Euclid preparation. LI. Forecasting the recovery of galaxy physical properties and their relations with template-fitting and machine-learning methods

Abstract

Euclid will collect an enormous amount of data during the mission's lifetime, observing billions of galaxies in the extragalactic sky. Along with traditional template-fitting methods, numerous machine learning algorithms have been presented for computing their photometric redshifts and physical parameters (PPs), requiring significantly less computing effort while producing equivalent performance measures. However, their performance is limited by the quality and amount of input information, to the point where the recovery of some well-established physical relationships between parameters might not be guaranteed. To forecast the reliability of Euclid photo-$z$s and PPs calculations, we produced two mock catalogs simulating Euclid photometry. We simulated the Euclid Wide Survey (EWS) and Euclid Deep Fields (EDF). We tested the performance of a template-fitting algorithm (Phosphoros) and four ML methods in recovering photo-$z$s, PPs (stellar masses and star formation rates), and the SFMS. To mimic the Euclid processing as closely as possible, the models were trained with Phosphoros-recovered labels. For the EWS, we found that the best results are achieved with a mixed labels approach, training the models with wide survey features and labels from the Phosphoros results on deeper photometry, that is, with the best possible set of labels for a given photometry. This imposes a prior, helping the models to better discern cases in degenerate regions of feature space, that is, when galaxies have similar magnitudes and colors but different redshifts and PPs, with performance metrics even better than those found with Phosphoros. We found no more than 3% performance degradation using a COSMOS-like reference sample or removing u band data, which will not be available until after data release DR1. The best results are obtained for the EDF, with appropriate recovery of photo-$z$, PPs, and the SFMS.

26 pages, 13 figures. Accepted for publication on A&A

References in corpus (41)

• Array Programming with NumPy
• Mass and environment as drivers of galaxy evolution in SDSS and zCOSMOS and the origin of the Schechter function
• The Cosmic Evolution Survey (COSMOS) -- Overview
• Detection of quiescent galaxies in a bicolor sequence from z=0-2
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• Euclid. I. Overview of the Euclid mission
• COSMOS2020: A panchromatic view of the Universe to $z\sim 10$ from two complementary catalogs
• The galaxy stellar mass function at 3.5<z<7.5 in the CANDELS/UDS, GOODS-South, and HUDF fields
• Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks
• Photometric redshift estimation via deep learning
• A Multiwavelength Consensus on the Main Sequence of Star-Forming Galaxies at z~2
• Euclid. III. The NISP Instrument
• Euclid. II. The VIS Instrument
• The Canada-France Imaging Survey: First results from the u-band component
• Deep learning for galaxy surface brightness profile fitting
• Euclid preparation. XVIII. The NISP photometric system
• Euclid preparation: XVIII. Cosmic Dawn Survey. Spitzer observations of the Euclid deep fields and calibration fields
• Chasing passive galaxies in the early Universe: a critical analysis in CANDELS GOODS-South
• Massive and old quiescent galaxies at high redshift
• Star formation rates and stellar masses from machine learning
• A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
• Photometric redshift-aided classification using ensemble learning
• A cooperative approach among methods for photometric redshifts estimation: an application to KiDS data
• Attention-gating for improved radio galaxy classification
• Star Formation Rates for photometric samples of galaxies using machine learning methods
• Predicting star formation properties of galaxies using deep learning
• Forward modeling of galaxy populations for cosmological redshift distribution inference
• Improving the reliability of photometric redshift with machine learning
• COSMOS2020: Manifold Learning to Estimate Physical Parameters in Large Galaxy Surveys
• SED-dependent Galactic Extinction Prescription for Euclid and Future Cosmological Surveys
• Hierarchical Bayesian inference of photometric redshifts with stellar population synthesis models
• The Probabilistic Random Forest applied to the selection of quasar candidates in the QUBRICS Survey
• pop-cosmos: A comprehensive picture of the galaxy population from COSMOS data
• Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry
• Euclid preparation. XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning
• LeMoN: Lens Modelling with Neural networks -- I. Automated modelling of strong gravitational lenses with Bayesian Neural Networks
• Euclid Preparation XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong lensing events
• Selection of powerful radio galaxies with machine learning
• MAMBO -- An empirical galaxy and AGN mock catalogue for the exploitation of future surveys
• Euclid: Identifying the reddest high-redshift galaxies in the Euclid Deep Fields with gradient-boosted trees
• Euclid preparation. XXXI. The effect of the variations in photometric passbands on photometric-redshift accuracy

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• Euclid. V. The Flagship galaxy mock catalogue: a comprehensive simulation for the Euclid mission
• Euclid preparation. LXVIII. Extracting physical parameters from galaxies with machine learning
• MAMBO -- An empirical galaxy and AGN mock catalogue for the exploitation of future surveys
• Euclid preparation. Estimating galaxy physical properties using CatBoost chained regressors with attention
• A Value-added Physical Properties Catalog for Low-redshift Galaxies from DESI Legacy Imaging Surveys DR10