We present an analysis of Euclid observations of a 0.5 deg$^2$ field in the central region of the Fornax galaxy cluster that were acquired during the performance verification phase. With these data, we investigate the potential of Euclid for identifying GCs at 20 Mpc, and validate the search methods using artificial GCs and known GCs within the field from the literature. Our analysis of artificial GCs injected into the data shows that Euclid's data in $I_{\rm E}$ band is 80% complete at about $I_{\rm E} \sim 26.0$ mag ($M_{V\rm } \sim -5.0$ mag), and resolves GCs as small as $r_{\rm h} = 2.5$ pc. In the $I_{\rm E}$ band, we detect more than 95% of the known GCs from previous spectroscopic surveys and GC candidates of the ACS Fornax Cluster Survey, of which more than 80% are resolved. We identify more than 5000 new GC candidates within the field of view down to $I_{\rm E}$ mag, about 1.5 mag fainter than the typical GC luminosity function turn-over magnitude, and investigate their spatial distribution within the intracluster field. We then focus on the GC candidates around dwarf galaxies and investigate their numbers, stacked luminosity distribution and stacked radial distribution. While the overall GC properties are consistent with those in the literature, an interesting over-representation of relatively bright candidates is found within a small number of relatively GC-rich dwarf galaxies. Our work confirms the capabilities of Euclid data in detecting GCs and separating them from foreground and background contaminants at a distance of 20 Mpc, particularly for low-GC count systems such as dwarf galaxies.

Context. ALMA observations show that dusty, distant, massive ( M similar to & 1011 M fi) galaxies usually have a remarkable star-formation activity, contributing of the order of 25% of the cosmic star-formation rate density at z similar to 3-5, and up to 30% at z similar to 7. Nonetheless, they are elusive in classical optical surveys, and current near-IR surveys are able to detect them only in very small sky areas. Since these objects have low space densities, deep and wide surveys are necessary to obtain statistically relevant results about them. Euclid will potentially be capable of delivering the required information, but, given the lack of spectroscopic features at these distances within its bands, it is still unclear if Euclid will be able to identify and characterise these objects. Aims. The goal of this work is to assess the capability of Euclid, together with ancillary optical and near-IR data, to identify these distant, dusty, and massive galaxies based on broadband photometry. Methods. We used a gradient-boosting algorithm to predict both the redshift and spectral type of objects at high z. To perform such an analysis, we made use of simulated photometric observations that mimic the Euclid Deep Survey, derived using the state-of-the-art Spectro-Photometric Realizations of Infrared-selected Targets at all-z (SPRITZ) software. Results. The gradient-boosting algorithm was found to be accurate in predicting both the redshift and spectral type of objects within the simulated Euclid Deep Survey catalogue at z > 2, while drastically decreasing the runtime with respect to spectral-energy-distribution-fitting methods. In particular, we studied the analogue of HIEROs (i.e. sources selected on the basis of a red H [4:5] > 2:25), combining Euclid and Spitzer data at the depth of the Deep Fields. These sources include the bulk of obscured and massive galaxies in a broad redshift range, 3 < z < 7. We find that the dusty population at 3. z. 7 is well identified, with a redshift root mean squared error and catastrophic outlier fraction of only 0:55 and 8:5% (HE similar to 26), respectively. Our findings suggest that with Euclid we will obtain meaningful insights into the impact of massive and dusty galaxies on the cosmic star-formation rate over time.

We analyze a sample of 3300 galaxies between redshifts z~3.5 and z~8.5 selected from JWST images in the Hubble Ultra Deep Field (HUDF) and UKIDSS Ultra Deep Survey field, including objects with stellar masses as low as ~ 10^8 Msun up to z~8. The depth and wavelength coverage of the JWST data allow us, for the first time, to derive robust stellar masses for such high-z, low stellar-mass galaxies on an individual basis. We compute the galaxy stellar mass function (GSMF), after complementing our sample with ancillary data from CANDELS to constrain the GMSF at high stellar masses (M > M*). Our results show a steepening of the low stellar-mass end slope (a) with redshift, with a = -1.61 (+/-0.05) at z~4 and a = -1.98 (+/-0.14) at z~7. We also observe an evolution of the normalization phi* from z~7 to z~4, with phi*(z~4)/phi*(z~7)= 130 (+210/-50). Our study incorporates a novel method for the estimation of the Eddington bias that takes into account its possible dependence both on stellar mass and redshift, while allowing for skewness in the error distribution. We finally compute the resulting cosmic stellar mass density and find a flatter evolution with redshift than previous studies.

We have characterized 26 Spitzer/IRAC-selected sources from the SMUVS program that are undetected in the UltraVISTA DR5 H- and/or K s-band images, covering 94 arcmin2 of the COSMOS field, which have deep multiwavelength JWST photometry. We analyzed the JWST/NIRCam imaging from the PRIMER survey and ancillary HST data to reveal the properties of these galaxies from spectral energy distribution (SED) fitting. We find that the majority of these galaxies are detected by NIRCam at lambda < 2 mu m, with only four remaining as near-infrared dropouts in the deeper JWST images. Our results indicate that the UltraVISTA dropouts candidates are primarily located at z > 3 and are characterized by high dust extinctions, with a typical color excess E(B - V) = 0.5 +/- 0.3 and stellar mass log ( M * / M-circle dot ) = 9.5 +/- 1.0 . Remarkably, similar to 75% of these sources show a flux enhancement between the observed photometry and modeled continuum SED that can be attributed to H alpha emission in the corresponding NIRCam bands. The derived (H alpha+ N [ii] + S [ii]) rest-frame equivalent widths and H alpha star formation rates (SFRs) span values similar to 100-2200 & Aring; and similar to 5-375 M-circle dot yr(-1), respectively. The locations of these sources on the SFR-M * plane indicate that 35% of them are starbursts, 40% are main-sequence galaxies, and the remaining 25% are located in the star formation valley. Our sample includes one active galactic nucleus and six submillimeter sources, as revealed from ancillary X-ray and submillimeter photometry. The high dust extinctions combined with the flux boosting from H alpha emission explain why these sources are relatively bright Spitzer galaxies and yet unidentified in the ultradeep UltraVISTA near-infrared images.

Recent controversy regarding the existence of massive ( log(M*/M⊙)≳11 ) galaxies at z > 6 poses a challenge for galaxy formation theories. Hence, it is of critical importance to understand the effects of SED fitting methods on stellar mass estimates of Epoch of Reionization galaxies. In this work, we perform a case study on the AGN host galaxy candidate COS-87259, with spectroscopic redshift z spec = 6.853, that is claimed to have an extremely high stellar mass of log(M*/M⊙)∼11.2 . We test a suite of different SED fitting algorithms and stellar population models on our independently measured photometry in 17 broad bands for this source. Between five different code setups, the stellar mass estimates for COS-87259 span log(M*/M⊙)=10.24 –11.00, while the reduced χ 2 values of the fits are all close to unity within Δχν2=1.2 , such that the quality of the SED fits is basically indistinguishable. Only when we adopt a nonparametric star formation history model within Prospector do we retrieve a stellar mass exceeding log(M*/M⊙)=11 . Although the derived stellar masses change when using previously reported photometry for this source, the nonparametric SED-fitting method always yields the highest values. As these models are becoming increasingly popular for James Webb Space Telescope high-redshift science, we stress the absolute importance of testing various SED fitting routines particularly on apparently very massive galaxies at such high redshifts.

The ESA Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real time is being developed by the Science Ground Segment of the Euclid Consortium; this pipeline will include a model-fitting algorithm, which will provide photometric and morphological estimates of paramount importance for the core science goals of the mission and for legacy science. The Euclid Morphology Challenge is a comparative investigation of the performance of five model-fitting software packages on simulated Euclid data, aimed at providing the baseline to identify the best suited algorithm to be implemented in the pipeline. In this paper we describe the simulated data set, and we discuss the photometry results. A companion paper (Euclid Collaboration: Bretonni\`ere et al. 2022) is focused on the structural and morphological estimates. We created mock Euclid images simulating five fields of view of 0.48 deg2 each in the $I_E$ band of the VIS instrument, each with three realisations of galaxy profiles (single and double S\'ersic, and 'realistic' profiles obtained with a neural network); for one of the fields in the double S\'ersic realisation, we also simulated images for the three near-infrared $Y_E$, $J_E$ and $H_E$ bands of the NISP-P instrument, and five Rubin/LSST optical complementary bands ($u$, $g$, $r$, $i$, and $z$). To analyse the results we created diagnostic plots and defined ad-hoc metrics. Five model-fitting software packages (DeepLeGATo, Galapagos-2, Morfometryka, ProFit, and SourceXtractor++) were compared, all typically providing good results. (cut)

The various Euclid imaging surveys will become a reference for studies of galaxy morphology by delivering imaging over an unprecedented area of 15 000 square degrees with high spatial resolution. In order to understand the capabilities of measuring morphologies from Euclid-detected galaxies and to help implement measurements in the pipeline, we have conducted the Euclid Morphology Challenge, which we present in two papers. While the companion paper by Merlin et al. focuses on the analysis of photometry, this paper assesses the accuracy of the parametric galaxy morphology measurements in imaging predicted from within the Euclid Wide Survey. We evaluate the performance of five state-of-the-art surface-brightness-fitting codes DeepLeGATo, Galapagos-2, Morfometryka, Profit and SourceXtractor++ on a sample of about 1.5 million simulated galaxies resembling reduced observations with the Euclid VIS and NIR instruments. The simulations include analytic S\'ersic profiles with one and two components, as well as more realistic galaxies generated with neural networks. We find that, despite some code-specific differences, all methods tend to achieve reliable structural measurements (10% scatter on ideal S\'ersic simulations) down to an apparent magnitude of about 23 in one component and 21 in two components, which correspond to a signal-to-noise ratio of approximately 1 and 5 respectively. We also show that when tested on non-analytic profiles, the results are typically degraded by a factor of 3, driven by systematics. We conclude that the Euclid official Data Releases will deliver robust structural parameters for at least 400 million galaxies in the Euclid Wide Survey by the end of the mission. We find that a key factor for explaining the different behaviour of the codes at the faint end is the set of adopted priors for the various structural parameters.

The technique of photometric redshifts has become essential for the exploitation of multi-band extragalactic surveys. While the requirements on photo-zs for the study of galaxy evolution mostly pertain to the precision and to the fraction of outliers, the most stringent requirement in their use in cosmology is on the accuracy, with a level of bias at the sub-percent level for the Euclid cosmology mission. A separate, and challenging, calibration process is needed to control the bias at this level of accuracy. The bias in photo-zs has several distinct origins that may not always be easily overcome. We identify here one source of bias linked to the spatial or time variability of the passbands used to determine the photometric colours of galaxies. We first quantified the effect as observed on several well-known photometric cameras, and found in particular that, due to the properties of optical filters, the redshifts of off-axis sources are usually overestimated. We show using simple simulations that the detailed and complex changes in the shape can be mostly ignored and that it is sufficient to know the mean wavelength of the passbands of each photometric observation to correct almost exactly for this bias; the key point is that this mean wavelength is independent of the spectral energy distribution of the source}. We use this property to propose a correction that can be computationally efficiently implemented in some photo-z algorithms, in particular template-fitting. We verified that our algorithm, implemented in the new photo-z code Phosphoros, can effectively reduce the bias in photo-zs on real data using the CFHTLS T007 survey, with an average measured bias Delta z over the redshift range 0.4

Next generation telescopes, like Euclid, Rubin/LSST, and Roman, will open new windows on the Universe, allowing us to infer physical properties for tens of millions of galaxies. Machine learning methods are increasingly becoming the most efficient tools to handle this enormous amount of data, because they are often faster and more accurate than traditional methods. We investigate how well redshifts, stellar masses, and star-formation rates (SFR) can be measured with deep learning algorithms for observed galaxies within data mimicking the Euclid and Rubin/LSST surveys. We find that Deep Learning Neural Networks and Convolutional Neutral Networks (CNN), which are dependent on the parameter space of the training sample, perform well in measuring the properties of these galaxies and have a better accuracy than methods based on spectral energy distribution fitting. CNNs allow the processing of multi-band magnitudes together with $H_{\scriptscriptstyle\rm E}$-band images. We find that the estimates of stellar masses improve with the use of an image, but those of redshift and SFR do not. Our best results are deriving i) the redshift within a normalised error of less than 0.15 for 99.9$\%$ of the galaxies with S/N>3 in the $H_{\scriptscriptstyle\rm E}$-band; ii) the stellar mass within a factor of two ($\sim0.3 \rm dex$) for 99.5$\%$ of the considered galaxies; iii) the SFR within a factor of two ($\sim0.3 \rm dex$) for $\sim$70$\%$ of the sample. We discuss the implications of our work for application to surveys as well as how measurements of these galaxy parameters can be improved with deep learning.

Submillimeter observations reveal the star formation activity obscured by dust in the young Universe. It still remains unclear how galaxies detected at submillimeter wavelengths are related to ultraviolet/optical-selected galaxies in terms of their observed quantities, physical properties, and evolutionary stages. Deep near- and mid-infrared observational data are crucial to characterize the stellar properties of galaxies detected with submillimeter emission. In this study, we make use of a galaxy catalog from the Spitzer Matching survey of the UltraVISTA ultra-deep Stripes. By crossmatching with a submillimeter source catalog constructed with archival data of the Atacama Large Millimeter/submillimeter Array (ALMA), we search for galaxies at z > 2 with a submillimeter detection in our galaxy catalog. We find that the ALMA-detected galaxies at z > 2 are systematically massive and have redder K _s –[4.5] colors than the nondetected galaxies. The redder colors are consistent with the larger dust reddening values of the ALMA-detected galaxies obtained from spectral energy distribution (SED) fitting. We also find that the ALMA-detected galaxies tend to have brighter 4.5 μ m magnitudes. This may suggest that they tend to have smaller mass-to-light ratios and thus to be younger than star-forming galaxies fainter at submillimeter wavelengths with similar stellar masses. We identify starburst galaxies with high specific star formation rates among both ALMA-detected and nondetected SMUVS sources. Irrespective of their brightness at submillimeter wavelengths, these populations have similar dust reddening values, which may suggest a variety of dust SED shapes among the starburst galaxies at z > 2.

We study the relation between stellar mass (M*) and star formation rate (SFR) for star-forming galaxies over approximately five decades in stellar mass (5.5 <~ log10(M*/Msun) <~ 10.5) at z ~ 3-6.5. This unprecedented coverage has been possible thanks to the joint analysis of blank non-lensed fields (COSMOS/SMUVS) and cluster lensing fields (Hubble Frontier Fields) which allow us to reach very low stellar masses. Previous works have revealed the existence of a clear bimodality in the SFR-M* plane with a star-formation Main Sequence and a starburst cloud at z ~ 4-5. Here we show that this bimodality extends to all star-forming galaxies and is valid in the whole redshift range z ~ 3-6.5. We find that starbursts constitute at least 20% of all star-forming galaxies with M* >~ 10^9 Msun at these redshifts and reach a peak of 40% at z=4-5. More importantly, 60% to 90% of the total SFR budget at these redshifts is contained in starburst galaxies, indicating that the starburst mode of star-formation is dominant at high redshifts. Almost all the low stellar-mass starbursts with log10(M*/Msun) <~ 8.5 have ages comparable to the typical timescales of a starburst event, suggesting that these galaxies are being caught in the process of formation. Interestingly, galaxy formation models fail to predict the starburst/main-sequence bimodality and starbursts overall, suggesting that the starburst phenomenon may be driven by physical processes occurring at smaller scales than those probed by these models.

(Abridged) The Euclid mission is expected to discover thousands of z>6 galaxies in three Deep Fields, which together will cover a ~40 deg2 area. However, the limited number of Euclid bands and availability of ancillary data could make the identification of z>6 galaxies challenging. In this work, we assess the degree of contamination by intermediate-redshift galaxies (z=1-5.8) expected for z>6 galaxies within the Euclid Deep Survey. This study is based on ~176,000 real galaxies at z=1-8 in a ~0.7 deg2 area selected from the UltraVISTA ultra-deep survey, and ~96,000 mock galaxies with 25.3$\leq$H<27.0, which altogether cover the range of magnitudes to be probed in the Euclid Deep Survey. We simulate Euclid and ancillary photometry from the fiducial, 28-band photometry, and fit spectral energy distributions (SEDs) to various combinations of these simulated data. Our study demonstrates that identifying z>6 with Euclid data alone will be very effective, with a z>6 recovery of 91(88)% for bright (faint) galaxies. For the UltraVISTA-like bright sample, the percentage of z=1-5.8 contaminants amongst apparent z>6 galaxies as observed with Euclid alone is 18%, which is reduced to 4(13)% by including ultra-deep Rubin (Spitzer) photometry. Conversely, for the faint mock sample, the contamination fraction with Euclid alone is considerably higher at 39%, and minimized to 7% when including ultra-deep Rubin data. For UltraVISTA-like bright galaxies, we find that Euclid (I-Y)>2.8 and (Y-J)<1.4 colour criteria can separate contaminants from true z>6 galaxies, although these are applicable to only 54% of the contaminants, as many have unconstrained (I-Y) colours. In the most optimistic scenario, these cuts reduce the contamination fraction to 1% whilst preserving 81% of the fiducial z>6 sample. For the faint mock sample, colour cuts are infeasible.