Cover Image

Combining datasets with different ground truths using Low-Rank Adaptation to generalize image-based CNN models for photometric redshift prediction

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Seenivasan, Vikram, Saikrishnan, Srinath, Lizarraga, Andrew, Soriano, Jonathan, Boscoe, Bernie, Do, Tuan
Format:	Preprint
Published:	2026
Subjects:	Instrumentation and Methods for 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)

Improving Generalization and Uncertainty Quantification of Photometric Redshift Models
by: Soriano, Jonathan, et al.
Published: (2026)

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

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

AquiLLM: a RAG Tool for Capturing Tacit Knowledge in Research Groups
by: Campbell, Chandler, et al.
Published: (2025)

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

Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion Models
by: Lizarraga, Andrew, et al.
Published: (2024)

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

GreenCrossingAI: A Camera Trap/Computer Vision Pipeline for Environmental Science Research Groups
by: Boscoe, Bernie, et al.
Published: (2025)

The Domain Adaptation problem in photometric redshift estimation: a solution applied to the HSC Survey
by: Treyer, M., et al.
Published: (2025)

Euclid preparation. XXXI. The effect of the variations in photometric passbands on photometric-redshift accuracy
by: Euclid Collaboration, et al.
Published: (2023)

Reconstructing redshift distributions with photometric galaxy clustering
by: Peng, Hui, et al.
Published: (2024)

UVCANDELS: Catalogs of photometric redshifts and galaxy physical properties
by: Mehta, Vihang, et al.
Published: (2024)

VitalVideos-Europe: A dataset of face videos with PPG and blood pressure ground truths
by: Toye, Pieter-Jan
Published: (2023)

Influence of photometric galaxies redshift distribution in BAO estimation
by: Ferreira, Paula S., et al.
Published: (2025)

Targeting cluster galaxies for the 4MOST CHANCES Low-z sub-survey with photometric redshifts
by: Méndez-Hernández, Hugo, et al.
Published: (2025)

Hypernetwork-Driven Low-Rank Adaptation Across Attention Heads
by: Diep, Nghiem T., et al.
Published: (2025)

Optimal Policy Sparsification and Low Rank Decomposition for Deep Reinforcement Learning
by: Goddla, Vikram
Published: (2024)

Reflectance spectra measured with ASD field spectrometer and ground truthing of vegetation heights and phenology for different crops and bare soil at the Hordorf (Central Germany) ground truthing sites
by: Steigerwald, Sophie, et al.
Published: (2021)

The PAU Survey: Enhancing photometric redshift estimation using DEEPz
by: Daza-Perilla, I. V., et al.
Published: (2024)

Dimensional reduction for sampled priors and application to photometric redshift distributions
by: Bernstein, Gary, et al.
Published: (2025)

A photometric view of the redshift distribution of dust-enshrouded galaxies
by: David H. Hughes
Published: (2005)

Galaxy-dark matter connection from weak lensing in imaging surveys: Impact of photometric redshift errors
by: Chaurasiya, Navin, et al.
Published: (2024)

Learning CNN on ViT: A Hybrid Model to Explicitly Class-specific Boundaries for Domain Adaptation
by: Ngo, Ba Hung, et al.
Published: (2024)

HTR ground-truth of Chinese xylographic editions
by: Bizais-Lillig, Marie
Published: (2024)

Mechanisms for belief elicitation without ground truth
by: Lehmann, Niklas Valentin
Published: (2024)

The ground truth of the calving fronts in Jakobshavn Isbræ
by: Zhang, Enze
Published: (2019)

Extremal Testing for Network Software using LLMs
by: Singha, Rathin, et al.
Published: (2025)

Communication-Efficient and Accurate Approach for Aggregation in Federated Low-Rank Adaptation
by: Nguyen, Le-Tuan, et al.
Published: (2025)

Measurement of the photometric Baryon Acoustic Oscillations with self-calibrated redshift distribution
by: Song, Ruiyu, et al.
Published: (2024)

The J-PAS survey: The effect of photometric redshift errors on cosmic voids
by: Mansour, J. A., et al.
Published: (2025)

ANNZ+: an enhanced photometric redshift estimation algorithm with applications on the PAU Survey
by: Pathi, Imdad Mahmud, et al.
Published: (2024)

Monte Carlo conformal prediction for quantifying uncertainty in radio galaxy classification under ambiguous ground truth
by: Walls, Alex, et al.
Published: (2026)

Electroencephalographic source imaging of spikes with concurrent high‐frequency oscillations is concordant with the clinical ground truth
by: Colton B. Gonsisko, et al.
Published: (2024)

Benchmarking ground truth trajectories with robotic total stations
by: Daum, Effie, et al.
Published: (2023)

RepLoRA: Reparameterizing Low-Rank Adaptation via the Perspective of Mixture of Experts
by: Truong, Tuan, et al.
Published: (2025)

Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs)
by: Garcia-Fernandez, M.
Published: (2025)

The Quasar Catalogue for S-PLUS DR4 (QuCatS) and the estimation of photometric redshifts
by: Nakazono, L., et al.
Published: (2024)

Estimating the redshift of galaxies from their photometric colors using machine learning methods
by: Felipe Meza-Obando
Published: (2020)

Radar-Camera Fusion 3D Occupancy prediction (left) compared to LiDAR ground truth (right)
by: Dragota, Simina Cristina
Published: (2026)