Micol Bolzonella

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 the discovery of two disky titans in the first data release of the Euclid satellite. These sources are massive ( M _* > 10 ¹¹ M _⊙ ) star-forming (SFR ∼ 20 M _⊙ yr ⁻¹ ) disks located in strong over-dense environments at intermediate redshifts ( z ∼ 0.75). They represent a small fraction of the massive galaxies in over-dense regions (just four candidates in over 20 deg ² analysed in this study). Their existence is puzzling, given the abundance of passive, bulge-dominated sources commonly found at the centre of groups and clusters at low redshifts. First, our analysis shows that these objects are located in massive groups ( M _h ∼ 10 ^13.8 M _⊙ ), where rapid accretion of cold gas should be prevented by the formation of a static hot halo. Despite this, a millimetre follow-up with NOEMA shows significant cold gas reservoirs ( M H ₂ ∼ 10 ^10.3 M _⊙ ) within these sources. Second, our morphological analysis indicates a massive, passive bulge in these galaxies. This bulge is expected to stabilise the disk against fragmentation, thereby suppressing further star formation. However, these sources lie on the Schmidt–Kennicutt relation or even slightly above. Building on these observations, we propose a scenario where these disky titans are the product of a merger-induced rejuvenation episode, in which the most massive galaxy in a group accretes cold gas from another member and briefly restarts star formation. Such a scenario is supported by a comparison with the TNG300 simulation and easily explains the persistence of star formation activity in massive galaxies in over-dense environments as temporary stages of more complex evolution. More generally, our study showcases the ability of Euclid to find rare objects, thanks to the unprecedented statistic offered by its surveys and the scientific potential residing in the synergy between Euclid and other facilities observing at longer wavelengths.

We present and investigate the properties of a sample of radio-selected, Euclid -dark galaxies, identified from LOFAR HBA observations at 144 MHz within the Euclid Deep Field-North (EDF-N), covering approximately 10 deg ² . Starting from 1051 radio sources lacking optical counterparts down to a 5σ depth of 23.9 in the y band in previous surveys (but detected with Spitzer /IRAC), we identified 166 galaxies with no emission at a depth of more than 3σ from Euclid Quick Release 1 (Q1) images, reaching a 5σ depth of 23.9 in the H _E band, and no matches in the Euclid Q1 catalogue. To minimise contamination from nearby sources, we selected a sub-sample of 88 isolated galaxies. By exploiting multi-band images and catalogues available for the EDF-N, we inferred the physical properties of our sample via SED fitting. The resulting redshift distribution spans 0.4 ≤ z _ph ≤ 5.0. We made use of recent sub-arcsecond imaging from the International LOFAR Telescope over an area of 2.5 deg × 2.5 deg to constrain the nature of the compact radio emission in our targets through brightness-temperature estimates. By combining this information with the radio excess relative to the infrared/radio-correlation (IRRC), we searched for possible active galactic nucleus (AGN) activity in our sample. Approximately 30% of our sources show a radio excess relative to star formation. This fraction increases to 40% when including AGNs identified via their brightness temperature. The Euclid -dark sources detected in the far-infrared (FIR) are consistent with a population of heavily obscured (⟨ A _V ^ISM ⟩ = 5.0) massive (⟨log ₁₀ ( M _⋆ / M _⊙ )⟩ = 11.6) star forming galaxies (SFGs) with high star formation rates (⟨log ₁₀ (SFR/ M _⊙ yr ⁻¹ )⟩ = 3.3). Their location above the star-forming main sequence is consistent with the high-mass and high-SFR end of similar near-infrared (NIR)-dark galaxy populations selected at higher radio frequencies reported in the literature. We performed a UV-to-radio median stacking analysis, dividing the sources with LOFAR ILT imaging according to the presence of radio excess. Both sub-samples are detected in the H _E band at 3σ and 9σ for the radio-excess and non–radio-excess sources, respectively. The two stacked populations exhibit similar global physical properties and differ primarily in their radio emission, with values consistent with those observed for individual sources, albeit with less extreme SFRs. These preliminary results indicate that, compared to similar selections carried out over smaller fields, the wide area covered by Euclid enables the identification of a higher fraction of systems in which intense star formation and AGN activity coexist, likely capturing a key phase of galaxy–black hole co-evolution.

Modern photometric and spectroscopic surveys enable the analysis of millions of sources, such as galaxies and active galactic nuclei, using a multiwavelength approach to study their physical characteristics. In order to exploit the scientific output from such extensive volumes of data, the analysis can be conducted with innovative methods such as those employing machine learning techniques and artificial intelligence algorithms. Here, we investigate the use of a specific type of neural network, a self-organizing map (SOM) with an early stopping feature implemented, capable of catching similarities among features in the input data. We consider a sample of galaxies from the COSMOS survey with photometric observations in the Y, J, H , and K s VISTA bands (and associated colors) and show that, by training the SOM and minimizing the over fitting problem, the neural network is able to identify clusters of data depending on the intrinsic redshift of the sources. We then study the behavior of the SOM to estimate the redshift of each input galaxy and address the performance of the algorithm by evaluating the purity and completeness of the output as well as the number of expected outliers.

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 a detailed visual morphology catalogue for the Euclid Quick Release 1 (Q1). Our catalogue includes galaxy features such as bars, spiral arms, and ongoing mergers, for the 378 000 bright ( I _E < 20.5) or extended (area ≥700 pixels) galaxies in Q1. The catalogue was created by finetuning the Zoobot galaxy foundation models on annotations from an intensive one-month campaign by Galaxy Zoo volunteers. Our measurements are fully automated, and hence fully scaleable. This catalogue is the first 0.4% of the approximately 100 million galaxies where Euclid will ultimately resolve detailed morphology.

The Euclid Wide Survey (EWS) is expected to identify in the order of 100 000 galaxy-galaxy strong lenses across 14 000deg ² . The Euclid Quick Data Release (Q1) of 63.1deg ² Euclid images provides an excellent opportunity to test our lens-finding ability, and to verify the anticipated lens frequency in the EWS. Following the Q1 data release, eight machine learning networks from five teams were applied to approximately one million images. This was followed by a citizen science inspection of a subset of around 100 000 images, of which 65% received high network scores, with the remainder randomly selected. The top scoring outputs were inspected by experts to establish confident (grade A), likely (grade B), possible (grade C), and unlikely lenses. In this paper we combine the citizen science and machine learning classifiers into an ensemble, demonstrating that a combined approach can produce a purer and more complete sample than the original individual classifiers. Using the expert-graded subset as ground truth, we find that this ensemble can provide a purity of 52 ± 2% (grade A/B lenses) with 50% completeness (for context, due to the rarity of lenses a random classifier would have a purity of 0.05% and the best machine learning network in this work achieved 7.3% purity for the same completeness). We discuss future lessons for the first major Euclid data release (DR1), where the big-data challenges will become more significant and will require analysing more than ∼300 million galaxies, and thus the time investment of both experts and citizens must be carefully managed.