Euclid preparation. Estimating galaxy physical properties using CatBoost chained regressors with attention
arXiv:2504.13020 · doi:10.1051/0004-6361/202452468
Abstract
Euclid will image ~14000 deg^2 of the extragalactic sky at visible and NIR wavelengths, providing a dataset of unprecedented size and richness that will facilitate a multitude of studies into the evolution of galaxies. In the vast majority of cases the main source of information will come from broad-band images and data products thereof. Therefore, there is a pressing need to identify or develop scalable yet reliable methodologies to estimate the redshift and physical properties of galaxies using broad-band photometry from Euclid, optionally including ground-based optical photometry also. To address this need, we present a novel method to estimate the redshift, stellar mass, star-formation rate, specific star-formation rate, E(B-V), and age of galaxies, using mock Euclid and ground-based photometry. The main novelty of our property-estimation pipeline is its use of the CatBoost implementation of gradient-boosted regression-trees, together with chained regression and an intelligent, automatic optimization of the training data. The pipeline also includes a computationally-efficient method to estimate prediction uncertainties, and, in the absence of ground-truth labels, provides accurate predictions for metrics of model performance up to z~2. We apply our pipeline to several datasets consisting of mock Euclid broad-band photometry and mock ground-based ugriz photometry, to evaluate the performance of our methodology for estimating the redshift and physical properties of galaxies detected in the Euclid Wide Survey. The quality of our photometric redshift and physical property estimates are highly competitive overall, validating our modeling approach. We find that the inclusion of ground-based optical photometry significantly improves the quality of the property estimation, highlighting the importance of combining Euclid data with ancillary ground-based optical data. (Abridged)
22 pages, 13 figures, 4 tables. Accepted for publication by Astronomy & Astrophysics
References in corpus (68)
- SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
- Array Programming with NumPy
- The Wide-field Infrared Survey Explorer (WISE): Mission Description and Initial On-orbit Performance
- Measuring Reddening with SDSS Stellar Spectra and Recalibrating SFD
- LSST: from Science Drivers to Reference Design and Anticipated Data Products
- Overview of the DESI Legacy Imaging Surveys
- Accurate photometric redshifts for the CFHT Legacy Survey calibrated using the VIMOS VLT Deep Survey
- A simple model to interpret the ultraviolet, optical and infrared emission from galaxies
- The COSMOS2015 Catalog: Exploring the 1<z<6 Universe with half a million galaxies
- Analysis of galaxy SEDs from far-UV to far-IR with CIGALE: Studying a SINGS test sample
- Spectral Energy Distributions of Hard X-ray selected AGNs in the XMDS Survey
- Inferring the star-formation histories of massive quiescent galaxies with BAGPIPES: Evidence for multiple quenching mechanisms
- Rotation-invariant convolutional neural networks for galaxy morphology prediction
- Stellar Population Inference with Prospector
- Determining Star Formation Rates for Infrared Galaxies
- Euclid. I. Overview of the Euclid mission
- The Herschel PEP/HerMES Luminosity Function. I: Probing the Evolution of PACS selected Galaxies to z~4
- Improving galaxy morphologies for SDSS with Deep Learning
- The fourth data release of the Kilo-Degree Survey: ugri imaging and nine-band optical-IR photometry over 1000 square degrees
- The SWIRE-VVDS-CFHTLS surveys: stellar mass assembly over the last 10 Gyears. Evidence for a major build up of the red sequence between z=2 and z=1
- A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning
- PHAT: PHoto-z Accuracy Testing
- The evolution of the near-IR galaxy Luminosity Function and colour bimodality up to z ~ 2 from the UKIDSS Ultra Deep Survey Early Data Release
- Photometric redshifts from SDSS images using a Convolutional Neural Network
- Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks
- The Art of Measuring Physical Parameters in Galaxies: A Critical Assessment of Spectral Energy Distribution Fitting Techniques
- Euclid preparation: X. The Euclid photometric-redshift challenge
- Euclid. III. The NISP Instrument
- Mass assembly and morphological transformations since from CANDELS
- Euclid. II. The VIS Instrument
- Galaxy And Mass Assembly (GAMA): the Stellar Mass Budget by Galaxy Type
- Deep learning for galaxy surface brightness profile fitting
- Euclid preparation. XVIII. The NISP photometric system
- Photometric redshifts for Quasars in multi band Surveys
- PEP: first Herschel probe of dusty galaxy evolution up to z~3
- Fitting Analysis using Differential Evolution Optimization (FADO): Spectral population synthesis through genetic optimization under self-consistency boundary conditions
- Identifying galaxies, quasars, and stars with machine learning: A new catalogue of classifications for 111 million SDSS sources without spectra
- The fraction of bolometric luminosity absorbed by dust in DustPedia galaxies
- Unsupervised star, galaxy, qso classification: Application of HDBSCAN
- Star formation rates and stellar masses from machine learning
- Photometric classification of emission line galaxies with Machine Learning methods
- Machine learning Applied to Star-Galaxy-QSO Classification and Stellar Effective Temperature Regression
- CPz: Classification-aided photometric-redshift estimation
- A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
- Photometric redshift-aided classification using ensemble learning
- Detecting outliers and learning complex structures with large spectroscopic surveys - a case study with APOGEE stars
- A cooperative approach among methods for photometric redshifts estimation: an application to KiDS data
- Attention-gating for improved radio galaxy classification
- Machine-learning classification of astronomical sources: estimating F1-score in the absence of ground truth
- Star Formation Rates for photometric samples of galaxies using machine learning methods
- Improving the reliability of photometric redshift with machine learning
- Stacking for machine learning redshifts applied to SDSS galaxies
- GAlaxy Light profile convolutional neural NETworks (GaLNets). I. fast and accurate structural parameters for billion galaxy samples
- Simulating the infrared sky with a Spritz
- Euclid preparation: XXII. Selection of Quiescent Galaxies from Mock Photometry using Machine Learning
- Euclid: The selection of quiescent and star-forming galaxies using observed colours
- The Probabilistic Random Forest applied to the selection of quasar candidates in the QUBRICS Survey
- GAME: GAlaxy Machine learning for Emission lines
- Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry
- Evolution of the Stellar Mass Function and Infrared Luminosity Function of Galaxies since
- LeMoN: Lens Modelling with Neural networks -- I. Automated modelling of strong gravitational lenses with Bayesian Neural Networks
- Galaxy classification: A machine learning analysis of GAMA catalogue data
- Improving machine learning-derived photometric redshifts and physical property estimates using unlabelled observations
- Euclid preparation. LI. Forecasting the recovery of galaxy physical properties and their relations with template-fitting and machine-learning methods
- Euclid: Identifying the reddest high-redshift galaxies in the Euclid Deep Fields with gradient-boosted trees
- The classification of real and bogus transients using active learning and semi-supervised learning
- Identifying type II quasars at intermediate redshift with few-shot learning photometric classification
- Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey