Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform | Paper Citation Record

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Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Unresolved cited work

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This paper cites The filters are parameterized as ˆψjl(⃗k) =F jl(⃗k, j), whereFjl is a neu- ral network that maps Fourier-space coordinates ⃗kto filter values.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform The filters are parameterized as ˆψjl(⃗k) =F jl(⃗k, j), whereFjl is a neu- ral network that maps Fourier-space coordinates ⃗kto filter values

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Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Unresolved cited work

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Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Unresolved cited work

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Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Unresolved cited work

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Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Unresolved cited work

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This paper cites To treat each summary statistic equally in the downstream analysis, we choose to pre-train the NFST and CNN summary statistics and freeze their trainable components.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform To treat each summary statistic equally in the downstream analysis, we choose to pre-train the NFST and CNN summary statistics and freeze their trainable components

Reference 7

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This paper cites We use the same MLP model and training setup as with pre-training, but in- stead train for 2000 epochs with a learning rate of 10−4.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform We use the same MLP model and training setup as with pre-training, but in- stead train for 2000 epochs with a learning rate of 10−4

Reference 8

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Observation b97df8fe-a2e6-4ec4-9b76-388e2b3b8027 · outbound

This paper cites To cap- ture these, we instead use Neural Posterior Estimation (NPE).

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform To cap- ture these, we instead use Neural Posterior Estimation (NPE)

Reference 9

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This paper cites We expand upon work from [40], which visualizes the information that an arbitrary summary statistic captures from a tar- get field.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform We expand upon work from [40], which visualizes the information that an arbitrary summary statistic captures from a tar- get field

Reference 10

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Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Unresolved cited work

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Observation c5da7334-0a4a-472e-9076-b52c2da41673 · outbound

This paper cites The Hubble constant tension: current status and future perspectives through new cosmological probes.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform The Hubble constant tension: current status and future perspectives through new cosmological probes

Reference 12

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This paper cites The Dark Energy Survey Supernova Program: Investigating Beyond-$\Lambda$CDM.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform The Dark Energy Survey Supernova Program: Investigating Beyond-$\Lambda$CDM

Reference 13

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This paper cites The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset

Reference 14

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This paper cites The Ups and Downs of Early Dark Energy solutions to the Hubble tension: a review of models, hints and constraints circa 2023.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform The Ups and Downs of Early Dark Energy solutions to the Hubble tension: a review of models, hints and constraints circa 2023

Reference 15

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This paper cites DESI 2024 VI: Cosmological Constraints from the Measurements of Baryon Acoustic Oscillations.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform DESI 2024 VI: Cosmological Constraints from the Measurements of Baryon Acoustic Oscillations

Reference 16

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This paper cites Full-Shape analysis of the power spectrum and bispectrum of DESI DR1 LRG and QSO samples.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Full-Shape analysis of the power spectrum and bispectrum of DESI DR1 LRG and QSO samples

Reference 17

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Observation effe9f74-660f-417c-a0e7-32f592f185a9 · outbound

This paper cites Secco, S.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Secco, S

Reference 18

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Observation a8b55b24-d66e-47dc-a1ca-6cd803ab8145 · outbound

This paper cites Asgari, C.-A.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Asgari, C.-A

Reference 19

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Observation 9b3e0d4e-478f-4793-b52a-8f5c82074a12 · outbound

This paper cites Hamana, M.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Hamana, M

Reference 20

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Observation 3c8b0211-3268-4f6a-aab8-65df40dd7af2 · outbound

This paper cites Abbott, M.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Abbott, M

Reference 21

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This paper cites Heymans, T.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Heymans, T

Reference 22

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This paper cites Hyper Suprime-Cam Year 3 Results: Measurements of Clustering of SDSS-BOSS Galaxies, Galaxy-Galaxy Lensing and Cosmic Shear.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Hyper Suprime-Cam Year 3 Results: Measurements of Clustering of SDSS-BOSS Galaxies, Galaxy-Galaxy Lensing and Cosmic Shear

Reference 23

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Observation d98d95df-31c3-4511-b6b1-aab18b7e7089 · outbound

This paper cites Gil-Mar ´ ın, J.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Gil-Mar ´ ın, J

Reference 24

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Observation d5ba549c-8cad-4924-b9f3-f0459d714f9b · outbound

This paper cites Gil-Mar ´ ın, W.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Gil-Mar ´ ın, W

Reference 25

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Observation 1b1781d0-345f-4609-965a-57dc5c710780 · outbound

This paper cites Slepian, D.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Slepian, D

Reference 26

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This paper cites A First Detection of the Connected 4-Point Correlation Function of Galaxies Using the BOSS CMASS Sample.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform A First Detection of the Connected 4-Point Correlation Function of Galaxies Using the BOSS CMASS Sample

Reference 27

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Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Unresolved cited work

Reference 28

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This paper cites Bispectrum constraints on Primordial non-Gaussianities with the eBOSS DR16 quasars.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Bispectrum constraints on Primordial non-Gaussianities with the eBOSS DR16 quasars

Reference 29

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This paper cites Cosmological information in the redshift-space bispectrum.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Cosmological information in the redshift-space bispectrum

Reference 30

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Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Unresolved cited work

Reference 31

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This paper cites Window convolution of the galaxy clustering bispectrum.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Window convolution of the galaxy clustering bispectrum

Reference 32

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This paper cites Fluri, T.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Fluri, T

Reference 33

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This paper cites Dark Energy Survey Year 3 results: likelihood-free, simulation-based $w$CDM inference with neural compression of weak-lensing map statistics.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Dark Energy Survey Year 3 results: likelihood-free, simulation-based $w$CDM inference with neural compression of weak-lensing map statistics

Reference 34

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This paper cites Lemos, L.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Lemos, L

Reference 35

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This paper cites Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

Reference 36

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This paper cites Going Beyond the Galaxy Power Spectrum: an Analysis of BOSS Data with Wavelet Scattering Transforms.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Going Beyond the Galaxy Power Spectrum: an Analysis of BOSS Data with Wavelet Scattering Transforms

Reference 37

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This paper cites Valogiannis and C.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Valogiannis and C

Reference 38

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This paper cites Precise Cosmological Constraints from BOSS Galaxy Clustering with a Simulation-Based Emulator of the Wavelet Scattering Transform.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Precise Cosmological Constraints from BOSS Galaxy Clustering with a Simulation-Based Emulator of the Wavelet Scattering Transform

Reference 39

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Observation 017362d6-cceb-470d-af62-4746b69ed0e5 · outbound

This paper cites Parametric Scattering Networks.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Parametric Scattering Networks

Reference 40

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Observation b671b983-e8dd-403b-a5c8-043980067d86 · outbound

This paper cites Khemani, M.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Khemani, M

Reference 41

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Observation 5427c9e9-1004-46a2-9251-684484be7a9f · outbound

This paper cites Unsupervised Searches for Cosmological Parity Violation: Improving Detection Power with the Neural Field Scattering Transform.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Unsupervised Searches for Cosmological Parity Violation: Improving Detection Power with the Neural Field Scattering Transform

Reference 42

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This paper cites Kuijken, C.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Kuijken, C

Reference 43

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Observation d4e68146-5572-4f9d-89c8-ef147b31535e · outbound

This paper cites Mellier, Abdurro’uf, J.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Mellier, Abdurro’uf, J

Reference 44

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This paper cites LSST: from Science Drivers to Reference Design and Anticipated Data Products.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform LSST: from Science Drivers to Reference Design and Anticipated Data Products

Reference 45

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This paper cites Cheng and B.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Cheng and B

Reference 46

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This paper cites Cranmer, J.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Cranmer, J

Reference 47

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This paper cites Tejero-Cantero, J.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Tejero-Cantero, J

Reference 48

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Observation 175ad0a2-2b94-4f76-bf47-5f2440e3061f · outbound

This paper cites Masked Autoregressive Flow for Density Estimation.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Masked Autoregressive Flow for Density Estimation

Reference 49

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This paper cites Benchmarking Simulation-Based Inference.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Benchmarking Simulation-Based Inference

Reference 50

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Observation 560ac964-851e-41a9-a507-1a3399133d4d · outbound

This paper cites How to quantify fields or textures? A guide to the scattering transform.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform How to quantify fields or textures? A guide to the scattering transform

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Observation 41e0bd44-c492-4f0b-8139-441a8b90398c · outbound

This paper cites Erhan, Y.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Erhan, Y

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Observation fba45034-81e2-442e-97e1-5dd114c7d973 · outbound

This paper cites Visualizing and Understanding Convolutional Networks.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Visualizing and Understanding Convolutional Networks

Reference 53

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Observation e465f002-a482-4d2b-977c-cee9a211675d · outbound

This paper cites Understanding Neural Networks Through Deep Visualization.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Understanding Neural Networks Through Deep Visualization

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Observation 607ca053-265e-46a8-bb2e-940f13e7b4dc · outbound

This paper cites A Unified Approach to Interpreting Model Predictions.

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform A Unified Approach to Interpreting Model Predictions

Reference 55

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Observation 423466b3-7fa2-4d65-9981-df4a963525db · outbound

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Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform Unresolved cited work

Reference 56

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