Instrumentation and Surveys

arXiv:2505.16738v1 Announce Type: new Abstract: The Fornax cluster, the second-largest galaxy cluster within 20 Mpc, presents an ideal environment for studying environmental effects on galaxy evolution. Utilizing data from the Southern Photometric Local Universe Survey (S-PLUS), this study explores the H$\alpha$+[NII] emission maps across an area of approximately 208 square degrees around NGC 1399. For such, a dedicated semi-automated pipeline, Pixel-to-Pixel Emission Line Estimate (PELE), was developed to generate emission line maps by processing S-PLUS images using the Three Filter Method. A morphological analysis was conducted using the ASTROMORPHLIB package to determine whether H$\alpha$+[NII] emitters exhibit perturbed features. The study successfully detected 77 H$\alpha$+[NII] emitters with $r<18$ mag, extending to four times the virial radius of the Fornax cluster. PELE demonstrated its ability to recover flux down to 2e-17 erg s$^{-1}$ cm$^{-2}$ when compared to H$\alpha$ maps from MUSE/VLT. Among the emitters, 25% are early-type galaxies (ETG) and 75% late-type galaxies (LTG). Signs of morphological perturbation or merger activity are observed in 44% of the LTG and in three ETG located beyond the cluster's virial radius. A significant fraction (91%) of the emitters are identified as recent infallers, primarily located in the northwestern region of the cluster, while others are associated with the infalling group Fornax A in the southwest. Disturbed, low-mass galaxies at larger cluster-centric distances provide evidence of galaxies begin transforming before entering the main cluster. This study demonstrates S-PLUS's effectiveness in detecting emitters, whose distribution reflects the Fornax cluster's assembly history, with LTG linked to recent infall from the field, possibly along a Fornax-Eridanus filament, and ETG may have evolved prior to entry.

arXiv:2505.16795v1 Announce Type: cross Abstract: Extreme mass-ratio inspirals pose a difficult challenge in terms of both search and parameter estimation for upcoming space-based gravitational-wave detectors such as LISA. Their signals are long and of complex morphology, meaning they carry a large amount of information about their source, but are also difficult to search for and analyse. We explore how sequential simulation-based inference methods, specifically truncated marginal neural ratio estimation, could offer solutions to some of the challenges surrounding extreme-mass-ratio inspiral data analysis. We show that this method can efficiently narrow down the volume of the complex 11-dimensional search parameter space by a factor of $10^6-10^7$ and provide 1-dimensional marginal proposal distributions for non-spinning extreme-mass-ratio inspirals. We discuss the current limitations of this approach and place it in the broader context of a global strategy for future space-based gravitational-wave data analysis.

arXiv:2505.10976v1 Announce Type: new Abstract: Context. Ongoing and upcoming large spectroscopic surveys are drastically increasing the number of observed quasar spectra, requiring the development of fast and accurate automated methods to estimate spectral continua. Aims. This study evaluates the performance of three neural networks (NN) - an autoencoder, a convolutional NN (CNN), and a U-Net - in predicting quasar continua within the rest-frame wavelength range of $1020~\text{\AA}$ to $2000~\text{\AA}$. The ability to generalize and predict galaxy continua within the range of $3500~\text{\AA}$ to $5500~\text{\AA}$ is also tested. Methods. The performance of these architectures is evaluated using the absolute fractional flux error (AFFE) on a library of mock quasar spectra for the WEAVE survey, and on real data from the Early Data Release observations of the Dark Energy Spectroscopic Instrument (DESI) and the VIMOS Public Extragalactic Redshift Survey (VIPERS). Results. The autoencoder outperforms the U-Net, achieving a median AFFE of 0.009 for quasars. The best model also effectively recovers the Ly$\alpha$ optical depth evolution in DESI quasar spectra. With minimal optimization, the same architectures can be generalized to the galaxy case, with the autoencoder reaching a median AFFE of 0.014 and reproducing the D4000n break in DESI and VIPERS galaxies.

arXiv:2503.15305v4 Announce Type: replace Abstract: The Euclid satellite is an ESA mission that was launched in July 2023. \Euclid is working in its regular observing mode with the target of observing an area of $14\,000~\text{deg}^2$ with two instruments, the Visible Camera (VIS) and the Near IR Spectrometer and Photometer (NISP) down to $I_{\rm E} = 24.5~\text{mag}$ ($10\, \sigma$) in the Euclid Wide Survey. Ground-based imaging data in the \textit{ugriz} bands complement the \Euclid data to enable photo-$z$ determination and VIS PSF modeling for week lensing analysis. Euclid investigates the distance-redshift relation and the evolution of cosmic structures by measuring shapes and redshifts of galaxies and clusters of galaxies out to $z\sim 2$. Generating the multi-wavelength catalogues from \Euclid and ground-based data is an essential part of the \Euclid data processing system. In the framework of the \Euclid Science Ground Segment (SGS), the aim of the MER Processing Function (PF) pipeline is to detect objects in the \Euclid imaging data, measure their properties, and MERge them into a single multi-wavelength catalogue. The MER PF pipeline performs source detection on both visible (VIS) and near-infrared (NIR) images and offers four different photometric measurements: Kron total flux, aperture photometry on PSF-matched images, template fitting photometry, and S\'ersic fitting photometry. Furthermore, the MER PF pipeline measures a set of ancillary quantities, spanning from morphology to quality flags, to better characterise all detected sources. In this paper, we show how the MER PF pipeline is designed, detailing its main steps, and we show that the pipeline products meet the tight requirements that Euclid aims to achieve on photometric accuracy. We also present the other measurements (e.g. morphology) that are included in the OU-MER output catalogues and we list all output products coming out of the MER PF pipeline.

arXiv:2503.15305v3 Announce Type: replace Abstract: The Euclid satellite is an ESA mission that was launched in July 2023. \Euclid is working in its regular observing mode with the target of observing an area of $14\,000~\text{deg}^2$ with two instruments, the Visible Camera (VIS) and the Near IR Spectrometer and Photometer (NISP) down to $I_{\rm E} = 24.5~\text{mag}$ ($10\, \sigma$) in the Euclid Wide Survey. Ground-based imaging data in the \textit{ugriz} bands complement the \Euclid data to enable photo-$z$ determination and VIS PSF modeling for week lensing analysis. Euclid investigates the distance-redshift relation and the evolution of cosmic structures by measuring shapes and redshifts of galaxies and clusters of galaxies out to $z\sim 2$. Generating the multi-wavelength catalogues from \Euclid and ground-based data is an essential part of the \Euclid data processing system. In the framework of the \Euclid Science Ground Segment (SGS), the aim of the MER Processing Function (PF) pipeline is to detect objects in the \Euclid imaging data, measure their properties, and MERge them into a single multi-wavelength catalogue. The MER PF pipeline performs source detection on both visible (VIS) and near-infrared (NIR) images and offers four different photometric measurements: Kron total flux, aperture photometry on PSF-matched images, template fitting photometry, and S\'ersic fitting photometry. Furthermore, the MER PF pipeline measures a set of ancillary quantities, spanning from morphology to quality flags, to better characterise all detected sources. In this paper, we show how the MER PF pipeline is designed, detailing its main steps, and we show that the pipeline products meet the tight requirements that Euclid aims to achieve on photometric accuracy. We also present the other measurements (e.g. morphology) that are included in the OU-MER output catalogues and we list all output products coming out of the MER PF pipeline.

arXiv:2505.04688v1 Announce Type: new Abstract: The Euclid survey aims to measure the spectroscopic redshift of emission-line galaxies by identifying the H$\,{\alpha}$ line in their slitless spectra. This method is sensitive to the signal-to-noise ratio of the line, as noise fluctuations or other strong emission lines can be misidentified as H$\,{\alpha}$, depending on redshift. These effects lead to catastrophic redshift errors and the inclusion of interlopers in the sample. We forecast the impact of such redshift errors on galaxy clustering measurements. In particular, we study the effect of interloper contamination on the two-point correlation function (2PCF), the growth rate of structures, and the Alcock-Paczynski (AP) parameters. We analyze 1000 synthetic spectroscopic catalogues, the EuclidLargeMocks, designed to match the area and selection function of the Data Release 1 (DR1) sample. We estimate the 2PCF of the contaminated catalogues, isolating contributions from correctly identified galaxies and from interlopers. We explore different models with increasing complexity to describe the measured 2PCF at fixed cosmology. Finally, we perform a cosmological inference and evaluate the systematic error on the inferred $f\sigma_8$, $\alpha_{\parallel}$ and $\alpha_{\perp}$ values associated with different models. Our results demonstrate that a minimal modelling approach, which only accounts for an attenuation of the clustering signal regardless of the type of contaminants, is sufficient to recover the correct values of $f\sigma_8$, $\alpha_{\parallel}$, and $\alpha_{\perp}$ at DR1. The accuracy and precision of the estimated AP parameters are largely insensitive to the presence of interlopers. The adoption of a minimal model induces a 1%-3% systematic error on the growth rate of structure estimation, depending on the redshift. However, this error remains smaller than the statistical error expected for the Euclid DR1 analysis.

arXiv:2504.17867v1 Announce Type: new Abstract: The Cosmic Dawn Survey Pre-launch (PL) catalogues cover an effective 10.13 deg$^{2}$ area with uniform deep Spitzer/IRAC data ($m\sim25$ mag, 5$\sigma$), the largest area covered to these depths in the infrared. These data are used to gain new insight into the growth of stellar mass across cosmic history by characterising the evolution of the galaxy stellar mass function (GSMF) through $0.2 < z \leq 6.5$. The total volume (0.62 Gpc$^{3}$) represents a tenfold increase compared to previous works that have explored $z > 3$ and significantly reduces cosmic variance, yielding strong constraints on the abundance of massive galaxies. Results are generally consistent with the literature but now provide firm estimates of number density where only upper limits were previously available. Contrasting the GSMF with the dark matter halo mass function suggests that massive galaxies ($M \gtrsim10^{11}$ M$_{\odot}$) at $z > 3.5$ required integrated star-formation efficiencies of $M/(M_{\rm h}f_{\rm b}) \gtrsim$ 0.25--0.5, in excess of the commonly-held view of ``universal peak efficiency" from studies on the stellar-to-halo mass relation (SHMR). Such increased efficiencies imply an evolving peak in the SHMR at $z > 3.5$ which can be maintained if feedback mechanisms from active galactic nuclei and stellar processes are ineffective at early times. In addition, a significant fraction of the most massive quiescent galaxies are observed to be in place already by $z\sim 2.5$--3. The apparent lack in change of their number density by $z\sim 0.2$ is consistent with relatively little mass growth from mergers. Utilising the unique volume, evidence for an environmental dependence of the galaxy stellar mass function is found all the way through $z\sim 3.5$ for the first time, though a more careful characterisation of the density field is ultimately required for confirmation.

arXiv:2503.21728v2 Announce Type: replace Abstract: Radio-frequency interference (RFI) is a major systematic limitation in radio astronomy, particularly for science cases requiring high sensitivity, such as 21 cm cosmology. Traditionally, RFI is dealt with by identifying its signature in the dynamic spectra of visibility data and flagging strongly affected regions. However, for RFI sources that do not occupy narrow regions in the time-frequency space, such as persistent local RFI, modeling these sources could be essential to mitigating their impact. This paper introduces two methods for detecting and characterizing local RFI sources from radio interferometric visibilities: matched filtering and maximum a posteriori (MAP) imaging. These algorithms use the spherical wave equation to construct three-dimensional near-field image cubes of RFI intensity from the visibilities. The matched filter algorithm can generate normalized maps by cross-correlating the expected contributions from RFI sources with the observed visibilities, while the MAP method performs a regularized inversion of the visibility equation in the near field. We developed a full polarization simulation framework for RFI and demonstrated the methods on simulated observations of local RFI sources. The stability, speed, and errors introduced by these algorithms were investigated, and, as a demonstration, the algorithms were applied to a subset of NenuFAR observations to perform spatial, spectral, and temporal characterization of two local RFI sources. We used simulations to assess the impact of local RFI on images, the uv plane, and cylindrical power spectra, and to quantify the level of bias introduced by the algorithms in order to understand their implications for the estimated 21 cm power spectrum with radio interferometers. The near-field imaging and simulation codes are publicly available in the Python library nfis.

arXiv:2501.14408v3 Announce Type: replace Abstract: The Euclid mission is generating a vast amount of imaging data in four broadband filters at high angular resolution. This will allow the detailed study of mass, metallicity, and stellar populations across galaxies, which will constrain their formation and evolutionary pathways. Transforming the Euclid imaging for large samples of galaxies into maps of physical parameters in an efficient and reliable manner is an outstanding challenge. We investigate the power and reliability of machine learning techniques to extract the distribution of physical parameters within well-resolved galaxies. We focus on estimating stellar mass surface density, mass-averaged stellar metallicity and age. We generate noise-free, synthetic high-resolution imaging data in the Euclid photometric bands for a set of 1154 galaxies from the TNG50 cosmological simulation. The images are generated with the SKIRT radiative transfer code, taking into account the complex 3D distribution of stellar populations and interstellar dust attenuation. We use a machine learning framework to map the idealised mock observational data to the physical parameters on a pixel-by-pixel basis. We find that stellar mass surface density can be accurately recovered with a $\leq 0.130 {\rm \,dex}$ scatter. Conversely, stellar metallicity and age estimates are, as expected, less robust, but still contain significant information which originates from underlying correlations at a sub-kpc scale between stellar mass surface density and stellar population properties.

arXiv:2409.03523v2 Announce Type: replace Abstract: The Euclid mission will measure cosmological parameters with unprecedented precision. To distinguish between cosmological models, it is essential to generate realistic mock observables from cosmological simulations that were run in both the standard $\Lambda$-cold-dark-matter ($\Lambda$CDM) paradigm and in many non-standard models beyond $\Lambda$CDM. We present the scientific results from a suite of cosmological N-body simulations using non-standard models including dynamical dark energy, k-essence, interacting dark energy, modified gravity, massive neutrinos, and primordial non-Gaussianities. We investigate how these models affect the large-scale-structure formation and evolution in addition to providing synthetic observables that can be used to test and constrain these models with Euclid data. We developed a custom pipeline based on the Rockstar halo finder and the nbodykit large-scale structure toolkit to analyse the particle output of non-standard simulations and generate mock observables such as halo and void catalogues, mass density fields, and power spectra in a consistent way. We compare these observables with those from the standard $\Lambda$CDM model and quantify the deviations. We find that non-standard cosmological models can leave significant imprints on the synthetic observables that we have generated. Our results demonstrate that non-standard cosmological N-body simulations provide valuable insights into the physics of dark energy and dark matter, which is essential to maximising the scientific return of Euclid.

arXiv:2503.20875v1 Announce Type: new Abstract: We present the Cambridge Exoplanet Transit Recovery Algorithm (CETRA), a fast and sensitive transit detection algorithm, optimised for GPUs. CETRA separates the task into a search for transit signals across linear time space, followed by a phase-folding of the former to enable a periodic signal search, using a physically motivated transit model to improve detection sensitivity. It outperforms traditional methods like Box Least Squares and Transit Least Squares in both sensitivity and speed. Tests on synthetic light curves demonstrate that CETRA can identify at least 20 per cent more low-SNR transits than Transit Least Squares in the same data, particularly those of long period planets. It is also shown to be up to a few orders of magnitude faster for high cadence light curves, enabling rapid large-scale searches. Through application of CETRA to Transiting Exoplanet Survey Satellite short cadence data, we recover the three planets in the HD 101581 system with improved significance. In particular, the transit signal of the previously unvalidated planet TOI-6276.03 is enhanced from ${\rm SNR}=7.9$ to ${\rm SNR}=16.0$, which means it may now meet the criteria for statistical validation. CETRA's speed and sensitivity make it well-suited for current and future exoplanet surveys, particularly in the search for Earth analogues. Our implementation of this algorithm uses NVIDIA's CUDA platform and requires an NVIDIA GPU, it is open-source and available from GitHub and PyPI.

arXiv:2503.21728v1 Announce Type: new Abstract: Radio-frequency interference (RFI) is a major systematic limitation in radio astronomy, particularly for science cases requiring high sensitivity, such as 21-cm cosmology. Traditionally, RFI is dealt with by identifying its signature in the dynamic spectra of visibility data and flagging strongly affected regions. However, for RFI sources that do not occupy narrow regions in the time-frequency space, such as persistent local RFI, modeling these sources could be essential to mitigating their impact. This paper introduces two methods for detecting and characterizing local RFI sources from radio interferometric visibilities: matched filtering and maximum a posteriori (MAP) imaging. These algorithms use the spherical wave equation to construct three-dimensional near-field image cubes of RFI intensity from the visibilities. The matched filter algorithm can generate normalized maps by cross-correlating the expected contributions from RFI sources with the observed visibilities, while the MAP method performs a regularized inversion of the visibility equation in the near field. We also develop a full polarization simulation framework for RFI and demonstrate the methods on simulated observations of local RFI sources. The stability, speed, and errors introduced by these algorithms are investigated, and, as a demonstration, the algorithms are applied to a subset of NenuFAR observations to perform spatial, spectral, and temporal characterization of two local RFI sources. We assess the impact of local RFI on images, the uv plane, and cylindrical power spectra through simulations and describe these effects qualitatively. We also quantify the level of errors and biases that these algorithms induce and assess their implications for the estimated 21-cm power spectrum with radio interferometers. The near-field imaging and simulation codes are made available publicly in the Python library nfis.

arXiv:2503.15305v2 Announce Type: replace Abstract: The Euclid satellite is an ESA mission that was launched in July 2023. \Euclid is working in its regular observing mode with the target of observing an area of $14\,000~\text{deg}^2$ with two instruments, the Visible Camera (VIS) and the Near IR Spectrometer and Photometer (NISP) down to $I_{\rm E} = 24.5~\text{mag}$ ($10\, \sigma$) in the Euclid Wide Survey. Ground-based imaging data in the \textit{ugriz} bands complement the \Euclid data to enable photo-$z$ determination and VIS PSF modeling for week lensing analysis. Euclid investigates the distance-redshift relation and the evolution of cosmic structures by measuring shapes and redshifts of galaxies and clusters of galaxies out to $z\sim 2$. Generating the multi-wavelength catalogues from \Euclid and ground-based data is an essential part of the \Euclid data processing system. In the framework of the \Euclid Science Ground Segment (SGS), the aim of the MER Processing Function (PF) pipeline is to detect objects in the \Euclid imaging data, measure their properties, and MERge them into a single multi-wavelength catalogue. The MER PF pipeline performs source detection on both visible (VIS) and near-infrared (NIR) images and offers four different photometric measurements: Kron total flux, aperture photometry on PSF-matched images, template fitting photometry, and S\'ersic fitting photometry. Furthermore, the MER PF pipeline measures a set of ancillary quantities, spanning from morphology to quality flags, to better characterise all detected sources. In this paper, we show how the MER PF pipeline is designed, detailing its main steps, and we show that the pipeline products meet the tight requirements that Euclid aims to achieve on photometric accuracy. We also present the other measurements (e.g. morphology) that are included in the OU-MER output catalogues and we list all output products coming out of the MER PF pipeline.

arXiv:2503.15328v1 Announce Type: new Abstract: The Euclid Wide Survey (EWS) is expected to identify of order $100\,000$ galaxy-galaxy strong lenses across $14\,000$deg$^2$. The Euclid Quick Data Release (Q1) of $63.1$deg$^2$ 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 $52\pm2\%$ (grade A/B lenses) with $50\%$ completeness (for context, due to the rarity of lenses a random classifier would have a purity of $0.05\%$). 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 $\sim300$ million galaxies, and thus time investment of both experts and citizens must be carefully managed.

arXiv:2503.15326v1 Announce Type: new Abstract: Strong gravitational lensing has the potential to provide a powerful probe of astrophysics and cosmology, but fewer than 1000 strong lenses have been confirmed previously. With \ang{;;0.16} resolution covering a third of the sky, the \Euclid telescope will revolutionise strong lens finding, with \num{170000} lenses forecasted to be discovered amongst its 1.5 billion galaxies. We present an analysis of the performance of five machine-learning models at finding strong gravitational lenses in the quick release of \Euclid data (Q1), covering 63\,deg$^{2}$. The models are validated with citizen scientists and expert visual inspection. We focus on the best performing network: a fine-tuned version of the \texttt{Zoobot} pretrained model, originally trained to classify galaxy morphologies in heterogeneous astronomical imaging surveys. Of the one million Q1 objects that \texttt{Zoobot} was tasked to find strong lenses within, the top 1000 ranked objects contained 122 grade A lenses (almost certain lenses), and 41 grade B lenses (probable lenses). A deeper search with the five networks combined with visual inspection discovered 250 (247) grade A (B) lenses, of which 224 (182) are ranked in the top \num{20000} by \texttt{Zoobot}. When extrapolated to the full \Euclid survey, the highest ranked one million images will contain \num{75000} grade A or B strong gravitational lenses.

arXiv:2503.15324v1 Announce Type: new Abstract: We present a catalogue of 497 galaxy-galaxy strong lenses in the Euclid Quick Release 1 data (63 deg$^2$). In the initial 0.45\% of Euclid's surveys, we double the total number of known lens candidates with space-based imaging. Our catalogue includes 250 grade A candidates, the vast majority of which (243) were previously unpublished. Euclid's resolution reveals rare lens configurations of scientific value including double-source-plane lenses, edge-on lenses, complete Einstein rings, and quadruply-imaged lenses. We resolve lenses with small Einstein radii ($\theta_{\rm E} < 1''$) in large numbers for the first time. These lenses are found through an initial sweep by deep learning models, followed by Space Warps citizen scientist inspection, expert vetting, and system-by-system modelling. Our search approach scales straightforwardly to Euclid Data Release 1 and, without changes, would yield approximately 7000 high-confidence (grade A or B) lens candidates by late 2026. Further extrapolating to the complete Euclid Wide Survey implies a likely yield of over 100000 high-confidence candidates, transforming strong lensing science.

arXiv:2503.14745v1 Announce Type: new Abstract: In 2021 May the Dark Energy Spectroscopic Instrument (DESI) collaboration began a 5-year spectroscopic redshift survey to produce a detailed map of the evolving three-dimensional structure of the universe between $z=0$ and $z\approx4$. DESI's principle scientific objectives are to place precise constraints on the equation of state of dark energy, the gravitationally driven growth of large-scale structure, and the sum of the neutrino masses, and to explore the observational signatures of primordial inflation. We present DESI Data Release 1 (DR1), which consists of all data acquired during the first 13 months of the DESI main survey, as well as a uniform reprocessing of the DESI Survey Validation data which was previously made public in the DESI Early Data Release. The DR1 main survey includes high-confidence redshifts for 18.7M objects, of which 13.1M are spectroscopically classified as galaxies, 1.6M as quasars, and 4M as stars, making DR1 the largest sample of extragalactic redshifts ever assembled. We summarize the DR1 observations, the spectroscopic data-reduction pipeline and data products, large-scale structure catalogs, value-added catalogs, and describe how to access and interact with the data. In addition to fulfilling its core cosmological objectives with unprecedented precision, we expect DR1 to enable a wide range of transformational astrophysical studies and discoveries.

arXiv:2503.15329v1 Announce Type: new Abstract: The Euclid mission aims to survey around 14000 deg^{2} of extragalactic sky, providing around 10^{5} gravitational lens images. Modelling of gravitational lenses is fundamental to estimate the total mass of the lens galaxy, along with its dark matter content. Traditional modelling of gravitational lenses is computationally intensive and requires manual input. In this paper, we use a Bayesian neural network, LEns MOdelling with Neural networks (LEMON), for modelling Euclid gravitational lenses with a singular isothermal ellipsoid mass profile. Our method estimates key lens mass profile parameters, such as the Einstein radius, while also predicting the light parameters of foreground galaxies and their uncertainties. We validate LEMON's performance on both mock Euclid data sets, real Euclidised lenses observed with Hubble Space Telescope (hereafter HST), and real Euclid lenses found in the Perseus ERO field, demonstrating the ability of LEMON to predict parameters of both simulated and real lenses. Results show promising accuracy and reliability in predicting the Einstein radius, axis ratio, position angle, effective radius, S\'ersic index, and lens magnitude for simulated lens galaxies. The application to real data, including the latest Quick Release 1 strong lens candidates, provides encouraging results, particularly for the Einstein radius. We also verified that LEMON has the potential to accelerate traditional modelling methods, by giving to the classical optimiser the LEMON predictions as starting points, resulting in a speed-up of up to 26 times the original time needed to model a sample of gravitational lenses, a result that would be impossible with randomly initialised guesses. This work represents a significant step towards efficient, automated gravitational lens modelling, which is crucial for handling the large data volumes expected from Euclid.

arXiv:2503.15323v1 Announce Type: new Abstract: Recent James Webb Space Telescope (JWST) observations have revealed a population of sources with a compact morphology and a `v-shaped' continuum, namely blue at rest-frame $\lambda<4000$A and red at longer wavelengths. The nature of these sources, called `little red dots' (LRDs), is still debated, since it is unclear if they host active galactic nuclei (AGN) and their number seems to drastically drop at z<4. We utilise the 63 $deg^2$ covered by the quick Euclid Quick Data Release (Q1) to extend the search for LRDs to brighter magnitudes and to lower z than what has been possible with JWST to have a broader view of the evolution of this peculiar galaxy population. The selection is done by fitting the available photometric data (Euclid, Spitzer/IRAC, and ground-based griz data) with two power laws, to retrieve the rest-frame optical and UV slopes consistently over a large redshift range (i.e, z<7.6). We exclude extended objects and possible line emitters, and perform a visual inspection to remove imaging artefacts. The final selection includes 3341 LRD candidates from z=0.33 to z=3.6, with 29 detected in IRAC. Their rest-frame UV luminosity function, in contrast with previous JWST studies, shows that the number density of LRD candidates increases from high-z down to z=1.5-2.5 and decreases at even lower z. Less evolution is apparent focusing on the subsample of more robust LRD candidates having IRAC detections, which is affected by low statistics and limited by the IRAC resolution. The comparison with previous quasar UV luminosity functions shows that LRDs are not the dominant AGN population at z<4. Follow-up studies of these LRD candidates are key to confirm their nature, probe their physical properties and check for their compatibility with JWST sources, since the different spatial resolution and wavelength coverage of Euclid and JWST could select different samples of compact sources.

arXiv:2503.15327v1 Announce Type: new Abstract: Strong gravitational lensing systems with multiple source planes are powerful tools for probing the density profiles and dark matter substructure of the galaxies. The ratio of Einstein radii is related to the dark energy equation of state through the cosmological scaling factor $\beta$. However, galaxy-scale double-source-plane lenses (DSPLs) are extremely rare. In this paper, we report the discovery of four new galaxy-scale double-source-plane lens candidates in the Euclid Quick Release 1 (Q1) data. These systems were initially identified through a combination of machine learning lens-finding models and subsequent visual inspection from citizens and experts. We apply the widely-used {\tt LensPop} lens forecasting model to predict that the full \Euclid survey will discover 1700 DSPLs, which scales to $6 \pm 3$ DSPLs in 63 deg$^2$, the area of Q1. The number of discoveries in this work is broadly consistent with this forecast. We present lens models for each DSPL and infer their $\beta$ values. Our initial Q1 sample demonstrates the promise of \Euclid to discover such rare objects.