Photometric Redshift Estimation
3 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Latest papers with no code
Preliminary Report on Mantis Shrimp: a Multi-Survey Computer Vision Photometric Redshift Model
We reason about the behavior of the CNNs from the interpretability metrics, specifically framing the result in terms of physically-grounded knowledge of galaxy properties.
Photometric Redshift Estimation with Convolutional Neural Networks and Galaxy Images: A Case Study of Resolving Biases in Data-Driven Methods
Deep Learning models have been increasingly exploited in astrophysical studies, yet such data-driven algorithms are prone to producing biased outputs detrimental for subsequent analyses.
Scalable Statistical Inference of Photometric Redshift via Data Subsampling
Handling big data has largely been a major bottleneck in traditional statistical models.
The effect of emission lines on the performance of photometric redshift estimation algorithms
We investigate the effect of strong emission line galaxies on the performance of empirical photometric redshift estimation methods.
Spectroscopic and Photometric Redshift Estimation by Neural Networks For the China Space Station Optical Survey (CSS-OS)
This indicates that the neural network method is feasible and powerful for spec-z and photo-z estimations in future cosmological surveys.
A Sparse Gaussian Process Framework for Photometric Redshift Estimation
The proposed framework reaches a mean absolute $\Delta z = 0. 0026(1+z_\textrm{s})$, over the redshift range of $0 \le z_\textrm{s} \le 2$ on the simulated data, and $\Delta z = 0. 0178(1+z_\textrm{s})$ over the entire redshift range on the SDSS DR12 survey, outperforming the standard ANNz used in the literature.
