REVIEW 7 cited by
The Role of Machine Learning in the Next Decade of Cosmology
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmological discovery, but will also present new challenges for adopting ML methodologies and understanding the results. ML could transform our field, but this transformation will require the astronomy community to both foster and promote interdisciplinary research endeavors.
Forward citations
Cited by 7 Pith papers
-
Emulating Cosmic Structure Formation with a Lagrangian Neural Cellular Automaton
A ~4,000-parameter recurrent local network correcting the Zeldovich approximation reaches percent-level matter power-spectrum accuracy at k≲0.5 h/Mpc at z=0, matching larger U-Net emulators on Quijote N-body tests.
-
DeepCHART: Mapping the 3D dark matter density field from Ly$\alpha$ forest surveys using deep learning
A 3D neural network reconstructs the dark matter density field at redshift 2.5 from simulated Lyman-alpha forest spectra, reaching voxel-wise correlation of roughly 0.77 for current surveys and 0.90 for denser future ...
-
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference
Nonparametric conditional density estimation packages and diagnostics are released for Python and R, with applications to photometric redshifts and likelihood-free cosmology.
-
AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution
A UNet trained on N-body simulations reconstructs dark matter velocity and momentum fields from sparse redshift-space halo maps, with power spectra matching simulation truth within 2σ up to k=0.3 h/Mpc and correcting ...
-
Cosmological parameter estimation from large-scale structure deep learning
A light CNN estimates Omega_m and sigma_8 from simulated 3D dark matter density fields with statistical errors of 0.0015 and 0.0029 after a polynomial bias correction, several times tighter than 2-point correlation fu...
-
Stacked Hybrid RNN-CNN Reconstruction of X-ray Influence on 21-cm Brightness Temperature
A stacked LSTM-GRU-CNN emulator reportedly reconstructs the global 21-cm brightness temperature with 99.93% accuracy, but a residual feature derived from the target values makes the reported accuracy invalid.
-
Anisotropic cosmology using observational datasets: exploring via machine learning approaches
The authors constrain a Bianchi I anisotropic model to near-isotropy (Omega_sigma0 about 0.0009) and show polynomial regression tracks the fitted Hubble curve better than linear regression or ANN.
Discussion (0). Continue with ORCID to comment.