arXiv subjects

S. Arnouts

Publications and source records attributed to S. Arnouts.

At least 19 recordsLinked to original sources

arXiv2026

Photometric redshifts for active galactic nuclei with LePHARE for the Vera C. Rubin Observatory

Active Galactic Nuclei (AGN) play a crucial role in galaxy evolution, but they are a minority of extragalactic sources with diverse Spectral Energy Distributions (SEDs), which depend on their means of selection. Upcoming large-scale surveys such as LSST will identify many AGN, but analysis tools are not optimized for them. The limited number of photometric bands in these surveys impacts the calculation of photometric redshifts for AGN, which are essential for scientific advancement. We use LePHARE to demonstrate the impact that a limited number of bands and erroneous assumptions have on the determination of the photometric redshifts of AGN. We conduct tests on six AGN samples selected using X-ray, radio, infrared, variability, color, and spectroscopic criteria in the COSMOS field, using photometry from HSC-CLAUDS, which is closest in depth and wavelength coverage to LSST. We present the LSST pipeline for LePHARE within the Redshift Assessment Infrastructure Layers (RAIL), facilitating comparison between SED fitting and machine learning algorithms. AGN that appear as point-like sources in optical data will be assigned highly unreliable photometric redshifts if they are processed using galaxy templates. Additionally, shallow all-sky surveys (like eROSITA, WISE, and ZTF) miss many AGN. As a result, these "hidden" AGN are often misidentified as galaxies in public survey data, leading to incorrect photometric redshift. We provide the configurations that are suggested for each type of AGN alongside measures of expected performance as a function of redshift, magnitude, and selection. To facilitate studies with a panchromatic view of AGN, we also release photometric redshifts and posterior distributions for all AGN sources identified in the COSMOS field using the six criteria, based on 28-band photometry.

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arXiv2025

The Domain Adaptation problem in photometric redshift estimation: a solution applied to the HSC Survey

The multi-band HSC-CLAUDS survey comprises several sky regions with varying observing conditions, only one of which, the COSMOS Ultra Deep Field (UDF), offers extensive redshift coverage. We aim to exploit a complete sample of labeled galaxies from the COSMOS UDF at i<25 (z<~5) to train a convolutional neural network (CNN) and infer more accurate photometric redshifts in the other regions than those currently available from SED-fitting methods. To address the severe domain mismatch problem we observed when applying the trained CNN to regions other than the COSMOS UDF, we developed an unsupervised adversarial domain adaptation network that we grafted onto the CNN. The method is validated by three tests: the predicted redshifts are compared to the spectroscopic redshifts that are available for limited samples of mostly bright galaxies; the predicted redshift distributions of the entire galaxy population of a given field in several intervals of magnitude are compared to those of the COSMOS UDF, assumed to be representative; the redshifts predicted for a sample of galaxies selected by narrow-band filter observations sensitive to [OII] emitters at z~1.47 are compared to those of confirmed [OII] emission line galaxies. The results show successful domain adaptation: the network is able to transfer its redshift classification capability learnt from the COSMOS UDF to other regions of HSC-CLAUDS. Accuracy varies depending on magnitude and redshift, following that of the labels we used, but far exceeds that of currently available photometric redshifts. The catalogs of CNN redshifts we inferred for the XMM, DEEP2 and ELAIS fields and for the remaining COSMOS region (~4 million sources in total at i<25) are made public.

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arXiv2024

Euclid preparation. The Cosmic Dawn Survey (DAWN) of the Euclid Deep and Auxiliary Fields

Euclid will provide deep NIR imaging to $\sim$26.5 AB magnitude over $\sim$59 deg$^2$ in its deep and auxiliary fields. The Cosmic DAWN survey complements the deep Euclid data with matched depth multiwavelength imaging and spectroscopy in the UV--IR to provide consistently processed Euclid selected photometric catalogs, accurate photometric redshifts, and measurements of galaxy properties to a redshift of $z\sim 10$. In this paper, we present an overview of the survey, including the footprints of the survey fields, the existing and planned observations, and the primary science goals for the combined data set.

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arXiv2024

Euclid. I. Overview of the Euclid mission

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance.

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arXiv2023

CNN photometric redshifts in the SDSS at $r\leq 20$

We release photometric redshifts, reaching $\sim$0.7, for $\sim$14M galaxies at $r\leq 20$ in the 11,500 deg$^2$ of the SDSS north and south galactic caps. These estimates were inferred from a convolution neural network (CNN) trained on $ugriz$ stamp images of galaxies labelled with a spectroscopic redshift from the SDSS, GAMA and BOSS surveys. Representative training sets of $\sim$370k galaxies were constructed from the much larger combined spectroscopic data to limit biases, particularly those arising from the over-representation of Luminous Red Galaxies. The CNN outputs a redshift classification that offers all the benefits of a well-behaved PDF, with a width efficiently signaling unreliable estimates due to poor photometry or stellar sources. The dispersion, mean bias and rate of catastrophic failures of the median point estimate are of order $σ_{\rm MAD}=0.014$, <$Δz_{\rm norm}$>$=0.0015$, $η(|Δz_{\rm norm}|>0.05)=4\%$ on a representative test sample at $r<19.8$, out-performing currently published estimates. The distributions in narrow intervals of magnitudes of the redshifts inferred for the photometric sample are in good agreement with the results of tomographic analyses. The inferred redshifts also match the photometric redshifts of the redMaPPer galaxy clusters for the probable cluster members. The CNN input and output are available at: https://deepdip.iap.fr/treyer+2023.

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arXiv2023

Multimodality for improved CNN photometric redshifts

Photometric redshift estimation plays a crucial role in modern cosmological surveys for studying the universe's large-scale structures and the evolution of galaxies. Deep learning has emerged as a powerful method to produce accurate photometric redshift estimates from multi-band images of galaxies. Here, we introduce a multimodal approach consisting of the parallel processing of several subsets of image bands prior, the outputs of which are then merged for further processing through a convolutional neural network (CNN). We evaluate the performance of our method using three surveys: the Sloan Digital Sky Survey (SDSS), The Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) and Hyper Suprime-Cam (HSC). By improving the model's ability to capture information embedded in the correlation between different bands, our technique surpasses the state-of-the-art photometric redshift precision. We find that the positive gain does not depend on the specific architecture of the CNN and that it increases with the number of photometric filters available.

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arXiv2023

HSC-CLAUDS survey: The star formation rate functions since z ~ 2 and comparison with hydrodynamical simulations

Star formation rate functions (SFRFs) give an instantaneous view of the distribution of star formation rates (SFRs) in galaxies at different epochs. They are a complementary and more stringent test for models than the galaxy stellar mass function, which gives an integrated view of the past star formation activity. However, the exploration of SFRFs has been limited thus far due to difficulties in assessing the SFR from observed quantities and probing the SFRF over a wide range of SFRs. We overcome these limitations thanks to an original method that predicts the infrared luminosity from the rest-frame UV/optical color of a galaxy and then its SFR over a wide range of stellar masses and redshifts. We applied this technique to the deep imaging survey HSC-CLAUDS combined with near-infrared and UV photometry. We provide the first SFR functions with reliable measurements in the high- and low-SFR regimes up to $z=2$ and compare our results with previous observations and four state-of-the-art hydrodynamical simulations.

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arXiv2023

Combining the CLAUDS & HSC-SSP surveys: U+grizy(+YJHKs) photometry and photometric redshifts for 18M galaxies in the 20 deg2 of the HSC-SSP Deep and ultraDeep fields

We present the combination of the Canada-France-Hawaii Telescope (CHFT) Large Area $U$-bands Deep Survey (CLAUDS) and the Hyper-Suprime-Cam (HSC) Subaru Strategic Program (HSC-SSP) data over their four deep fields. We provide photometric catalogs for $u$, $u^*$ (CFHT--MegaCam), $g$, $r$, $i$, $z$, and $y$ (Subaru--HSC) bands over $\sim 20~{\rm deg}^2$, complemented in two fields by data from the Visible and Infrared Survey Telescope for Astronomy (VISTA) Deep Extragalactic Observations (VIDEO) survey and the UltraVISTA survey, thus extending the wavelength coverage toward near-infrared with VIRCAM $Y$, $J$, $H$, and $K_s$ observations over $5.5~{\rm deg}^2$. The extraction of the photometry was performed with two different softwares: the HSC pipeline hscPipe and the standard and robust SExtractor software. Photometric redshifts were computed with template-fitting methods using the new Phosphoros code for the hscPipe photometry and the well-known Le Phare code for the SExtractor photometry. The products of these methods were compared with each other in detail. We assessed their quality using the large spectroscopic sample available in those regions, together with photometry and photometric redshifts from COSMOS2020, the latest version of the Cosmic Evolution Survey catalogs. We find that both photometric data sets are in good agreement in $Ugrizy$ down to magnitude$\sim26$, and to magnitude$\sim24.5$ in the $YJHK_s$ bands. We achieve good performance for the photometric redshifts, reaching precisions of $σ_{NMAD} \lesssim 0.04$ down to ${m}_i\sim25$, even using only the CLAUDS and HSC bands. At the same magnitude limit, we measured an outlier fraction of $η\lesssim 10\%$ when using the $Ugrizy$ bands, and down to $η\lesssim 6\%$ when considering near-infrared data. [abridged]

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arXiv2022

Photometric 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 major forms of biases, i.e., class-dependent residuals and mode collapse, in a case study of estimating photometric redshifts as a classification problem using Convolutional Neural Networks (CNNs) and galaxy images with spectroscopic redshifts. We focus on point estimates and propose a set of consecutive steps for resolving the two biases based on CNN models, involving representation learning with multi-channel outputs, balancing the training data and leveraging soft labels. The residuals can be viewed as a function of spectroscopic redshifts or photometric redshifts, and the biases with respect to these two definitions are incompatible and should be treated in a split way. We suggest that resolving biases in the spectroscopic space is a prerequisite for resolving biases in the photometric space. Experiments show that our methods possess a better capability in controlling biases compared to benchmark methods, and exhibit robustness under varying implementing and training conditions provided with high-quality data. Our methods have promises for future cosmological surveys that require a good constraint of biases, and may be applied to regression problems and other studies that make use of data-driven models. Nonetheless, the bias-variance trade-off and the demand on sufficient statistics suggest the need for developing better methodologies and optimizing data usage strategies.

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arXiv2022

A Cautionary Tale of LyC Escape Fraction Estimates from High Redshift Galaxies

Measuring the escape fraction, $f_{\rm esc}$, of ionizing, Lyman Continuum (LyC) radiation is key to our understanding of the process of cosmic reionization. In this paper we provide a methodology for recovering the posterior probability distribution of the LyC escape fraction, $f_{\rm esc}^{\rm PDF}$, considering both the observational uncertainties and ensembles of simulated transmission functions through the intergalactic medium (IGM). We present an example of this method applied to a VUDS galaxy at $z=3.64$ and find $f_{\rm esc}^{\rm PDF}$ = 0.51$^{+0.33}_{-0.34}$ and compare this to the values computed assuming averaged IGM transmission with and without consideration of detection bias along average sightlines yielding $f_{\rm esc}^{\langle T \rangle}$ = 1.40$^{+0.80}_{-0.42}$, and $f_{\rm esc}^{\rm bias}$ = 0.82$^{+0.33}_{-0.16}$. Our results highlight the limitations of methods assuming average, smooth transmission functions. We also present MOSFIRE data for a sample of seven LyC candidates selected based on photometric redshifts at $z > 3.4$, but find that all seven have overestimated photometric redshifts by $Δz \sim 0.2$ making them unsuitable for LyC measurements. This results likely due to a bias induced by our selection criteria.

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arXiv2021

COSMOS2020: A panchromatic view of the Universe to $z\sim10$ from two complementary catalogs

The Cosmic Evolution Survey (COSMOS) has become a cornerstone of extragalactic astronomy. Since the last public catalog in 2015, a wealth of new imaging and spectroscopic data has been collected in the COSMOS field. This paper describes the collection, processing, and analysis of this new imaging data to produce a new reference photometric redshift catalog. Source detection and multi-wavelength photometry is performed for 1.7 million sources across the $2\,\mathrm{deg}^{2}$ of the COSMOS field, $\sim$966,000 of which are measured with all available broad-band data using both traditional aperture photometric methods and a new profile-fitting photometric extraction tool, The Farmer, which we have developed. A detailed comparison of the two resulting photometric catalogs is presented. Photometric redshifts are computed for all sources in each catalog utilizing two independent photometric redshift codes. Finally, a comparison is made between the performance of the photometric methodologies and of the redshift codes to demonstrate an exceptional degree of self-consistency in the resulting photometric redshifts. The $i<21$ sources have sub-percent photometric redshift accuracy and even the faintest sources at $25<i<27$ reach a precision of $5\,\%$. Finally, these results are discussed in the context of previous, current, and future surveys in the COSMOS field. Compared to COSMOS2015, reaches the same photometric redshift precision at almost one magnitude deeper. Both photometric catalogs and their photometric redshift solutions and physical parameters will be made available through the usual astronomical archive systems (ESO Phase 3, IPAC IRSA, and CDS).

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arXiv2020

Outside the Lyman-break box: detecting Lyman continuum emitters at $3.5<z<5.1$ with CLAUDS

Identifying non-contaminated sample of high-redshift galaxies with escaping Lyman continuum (LyC) flux is important for understanding the sources and evolution of cosmic reionization. We present CLAUDS $u$-band photometry of the COSMOS field to probe LyC radiation from spectroscopically confirmed galaxies at $z\geq3.5$ and outside the standard Lyman-break galaxy colour selection expectations. Complementary to the CLAUDS data, we use Subaru multi-filter photometry, \textit {Hubble Space Telescope} ($HST$) multi-filter imaging, and the spectroscopic surveys D10K, VUDS and 3D-HST. We present a sample of Lyman continuum galaxy (LCG) candidates in the redshift range $3.5\lesssim z\lesssim5.1$. Here, we introduce 5 LCG candidates, where two are flagged quality 1 and three quality 2. The estimated $f_{\rm esc}^{\rm abs}$ for quality 1 candidates are in the range $\sim5\% - 73\%$ and $\sim30\% - 93\%$. These estimates are based on our derived parameters from individual galaxies as inputs to a range of BPASS models as well as mean intergalactic medium (IGM) and maximal intergalactic and circumgalactic media (IGM+CGM) transmission. We conclude that our search for LCGs is most likely biased to lines of sight with low HI densities or free from Lyman limit systems. Our two best LCG candidates have EW (Ly$α)\leq50$Å and we find no correlation or anti-correlation between EW (Ly$α$), $f_{\rm esc}^{\rm abs}$, and $R_{\rm obs}$, the ratio of ionizing to non-ionizing observed flux in the measured passbands. Stacking candidates without solid LyC detections ($S/N<3$) results in an estimated $f_{\rm esc}^{\rm abs}$ from galaxies not greater than $1\%$.

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arXiv2020

PhotoWeb redshift: boosting photometric redshift accuracy with large spectroscopic surveys

Improving distance measurements in large imaging surveys is a major challenge to better reveal the distribution of galaxies on a large scale and to link galaxy properties with their environments. Photometric redshifts can be efficiently combined with the cosmic web (CW) extracted from overlapping spectroscopic surveys to improve their accuracy. We apply a similar method using a new generation of photometric redshifts based on a convolution neural network (CNN). The CNN is trained on the SDSS images with the main galaxy sample (SDSS-MGS, $r \leq 17.8$) and the GAMA spectroscopic redshifts up tor $\sim 19.8$. The mapping of the CW is obtained with 680,000 spectroscopic redshifts from the MGS and BOSS surveys. The redshift probability distribution functions (PDF), which are well calibrated (unbiased and narrow, $\leq 120$ Mpc), intercept a few CW structure along the line of sight. Combining these PDFs with the density field distribution provides new photometric redshifts, $z_{web}$, whose accuracy is improved by a factor of two (i.e.,$σ \sim 0.004(1+z)$) for galaxies with $r \leq 17.8$. For half of them, the distance accuracy is better than 10 cMpc. The narrower the original PDF, the larger the boost in accuracy. No gain is observed for original PDFs wider than 0.03. The final $z_{web}$ PDFs also appear well calibrated. The method performs slightly better for passive galaxies than star-forming ones, and for galaxies in massive groups since these populations better trace the underlying large-scale structure. Reducing the spectroscopic sampling by a factor of 8 still improves the photometric redshift accuracy by 25%. Extending the method to galaxies fainter than the MGS limit still improves the redshift estimates for 70% of the galaxies, with a gain in accuracy of 20% at low $z$ where the resolution of the CW is the highest.

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arXiv2019

The VIMOS Public Extragalactic Redshift Survey (VIPERS) -- Environment-size relation of massive passive galaxies at 0.5 < z < 0.8

We use the statistics of the VIPERS survey to investigate the relation between the surface mean stellar mass density Sigma=Mstar/(2*pi*Re^2) of massive passive galaxies (MPGs, Mstar>10^11 Msun) and their environment in the redshift range 0.5 2*10^11 Msun, we find an excess of MPGs with low Sigma and a deficit of high-Sigma MPGs in the densest regions wrt other environments. We interpret this result as due to the migration of some high-Sigma MPGs (<1% of the total population of MPGs) into low-Sigma MPGs, probably through mergers or cannibalism of small satellites. In summary, our results imply that the accretion of satellite galaxies has a marginal role in the mass-assembly history of most MPGs. We have previously found that the number density of VIPERS massive star-forming galaxies (MSFGs) declines rapidily from z=0.8 to z=0.5, which mirrors the rapid increase in the number density of MPGs. This indicates that the MSFGs at z>0.8 migrate to the MPG population. Here, we investigate the Sigma-delta relation of MSFGs at z>0.8 and find that it is consistent within 1 sigma with that of low-Sigma MPGs at z<0.8. Thus, the results of this and our previous paper show that MSFGs at z>0.8 are consistent in terms of number and environment with being the progenitors of low-Sigma MPGs at z<0.8.

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arXiv2019

Group connectivity in COSMOS: a tracer of mass assembly history

Cosmic filaments are the channel through which galaxy groups assemble their mass. Cosmic connectivity, namely the number of filaments connected to a given group, is therefore expected to be an important ingredient in shaping group properties. The local connectivity is measured in COSMOS around X-Ray detected groups between redshift 0.5 and 1.2. To this end, large-scale filaments are extracted using the accurate photometric redshifts of the COSMOS2015 catalogue in two-dimensional slices of thickness 120 comoving Mpc centred on the group's redshift. The link between connectivity, group mass and the properties of the brightest group galaxy (BGG) is investigated. The same measurement is carried out on mocks extracted from the lightcone of the hydrodynamical simulation Horizon-AGN in order to control systematics. More massive groups are on average more connected. At fixed group mass in low-mass groups, BGG mass is slightly enhanced at high connectivity, while in high mass groups BGG mass is lower at higher connectivity. Groups with a star-forming BGG have on average a lower connectivity at given mass. From the analysis of the Horizon-AGN simulation, we postulate that different connectivities trace different paths of group mass assembly: at high group mass, groups with higher connectivity are more likely to have grown through a recent major merger, which might be in turn the reason for the quenching of the BGG. Future large-field photometric surveys, such as Euclid and LSST, will be able to confirm and extend these results by probing a wider mass range and a larger variety of environment.

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arXiv2019

Horizon-AGN virtual observatory - 1. SED-fitting performance and forecasts for future imaging surveys

Using the ligthcone from the cosmological hydrodynamical simulation Horizon-AGN, we produced a photometric catalogue over $0<z<4$ with apparent magnitudes in COSMOS, DES, LSST-like, and Euclid-like filters at depths comparable to these surveys. The virtual photometry accounts for the complex star formation history and metal enrichment of Horizon-AGN galaxies, and consistently includes magnitude errors, dust attenuation and absorption by inter-galactic medium. The COSMOS-like photometry is fitted in the same configuration as the COSMOS2015 catalogue. We then quantify random and systematic errors of photometric redshifts, stellar masses, and star-formation rates (SFR). Photometric redshifts and redshift errors capture the same dependencies on magnitude and redshift as found in COSMOS2015, excluding the impact of source extraction. COSMOS-like stellar masses are well recovered with a dispersion typically lower than 0.1 dex. The simple star formation histories and metallicities of the templates induce a systematic underestimation of stellar masses at $z<1.5$ by at most 0.12 dex. SFR estimates exhibit a dust-induced bimodality combined with a larger scatter (typically between 0.2 and 0.6 dex). We also use our mock catalogue to predict photometric redshifts and stellar masses in future imaging surveys. We stress that adding Euclid near-infrared photometry to the LSST-like baseline improves redshift accuracy especially at the faint end and decreases the outlier fraction by a factor $\sim$2. It also considerably improves stellar masses, reducing the scatter up to a factor 3. It would therefore be mutually beneficial for LSST and Euclid to work in synergy.

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arXiv2018

The VIMOS Public Extragalactic Redshift Survey (VIPERS). Unsupervised classification with photometric redshifts: a method to accurately classify large galaxy samples without spectroscopic information

Techniques to classify galaxies solely based on photometry will be necessary for future large cosmology missions, such as Euclid or LSST. However, the precision of classification is always lower in photometric surveys and can be systematically biased with respect to classifications based upon spectroscopic data. We verified how precisely the detailed classification scheme introduced by Siudek et al. (2018, hereafter: S1) for galaxies at z~0.7 could be reproduced if only photometric data are available. We applied the Fisher Expectation-Maximization (FEM) unsupervised clustering algorithm to 54,293 VIPERS galaxies working in a parameter space of reliable photometric redshifts and 12 corresponding rest-frame magnitudes. The FEM algorithm distinguishes four main groups: (1) red, (2) green, (3) blue, and (4) outliers. Each group is further divided into 3, 3, 4, and 2 subclasses, respectively. The accuracy of reproducing galaxy classes using spectroscopic data is high: 92%, 84%, 96% for red, green, and blue classes, respectively, except for dusty star-forming galaxies. The presented verification of the photometric classification demonstrates that large photometric samples can be used to distinguish different galaxy classes at z > 0.5 with an accuracy provided so far only by spectroscopic data except for particular galaxy classes.

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arXiv2018

The VIMOS Public Extragalactic Redshift Survey (VIPERS). The complexity of galaxy populations at 0.4< z<1.3 revealed with unsupervised machine-learning algorithms

Various galaxy classification schemes have been developed so far to constrain the main physical processes regulating evolution of different galaxy types. In the era of a deluge of astrophysical information and recent progress in machine learning, a new approach to galaxy classification becomes imperative. We employ a Fisher Expectation-Maximization unsupervised algorithm working in a parameter space of 12 rest-frame magnitudes and spectroscopic redshift. The model (DBk) and the number of classes (12) were established based on the joint analysis of standard statistical criteria and confirmed by the analysis of the galaxy distribution with respect to a number of classes and their properties. This new approach allows us to classify galaxies based just on their redshifts and UV-NIR spectral energy distributions. The FEM unsupervised algorithm has automatically distinguished 12 classes: 11 classes of VIPERS galaxies and an additional class of broad-line AGNs. After a first broad division into blue, green and red categories we obtained a further sub-division into three red, three green, and five blue galaxy classes. The FEM classes follow the galaxy sequence from the earliest to the latest types that is reflected in their colours (which are constructed from rest-frame magnitudes used in classification procedure) but also their morphological, physical, and spectroscopic properties (not included in the classification scheme). We demonstrate that the members of each class share similar physical and spectral properties. In particular, we are able to find three different classes of red passive galaxy populations. Thus, we demonstrate the potential of an unsupervised approach to galaxy classification and we retrieve the complexity of galaxy populations at z~0.7, a task that usual simpler colour-based approaches cannot fulfil.

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