arXiv subjects

N. Aghanim

Publications and source records attributed to N. Aghanim.

At least 19 recordsLinked to original sources

arXiv2026

Euclid. A two-point correlation approach to diagnosing star-related systematics in the Euclid spectroscopic survey

The Euclid spectroscopic survey will measure galaxy clustering with unprecedented precision, requiring stringent control of observational and instrumental systematics. Star-related effects may contaminate spectroscopic images through photometric persistence and imperfect masking of stars. We characterized their impact on galaxy clustering analyses, focusing on angular features identifiable in the data. We used angular and spatial auto- and cross-correlation statistics. For galaxies, we used mock spectroscopic catalogues from the EuclidLargeMocks in a 330 deg2 region of the Euclid Wide Survey (EWS). For stars, we used Gaia and 2MASS catalogues in the same area. To simulate photometric persistence, we implemented a simplified detector-level model calibrated on spectroscopic measurements and varied its strength to introduce different interloper fractions. For stellar masking, we modelled inconsistencies between the mask applied to the data and to the random catalogue. We measured star-star, galaxy-galaxy, and star-galaxy angular correlation functions using the Landy--Szalay estimator, and quantified deviations from the expected null star-galaxy correlation. We also evaluated the large-scale impact of such systematics through the three-dimensional two-point correlation function (2PCF). Photometric persistence produces a characteristic feature in the star-galaxy angular cross-correlation at scales of order 100", corresponding to the Euclid dithering pattern and grism dispersion geometry, and a spurious positive signal approximately constant up to 1{\deg}. Star-galaxy cross-correlation can detect residual persistence at contamination levels as low as 10% in a Data Release 1 spectroscopic catalogue. In contrast, stellar-mask mismatches produce strong small-scale angular signatures but have negligible impact on the large-scale 2PCF under realistic EWS conditions.

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arXiv2026

Euclid preparation. Three-dimensional galaxy clustering in configuration space: Three-point correlation function estimation

Higher-order correlation functions are firmly established as a fundamental tool for the statistical analysis of clustering in modern galaxy surveys. It was demonstrated that they greatly enrich the information content extracted by two-point statistics, allowing us to break the degeneracies between model parameters and constrain departures from Gaussianity. This paper presents the statistical estimators adopted to evaluate the galaxy three-point correlation function and its numerical implementation within the data analysis pipeline of the Euclid Science Ground Segment. Two different algorithms are adopted to count triplets: a direct and exact counting method capable of providing a robust three-point correlation function measurement for any triangular configuration, and a more efficient method based on spherical harmonic decomposition, designed to address the computational challenges of measuring the three-point statistics for data sets as large as those of the final Euclid survey. The spherical harmonic decomposition estimates the Legendre coefficients of the three-point correlation function up to a finite expansion order. Despite being an approximation, the three-point function measured with this approach satisfies the scientific requirements of the mission. We also introduce, implement, and validate the random split technique, which reduces the computational cost of counting triplets in the reference random sample by a factor of 10, without significantly compromising numerical accuracy. We evaluated the robustness, precision, and accuracy of the numerical estimates through an extensive campaign of validation tests, the results of which are presented. Finally, we quantify the computational requirements and their scaling with the expected size of Euclid data set, showing that a complete three-point analysis of the final Euclid survey is within computational reach.

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arXiv2026

Euclid preparation. Impact of redshift distribution uncertainties on the joint analysis of photometric galaxy clustering and weak gravitational lensing

One of the $\textit{Euclid}$ mission's key projects is the so-called 3$\times$2pt analysis, that is, the combination of cosmic shear, photometric galaxy clustering, and galaxy-galaxy lensing. Although $\textit{Euclid}$ has established quality requirements for the photo-$z$ accuracy needed for the weak lensing galaxy sample, no such requirements have been set for the photometric clustering sample. In this paper, we investigate the impact of redshift uncertainties on $\textit{Euclid}$'s photometric galaxy clustering analysis and its combination with weak gravitational lensing, focusing on data release 1 (DR1). In particular, we study whether having precise knowledge of the mean of the redshift distributions per bin is sufficient to avoid biases in the resulting cosmological constraints or whether accuracy in the higher-order moments of the distribution is required. We evaluate the results based on their constraining power on $w_{\mathrm{0}}$ and $w_{a}$ and define thresholds for the precision and accuracy of $\textit{Euclid}$'s redshift distribution of the photometric clustering sample. We find that the redshift distributions of the photometric clustering sample must be known at an accuracy of 0.004(1+$z$) in the mean in order to recover 80$\%$ of the constraining power in $\textit{Euclid}$'s DR1 $w_{\mathrm{0}}w_{a}$CDM 3$\times$2pt analysis. The impact of the uncertainty on the width is negligible, provided the mean redshift is constrained with sufficient accuracy. For most sources of redshift distribution error, attaining the requirement on the mean will also reduce uncertainty in the width well below the required level.

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arXiv2026

Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine F -- Bright and low-redshift strong lenses

We present 72 additional galaxy-galaxy strong lenses that complement the sample discovered in the Euclid Quick Release 1 data (63.1 deg^2) of the Strong Lens Discovery Engine (SLDE) papers A-E. It is shown that previous pre-selection of potential lenses, which excluded objects from the Gaia catalogue, led to missing several bright and low-redshift strong lenses, adding more than 10% new strong lens candidates compared to the previous search. In total, the catalogue includes 38 "grade A" (confident) and 34 "grade B" (probable) candidates. These lenses are identified through a combination of two independent searches for bright nearby objects: one based on machine-learning models followed by expert visual inspection, and the other based solely on expert visual inspection, targeting objects not included in the initial machine-learning selection (a limitation identified only after extensive visual inspection). With these additional strong lens candidates, we augment the expected number of high-confidence candidates in the Euclid Wide Survey from previous forecasts to 120000. Detailed semi-automated lens modelling confirms at least 41 systems out of 72, a fraction consistent with that found in SLDE A (315 out of 488). These include: multiple edge-on disc lenses; sources with arcs near the lens centre; "red sources"; and an edge-on disk galaxy lensing a galaxy merger, producing two sets of lensed features, an Einstein ring and a doubly imaged component. The median redshift of these systems is $\Delta$ z ~ 0.3 lower than that of the SLDE A sample.

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arXiv2026

Euclid preparation. Far-infrared predictions for Euclid galaxy catalogues: cluster, protocluster, and field

The MAMBO mock galaxy catalogue, based on the Millennium Simulation with empirically assigned galaxy properties, provides predictions of FIR fluxes and physical parameters of Euclid-detectable galaxies. Predicted FIR flux distributions confirm that only the brightest Euclid sources will be detectable in existing FIR surveys. We employ stacking to measure the mean dust properties as a function of stellar mass and redshift. We find dust temperatures and infrared luminosities increase with redshift across all mass bins, while dust masses remain roughly constant. FIR number counts from MAMBO show overall good agreement with observations, and the total infrared luminosity function reproduces published estimates across most redshift ranges, extending to z~10. Comparing the Euclid Wide and Deep Surveys, we find that the EDS recovers the total IRLF to fainter luminosities and higher redshifts (up to z~6 in $I_E$), although its detectability falls below 80% at z>4, whereas the EWS becomes strongly incomplete beyond z~2. We also examine the dependence of the IRLF on environment. Schechter fits indicate that the faint-end slope $\alpha$ flattens with redshift for cluster and protocluster galaxies, while remaining approximately constant for field populations. Imposing additional detection limits from Herschel-PACS and SPIRE shows that only the most luminous ($L_{IR}$ > $10^{12.5}$ $L_{\odot}$) galaxies remain detectable at z~4, but the limited MAMBO area (3.14$deg^2$) is inadequate for statistically robust (>3$\sigma$) constraints. Survey areas at least 30 times larger are required. Overall, the MAMBO FIR extension reproduces key number count and IRLF trends, provides realistic predictions for FIR-detected Euclid galaxies, and highlights the importance of synergies with current and future FIR/sub-mm facilities to probe environmental dependence with sufficient depth and area.

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arXiv2026

Euclid. Properties and performance of the NISP signal estimator

The Euclid spacecraft, located at the second Lagrangian point of the Sun-Earth system, hosts the Near-Infrared Spectrometer and Photometer (NISP) instrument. NISP is equipped with a mosaic of 16 HgCdTe-based detectors to acquire near-infrared photometric and spectroscopic data. To meet the spacecraft's constraints on computational resources and telemetry bandwidth, the near-infrared signal is processed onboard via a dedicated hardware-software architecture designed to fulfil the stringent Euclid's data-quality requirements. A custom application software, running on the two NISP data processing units, implements the NISP signal estimator: an ad-hoc algorithm which delivers accurate flux measurements and simultaneously estimates the quality of signal estimation through the quality factor parameter. This paper investigates the properties of the NISP signal estimator by evaluating its performance during the early flight operations of the NISP instrument. First, we revisit the assumptions on which the inference of the near-infrared signal is based and investigate the origin of the main systematics of the signal estimator through Monte Carlo simulations. Then, we test the flight performance of the NISP signal estimator. Results indicate a systematic bias lower than 0.01 e/s for 99% of the NISP pixel array, well within the noise budget of the estimated signal. We also derive an analytical expression for the variance of the NISP signal estimator, demonstrating its validity, particularly when the covariance matrix is not pre-computed. Finally, we provide a robust statistical framework to interpret the QF parameter, analyse its dependence on the signal estimator bias, and show its sensitivity to cosmic ray hits on NISP detectors. Our findings corroborate previous results on the NISP signal estimator and suggest a leading-order correction based on the agreement between flight data and simulations.

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arXiv2026

Euclid preparation. 3D reconstruction of the cosmic web with simulated Euclid Deep spectroscopic samples

The ongoing Euclid mission aims to measure spectroscopic redshifts for approximately two million galaxies using the H $\alpha$ line emission detected in near-infrared slitless spectroscopic data from the Euclid Deep Fields (EDFs). These measurements will reach a flux limit of $5\times 10^{-17}\,{\rm erg}\,{\rm cm}^{-2}\,{\rm s}^{-1}$ in the redshift range $0.4<z<1.8$, opening the door to numerous investigations involving galaxy evolution, extending well beyond the mission's core objectives. The achieved H $\alpha$ luminosity depth will lead to a sufficiently high sampling, enabling the reconstruction of the large-scale galaxy environment. We assess the quality of the reconstruction of the galaxy cosmic web environment with the expected spectroscopic dataset in EDFs. The analysis is carried out on the Flagship and GAEA galaxy mock catalogues. The quality of the reconstruction is first evaluated using geometrical and topological statistics measured on the cosmic web, namely the length of filaments, the area of walls, the volume of voids, and its connectivity and multiplicity. We then quantify how accurately gradients in galaxy properties with distance from filaments can be recovered. As expected, the small-scale redshift-space distortions, have a strong impact on filament lengths and connectivity, but can be mitigated by compressing galaxy groups before skeleton extraction. The cosmic web reconstruction is biased when relying solely on H $\alpha$ emitters. This limitation can be mitigated by applying stellar mass weighting during the reconstruction. However, this approach introduces non-trivial biases that need to be accounted for when comparing to theoretical predictions. Redshift uncertainties pose the greatest challenge in recovering the expected dependence of galaxy properties, though the well-established stellar mass transverse gradients towards filaments can still be observed.

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arXiv2026

Euclid preparation. Galaxy 2-point correlation function modelling in redshift space

The Euclid satellite will measure spectroscopic redshifts for tens of millions of emission-line galaxies. In the context of Stage-IV surveys, the 3-dimensional clustering of galaxies plays a key role in providing cosmological constraints. In this paper, we conduct a model comparison for the multipole moments of the galaxy 2-point correlation function (2PCF) in redshift space. We test state-of-the-art models, in particular the effective field theory of large-scale structure (EFT), one based on the velocity difference generating function (VDG$_{\infty}$), and different variants of Lagrangian perturbation theory (LPT) models, such as convolutional LPT (CLPT) and its effective-field-theory extension (CLEFT). We analyse the first three even multipoles of the 2PCF in the Flagship 1 simulation, which consists of four snapshots at $z\in\{0.9,1.2,1.5,1.8\}$. We study both template-fitting and full-shape approaches and find that with the template-fitting approach, only the VDG$_{\infty}$ model is able to reach a minimum fitting scale of $s_{\rm min}=20\,h^{-1}\,{\rm Mpc}$ at $z=0.9$ without biasing the recovered parameters. Indeed, the EFT model becomes inaccurate already at $s_{\rm min}=30\,h^{-1}\,{\rm Mpc}$. Conversely, in the full-shape analysis, the CLEFT and VDG$_{\infty}$ models perform similarly well, but only the CLEFT model can reach $s_{\rm min}=20\,h^{-1}\,{\rm Mpc}$ while the VDG$_{\infty}$ model is unbiased down to $s_{\rm min}=25\,h^{-1}\,{\rm Mpc}$ at the lowest redshift. Overall, in order to achieve the accuracy required by Euclid, non-perturbative modelling such as in the VDG$_{\infty}$ or CLEFT models should be considered. At $z=1.8$, the CLPT model is sufficient to describe the data with high figure of merit. This comparison selects baseline models that perform best in ideal conditions and sets the stage for an optimal analysis of Euclid data in configuration space.

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arXiv2026

Euclid: Improving redshift distribution reconstruction using a deep-to-wide transfer function

The Euclid mission seeks to understand the Universe expansion history and the nature of dark energy, which requires a very accurate estimate of redshift distribution. Achieving this accuracy relies on reference samples with spectroscopic redshifts, together with a procedure to match them to survey sources for which only photometric redshifts are available. One important source of systematic uncertainty is the mismatch in photometric properties between galaxies in the Euclid survey and the reference objects. We develop a method to degrade the photometry of objects with deep photometry to match the properties of any shallower survey in the multi-band photometric space, preserving all the correlations between the fluxes and their uncertainties. We compare our transfer method with more demanding image-based methods, such as Balrog from the Dark Energy Survey Collaboration. According to metrics, our method outperforms Balrog. We implement it in the redshift distribution reconstruction, based on the self-organising map approach of arXiv:1509.03318, and test it using a realistic sample from the Euclid Flagship Simulation. We find that the key ingredient is to ensure that the reference objects are distributed in the colour space the same way as the wide-survey objects, which can be efficiently achieved with our transfer method. In our best implementation, the mean redshift biases are consistently reduced across the tomographic bins, bringing a significant fraction of them within the Euclid accuracy requirements in all tomographic bins. Equally importantly, the tests allow us to pinpoint which step in the calibration pipeline has the strongest impact on achieving the required accuracy. Our approach also reproduces the overall redshift distributions, which are crucial for applications such as angular clustering.

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arXiv2025

Euclid Quick Data Release (Q1). From simulations to sky: Advancing machine-learning lens detection with real Euclid data

In the era of large-scale surveys like Euclid, machine learning has become an essential tool for identifying rare yet scientifically valuable objects, such as strong gravitational lenses. However, supervised machine-learning approaches require large quantities of labelled examples to train on, and the limited number of known strong lenses has lead to a reliance on simulations for training. A well-known challenge is that machine-learning models trained on one data domain often underperform when applied to a different domain: in the context of lens finding, this means that strong performance on simulated lenses does not necessarily translate into equally good performance on real observations. In Euclid's Quick Data Release 1 (Q1), covering 63 deg2, 500 strong lens candidates were discovered through a synergy of machine learning, citizen science, and expert visual inspection. These discoveries now allow us to quantify this performance gap and investigate the impact of training on real data. We find that a network trained only on simulations recovers up to 92% of simulated lenses with 100% purity, but only achieves 50% completeness with 24% purity on real Euclid data. By augmenting training data with real Euclid lenses and non-lenses, completeness improves by 25-30% in terms of the expected yield of discoverable lenses in Euclid DR1 and the full Euclid Wide Survey. Roughly 20% of this improvement comes from the inclusion of real lenses in the training data, while 5-10% comes from exposure to a more diverse set of non-lenses and false-positives from Q1. We show that the most effective lens-finding strategy for real-world performance combines the diversity of simulations with the fidelity of real lenses. This hybrid approach establishes a clear methodology for maximising lens discoveries in future data releases from Euclid, and will likely also be applicable to other surveys such as LSST.

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arXiv2025

Euclid: The first statistical census of dusty and massive objects in the ERO/Perseus field

Our comprehension of the history of star formation at $z>3$ relies on rest-frame UV observations, yet this selection misses the most dusty and massive sources, yielding an incomplete census at early times. Infrared facilities such as Spitzer and the James Webb Space Telescope have revealed a hidden population at $z=3$-$6$ with extreme red colours, named HIEROs (HST-to-IRAC extremely red objects), identified by the criterion $H_{\mathrm{E}}-\mathrm{ch2}>2.25$. Recently, Euclid Early Release Observations (ERO) have made it possible to further study such objects by comparing Euclid data with ancillary Spitzer/IRAC imaging. We investigate a $232$ arcmin$^2$ area in the Perseus field using VIS and NISP photometry, complemented by the four Spitzer channels and ground-based MegaCam bands ($u$, $g$, $r$, ${\rm H}\alpha$, $i$, $z$). Applying the colour cut yields $121$ HIEROs; after removing globular clusters, brown dwarfs, and unreliable cases through visual inspection of multiband cutouts, we obtain a final sample of $42$ robust HIEROs. Photometric redshifts and physical properties are estimated with the SED-fitting code Bagpipes. From the resulting $z_{\mathrm{phot}}$ and $M_*$ values, we compute the galaxy stellar mass function at $3.5<z<5.5$. Even after excluding possible AGN hosts or systems where the stellar mass may be overestimated, the high-mass end remains comparable to previous determinations, suggesting the true abundance could be higher. These results highlight the importance of further study of this obscured population to assess its role in the cosmic star-formation rate density and its consistency with galaxy-formation models, demonstrating Euclid's capability to advance our understanding of dust-hidden star formation across early epochs.

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arXiv2025

Euclid preparation: LXXXI. The impact of nonparametric star formation histories on spatially resolved galaxy property estimation using synthetic Euclid images

We analyzed the spatially resolved and global star formation histories (SFHs) for a sample of 25 TNG50-SKIRT Atlas galaxies to assess the feasibility of reconstructing accurate SFHs from Euclid-like data. This study provides a proof of concept for extracting the spatially resolved SFHs of local galaxies with Euclid, highlighting the strengths and limitations of SFH modeling in the context of next-generation galaxy surveys. We used the spectral energy distribution (SED) fitting code Prospector to model both spatially resolved and global SFHs using parametric and nonparametric configurations. The input consisted of mock ultraviolet--near-infrared photometry derived from the TNG50 cosmological simulation and processed with the radiative transfer code SKIRT. We show that nonparametric SFHs provide a more effective approach to mitigating the outshining effect by recent star formation, offering improved accuracy in the determination of galaxy stellar properties. Also, we find that the nonparametric SFH model at resolved scales closely recovers the stellar mass formation times (within 0.1~dex) and the ground truth values from TNG50, with an absolute average bias of $0.03$~dex in stellar mass and $0.01$~dex in both specific star formation rate and mass-weighted age. In contrast, larger offsets are estimated for all stellar properties and formation times when using a simple $\tau$-model SFH, at both resolved and global scales, highlighting its limitations. These results emphasize the critical role of nonparametric SFHs in both global and spatially resolved analyses, as they better capture the complex evolutionary pathways of galaxies and avoid the biases inherent in simple parametric models.

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arXiv2025

Cosmology Likelihood for Observables in \Euclid (CLOE). 1. Theoretical recipe

As the statistical precision of cosmological measurements increases, the accuracy of the theoretical description of these measurements needs to increase correspondingly in order to infer the underlying cosmology that governs the Universe. To this end, we have created the Cosmology Likelihood for Observables in Euclid (CLOE), which is a novel cosmological parameter inference pipeline developed within the Euclid Consortium to translate measurements and covariances into cosmological parameter constraints. In this first in a series of six papers, we describe the theoretical recipe of this code for the Euclid primary probes. These probes are composed of the photometric 3x2pt observables of cosmic shear, galaxy-galaxy lensing, and galaxy clustering, along with spectroscopic galaxy clustering. We provide this description in both Fourier and configuration space for standard and extended summary statistics, including the wide range of systematic uncertainties that affect them. This includes systematic uncertainties such as intrinsic galaxy alignments, baryonic feedback, photometric and spectroscopic redshift uncertainties, shear calibration uncertainties, sample impurities, photometric and spectroscopic galaxy biases, as well as magnification bias. The theoretical descriptions are further able to accommodate both Gaussian and non-Gaussian likelihoods and extended cosmologies with non-zero curvature, massive neutrinos, evolving dark energy, and simple forms of modified gravity. These theoretical descriptions that underpin CLOE will form a crucial component in revealing the true nature of the Universe with next-generation cosmological surveys such as Euclid.

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arXiv2025

Euclid preparation. Cosmology Likelihood for Observables in Euclid (CLOE). 4: Validation and Performance

The Euclid satellite will provide data on the clustering of galaxies and on the distortion of their measured shapes, which can be used to constrain and test the cosmological model. However, the increase in precision places strong requirements on the accuracy of the theoretical modelling for the observables and of the full analysis pipeline. In this paper, we investigate the accuracy of the calculations performed by the Cosmology Likelihood for Observables in Euclid (CLOE), a software able to handle both the modelling of observables and their fit against observational data for both the photometric and spectroscopic surveys of Euclid, by comparing the output of CLOE with external codes used as benchmark. We perform such a comparison on the quantities entering the calculations of the observables, as well as on the final outputs of these calculations. Our results highlight the high accuracy of CLOE when comparing its calculation against external codes for Euclid observables on an extended range of operative cases. In particular, all the summary statistics of interest always differ less than $0.1\,\sigma$ from the chosen benchmark, and CLOE predictions are statistically compatible with simulated data obtained from benchmark codes. The same holds for the comparison of correlation function in configuration space for spectroscopic and photometric observables.

astro-ph.CO↗

arXiv2025

Euclid preparation. Predicting star-forming galaxy scaling relations with the spectral stacking code SpectraPyle

We introduce SpectraPyle, a versatile spectral stacking pipeline developed for the Euclid mission's NISP spectroscopic surveys, aimed at extracting faint emission lines and spectral features from large galaxy samples in the Wide and Deep Surveys. Designed for computational efficiency and flexible configuration, SpectraPyle supports the processing of extensive datasets critical to Euclid's non-cosmological science goals. We validate the pipeline using simulated spectra processed to match Euclid's expected final data quality. Stacking enables robust recovery of key emission lines, including Halpha, Hbeta, [O III], and [N II], below individual detection limits. However, the measurement of galaxy properties such as star formation rate, dust attenuation, and gas-phase metallicity are biased at stellar mass below log10(M*/Msol) ~ 9 due to the flux-limited nature of Euclid spectroscopic samples, which cannot be overcome by stacking. The SFR-stellar mass relation of the parent sample is recovered reliably only in the Deep survey for log10(M*/Msol) > 10, whereas the metallicity-mass relation is recovered more accurately over a wider mass range. These limitations are caused by the increased fraction of redshift measurement errors at lower masses and fluxes. We examine the impact of residual redshift contaminants that arises from misidentified emission lines and noise spikes, on stacked spectra. Even after stringent quality selections, low-level contamination (< 6%) has minimal impact on line fluxes due to the systematically weaker emission of contaminants. Percentile-based analysis of stacked spectra provides a sensitive diagnostic for detecting contamination via coherent spurious features at characteristic wavelengths. While our simulations include most instrumental effects, real Euclid data will require further refinement of contamination mitigation strategies.

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arXiv2025

Implications of SPT and eROSITA cosmologies for Planck cluster number counts and t-SZ power spectrum

Comparison between cosmological studies is usually performed in a statistical manner at the level of the posteriors of cosmological parameters. In this Letter, we show how this approach poorly reflects the differences between cosmological analyses, when applied to cosmological studies using galaxy cluster abundances. We illustrate this by deriving the implications of the best-fit cosmologies from the recent SPT and eROSITA cluster number counts analyses on the Planck thermal Sunyaev-Zeldovich (t-SZ) probes. We first fix the mass calibration, and find that the Planck cluster sample would theoretically contain 498 clusters with the SPT cosmology, and 1098 clusters with the eROSITA cosmology, instead of the 439 clusters observed. We then fit the Planck number counts to both cosmologies, only varying the hydrostatic mass bias, and find required biases of $0.790 \pm 0.070$ and $0.630 \pm 0.034$ for SPT and eROSITA respectively, instead of the $0.844^{+0.055}_{-0.062}$ derived in Aymerich et al. (2025). Lastly, we compute the expected t-SZ power spectrum obtained from the SPT and eROSITA cosmologies, and compare these to the Planck measurement. While the predicted SPT angular power spectrum is in good agreement with the Planck measurements, the normalisation of the predicted eROSITA angular power spectrum is two times higher at all scales. These two tests highlight the power of comparing predicted cluster abundances and t-SZ power spectra to measured data in a physically interpretable way.

astro-ph.CO↗

arXiv2025

Euclid preparation. LXXIV. Euclidised observations of Hubble Frontier Fields and CLASH galaxy clusters

We present HST2EUCLID, a novel Python code to generate Euclid realistic mock images in the $H_{\rm E}$, $J_{\rm E}$, $Y_{\rm E}$, and $I_{\rm E}$ photometric bands based on panchromatic Hubble Space Telescope observations. The software was used to create a simulated database of Euclid images for the 27 galaxy clusters observed during the Cluster Lensing And Supernova survey with Hubble (CLASH) and the Hubble Frontier Fields (HFF) program. Since the mock images were generated from real observations, they incorporate, by construction, all the complexity of the observed galaxy clusters. The simulated Euclid data of the galaxy cluster MACS J0416.1$-$2403 were then used to explore the possibility of developing strong lensing models based on the Euclid data. In this context, complementary photometric or spectroscopic follow-up campaigns are required to measure the redshifts of multiple images and cluster member galaxies. By Euclidising six parallel blank fields obtained during the HFF program, we provide an estimate of the number of galaxies detectable in Euclid images per ${\rm deg}^2$ per magnitude bin (number counts) and the distribution of the galaxy sizes. Finally, we present a preview of the Chandra Deep Field South that will be observed during the Euclid Deep Survey and two examples of galaxy-scale strong lensing systems residing in regions of the sky covered by the Euclid Wide Survey. The methodology developed in this work lends itself to several additional applications, as simulated Euclid fields based on HST (or JWST) imaging with extensive spectroscopic information can be used to validate the feasibility of legacy science cases or to train deep learning techniques in advance, thus preparing for a timely exploitation of the Euclid Survey data.

astro-ph.CO↗

arXiv2025

Euclid preparation. Establishing the quality of the 2D reconstruction of the filaments of the cosmic web with DisPerSE using Euclid photometric redshifts

Cosmic filaments are prominent structures of the matter distribution of the Universe. Modern detection algorithms are an efficient way to identify filaments in large-scale observational surveys of galaxies. Many of these methods were originally designed to work with simulations and/or well-sampled spectroscopic surveys. When spectroscopic redshifts are not available, the filaments of the cosmic web can be detected in projection using photometric redshifts in slices along the Line of Sight, which enable the exploration of larger cosmic volumes. However, this comes at the expense of a lower redshift precision. It is therefore crucial to assess the differences between filaments extracted from exact redshifts and from photometric redshifts for a specific survey. We apply this analysis to capture the uncertainties and biases of filament extractions introduced by using the photometric sample of the Euclid Wide Survey. The question that we address in this work is how can we compare two filament samples derived with redshifts of different precisions in the Euclid Wide Survey context. We apply the cosmic web detection algorithm DisPerSE, in the redshift range $0.1 \leq z \leq 0.5$, to the GAlaxy Evolution and Assembly (GAEA) simulated galaxy sample which reproduces several characteristics of the Euclid Wide Survey. We develop a method to compare skeletons derived from photometric redshifts to those derived from true galaxy positions. This method expands the commonly used measure of distance between filaments to include geometrical (angles between filaments) and astrophysical considerations (galaxy mass gradients and connectivity-mass relations). We assess whether this approach strengthens our ability to correctly identify filaments in very large surveys such as the Euclid Wide Survey. [abridged]

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