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Paper Citation Record · LEDGER

NAUTILUS: boosting Bayesian importance nested sampling with deep learning

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2306.16923.

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pith.paper-citation-record.v1
2306.16923 v1

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Pith citing papers itemized under the disclosed page cap.

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Pith citing papers

Observation 26472c1d-c8f5-42b0-86d0-9334fd88b612 · inbound

Effect of Peak Absolute Magnitude of Type Ia Supernovae and Sound Horizon Values on Hubble Tension using DESI results cites this paper.

Effect of Peak Absolute Magnitude of Type Ia Supernovae and Sound Horizon Values on Hubble Tension using DESI results NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 103

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Attention-based Neural Network Emulators for Multi-Probe Data Vectors Part III: Modeling The Next Generation Surveys cites this paper.

Attention-based Neural Network Emulators for Multi-Probe Data Vectors Part III: Modeling The Next Generation Surveys NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 13

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Constraints on Dark Energy Models Using Late Universe Probes cites this paper.

Constraints on Dark Energy Models Using Late Universe Probes NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 174

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Progress toward the detection of the gravitational-wave background from stellar-mass binary black holes: a mock data challenge cites this paper.

Progress toward the detection of the gravitational-wave background from stellar-mass binary black holes: a mock data challenge NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 69

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Sampler-free gravitational wave inference using matrix multiplication cites this paper.

Sampler-free gravitational wave inference using matrix multiplication NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 78

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Constraints on Logarithmic Model Extensions of Symmetric Teleparallel Gravity cites this paper.

Constraints on Logarithmic Model Extensions of Symmetric Teleparallel Gravity NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 97

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Observation 2bd9e3f5-e906-47ae-9ae1-42fcb9285386 · inbound

Dark Energy Survey Year 6 Results: Redshift Calibration of the MagLim++ Lens Sample cites this paper.

Dark Energy Survey Year 6 Results: Redshift Calibration of the MagLim++ Lens Sample NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 35

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Observation a4afd1ca-b235-4aa8-8fed-659d2aab8ff4 · inbound

Dynamical Dark Energy or Modified Gravity? Signatures in Gravitational Wave Propagation cites this paper.

Dynamical Dark Energy or Modified Gravity? Signatures in Gravitational Wave Propagation NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 76

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Observation 62f80e53-abe3-44cd-884a-dd7914b66552 · inbound

The simple way to measure evolving dark energy without prior-volume effects cites this paper.

The simple way to measure evolving dark energy without prior-volume effects NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 36

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Fingerprints of Individual Supermassive Black Hole Binaries in Pulsar Timing Arrays cites this paper.

Fingerprints of Individual Supermassive Black Hole Binaries in Pulsar Timing Arrays NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 53

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Observation 8ef92ae5-eee8-4846-beb6-2a8473b26be9 · inbound

Generalizing the CPL Parametrization through Dark Sector Interaction cites this paper.

Generalizing the CPL Parametrization through Dark Sector Interaction NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 84

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Observation 21aacea4-e85c-4bfc-9b0b-19ea436b31e2 · inbound

Generalizing the CPL Parametrization through Dark Sector Interaction cites this paper.

Generalizing the CPL Parametrization through Dark Sector Interaction NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 84

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Observation 1a5e99cf-d5d8-4ae0-bee3-c83c3b833b5c · inbound

Testing General Relativity with Individual Supermassive Black Hole Binaries cites this paper.

Testing General Relativity with Individual Supermassive Black Hole Binaries NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 86

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Testing General Relativity with Individual Supermassive Black Hole Binaries cites this paper.

Testing General Relativity with Individual Supermassive Black Hole Binaries NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 86

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Constraints on Dynamical Dark Energy from Multiple Probes in the Full Dark Energy Survey cites this paper.

Constraints on Dynamical Dark Energy from Multiple Probes in the Full Dark Energy Survey NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 64

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Impact and measurability of linear relativistic effects in galaxy surveys NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 141

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First detection of ultra-fast outflows in a quiescent galaxy NAUTILUS: boosting Bayesian importance nested sampling with deep learning

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Web-Halo Model Peak-Background Split (WHM-PBS): halo bias as a distribution, not a number cites this paper.

Web-Halo Model Peak-Background Split (WHM-PBS): halo bias as a distribution, not a number NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 75

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Searching for signatures of inflationary massive fields in DESI Imaging data and Stage-V galaxy surveys cites this paper.

Searching for signatures of inflationary massive fields in DESI Imaging data and Stage-V galaxy surveys NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 54

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Growth, geometry, and early-universe split of the matter density parameter $\Omega_{\rm m}$ cites this paper.

Growth, geometry, and early-universe split of the matter density parameter $\Omega_{\rm m}$ NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 16

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A Physically Driven Parameterisation of Multidimensional Atmospheres: Application to the JWST Phase Curve of WASP-121b cites this paper.

A Physically Driven Parameterisation of Multidimensional Atmospheres: Application to the JWST Phase Curve of WASP-121b NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 14

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Unveiling and Characterising Ubiquitous Nitrogen Enhancement in $6 \leq z \leq 10$ Galaxies with JWST Spectroscopy cites this paper.

Unveiling and Characterising Ubiquitous Nitrogen Enhancement in $6 \leq z \leq 10$ Galaxies with JWST Spectroscopy NAUTILUS: boosting Bayesian importance nested sampling with deep learning

Reference 107

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