Photometric Redshift Estimation
5 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Photometric Redshift Estimation
Most implemented papers
Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference
We provide sample code in $\texttt{Python}$ and $\texttt{R}$ as well as examples of applications to photometric redshift estimation and likelihood-free cosmological inference via CDE.
Self-Supervised Representation Learning for Astronomical Images
We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks.
Towards Instance-Wise Calibration: Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR)
There is a growing interest in conditional density estimation and generative modeling of a target $y$ given complex inputs $\mathbf{x}$.
AstroCLIP: A Cross-Modal Foundation Model for Galaxies
These embeddings can then be used - without any model fine-tuning - for a variety of downstream tasks including (1) accurate in-modality and cross-modality semantic similarity search, (2) photometric redshift estimation, (3) galaxy property estimation from both images and spectra, and (4) morphology classification.
CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimation
QiufanLin/CLAP-I
•
•
It leverages supervised contrastive learning (SCL) and k-nearest neighbours (KNN) to construct and calibrate raw probability density estimates, and implements a refitting procedure to resume end-to-end discriminative models ready to produce final estimates for large-scale imaging data.
