Paper Citation Record · LEDGER

On the cosmological performance of photometrically classified supernovae with machine learning

As of 15 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:1908.04210.

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pith.paper-citation-record.v1
1908.04210 v3

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Outbound references

Observation 6ee624fd-ccf4-414b-8bf5-1158940b52a7 · outbound

This paper cites The Dark Energy Survey: more than dark energy - an overview.

On the cosmological performance of photometrically classified supernovae with machine learning The Dark Energy Survey: more than dark energy - an overview

Reference 1

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This paper cites A Comparison of Six Photometric Redshift Methods Applied to 1.5 Million Luminous Red Galaxies.

On the cosmological performance of photometrically classified supernovae with machine learning A Comparison of Six Photometric Redshift Methods Applied to 1.5 Million Luminous Red Galaxies

Reference 2

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Observation d2892736-9e71-42e6-893b-435487157624 · outbound

This paper cites Measuring the Hubble function with standard candle clustering.

On the cosmological performance of photometrically classified supernovae with machine learning Measuring the Hubble function with standard candle clustering

Reference 3

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Observation 77115b8c-0ce0-4e9f-9067-2d70ed87212f · outbound

This paper cites The Core-collapse rate from the Supernova Legacy Survey.

On the cosmological performance of photometrically classified supernovae with machine learning The Core-collapse rate from the Supernova Legacy Survey

Reference 4

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Observation 56fa1ae8-6241-4134-b066-df498ad8e820 · outbound

This paper cites The Zwicky Transient Facility.

On the cosmological performance of photometrically classified supernovae with machine learning The Zwicky Transient Facility

Reference 5

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Observation 4c9f453a-f9ea-413a-9f45-2cb3fd1ed6bf · outbound

This paper cites J-PAS: The Javalambre-Physics of the Accelerated Universe Astrophysical Survey.

On the cosmological performance of photometrically classified supernovae with machine learning J-PAS: The Javalambre-Physics of the Accelerated Universe Astrophysical Survey

Reference 6

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Observation 493155f0-a47e-430e-9adf-a3ae7f76232c · outbound

This paper cites Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples.

On the cosmological performance of photometrically classified supernovae with machine learning Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples

Reference 7

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Observation d2691a24-0599-477d-9eda-8d135a614385 · outbound

This paper cites H0LiCOW - IX. Cosmographic analysis of the doubly imaged quasar SDSS 1206+4332 and a new measurement of the Hubble constant.

On the cosmological performance of photometrically classified supernovae with machine learning H0LiCOW - IX. Cosmographic analysis of the doubly imaged quasar SDSS 1206+4332 and a new measurement of the Hubble constant

Reference 8

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Observation 875c6a9c-b53e-41e0-984a-0cbaeb0483e5 · outbound

This paper cites H0LiCOW V. New COSMOGRAIL time delays of HE0435-1223: $H_0$ to 3.8% precision from strong lensing in a flat $\Lambda$CDM model.

On the cosmological performance of photometrically classified supernovae with machine learning H0LiCOW V. New COSMOGRAIL time delays of HE0435-1223: $H_0$ to 3.8% precision from strong lensing in a flat $\Lambda$CDM model

Reference 9

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Observation 8090369e-7db4-4e35-9495-8d70c19df615 · outbound

This paper cites The Carnegie Supernova Project: Absolute Calibration and the Hubble Constant.

On the cosmological performance of photometrically classified supernovae with machine learning The Carnegie Supernova Project: Absolute Calibration and the Hubble Constant

Reference 10

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Observation 7cd475bf-b696-46a4-9825-5b964dc84993 · outbound

This paper cites First measurement of $\sigma_8$ using supernova magnitudes only.

On the cosmological performance of photometrically classified supernovae with machine learning First measurement of $\sigma_8$ using supernova magnitudes only

Reference 11

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Observation 010d0019-f1bf-4e0b-a1fa-c0f307257fc1 · outbound

This paper cites Turning noise into signal: learning from the scatter in the Hubble diagram.

On the cosmological performance of photometrically classified supernovae with machine learning Turning noise into signal: learning from the scatter in the Hubble diagram

Reference 12

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Observation 78993790-f6ac-4100-a5aa-7637d5c443c4 · outbound

This paper cites J., et al., 2019, A&A, 622, A176, 1804.02667.

On the cosmological performance of photometrically classified supernovae with machine learning J., et al., 2019, A&A, 622, A176, 1804.02667

Reference 13

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Observation 64d63d11-1e39-454f-a1f5-bb35911f9d3d · outbound

This paper cites Deep Recurrent Neural Networks for Supernovae Classification.

On the cosmological performance of photometrically classified supernovae with machine learning Deep Recurrent Neural Networks for Supernovae Classification

Reference 14

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Observation 1cdfcf7f-a1f1-4175-a242-3ccd2b2470ae · outbound

This paper cites Probing the anisotropic local universe and beyond with SNe Ia data.

On the cosmological performance of photometrically classified supernovae with machine learning Probing the anisotropic local universe and beyond with SNe Ia data

Reference 15

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Observation 4c0e22b7-89e4-4553-9640-05be2a8bfacf · outbound

This paper cites A Critical Assessment of Photometric Redshift Methods: A CANDELS Investigation.

On the cosmological performance of photometrically classified supernovae with machine learning A Critical Assessment of Photometric Redshift Methods: A CANDELS Investigation

Reference 16

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This paper cites A Measurement of the Rate of type-Ia Supernovae at Redshift $z\approx$ 0.1 from the First Season of the SDSS-II Supernova Survey.

On the cosmological performance of photometrically classified supernovae with machine learning A Measurement of the Rate of type-Ia Supernovae at Redshift $z\approx$ 0.1 from the First Season of the SDSS-II Supernova Survey

Reference 17

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On the cosmological performance of photometrically classified supernovae with machine learning Unresolved cited work

Reference 18

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Observation 806aa1a4-6cd4-40d8-9599-72dcd475e4ae · outbound

This paper cites J., Mandel K., 2013, The Astrophysical Journal, 778, 167.

On the cosmological performance of photometrically classified supernovae with machine learning J., Mandel K., 2013, The Astrophysical Journal, 778, 167

Reference 19

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Observation aa1d64b8-e0e2-4e1e-8ac5-55a30c2d205a · outbound

This paper cites On the amount of peculiar velocity field information in supernovae from LSST and beyond.

On the cosmological performance of photometrically classified supernovae with machine learning On the amount of peculiar velocity field information in supernovae from LSST and beyond

Reference 20

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Observation 263096fb-712e-4f14-86a3-e395b665cf77 · outbound

This paper cites Improving Photometric Redshift Estimation using GPz: size information, post processing and improved photometry.

On the cosmological performance of photometrically classified supernovae with machine learning Improving Photometric Redshift Estimation using GPz: size information, post processing and improved photometry

Reference 21

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Observation 8ee64519-b6a2-44d2-9058-7b56770b08aa · outbound

This paper cites Cosmological Constraints from Type Ia Supernovae Peculiar Velocity Measurements.

On the cosmological performance of photometrically classified supernovae with machine learning Cosmological Constraints from Type Ia Supernovae Peculiar Velocity Measurements

Reference 22

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This paper cites SALT2: using distant supernovae to improve the use of Type Ia supernovae as distance indicators.

On the cosmological performance of photometrically classified supernovae with machine learning SALT2: using distant supernovae to improve the use of Type Ia supernovae as distance indicators

Reference 23

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

On the cosmological performance of photometrically classified supernovae with machine learning Measuring the growth rate of structure with Type IA Supernovae from LSST

Reference 24

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This paper cites Strongly lensed SNe Ia in the era of LSST: observing cadence for lens discoveries and time-delay measurements.

On the cosmological performance of photometrically classified supernovae with machine learning Strongly lensed SNe Ia in the era of LSST: observing cadence for lens discoveries and time-delay measurements

Reference 25

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On the cosmological performance of photometrically classified supernovae with machine learning Unresolved cited work

Reference 26

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Observation 54ac3d8e-ec2a-42ff-9f95-893d1090a9cc · outbound

This paper cites Kernel PCA for type Ia supernovae photometric classification.

On the cosmological performance of photometrically classified supernovae with machine learning Kernel PCA for type Ia supernovae photometric classification

Reference 27

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This paper cites Improved Distances to Type Ia Supernovae with Multicolor Light Curve Shapes: MLCS2k2.

On the cosmological performance of photometrically classified supernovae with machine learning Improved Distances to Type Ia Supernovae with Multicolor Light Curve Shapes: MLCS2k2

Reference 28

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This paper cites Measuring Dark Energy Properties with Photometrically Classified Pan-STARRS Supernovae. II. Cosmological Parameters.

On the cosmological performance of photometrically classified supernovae with machine learning Measuring Dark Energy Properties with Photometrically Classified Pan-STARRS Supernovae. II. Cosmological Parameters

Reference 29

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This paper cites V., Feroz F., Hobson M.

On the cosmological performance of photometrically classified supernovae with machine learning V., Feroz F., Hobson M

Reference 30

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This paper cites First-year Sloan Digital Sky Survey-II (SDSS-II) Supernova Results: Hubble Diagram and Cosmological Parameters.

On the cosmological performance of photometrically classified supernovae with machine learning First-year Sloan Digital Sky Survey-II (SDSS-II) Supernova Results: Hubble Diagram and Cosmological Parameters

Reference 31

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This paper cites Results from the Supernova Photometric Classification Challenge.

On the cosmological performance of photometrically classified supernovae with machine learning Results from the Supernova Photometric Classification Challenge

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On the cosmological performance of photometrically classified supernovae with machine learning Supernova Photometric Classification Challenge

Reference 33

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On the cosmological performance of photometrically classified supernovae with machine learning First Cosmology Results using Type Ia Supernova from the Dark Energy Survey: Simulations to Correct Supernova Distance Biases

Reference 34

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On the cosmological performance of photometrically classified supernovae with machine learning Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples

Reference 35

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On the cosmological performance of photometrically classified supernovae with machine learning General classification of light curves using extreme boosting

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On the cosmological performance of photometrically classified supernovae with machine learning On the Possibility of Anisotropic Curvature in Cosmology

Reference 37

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On the cosmological performance of photometrically classified supernovae with machine learning Bayesian Estimation Applied to Multiple Species: Towards cosmology with a million supernovae

Reference 38

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On the cosmological performance of photometrically classified supernovae with machine learning Photometric Supernova Classification With Machine Learning

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On the cosmological performance of photometrically classified supernovae with machine learning LSST Science Book, Version 2.0

Reference 40

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On the cosmological performance of photometrically classified supernovae with machine learning The effects of velocities and lensing on moments of the Hubble diagram

Reference 41

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On the cosmological performance of photometrically classified supernovae with machine learning The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Selection of a performance metric for classification probabilities balancing diverse science goals

Reference 42

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On the cosmological performance of photometrically classified supernovae with machine learning Using Random Forest Machine Learning Algorithms in Binary Supernovae Classification

Reference 43

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On the cosmological performance of photometrically classified supernovae with machine learning The Southern Photometric Local Universe Survey (S-PLUS): improved SEDs, morphologies and redshifts with 12 optical filters

Reference 44

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On the cosmological performance of photometrically classified supernovae with machine learning SuperNNova: an open-source framework for Bayesian, Neural Network based supernova classification

Reference 45

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On the cosmological performance of photometrically classified supernovae with machine learning Improved Photometric Classification of Supernovae using Deep Learning

Reference 46

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On the cosmological performance of photometrically classified supernovae with machine learning Parameter Estimation with BEAMS in the presence of biases and correlations

Reference 47

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On the cosmological performance of photometrically classified supernovae with machine learning Statistical Classification Techniques for Photometric Supernova Typing

Reference 48

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On the cosmological performance of photometrically classified supernovae with machine learning Gravitationally lensed quasars and supernovae in future wide-field optical imaging surveys

Reference 49

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On the cosmological performance of photometrically classified supernovae with machine learning Scikit-learn: Machine Learning in Python

Reference 50

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This paper cites Accurate Weak Lensing of Standard Candles. II. Measuring sigma8 with Supernovae.

On the cosmological performance of photometrically classified supernovae with machine learning Accurate Weak Lensing of Standard Candles. II. Measuring sigma8 with Supernovae

Reference 51

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This paper cites A 2.4% Determination of the Local Value of the Hubble Constant.

On the cosmological performance of photometrically classified supernovae with machine learning A 2.4% Determination of the Local Value of the Hubble Constant

Reference 52

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On the cosmological performance of photometrically classified supernovae with machine learning ANNz2 - photometric redshift and probability distribution function estimation using machine learning

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On the cosmological performance of photometrically classified supernovae with machine learning Unresolved cited work

Reference 54

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On the cosmological performance of photometrically classified supernovae with machine learning The Sloan Digital Sky Survey-II Supernova Survey: Search Algorithm and Follow-up Observations

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On the cosmological performance of photometrically classified supernovae with machine learning The Data Release of the Sloan Digital Sky Survey-II Supernova Survey

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This paper cites The Complete Light-curve Sample of Spectroscopically Confirmed Type Ia Supernovae from Pan-STARRS1 and Cosmological Constraints from The Combined Pantheon Sample.

On the cosmological performance of photometrically classified supernovae with machine learning The Complete Light-curve Sample of Spectroscopically Confirmed Type Ia Supernovae from Pan-STARRS1 and Cosmological Constraints from The Combined Pantheon Sample

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This paper cites Percent-Level Test of Isotropic Expansion Using Type Ia Supernovae.

On the cosmological performance of photometrically classified supernovae with machine learning Percent-Level Test of Isotropic Expansion Using Type Ia Supernovae

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This paper cites A., Dawes R.

On the cosmological performance of photometrically classified supernovae with machine learning A., Dawes R

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This paper cites Supernova Photometric Classification Pipelines Trained on Spectroscopically Classified Supernovae from the Pan-STARRS1 Medium-Deep Survey.

On the cosmological performance of photometrically classified supernovae with machine learning Supernova Photometric Classification Pipelines Trained on Spectroscopically Classified Supernovae from the Pan-STARRS1 Medium-Deep Survey

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Modeling the probability distribution for cosmological analysis with photometrically classified samples cites this paper.

Modeling the probability distribution for cosmological analysis with photometrically classified samples On the cosmological performance of photometrically classified supernovae with machine learning

Reference 31

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