosmological probe?

Reference 10

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Observation 737c8ee6-680b-41bd-a778-827e987bd1eb · outbound

This paper cites Cosmological Forecasts for Combined and Next Generation Peculiar Velocity Surveys.

Anomaly detection with spiking neural networks for LHC physics Cosmological Forecasts for Combined and Next Generation Peculiar Velocity Surveys

Reference 11

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Observation c45f42dd-b0c7-4dd6-a666-80c1979f3c3f · outbound

This paper cites How to measure redshift-space distortions without sample variance.

Anomaly detection with spiking neural networks for LHC physics How to measure redshift-space distortions without sample variance

Reference 12

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Observation f66b7f89-5b27-44b3-a24b-7b90d2c10e01 · outbound

This paper cites The 6dF Galaxy Survey: Peculiar Velocity Field and Cosmography.

Anomaly detection with spiking neural networks for LHC physics The 6dF Galaxy Survey: Peculiar Velocity Field and Cosmography

Reference 13

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Observation 24519e26-5501-49c0-ac91-697cc3728dcb · outbound

This paper cites Cosmicflows-3.

Anomaly detection with spiking neural networks for LHC physics Cosmicflows-3

Reference 14

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Observation a7ec5d7e-5c67-4633-b68f-c41dcb13c35d · outbound

This paper cites 2MTF - VII. 2MASS Tully-Fisher survey final data release: distances for 2,062 nearby spiral galaxies.

Anomaly detection with spiking neural networks for LHC physics 2MTF - VII. 2MASS Tully-Fisher survey final data release: distances for 2,062 nearby spiral galaxies

Reference 15

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Observation 6a7cc4b9-3053-4618-a198-70f32db174ba · outbound

This paper cites Cosmic Flow Measurement and Mock Sampling Algorithm of Cosmicflows-4 Tully-Fisher Catalogue.

Anomaly detection with spiking neural networks for LHC physics Cosmic Flow Measurement and Mock Sampling Algorithm of Cosmicflows-4 Tully-Fisher Catalogue

Reference 16

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Observation f715351a-f372-4fa2-b1a5-6ec28d3ed560 · outbound

This paper cites The Sloan Digital Sky Survey Peculiar Velocity Catalogue.

Anomaly detection with spiking neural networks for LHC physics The Sloan Digital Sky Survey Peculiar Velocity Catalogue

Reference 17

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Observation 4d9fa51b-0b93-4872-987a-cb245eafce57 · outbound

This paper cites Cosmicflows-4.

Anomaly detection with spiking neural networks for LHC physics Cosmicflows-4

Reference 18

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Observation 5f1244ec-7984-4f4b-a7b1-8c4876bbfdf3 · outbound

This paper cites The 6dF Galaxy Velocity Survey: Cosmological constraints from the velocity power spectrum.

Anomaly detection with spiking neural networks for LHC physics The 6dF Galaxy Velocity Survey: Cosmological constraints from the velocity power spectrum

Reference 19

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Observation fa5e6448-f938-418e-acdd-8a4029bbffac · outbound

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Anomaly detection with spiking neural networks for LHC physics 2MTF VI. Measuring the velocity power spectrum

Reference 20

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Observation a7191483-83dc-44ad-a0dd-606c210fccf6 · outbound

This paper cites Adams and C.

Anomaly detection with spiking neural networks for LHC physics Adams and C

Reference 21

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This paper cites The Redshift Space Momentum Power Spectrum II: measuring the growth rate from the combined 2MTF and 6dFGSv surveys.

Anomaly detection with spiking neural networks for LHC physics The Redshift Space Momentum Power Spectrum II: measuring the growth rate from the combined 2MTF and 6dFGSv surveys

Reference 22

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Observation abd7157a-09ad-4d39-96f4-475220307341 · outbound

This paper cites Joint growth rate measurements from redshift-space distortions and peculiar velocities in the 6dF Galaxy Survey.

Anomaly detection with spiking neural networks for LHC physics Joint growth rate measurements from redshift-space distortions and peculiar velocities in the 6dF Galaxy Survey

Reference 23

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Observation a7b1ed54-db0f-4148-b27a-5cb1e0881a96 · outbound

This paper cites Using peculiar velocity surveys to constrain the growth rate of structure with the wide-angle effect.

Anomaly detection with spiking neural networks for LHC physics Using peculiar velocity surveys to constrain the growth rate of structure with the wide-angle effect

Reference 24

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Observation b218df0e-b2eb-418a-8f71-60f290cf40ff · outbound

This paper cites A local measurement of the growth rate from peculiar velocities and galaxy clustering correlations in the 6dF Galaxy Survey.

Anomaly detection with spiking neural networks for LHC physics A local measurement of the growth rate from peculiar velocities and galaxy clustering correlations in the 6dF Galaxy Survey

Reference 25

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Anomaly detection with spiking neural networks for LHC physics Reconstructing the cosmological density and velocity fields from redshifted galaxy distributions using V-net

Reference 26

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Observation 70649917-4933-4475-866b-f6f39cdf3da3 · outbound

This paper cites Constraining modified gravity scenarios with the 6dFGS and SDSS galaxy peculiar velocity datasets.

Anomaly detection with spiking neural networks for LHC physics Constraining modified gravity scenarios with the 6dFGS and SDSS galaxy peculiar velocity datasets

Reference 27

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This paper cites The Redshift-Space Momentum Power Spectrum III: measuring the growth rate from the SDSSv survey using auto- and cross- power spectrum of the galaxy density and momentum fields.

Anomaly detection with spiking neural networks for LHC physics The Redshift-Space Momentum Power Spectrum III: measuring the growth rate from the SDSSv survey using auto- and cross- power spectrum of the galaxy density and momentum fields

Reference 28

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Observation 471547ee-5745-4e37-9ac7-5862c26669a3 · outbound

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Anomaly detection with spiking neural networks for LHC physics Target Selection for the DESI Peculiar Velocity Survey

Reference 29

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Anomaly detection with spiking neural networks for LHC physics Taylor, M

Reference 30

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Observation 8bd85d3c-e037-42f4-a7b4-af4b0a1604bc · outbound

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Anomaly detection with spiking neural networks for LHC physics Growth-rate measurement with type-Ia supernovae using ZTF survey simulations

Reference 31

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Observation c2df623c-a7a1-4108-80ea-57c69f9ff2a7 · outbound

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Anomaly detection with spiking neural networks for LHC physics WALLABY -- An SKA Pathfinder HI Survey

Reference 32

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Observation 31dcb2df-ca62-4abe-972c-81778b4affc4 · outbound

This paper cites Measuring the growth rate of structure with Type IA Supernovae from LSST.

Anomaly detection with spiking neural networks for LHC physics Measuring the growth rate of structure with Type IA Supernovae from LSST

Reference 33

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Observation b420e723-6aa4-4247-9e5d-623271f0c716 · outbound

This paper cites Forecast for growth-rate measurement using peculiar velocities from LSST supernovae.

Anomaly detection with spiking neural networks for LHC physics Forecast for growth-rate measurement using peculiar velocities from LSST supernovae

Reference 34

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Observation e503b6e8-47bc-4fb4-a3e7-77651a529676 · outbound

This paper cites Cosmological Parameters from the BOSS Galaxy Power Spectrum.

Anomaly detection with spiking neural networks for LHC physics Cosmological Parameters from the BOSS Galaxy Power Spectrum

Reference 35

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This paper cites The Cosmological Analysis of the SDSS/BOSS data from the Effective Field Theory of Large-Scale Structure.

Anomaly detection with spiking neural networks for LHC physics The Cosmological Analysis of the SDSS/BOSS data from the Effective Field Theory of Large-Scale Structure

Reference 36

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Observation 6791a409-4d24-4318-a397-b94e8423b6a1 · outbound

This paper cites A new analysis of galaxy 2-point functions in the BOSS survey, including full-shape information and post-reconstruction BAO.

Anomaly detection with spiking neural networks for LHC physics A new analysis of galaxy 2-point functions in the BOSS survey, including full-shape information and post-reconstruction BAO

Reference 37

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Observation d4b34d05-416e-458a-9bd0-b88f88ba0a63 · outbound

This paper cites Clustering of dark matter tracers: generalizing bias for the coming era of precision LSS.

Anomaly detection with spiking neural networks for LHC physics Clustering of dark matter tracers: generalizing bias for the coming era of precision LSS

Reference 38

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source=pdf_text observed=2026-08-06T10:25:43.633042Z digest=sha256:13f0addf837ea272e9f3402c4b32ea1b4a8c82f09168590a6b75be3fa1b87f97

Observation 3fb3324d-1977-4c3b-b97c-7c89cf93493d · outbound

This paper cites The Effective Field Theory of Cosmological Large Scale Structures.

Anomaly detection with spiking neural networks for LHC physics The Effective Field Theory of Cosmological Large Scale Structures

Reference 39

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source=pdf_text observed=2026-08-06T10:25:43.637705Z digest=sha256:680bb3c449d7bedf52fd3d7785447fbf39daea1575fca6ef495a8cae58a94fc6

Observation 354b84cc-4ba3-4cc2-9d06-1b4c8cfee333 · outbound

This paper cites The Lagrangian-space Effective Field Theory of Large Scale Structures.

Anomaly detection with spiking neural networks for LHC physics The Lagrangian-space Effective Field Theory of Large Scale Structures

Reference 40

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source=pdf_text observed=2026-08-06T10:25:43.642239Z digest=sha256:b3290ebdccdab5f1413cee99b367f1e28bc152fa15c8a3c49baf0041c87507f7

Observation aa27a6bf-6a58-4d43-9038-561ee6cee479 · outbound

This paper cites A Lagrangian effective field theory.

Anomaly detection with spiking neural networks for LHC physics A Lagrangian effective field theory

Reference 41

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source=pdf_text observed=2026-08-06T10:25:43.646968Z digest=sha256:8c26b90f3b1292ec93c5d4666f435ee5a8287157da764991161746a8100bbbba

Observation 7f48ccc9-4ed6-4572-b3c8-391b46be6b2a · outbound

This paper cites Biased Tracers in Redshift Space in the EFT of Large-Scale Structure.

Anomaly detection with spiking neural networks for LHC physics Biased Tracers in Redshift Space in the EFT of Large-Scale Structure

Reference 42

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source=pdf_text observed=2026-08-06T10:25:43.651461Z digest=sha256:d49a3678f3bbf8e9f090058c37802b4a9e000e576c49b0b2d394098ef5c071df

Observation 71fa996d-34ff-403e-88b0-83ec8eda188b · outbound

This paper cites Large-Scale Galaxy Bias.

Anomaly detection with spiking neural networks for LHC physics Large-Scale Galaxy Bias

Reference 43

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source=pdf_text observed=2026-08-06T10:25:43.655971Z digest=sha256:8e97cec9f5850d59234f41d916f561f39960688e5dc0c4faf0edcd86f53b8c8f

Observation b534eac8-0a98-46af-890e-eefd456435e1 · outbound

This paper cites Consistent Modeling of Velocity Statistics and Redshift-Space Distortions in One-Loop Perturbation Theory.

Anomaly detection with spiking neural networks for LHC physics Consistent Modeling of Velocity Statistics and Redshift-Space Distortions in One-Loop Perturbation Theory

Reference 44

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source=pdf_text observed=2026-08-06T10:25:43.660563Z digest=sha256:353537493653dd78ac149f12d43ea29bdc9f45929a08dfe8df2746f8e9a3a7f5

Observation d8ffa5c4-d5f9-4475-ab9b-a8fe69f89aa1 · outbound

This paper cites Blinded challenge for precision cosmology with large-scale structure: results from effective field theory for the redshift-space galaxy power spectrum.

Anomaly detection with spiking neural networks for LHC physics Blinded challenge for precision cosmology with large-scale structure: results from effective field theory for the redshift-space galaxy power spectrum

Reference 45

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source=pdf_text observed=2026-08-06T10:25:43.665140Z digest=sha256:327192a1d85f6bb50457e20ed1c277e2c18a371386eca7b75553835f4175c72e

Observation 2a672243-fd02-4488-8da7-0197f07cc270 · outbound

This paper cites Cosmology with the Galaxy Bispectrum Multipoles: Optimal Estimation and Application to BOSS Data.

Anomaly detection with spiking neural networks for LHC physics Cosmology with the Galaxy Bispectrum Multipoles: Optimal Estimation and Application to BOSS Data

Reference 46

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source=pdf_text observed=2026-08-06T10:25:43.669966Z digest=sha256:43497eb0917c1bc79f5281caa40e0ef03b40df2cfb0afddaa7cdd136f3276c85

Observation db7ec2ff-c74f-4a5d-9b62-37cdd696679e · outbound

This paper cites The BOSS bispectrum analysis at one loop from the Effective Field Theory of Large-Scale Structure.

Anomaly detection with spiking neural networks for LHC physics The BOSS bispectrum analysis at one loop from the Effective Field Theory of Large-Scale Structure

Reference 47

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source=pdf_text observed=2026-08-06T10:25:43.674834Z digest=sha256:f3b002a1f0868308f7d8e74194ce7436174218e5cddda7b442c3523ceee8e1bb

Observation e035bfc9-d7f8-4ca9-b21b-07012495c377 · outbound

This paper cites A comparison of effective field theory models of redshift space galaxy power spectra for DESI 2024 and future surveys.

Anomaly detection with spiking neural networks for LHC physics A comparison of effective field theory models of redshift space galaxy power spectra for DESI 2024 and future surveys

Reference 48

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source=pdf_text observed=2026-08-06T10:25:43.679566Z digest=sha256:39c4f11064120e0015057348726266043cfce61a6d99a6bf08a88fc40f4bcfee

Observation 6d873f9a-ea27-4dd4-bf88-7163f8da2989 · outbound

This paper cites Peculiar velocities in redshift space: formalism, N-body simulations and perturbation theory.

Anomaly detection with spiking neural networks for LHC physics Peculiar velocities in redshift space: formalism, N-body simulations and perturbation theory

Reference 49

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verified exact
local_arxiv, observed 2026-08-06T10:25:44.649364Z

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source=pdf_text observed=2026-08-06T10:25:43.684131Z digest=sha256:1ad9d2ace7754db59c8fad61e8c6692a200291082a2d2c4ea3f00c85d9930090

Observation 9f39fb90-04cb-411a-b43c-9274cbd55cb7 · outbound

This paper cites Understanding redshift space distortions in density-weighted peculiar velocity.

Anomaly detection with spiking neural networks for LHC physics Understanding redshift space distortions in density-weighted peculiar velocity

Reference 50

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verified exact
local_arxiv, observed 2026-08-06T10:25:44.628510Z

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

source=pdf_text observed=2026-08-06T10:25:43.689177Z digest=sha256:5da566508aa440aceed0b05a9eb85d8a474728438de15006766c02406445a1b3

Observation 877cbd2c-3769-4cff-ba24-687f2c5ffff8 · outbound

This paper cites Exploring the Redshift-Space Peculiar Velocity Field and its Power Spectrum.

Anomaly detection with spiking neural networks for LHC physics Exploring the Redshift-Space Peculiar Velocity Field and its Power Spectrum

Reference 51

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source=pdf_text observed=2026-08-06T10:25:43.693783Z digest=sha256:8bded022bb4b1268769e83c0fe71094eb138def6faedef658741aa6c445962fd

Observation 45cbd872-aca5-47d3-9b5b-79213b64775c · outbound

This paper cites Equivalence Principle and the Baryon Acoustic Peak.

Anomaly detection with spiking neural networks for LHC physics Equivalence Principle and the Baryon Acoustic Peak

Reference 52

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source=pdf_text observed=2026-08-06T10:25:43.698484Z digest=sha256:3b0746951f0f15c83c18e58cd647a3b18dc2f653c7673a27c5b0739758bc14cd

Observation e0c1d9ba-6723-477e-ac58-aecbf42c1e55 · outbound

This paper cites The IR-resummed Effective Field Theory of Large Scale Structures.

Anomaly detection with spiking neural networks for LHC physics The IR-resummed Effective Field Theory of Large Scale Structures

Reference 53

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source=pdf_text observed=2026-08-06T10:25:43.703026Z digest=sha256:c660d68f9046c844a3c4b3ee1db750b3a03372bf0b51cc77843f08b58eb932fb

Observation b309671c-3fcb-4699-93ae-9d16e29ee3dc · outbound

This paper cites Perturbation theory, effective field theory, and oscillations in the power spectrum.

Anomaly detection with spiking neural networks for LHC physics Perturbation theory, effective field theory, and oscillations in the power spectrum

Reference 54

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source=pdf_text observed=2026-08-06T10:25:43.707662Z digest=sha256:71f1006e177391a677efc5b2cc1d94ce92ddeb9c8efbc4c653e114c988bb05ab

Observation 805e5f7a-23a2-47df-9672-c24b01bd619a · outbound

This paper cites Time-Sliced Perturbation Theory II: Baryon Acoustic Oscillations and Infrared Resummation.

Anomaly detection with spiking neural networks for LHC physics Time-Sliced Perturbation Theory II: Baryon Acoustic Oscillations and Infrared Resummation

Reference 55

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source=pdf_text observed=2026-08-06T10:25:43.712920Z digest=sha256:2b32d950641930e6ef495fc10004b29e77594af4e52700c9ed6480f2ae7b25c6

Observation 9c3e3b29-cd3d-4f4a-a09e-1125b1159478 · outbound

This paper cites The Bispectrum in Lagrangian Perturbation Theory.

Anomaly detection with spiking neural networks for LHC physics The Bispectrum in Lagrangian Perturbation Theory

Reference 56

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source=pdf_text observed=2026-08-06T10:25:43.718025Z digest=sha256:5eb13f18b7f735ff8b094d6e64ff82644e205b8b17fe54afe94447e469004fe9

Observation 6706a481-cd55-4aab-ab5f-f2702d4c5d11 · outbound

This paper cites Redshift-Space Distortions, Pairwise Velocities and Nonlinearities.

Anomaly detection with spiking neural networks for LHC physics Redshift-Space Distortions, Pairwise Velocities and Nonlinearities

Reference 57

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source=pdf_text observed=2026-08-06T10:25:43.722595Z digest=sha256:cf5483f704be3e72cf96ac27c8f31d535c89c452df962013bd7fb327a7c4d15a

Observation 564d507f-4301-4261-9a79-4944301be45e · outbound

This paper cites Exploring redshift-space distortions in large-scale structure.

Anomaly detection with spiking neural networks for LHC physics Exploring redshift-space distortions in large-scale structure

Reference 58

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source=pdf_text observed=2026-08-06T10:25:43.727009Z digest=sha256:c9174ae12d101ef7f222bff2ade5e3cf4b09aec940c2269134f817ceffd1c6b2

Observation b7551601-7633-4c25-ac05-fac9c0a81369 · outbound

This paper cites Wide angle effects for peculiar velocities.

Anomaly detection with spiking neural networks for LHC physics Wide angle effects for peculiar velocities

Reference 59

Resolution
verified exact
local_arxiv, observed 2026-08-06T10:25:44.489479Z

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.

source=pdf_text observed=2026-08-06T10:25:43.731347Z digest=sha256:6e9309f15621a5fc31885fcf0451b96cdcc2e17388101140b568a560b96a2724

Observation cf49f37a-5309-4a34-ac5c-050ae14f0d78 · outbound

This paper cites The power spectrum of luminosity distance fluctuations in General Relativity.

Anomaly detection with spiking neural networks for LHC physics The power spectrum of luminosity distance fluctuations in General Relativity

Reference 60

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source=pdf_text observed=2026-08-06T10:25:43.735728Z digest=sha256:9c48e8ea0be52f4777285f3ea321ac575c9bfd9dd87ac86000cfd24ecd55f555

Observation 52b2ef86-2729-408d-bd6c-9ac222a89683 · outbound

This paper cites Gorski, M.

Anomaly detection with spiking neural networks for LHC physics Gorski, M

Reference 61

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source=pdf_text observed=2026-08-06T10:25:43.740333Z digest=sha256:702f172a9c1b782d4e55fa272f0630eeebf26cfeb2320e630e3d9b1df6b3d22d

Observation 8c305743-eaec-400a-b54a-1de3721accd2 · outbound

This paper cites Groth, R.

Anomaly detection with spiking neural networks for LHC physics Groth, R

Reference 62

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source=pdf_text observed=2026-08-06T10:25:43.744962Z digest=sha256:22a4a78181df8268b4fc350ffec8e400b4b67f3e556d8d4f77000e2d20169d3e

Observation 4bd93246-2dfe-4983-a819-bd9de24656e7 · outbound

This paper cites Cosmic Momentum Field and Mass Fluctuation Power Spectrum.

Anomaly detection with spiking neural networks for LHC physics Cosmic Momentum Field and Mass Fluctuation Power Spectrum

Reference 63

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verified exact
local_arxiv, observed 2026-08-06T10:25:44.452563Z

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

source=pdf_text observed=2026-08-06T10:25:43.749532Z digest=sha256:af82dc86d23a02333db91af367f74609d9c880ce132e776831bb50f26cc1d89d

Observation 222141d9-a88b-429e-b912-228f0ca14132 · outbound

This paper cites Streaming velocities as a dynamical estimator of Omega.

Anomaly detection with spiking neural networks for LHC physics Streaming velocities as a dynamical estimator of Omega

Reference 64

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source=pdf_text observed=2026-08-06T10:25:43.754216Z digest=sha256:3b2a12cb6d31953c27e685270e27eb7dd2e71e524fe43ece19bef26ec49b2793

Observation 8148997b-8d95-44df-b69a-f8603c52784c · outbound

This paper cites Juszkiewicz and A.

Anomaly detection with spiking neural networks for LHC physics Juszkiewicz and A

Reference 65

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source=pdf_text observed=2026-08-06T10:25:43.759073Z digest=sha256:0b478afe662ee929841554adfc0272a9aed5466bc1d175ce9382a3596ec96a5f

Observation 3c71b5c7-1ef5-4c06-abf0-778c7a8af4c7 · outbound

This paper cites Distribution function approach to redshift space distortions.

Anomaly detection with spiking neural networks for LHC physics Distribution function approach to redshift space distortions

Reference 66

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source=pdf_text observed=2026-08-06T10:25:43.763346Z digest=sha256:3650cc23db6180e68a70310ec7a507dc1489ffdb6082af64d93f78288b3898ce

Observation 6b88fbd0-74a8-4807-a328-34f82c6dc7e6 · outbound

This paper cites Resumming Cosmological Perturbations via the Lagrangian Picture: One-loop Results in Real Space and in Redshift Space.

Anomaly detection with spiking neural networks for LHC physics Resumming Cosmological Perturbations via the Lagrangian Picture: One-loop Results in Real Space and in Redshift Space

Reference 67

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source=pdf_text observed=2026-08-06T10:25:43.768141Z digest=sha256:ad55d15f4bececde8d00f1c838567f3243abf094fdc410c84833c2866b700b1e

Observation ff2cdd5a-5d65-4973-8efe-af92fd66b517 · outbound

This paper cites Convolution Lagrangian perturbation theory for biased tracers.

Anomaly detection with spiking neural networks for LHC physics Convolution Lagrangian perturbation theory for biased tracers

Reference 68

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source=pdf_text observed=2026-08-06T10:25:43.772830Z digest=sha256:c51f9a305177b737216d6703732cd7d0ae9f8a1b48b778662c9836b1d44afcc1

Observation 14976b9f-4f54-4504-af7d-21b158a3f3f2 · outbound

This paper cites Lagrangian perturbation theory at one loop order: successes, failures, and improvements.

Anomaly detection with spiking neural networks for LHC physics Lagrangian perturbation theory at one loop order: successes, failures, and improvements

Reference 69

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source=pdf_text observed=2026-08-06T10:25:43.777364Z digest=sha256:3391047786fa19720a5e38cf5589b4564971a6a71a160827809f893215e147dd

Observation f7533b1f-66b5-43a2-b3ab-e316e404f80e · outbound

This paper cites Nonlinear perturbation theory with halo bias and redshift-space distortions via the Lagrangian picture.

Anomaly detection with spiking neural networks for LHC physics Nonlinear perturbation theory with halo bias and redshift-space distortions via the Lagrangian picture

Reference 70

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source=pdf_text observed=2026-08-06T10:25:43.782200Z digest=sha256:33d34b034b575a18a0d7dee0f33a0b87ad74cde1bf96e00fdd387043cee1e343

Observation e08d705d-f8b6-4f0d-91ba-77d4fdcdccce · outbound

This paper cites The Gaussian streaming model and Lagrangian effective field theory.

Anomaly detection with spiking neural networks for LHC physics The Gaussian streaming model and Lagrangian effective field theory

Reference 71

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source=pdf_text observed=2026-08-06T10:25:43.786723Z digest=sha256:93cf3ad252d2f18fea2fb227f6457ffafe86c7b34c2c8925316c320d93178cbc

Observation 31111d65-c17b-43b6-8bc2-4d87e4f996e9 · outbound

This paper cites The Reconstructed Power Spectrum in the Zeldovich Approximation.

Anomaly detection with spiking neural networks for LHC physics The Reconstructed Power Spectrum in the Zeldovich Approximation

Reference 72

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source=pdf_text observed=2026-08-06T10:25:43.791905Z digest=sha256:e4ac6dfa26d3f0ee3a5979dce916924e24db04459207fcde78909f40d60fc164

Observation d6a61445-6bc9-4f37-bc70-c121258d2cc2 · outbound

This paper cites Redshift-Space Distortions in Lagrangian Perturbation Theory.

Anomaly detection with spiking neural networks for LHC physics Redshift-Space Distortions in Lagrangian Perturbation Theory

Reference 73

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source=pdf_text observed=2026-08-06T10:25:43.796852Z digest=sha256:7b890d0d2506f35d314cdbeafc93ed96391fcfa19d28ec49c12c567961eb163a

Observation 70bdca7a-f967-410d-b81d-7ce71efc5512 · outbound

This paper cites Non-linear perturbation theory extension of the Boltzmann code CLASS.

Anomaly detection with spiking neural networks for LHC physics Non-linear perturbation theory extension of the Boltzmann code CLASS

Reference 74

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source=pdf_text observed=2026-08-06T10:25:43.801463Z digest=sha256:c1ee655bd8cd2bfb7e18bea3190bd991971450b94e1f028743e0a573f19acfe0

Observation 4d00bd31-004e-46e9-917a-149a96387672 · outbound

This paper cites Limits on $w$CDM from the EFTofLSS with the PyBird code.

Anomaly detection with spiking neural networks for LHC physics Limits on $w$CDM from the EFTofLSS with the PyBird code

Reference 75

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source=pdf_text observed=2026-08-06T10:25:43.806628Z digest=sha256:4b17df9749f80a4f3d5b3730ac3d794cf0015bbca46a461b6c1ada57c8bc5abd

Observation b246de1d-6304-48b8-8d4f-68299e117da1 · outbound

This paper cites Very Massive Tracers and Higher Derivative Biases.

Anomaly detection with spiking neural networks for LHC physics Very Massive Tracers and Higher Derivative Biases

Reference 76

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source=pdf_text observed=2026-08-06T10:25:43.811622Z digest=sha256:f8726be88868c1450402a5b17663d8c80d85775717e750c9399dcefaf9273b04

Observation cb120ce6-b05b-4825-8f72-fdaec4eb3e22 · outbound

This paper cites Cosmology before noon with multiple galaxy populations.

Anomaly detection with spiking neural networks for LHC physics Cosmology before noon with multiple galaxy populations

Reference 77

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Observation ecd1ceee-f3d4-4cc7-9b32-3bbfd05b3bbf · outbound

This paper cites An analysis of parameter compression and full-modeling techniques with Velocileptors for DESI 2024 and beyond.

Anomaly detection with spiking neural networks for LHC physics An analysis of parameter compression and full-modeling techniques with Velocileptors for DESI 2024 and beyond

Reference 78

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Observation f398dfcc-479a-4bb4-b7fb-47b251143e02 · outbound

This paper cites Infrared Resummation for Biased Tracers in Redshift Space.

Anomaly detection with spiking neural networks for LHC physics Infrared Resummation for Biased Tracers in Redshift Space

Reference 79

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Observation da5c0fd2-ad51-4ed0-a6e6-d0b63fa65cc5 · outbound

This paper cites Precision Redshift-Space Galaxy Power Spectra using Zel'dovich Control Variates.

Anomaly detection with spiking neural networks for LHC physics Precision Redshift-Space Galaxy Power Spectra using Zel'dovich Control Variates

Reference 80

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Observation ceb2417b-7162-4be8-bd4c-5be882572de3 · outbound

This paper cites Garrison, D.J.

Anomaly detection with spiking neural networks for LHC physics Garrison, D.J

Reference 81

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Observation 9f92ade0-21aa-4f96-86ca-fd80a33cdf33 · outbound

This paper cites Maksimova, L.H.

Anomaly detection with spiking neural networks for LHC physics Maksimova, L.H

Reference 82

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Observation 18d1bb5b-986c-4b4f-a092-cd437d2a41fd · outbound

This paper cites \textsc{CompaSO}: A new halo finder for competitive assignment to spherical overdensities.

Anomaly detection with spiking neural networks for LHC physics \textsc{CompaSO}: A new halo finder for competitive assignment to spherical overdensities

Reference 83

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Observation 8f5ac8d6-f918-4d6c-ba98-6323a59e3e68 · outbound

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

Anomaly detection with spiking neural networks for LHC physics Planck 2018 results. VI. Cosmological parameters

Reference 84

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Observation dae021e9-bdd7-4717-8938-4bdbc4ac08cc · outbound

This paper cites nbodykit: an open-source, massively parallel toolkit for large-scale structure.

Anomaly detection with spiking neural networks for LHC physics nbodykit: an open-source, massively parallel toolkit for large-scale structure

Reference 85

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Observation 9d26ba4a-232f-45c6-ae1b-66d9d7b0dd1e · outbound

This paper cites Will Kinematic Sunyaev-Zel'dovich Measurements Enhance the Science Return from Galaxy Redshift Surveys?.

Anomaly detection with spiking neural networks for LHC physics Will Kinematic Sunyaev-Zel'dovich Measurements Enhance the Science Return from Galaxy Redshift Surveys?

Reference 86

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Observation f687f317-0654-4b96-aab1-429d039bb7b6 · outbound

This paper cites Ravoux, B.

Anomaly detection with spiking neural networks for LHC physics Ravoux, B

Reference 87

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T10:25:43.863502Z digest=sha256:3d1652ff8bcb21b13735e3f4b1702bde780d90abb456e196fe55244f18dfdf5b

Observation 6a9af176-d925-4019-83fa-9a54459bd2c0 · outbound

This paper cites KSZ tomography and the bispectrum.

Anomaly detection with spiking neural networks for LHC physics KSZ tomography and the bispectrum

Reference 88

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Observation 09f316ef-82d5-4924-ad66-33c182ae3c99 · outbound

This paper cites Uncorrelated Modes of the Nonlinear Power Spectrum.

Anomaly detection with spiking neural networks for LHC physics Uncorrelated Modes of the Nonlinear Power Spectrum

Reference 89

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Observation 65713b23-2ba8-4352-a243-a17e94e6b669 · outbound

This paper cites Cobaya: Code for Bayesian Analysis of hierarchical physical models.

Anomaly detection with spiking neural networks for LHC physics Cobaya: Code for Bayesian Analysis of hierarchical physical models

Reference 90

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Observation 5c749050-dd1c-4659-b6f2-4c144ba8892d · outbound

This paper cites Analytic Methods for Cosmological Likelihoods.

Anomaly detection with spiking neural networks for LHC physics Analytic Methods for Cosmological Likelihoods

Reference 91

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