Cover Image

Improving Generalization and Uncertainty Quantification of Photometric Redshift Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Soriano, Jonathan, Do, Tuan, Saikrishnan, Srinath, Seenivasan, Vikram, Boscoe, Bernie, Singal, Jack, Jones, Evan
Format:	Preprint
Published:	2026
Subjects:	Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Using different sources of ground truths and transfer learning to improve the generalization of photometric redshift estimation
by: Soriano, Jonathan, et al.
Published: (2024)

Combining datasets with different ground truths using Low-Rank Adaptation to generalize image-based CNN models for photometric redshift prediction
by: Seenivasan, Vikram, et al.
Published: (2026)

Improving Photometric Redshift Estimation for Cosmology with LSST using Bayesian Neural Networks
by: Jones, Evan, et al.
Published: (2023)

GalaxiesML: a dataset of galaxy images, photometry, redshifts, and structural parameters for machine learning
by: Do, Tuan, et al.
Published: (2024)

Improving Photometric Redshift Estimates with Training Sample Augmentation
by: Moskowitz, Irene, et al.
Published: (2024)

Improved Weak Lensing Photometric Redshift Calibration via StratLearn and Hierarchical Modeling
by: Autenrieth, Maximilian, et al.
Published: (2024)

Diagnosing the Effects of Spectroscopic Training Set Imperfection on Photometric Redshift Performance
by: Crafford, Alice, et al.
Published: (2026)

Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts
by: Tortorelli, Luca, et al.
Published: (2026)

ColdPress: Efficient Quantile-Based Compression of Photometric Redshift PDFs
by: Hernán-Caballero, Antonio
Published: (2025)

Learning Spectral Templates for Photometric Redshift Estimation from Broadband Photometry
by: Crenshaw, John Franklin, et al.
Published: (2020)

Quantifying the Impact of LSST $u$-band Survey Strategy on Photometric Redshift Estimation and the Detection of Lyman-break Galaxies
by: Crenshaw, John Franklin, et al.
Published: (2025)

Bayesian Component Separation for DESI LAE Automated Spectroscopic Redshifts and Photometric Targeting
by: Uzsoy, Ana Sofía M., et al.
Published: (2025)

Multi-Modal Masked Autoencoders for Learning Image-Spectrum Associations for Galaxy Evolution and Cosmology
by: Himes, Morgan, et al.
Published: (2025)

The PAU Survey: Photometric Calibration of Narrow Band Images
by: Castander, F. J., et al.
Published: (2024)

The PAU Survey: Photometric redshift estimation in deep wide fields
by: Navarro-Gironés, D., et al.
Published: (2023)

HAYATE: Photometric redshift estimation by hybridising machine learning with template fitting
by: Tanigawa, Shingo, et al.
Published: (2024)

Unsupervised Discovery of High-Redshift Galaxy Populations with Variational Autoencoders
by: Saxena, Aayush
Published: (2025)

Photometric classification of supernovae detected by the Zwicky Transient Facility using noise augmentation
by: Townsend, A., et al.
Published: (2026)

Archetype-Based Redshift Estimation for the Dark Energy Spectroscopic Instrument Survey
by: Anand, Abhijeet, et al.
Published: (2024)

AI-Driven Reconstruction of Large-Scale Structure from Combined Photometric and Spectroscopic Surveys
by: Du, Wenying, et al.
Published: (2025)

RVSNUpy: A Python Package for Spectroscopic Redshift Measurement Based on Cross-Correlation
by: Kim, Taewan, et al.
Published: (2025)

Picture Perfect: Photometric Transient Classification Using the ParSNIP Model with Roman Hourglass Simulations
by: Abdelhadi, Belal, et al.
Published: (2024)

A Probabilistic Autoencoder for Galaxy SED Reconstruction and Redshift Estimation: Application to Mock SPHEREx Spectrophotometry
by: Feder, Richard M., et al.
Published: (2026)

Flexible Simulation Based Inference for Galaxy Photometric Fitting with Synthesizer
by: Harvey, Thomas, et al.
Published: (2025)

Propagating Uncertainties in the SALT3 Model Training Process to Cosmological Constraints
by: Dai, M., et al.
Published: (2022)

Target Selection for the Redshift-Limited WAVES-Wide with Machine Learning
by: Kaur, Gursharanjit, et al.
Published: (2025)

JWST Advanced Deep Extragalactic Survey (JADES) Data Release 5: Photometric Catalog
by: Robertson, Brant E., et al.
Published: (2026)

Generation of realistic input parameters for simulating atmospheric point-spread functions at astronomical observatories
by: Hébert, Claire-Alice, et al.
Published: (2023)

TITAN DR1: An Improved, Validated, and Systematically-Controlled Recalibration of ATLAS Photometry toward Type Ia Supernova Cosmology
by: Marlin, Elijah G., et al.
Published: (2025)

21 cm Intensity Mapping with the DSA-2000
by: Byrne, Ruby, et al.
Published: (2023)

Using Galaxy Evolution as Source of Physics-Based Ground Truth for Generative Models
by: Li, Yun Qi, et al.
Published: (2024)

Euclid Quick Data Release (Q1). Photometric redshifts and physical properties of galaxies through the PHZ processing function
by: Euclid Collaboration, et al.
Published: (2025)

Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production
by: The RAIL Team, et al.
Published: (2025)

Uncertainty-aware and Data-efficient Cosmological Emulation using Gaussian Processes and PCA
by: Günther, Sven
Published: (2023)

Attention-Based Neural Network Emulators for Multi-Probe Data Vectors Part II: Assessing Tension Metrics
by: Saraivanov, Evan, et al.
Published: (2024)

FarView: An In-Situ Manufactured Lunar Far Side Radio Array Concept for 21-cm Dark Ages Cosmology
by: Polidan, Ronald S., et al.
Published: (2024)

The FarView Low Frequency Radio Array on the Moon's Far Side: Science and Array Architecture
by: Burns, Jack O., et al.
Published: (2026)

STag II: Classification of Serendipitous Supernovae Observed by Galaxy Redshift Surveys
by: Davison, W., et al.
Published: (2024)

Variational Inference for Acceleration of SN Ia Photometric Distance Estimation with BayeSN
by: Uzsoy, Ana Sofía M., et al.
Published: (2024)

$\texttt{MEDEA}$: A New Model for Emulating Radio Antenna Beam Patterns for 21-cm Cosmology and Antenna Design Studies
by: Hibbard, Joshua J., et al.
Published: (2024)