Papers Photometric Redshift Estimation
“Photometric Redshift Estimation” 태그가 달린 논문 12편 · 필터 해제
Mantis Shrimp: Exploring Photometric Band Utilization in Computer Vision Networks for Photometric Redshift Estimation
We present Mantis Shrimp, a multi-survey deep learning model for photometric redshift estimation that fuses ultra-violet (GALEX), optical (PanSTARRS), and infrared (UnWISE) imagery. Machine learning is now an established…
Photometric Redshift EstimationDetermination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs)
Accurate and reliable photometric redshift determination is one of the key aspects for wide-field photometric surveys. Determination of photometric redshift for galaxies, has been traditionally solved by use of machine-l…
Photometric Redshift EstimationUsing different sources of ground truths and transfer learning to improve the generalization of photometric redshift estimation
In this work, we explore methods to improve galaxy redshift predictions by combining different ground truths. Traditional machine learning models rely on training sets with known spectroscopic redshifts, which are precis…
Photometric Redshift EstimationTransfer LearningCLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimation
Obtaining well-calibrated photometric redshift probability densities for galaxies without a spectroscopic measurement remains a challenge. Deep learning discriminative models, typically fed with multi-band galaxy images,…
Computational EfficiencyContrastive LearningDeep LearningPhotometric Redshift EstimationPreliminary Report on Mantis Shrimp: a Multi-Survey Computer Vision Photometric Redshift Model
The availability of large, public, multi-modal astronomical datasets presents an opportunity to execute novel research that straddles the line between science of AI and science of astronomy. Photometric redshift estimati…
AstronomyPhotometric Redshift EstimationAstroCLIP: A Cross-Modal Foundation Model for Galaxies
We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into a shared, physically meaningful latent space. These embeddings can then be used - without any model fine-tuning - for a v…
Contrastive LearningmodelMorphology classificationPhotometric Redshift Estimation+3Towards Instance-Wise Calibration: Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR)
Key science questions, such as galaxy distance and weather forecasting, often require knowing the full predictive distribution of a target variable $y$ given complex inputs $\mathbf{x}$. Despite recent advances in machin…
Conformal PredictionDensity EstimationMORPHPhotometric Redshift Estimation+3Photometric 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. In this work, we investigate two …
Photometric Redshift EstimationRepresentation LearningScalable Statistical Inference of Photometric Redshift via Data Subsampling
Handling big data has largely been a major bottleneck in traditional statistical models. Consequently, when accurate point prediction is the primary target, machine learning models are often preferred over their statisti…
Photometric Redshift EstimationSelf-Supervised Representation Learning for Astronomical Images
Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity. We show that, without the need for labels, self-supervised learning…
AstronomyContrastive LearningMorphology classificationPhotometric Redshift Estimation+3Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference
It is well known in astronomy that propagating non-Gaussian prediction uncertainty in photometric redshift estimates is key to reducing bias in downstream cosmological analyses. Similarly, likelihood-free inference appro…
AstronomyDensity EstimationDiagnosticModel Selection+1A Sparse Gaussian Process Framework for Photometric Redshift Estimation
Accurate photometric redshifts are a lynchpin for many future experiments to pin down the cosmological model and for studies of galaxy evolution. In this study, a novel sparse regression framework for photometric redshif…
Gaussian ProcessesPhotometric Redshift Estimationregression