Lorenzo Bazzanini

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 (Teledyne’s H2RG) 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 on board via a dedicated hardware-software architecture designed to fulfil Euclid ’s stringent data quality requirements. A custom application software, running on the two NISP data-processing units, implements the NISP signal estimator: an ad hoc algorithm that delivers accurate flux measurements and simultaneously estimates the quality of signal estimation through the quality factor (QF) parameter. This paper further 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 explain how we tested the flight performance of the NISP signal estimator. Results indicate a systematic bias lower than 0.01 e ⁻ s ⁻¹ for 99% of the NISP pixel array, which is well within the noise budget of the estimated signal. We also derived 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.

Stage-IV galaxy surveys will provide the opportunity to test cosmological models and the underlying theory of gravity with unparal-leled precision. In this context, it is crucial for the Euclid mission to leverage its spectroscopic and photometric probes to systematically investigate and incorporate non-standard cosmological models, including modified gravity, alternative dark energy scenarios, massive neutrinos, and primordial non-Gaussianity. For this, we produce and release publicly simulated galaxy catalogues from a broad suite of non-standard cosmological simulations, which we processed through a model-independent analytical pipeline, making use of ROCKSTAR for halo identification, and a modified version of the SciPic library for the galaxy-halo connection using the halo occupation distribution framework. One of the goals of this paper is also to test the validity of the template-fitting method for galaxy clustering analyses, even in cases where the template significantly differ from data. For this purpose, we investigate the multipoles of the two-point correlation function in redshift space and VDG _∞ , a highly performant, state-of-the-art model for galaxy clustering. Across a wide range of models, the linear growth rate multiplied by the matter density within spheres of radius of 12 Mpc, fσ ₁₂ , exhibits a notable robustness to the choice of cosmological template. Compared to previous works, our study extends this result to numerous scenarios with markedly distinct gravitational or dark energy dynamics. We find that the most of the scatter in cosmological parameter inference already appears when using the cosmological model of the simulations as templates. Using a ‘wrong’ template can also introduce an additional scatter, although with smaller amplitude. Often, we find deviations much larger than error bars, meaning that the Gaussian approximation for the covariance might need to be further studied. Looking ahead, future cosmological investigations must broaden their scope to include a diverse array of non-standard theoretical frameworks extending beyond ΛCDM and rudimentary dynamic dark energy models.

We present a strong-lensing analysis of the galaxy cluster MACS J0138.0−2155 ( z _l = 0.336), the first known cluster lens discovered to host two distinct multiply imaged type Ia supernovae (SNe): SN Requiem and SN Encore. Both transients are located in the massive, multiply imaged red galaxy MRG-M0138 at z _s = 1.949. The projected total mass distribution of this cluster has been investigated with several independent lens models, using a sample of 23 spectroscopically confirmed multiple images from eight background sources spanning 0.767 < z < 3.420, identified from Hubble Space Telescope and James Webb Space Telescope imaging, and VLT/MUSE spectroscopy. For this study, we developed a new lens model with the Bayesian parametric lens-modelling framework G RAVITY . JL , using the same strong-lensing dataset. Under a fiducial flat Lambda Cold Dark Matter cosmology, our reference mass model accurately reproduces the observed image positions, with an image-plane root-mean-square positional residual of Δ _rms = 0.15″. Assuming H ₀ = 70 km s ⁻¹ Mpc ⁻¹ , we predicted the future reappearances of highly delayed SN counter images, finding Δ t _1d,1a = 3122 ₋₇₀ ⁺⁷⁵ days (from approximately April–August 2032) for SN Encore and Δ t _2d,2a = 3122 ₋₇₂ ⁺⁶⁷ days (from approximately January–May 2027) for SN Requiem. By allowing the Hubble constant to vary and including the measured relative time delays of both SN Encore and SN Requiem, together with their statistical uncertainties, as observables, we inferred the value of H ₀ jointly with the lens-model parameters. We performed the cosmographic analysis with three alternative parametrisations of the large-scale cluster halo and combined the resulting H ₀ posteriors through Bayesian model averaging, using the corresponding Bayesian evidences as model weights. The resulting model-averaged constraint is H ₀ = 69.2 _−8.0 ^+9.6 km s ⁻¹ Mpc ⁻¹ , consistent with the value inferred from independent lens models of this cluster. The current uncertainty remains dominated by the relatively large fractional uncertainties of the measured SN time delays (≳10%), while the scatter among the mass parametrisations considered here is subdominant. The forthcoming long-baseline reappearance of SN Requiem offers an immediate opportunity to improve the statistical precision on the Hubble constant, provided that lens-model systematics are sufficiently controlled. Together, these results establish MACS J0138.0−2155 as a premier anchor for high-precision cluster-scale time-delay cosmography with multiply imaged SNe.

We present the first end-to-end validation of the Euclid baryon acoustic oscillation (BAO) analysis pipeline, encompassing density-field reconstruction, two-point correlation function measurement, and cosmological parameter inference. Using eight Euclid -like mock catalogues extracted from each of the four snapshots of the Flagship 1 (FS1) simulation, which is designed to replicate the statistical properties of the first Euclid data release (DR1), we assessed the performance of the two standard BAO reconstruction methods based on the Zeldovich approximation, R EC S YM and R EC I SO , in four redshift snapshots (0.9 ≤ z ≤ 1.8). The pipeline introduces several methodological advances: an emulator-based model evaluator ( Bora.jl ) combined with a Hamiltonian Monte Carlo sampler ( NUTS ), achieving a speed-up of more than 500 times relative to standard Monte Carlo Markov chains, and a semi-analytical covariance estimator ( BeXiCov+WinCov ) that enables robust error estimates with only eight mock realisations and remains stable under variations in the fiducial cosmology. Together, these components ensure computational efficiency while significantly reducing the risk of underestimating the parameter uncertainties. The two reconstruction schemes yield unbiased BAO measurements for all redshifts and user-defined choices, including the smoothing scale and fiducial cosmology. In each snapshot, reconstruction enhances the figure of merit for {Ω _m , H ₀ r _s } by a factor of ∼3, which is equivalent to tripling the effective survey volume. When we combined the four redshift bins, the improvement remained substantial, with BAO-only constraints reaching ∼10% precision on Ω _m and ∼3% on H ₀ r _s . The results from R EC S YM and R EC I SO are consistent within the uncertainties, but we recommend R EC S YM during testing because its sensitivity to covariance variations is lower. These findings establish the accuracy, robustness, and scalability of the Euclid BAO pipeline for DR1 and provide a solid foundation for future cosmological analyses.

The Euclid satellite will deliver a catalogue of optically selected galaxy clusters spanning from around 2000 deg ² in Data Release (DR) 1 to around 14 000 deg ² in DR3. In this work, we assess the validity of cluster clustering (CC) models for template-fitting, which complements the full-shape methodology by providing cosmological information from the anisotropy of the redshift-space two-point correlation function (2PCF). Both methods will be used to analyse the cluster 2PCF multipoles with Euclid . We examined the multipoles of the two-point redshift-space clustering of galaxy clusters simulated with the semi-analytic PINOCCHIO code using third-order Lagrangian perturbation theory, assuming a Euclid DR1-like footprint of 500 deg ² in the northern hemisphere and 1400 deg ² in the southern hemisphere. We estimated the first three even multipoles of the 2PCF and associated covariance matrix from 1000 DR1-like synthetic catalogues. We studied the impact of modelling the relevant non-linearities, halo bias, and photometric redshift uncertainties on the 2PCF. We applied three clustering models to the mock catalogues at 0 < z < 2 and with a virial mass of M _vir > 10 ¹⁴ h ⁻¹ M _⊙ under realistic and optimistic photometric redshift uncertainty scenarios. We formulated a set of permissive and conservative criteria that ought to be fulfilled by the multipole cut-off scales and validated them against 100 mock catalogues via an inference of the growth rate multiplied by the matter power spectrum normalisation parameter, fσ ₈ . We tested the dispersion, Scoccimarro, and Taruya–Nishimichi–Saito models. We find that the simplest of the three (i.e. the dispersion model) yields unbiased inferences on fσ ₈ from CC down to 10 h ⁻¹ Mpc in a DR1-like setting. All clustering models provide very similar goodness-of-fit metrics in the presence of DR1-like cluster redshift uncertainties.

Weak lensing surveys require accurate an correction for the point spread function (PSF) when measuring galaxy shapes. For a diffraction-limited PSF, which arises in the context of space-based missions, this correction depends on each galaxy’s spectral energy distribution (SED). Therefore, sufficiently accurate knowledge of the SED is needed for every galaxy. In the Euclid mission, galaxy SED reconstruction, which is one of the tasks of the photometric-redshift processing function (PHZ PF), relies on broad- and medium-band ancillary photometry. The limited wavelength sampling of the I _E passband and signal-to-noise ratio might affect the reconstruction accuracy and translate into biases in the weak lensing measurements. In this study, we present the methodology employed in the Euclid PHZ PF for reconstructing galaxy SEDs at 55 wavelengths, sampling the I _E passband every 10 nm, and we assess whether it fulfils the accuracy requirements imposed on the Euclid PSF model. We employed both physics- and data-driven methods, focussing on a new approach of template-based flux correction and Gaussian processes. For validation, we introduced an SED metric whose bias translates into inaccuracies in the PSF quadrupole moments. Our findings demonstrate that Gaussian processes and template fitting meet the requirements only in specific, but complementary redshift intervals. We therefore propose a hybrid approach that leverages both methods. This solution proves effective in meeting the Euclid accuracy requirements for most of the redshift range of the survey. Finally, we investigated the impact on the SED reconstruction of a new set of 16 evenly-spaced medium-band filters for the Subaru telescope, providing quasi-spectroscopic coverage of the I _E passband. This study offers promising results, ensuring accurate SED reconstruction and meeting the mission PSF requirements. This work thus provides not only the methodological foundation of galaxy SED reconstruction in the Euclid PHZ PF, but also a roadmap for future improvements using a new medium-band survey.

We present an end-to-end, iterative pipeline for efficient identification of strong galaxy-galaxy lensing systems, applied to the Euclid Q1 imaging data. Starting from the VIS catalogues, we rejected point sources, applied a magnitude cut ( I _E ≤ 24) on the deflectors, and ran a pixel-level artefact-and-noise filter to build 96 × 96 pixel cutouts. The VIS+NISP colour composites were constructed with a VIS-anchored luminance scheme that preserves VIS morphology and NISP colour contrast. A VIS-only seed classifier supplies clear positives and typical impostors, which we used to curate a morphology-balanced negative set and augment scarce positives. Among the six compact convolutional neural networks (CNNs) that had been studied initially, the modified VGG16 (GlobalAveragePooling + 256/128 dense layers with the last nine layers trainable) exhibited the best performance. In our run, the training set grew from 27 seed lenses (augmented 67× to 1809) plus 2000 negatives to a colour dataset of 30 686 images. After three rounds of iterative fine-tuning, a human grading of the top 4000 candidates ranked by the final model yielded 441 Grade A/B candidate lensing systems, including 311 overlapping with the existing Q1 strong-lens catalogue, and 130 additional A/B candidates (9 As and 121 Bs) not previously reported. Independently, the model recovered 740 out of 905 (81.8%) candidate Q1 lenses within its top 20 000 predictions, considering off-centred samples. Candidates span I _E ≃ 17–24 AB mag (median 21.3 AB mag) and are redder in Y _E − H _E than the parent population, consistent with massive early-type deflectors. Each training iteration required about a week for a small team and the approach can easily be scaled to future wide-area Euclid releases. Subsequent works will focus on calibrating the selection function via lens injection, extending recall through uncertainty-aware active learning, and exploring multi-scale or attention-based neural networks with fast post hoc vetters that incorporate lens models into the classification.

We present the first catalogue of strong-lensing galaxy clusters identified in the Euclid Quick Release 1 observations (covering 63.1 deg ² ). This catalogue is the result of the visual inspection of 1260 Euclid image cutouts of previously identified galaxy clusters. Each galaxy cluster was assigned a lensing score, 𝒫 _lens , derived from the aggregated votes of multiple visual inspectors, the majority of whom possess extensive expertise in strong gravitational lensing. Votes were assigned based on the assessed credibility and plausibility of the identified strong-lensing features. Specifically, we identified 83 gravitational lenses with 𝒫 _lens > 0.5, of which 14 have 𝒫 _lens = 1 and clearly exhibit secure strong-lensing features, such as giant tangential and radial arcs, and multiple images. Considering the measured number density of lensing galaxy clusters, approximately 0.3 deg ⁻² for 𝒫 _lens > 0.9, we predict that Euclid will likely observe more than 4500 strong-lensing clusters over the course of the mission. Notably, only three of the identified cluster-scale lenses had previously been observed from space. Thus, Euclid has provided the first high-resolution imaging for the remaining 80 strong-lensing galaxy cluster candidates, including those with the highest scores. The identified strong-lensing features will be used to train deep-learning models to automatically identify gravitational arcs and multiple images in the Euclid observations. This study confirms the great potential of Euclid for finding new strong-lensing clusters, enabling exciting new discoveries on the nature of dark matter and dark energy and the study of the high-redshift Universe.

The evolution of galaxies is profoundly influenced by the environment they reside in. Cosmic voids, the most underdense regions of the Universe, serve as pristine laboratories for studying galaxy evolution in the relative absence or weaker influence of the complex physical processes that dominate denser environments. We investigate the properties and merger histories of galaxies as a function of environment using the GAlaxy Evolution and Assembly (GAEA) mock-observation light cone, which replicates the Euclid Deep Survey as foreseen at the epoch of the first data release. The galaxy sample spans the redshift range 0.4<z<1.8, corresponding to the interval over which is accessible to Euclid slitless spectroscopy. We classified galaxies based on their void-centric distance and local density contrast. We compared their stellar masses, specific star formation rates, bulge-to-total stellar mass ratios, and halo masses across different environments, while controlling for the dependence on stellar mass of the other intrinsic properties. We further analysed the merger histories of these galaxies to study their assembly evolution. We find that galaxies located closer to void centres (d_ ̊m cc ̊m v ) are generally less massive, more actively star-forming, and more disc-dominated than galaxies in denser regions, with their distributions statistically distinct at more than 4 confidence according to Kolmogorov--Smirnov tests. Merger histories indicate that void galaxies do not experience fewer mergers but that these occur later, leading to stellar mass assembly delayed by approximately 0.5--1 Gyr relative to galaxies in high-density regions. This supports a scenario in which the environment regulates the timing and nature of mergers rather than their overall frequency, producing a slower evolutionary path in low-density regions. We conclude by discussing the extent to which these trends are shaped by environmental parametrisation methods and observational selection effects. Our analysis provides a framework for interpreting forthcoming Euclid data and demonstrates that Euclid will enable the identification of cosmic voids and characterisations of their galaxy populations with an unprecedented statistical power and redshift coverage, providing a quantitative forecast of Euclid's capability to probe environmental effects on galaxy evolution.