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Home All issues Volume 711 (July 2026) A&A, 711 (2026) A22 Full HTML
Issue
A&A
Volume 711, July 2026
Euclid Quick Data Release (Q1)
Article Number A22
Number of page(s) 11
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/202554615
Published online 30 June 2026

© The Authors 2026

Licence Creative CommonsOpen Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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1. Introduction

Understanding the evolution of galaxies throughout cosmic history has always been a fundamental objective of extragalactic astronomy. Observational constraints are crucial for validating theoretical predictions, which must be able to accurately replicate empirical phenomena. A key challenge in this endeavour lies in achieving a complete census of the galaxy population, particularly during the early epochs of the Universe.

The Hubble Space Telescope (HST) has been instrumental in the study of high-redshift galaxies, primarily through observations of their rest-frame ultraviolet (UV) emission. These kinds of galaxies have been extensively characterised across the redshift range 3 ≲ z ≲ 11 (Steidel & Hamilton 1993; Steidel et al. 1995, 1999; Madau et al. 1996; Bouwens et al. 2015; Oesch et al. 2016). However, this UV-based selection systematically misses massive, dusty galaxies, underestimating the true stellar mass function at different epochs (Rodighiero et al. 2007; Wang et al. 2019). Such dust-obscured sources are faint or undetected even in the deepest HST observations, earning them the label HST-dark galaxies or optically dark galaxies (ODGs).

Longer-wavelength facilities such as Spitzer and the Atacama Large Millimeter/submillimeter Array (ALMA) have played a crucial role in unveiling this population (Caputi et al. 2015; Franco et al. 2018; Dudzevičiūtė et al. 2020). Recently, the James Webb Space Telescope (JWST) has revolutionised the field by providing robust photometric redshifts and stellar mass estimates for these galaxies, thanks to its sensitivity and spatial resolution in the near-infrared (Gardner et al. 2023; Barrufet et al. 2023; Rodighiero et al. 2023). Nonetheless, JWST’s relatively narrow field of view limits its capacity for statistical investigations, which are essential for understanding the broader implications of this population on galaxy evolution.

The Euclid mission (Laureijs et al. 2012; Euclid Collaboration: Mellier et al. 2025), launched in July 2023 by the European Space Agency (ESA), provides an unparalleled opportunity to address these limitations. Equipped with the Visible Camera (VIS) for optical imaging (Euclid Collaboration: Cropper et al. 2025) and the Near-Infrared Spectrometer and Photometer (NISP) for near-infrared observations (Euclid Collaboration: Schirmer et al. 2023; Euclid Collaboration: Jahnke et al. 2025), Euclid is optimised for wide-field surveys, enabling statistical analyses of rare galaxy populations. With its Early Release Observations (EROs; Euclid Early Release Observations 2024), Euclid has already demonstrated its potential for identifying massive, dusty galaxies, especially when combined with ancillary data from the Spitzer/Infrared Array Camera (IRAC; Girardi et al. 2026).

With the first quick data release (Q1; Euclid Quick Release Q1 2025), we now have access to high-quality observations covering an area of approximately 60 deg2 area. This study focusses on a specific subset of ODGs known as HST-to-IRAC extremely red objects (HIEROs), identified using the colour criterion HE−IRAC2 > 2.25 (Wang et al. 2016; Caputi et al. 2012). By leveraging the overlap between Euclid Deep Field (EDF) and existing Spitzer imaging, we aim to refine the photometric redshifts and stellar mass estimates of this population. We are particularly interested in this population due to its contribution to the high-mass end of the stellar mass function at z ≳ 4 (Barrufet et al. 2023; Rodighiero et al. 2023, 2007; Wang et al. 2025; Traina et al. 2024; Gottumukkala et al. 2024).

The paper is structured as follows: In Sect. 2 we describe the Euclid data products and the creation of the Spitzer/IRAC photometric catalogue. In Sect. 3 we explain the selection of our sample and the methodology used to derive their physical properties. Lastly, in Sect. 4 we present our statistical analysis and discuss the results.

Throughout this work, we adopt a Λ cold dark matter (CDM) cosmology with parameters from Planck Collaboration XXIV (2016) and a Chabrier (2003) initial mass function (IMF). All magnitudes are reported in the AB system.

2. Data description

2.1. Euclid catalogues

We exploited the official catalogues released inside the Euclid Consortium for Q1. The detailed description of the data can be found in Euclid Collaboration: Aussel et al. (2026), Euclid Collaboration: McCracken et al. (2026), Euclid Collaboration: Polenta et al. (2026), and Euclid Collaboration: Romelli et al. (2026).

In summary, Q1 extragalactic observations cover 63 deg2 across three fields: the Euclid Deep Field Fornax (EDF-F), covering 12 deg2; the Euclid Deep Field North (EDF-N), with 22 deg2; and the Euclid Deep Field South (EDF-S), with 28 deg2. All the fields have been observed in the four Euclid bands, covering from the visible (IE, Euclid Collaboration: Cropper et al. 2025) to the near-infrared (NISP, YE, JE, and HE band; see Euclid Collaboration: Jahnke et al. 2025). These space-based observations are further supplemented by ground-based data collected with different instruments, covering wavelengths from 0.3 μm to 0.9 μm. The ground-based data are included in the officially released dataset as part of the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS, Gwyn et al. 2025) or the Dark Energy Survey (Abbott et al. 2018). The available bands are reported in Table 1.

Table 1.

All the available bands in the Q1 data release, differentiated by the three fields.

2.2. Spitzer/IRAC photometry

We analysed IRAC images1 described in Euclid Collaboration: Moneti et al. (2022). These observations partially overlap with the EDFs and are part of the Cosmic Dawn survey (Euclid Collaboration: McPartland et al. 2025). It is worth noting that the images do not have uniform coverage, both in area and depth, since they were collected by different programmes.

For consistency, we adopted the same IRAC photometry included in the pipeline used to derive the physical parameters for the Euclid sources (Euclid Collaboration: Enia et al. 2026). We exploited the two available IRAC bands at 3.6 μm (IRAC1) and 4.5 μm (IRAC2). In this work we did not consider the photometry at 5.8 μm (IRAC3) and 8.0 μm (IRAC4), since that is much shallower and inhomogeneous with respect to the other bands (see Table 1).

While more details on the IRAC photometry measurements can be found in Euclid Collaboration: Bisigello et al. (2026), in the following we briefly summarise the methodology. Using the photutils Python package (Bradley et al. 2024), we subtracted the background from each image by calculating the median with a 3 × 3 pixel filter. Again with photutils, aperture photometry was then performed on the IRAC images, forcing the detection to the Euclid source positions and using a 1″ radius aperture. This choice excludes any source that would be detectable by IRAC but not by the Euclid bands.

However, due to the different point spread functions (PSFs) of the two instruments, which cause significant source blending in the IRAC images, and in line with our goal of obtaining a reliable rather than a complete sample, we chose not to include in this study objects presenting blending or contamination effects. When available, future works will exploit the official Euclid catalogue where the de-blending has been performed on the Spitzer images.

For EDF-N, we conducted a separate extraction using co-added IRAC1 and IRAC2 Spitzer images, weighted by uncertainty maps, and measured fluxes within Kron apertures (Graham & Driver 2005). We applied a Kron scaling factor of 1.8 and a minimum unscaled radius of 2.5 pixels. Aperture and Kron fluxes from the separate extractions were compared to derive aperture-to-total corrections, which were uniformly applied to all filters. The final fluxes are consistent with the catalogues described in Euclid Collaboration: Zalesky et al. (2025), which cover two of the three EDFs.

3. Methods

3.1. HIERO sample selection

We produced a merged Euclid + Spitzer catalogue by matching the IDs of the sources, given the application of the photometry performed on the Spitzer images at the Euclid positions. The available bands and the respective observed depths are reported in Table 1.

To ensure the robustness of our sample, before applying our colour selection, we implemented a series of cuts:

  1. SPURIOUS_FLAG = 0;

  2. DET_QUALITY_FLAG <  4;

  3. MUMAX_MINUS_MAG >   − 2.6;

  4. 23.9 − 2.5 log10(FLUX_H_TOTAL) <  24.5;

  5. flag_H = 0.

The first cut ensures the removal of all the objects that have been labelled as spurious in the official catalogue. Similarly, the second flag requires that the photometry is good, and the fourth one requires a magnitude below 24.5 in the HE band. The decision to apply a magnitude cut in the HE band is due to the fact that this is one of the two bands used in our colour selection (see the next paragraph). This is a conservative choice, in order to deal only with the brightest and most massive sources. The MUMAX_MINUS_MAG quantity, instead, represents a sort of estimate of the compactness of the sources, and this cut should remove all the stars present in the catalogue. In fact, MU_MAX is the peak surface brightness above the background; thus, the estimator MUMAX_MINUS_MAG is related to the concentration of light at the peak versus the total magnitude (Euclid Collaboration: Romelli et al. 2026; Euclid Collaboration: Tucci et al. 2026). The last condition ensures that the HE band is not affected by spurious detections or artefacts. Again, we decided to include it given that our selection mainly relies on this band. We show in Table 2 the initial number of objects and the resulting number after applying these cuts.

Table 2.

Total number of objects in the merged Euclid + Spitzer catalogue and the remaining number of objects after each cleaning step, shown for the three fields.

To this clean catalogue, we applied the colour selection that identifies HIERO objects, as defined by Wang et al. (2016), and originally introduced by Caputi et al. (2012): HE−IRAC2 > 2.25. This colour selection is optimised to identify galaxies with AV ≳ 2 mag and log10(M*/M⊙)≈10 at z ≳ 3 (Gottumukkala et al. 2024). Figure 1 shows the entire clean parent sample, with the red line marking the HE−IRAC2 = 2.25 limit to our selection. All the objects falling above this line respect the HIERO definition. Out of the 14 737 506 objects in the clean sample, 26 906 candidates respect the colour criterion imposed and end up in our HIERO sample.

Thumbnail: Fig. 1. Refer to the following caption and surrounding text. Fig. 1.

Colour-magnitude plot of the clean sample. All the objects above the red line, representing HE−IRAC2 > 2.25, i.e. the HIERO colour selection (Wang et al. 2016), are included in our HIERO sample. The diagonal cut is due to the magnitude limit in the HE band.

3.2. Visual investigation of the candidate HIEROs

To maximise the reliability of the sources in our sample, we performed a visual check of all the HIERO selected according to the colour criterion presented in Fig. 1. This is mandatory to account for various issues that could affect these data and in order to provide the most conservative sample for the statistical purposes of this paper. We prioritised the purity at the expense of the completeness. As reported in Sect. 3.1, for a total of 26 906 objects we created a set of postage stamps, including the four Euclid bands and the first two IRAC channels with a size of 5″  ×  5″.

Some examples of good, isolated objects are reported in Fig. 2. We also highlight how the applied colour selection naturally includes dropout objects (bottom panel of Fig. 2). The difference in the PSFs of Euclid and Spitzer – with full widths a half maximum (FWHMs) of the order of 1 . ″ 5 Mathematical equation: $ {1{{\overset{\prime\prime}{.}}}5} $ in the first two IRAC channels – is evident from the images. This immediately leads to a large uncertainty in the physical association of objects detected with the two instruments. In particular, blending of Euclid sources in the IRAC images is a major issue. Figure 3 shows some such cases (middle panel), together with other examples of HIERO candidates that we decided to discard. For example, automatic masking of pixels from the Euclid pipeline can bias the measured fluxes in some bands, leading to artificially red galaxies (top panel). This does not imply that the object is not a valid candidate; rather, the available Euclid photometry is insufficient to accurately recover its true colours. An alternative approach would have been to mask the same pixels across all bands. However, given our goal of constructing a conservative and robust sample, we opted to discard such objects instead. Furthermore, the adopted IRAC photometry (see Sect. 2.2), extracted at the position of the Euclid sources, leads to cases where the corresponding IRAC flux is emitted by a different Spitzer object (bottom panel in Fig. 3, where a faint VIS detection is visible at the centre of the cutout). We further discarded sources falling at the edges of the maps or dominated by any other evident artefacts.

Thumbnail: Fig. 2. Refer to the following caption and surrounding text. Fig. 2.

Examples of two HIEROs that passed our visual check and that were kept in our final catalogue. From left to right: IE, YE, JE, HE, IRAC1, and IRAC2. Each cutout has a size of 5″ × 5″.

Thumbnail: Fig. 3. Refer to the following caption and surrounding text. Fig. 3.

Examples of three HIEROs that did not pass our visual check and were discarded from our final catalogue. From left to right: IE, YE, JE, HE, IRAC1, and IRAC2. Each cutout has a size of 5″  ×  5″.

To summarise, we determined whether to discard an object based on the following criteria:

  1. the presence of bad pixels invalidating the photometry;

  2. the presence of other Euclid sources within the IRAC FWHM;

  3. flux in the IRAC bands contaminated by nearby sources.

Our conservative approach leads to a significant reduction in the number of HIERO sources retained in this study, with only 15% surviving the selection process. The exact values for each deep field are provided in Table 2, resulting in a final sample of 3870 sources. While we acknowledge that discarding such a large number of objects is not ideal, the primary goal of this first work is to ensure a highly conservative selection. Consequently, we did not use this sample for statistical analyses, such as computing the stellar mass function, since completeness cannot be reliably reproduced. These analyses will be conducted once de-blended IRAC photometry becomes available for all three EDFs.

3.3. Physical parameter retrieval

In this study we exploited the Python code CIGALE (Boquien et al. 2019), given its fast response. We considered detections only the fluxes presenting S/N >  3, for all the others we set the flux to 0 and the error as 3 times the observed depth reported in Table 1. The setup is reported in Table 3.

Table 3.

Input models and main parameters for the CIGALE code.

We mimicked the setup used in the analogous work with the ERO data (Girardi et al. 2026). The AV value is up to 6, since we expect very dusty sources; while the redshift is free to go up to 15. This is because we expect both low- and high-z contaminants. With the wide nebular parameter range we ensure that we are not overestimating the mass due to mistaking the emission lines as the continuum, given the absence of data at longer wavelengths that could mitigate this problem. This could lead to an overestimation by up to a factor of 10 (Bisigello et al. 2019; Papovich et al. 2023; Wang et al. 2025).

4. Results and discussion

Given the significant uncertainties and degeneracies associated with photometric redshift estimation, we focussed on sources with at least three detections. When only one or two photometric points are available, even when considering upper limits, the fits cannot be considered reliable. Despite this restriction, the large sample size still enables a meaningful analysis. Prioritising robustness over completeness, we exclude these sources from further discussion. The resulting final sample consists of 2994 galaxies.

4.1. Constraints on the number density

While the primary focus of this work is the construction of a robust, clean sample, we also provide an approximate estimate of their number density, statistically accounting for sources of incompleteness and contamination, particularly those related to IRAC photometry.

A major challenge in selecting red sources like HIEROs arises from source blending in IRAC imaging, due to the large PSF. To mitigate this, we visually inspected all candidates and retained only those that appear isolated and uncontaminated in both the HE band and IRAC2 images (see Sect. 3.2). As a result, our final clean sample consists exclusively of visually confirmed isolated sources, ensuring that their IRAC photometry is reliable. However, this strict isolation criterion inevitably introduces incompleteness by systematically excluding genuine HIEROs that lie close to other sources and whose photometry is therefore blended.

To account for this, we applied a statistical correction inspired by the method used in Wang et al. (2019). Rather than applying an additional isolation cut, we used the fact that our sample already consists of isolated sources to estimate the fraction of HIEROs that would be missed due to blending. Specifically, we computed the probability that a genuine source would appear isolated within a radius of 2″, given the number density of Euclid sources.

From the reliable Euclid sample, we find a surface number density of N ∼ 0.026 arcsec−2. Under the assumption of a random spatial distribution, the probability that a galaxy has no neighbours within a radius r is p = exp(−Nπr2). For r = 2″, this yields p = 0.72, corresponding to a correction of approximately 28%. This correction could be underestimated, given that previous studies have found that HIEROs tend to be massive galaxies; therefore, they are expected to preferentially be in over-dense environments. Consequently, despite the reliability of our selection, a significant fraction of true HIEROs are likely excluded due to random superpositions with nearby sources, and this effect must be taken into consideration. Regarding the IRAC2 depth, a completeness correction should be negligible, as all sources lie above the 10σ observed depth reported in Table 1.

Although future improvements in de-blending techniques will enhance the accuracy of IRAC photometry, such methods may still struggle with very close source pairs. This approach, based on a statistical treatment of incompleteness, provides a conservative yet meaningful constraint on the true number density of such massive, red galaxies.

With this corrected estimate, it is instructive to compare our results with those found in the literature. We limited the comparison to studies where the applied selection is closely aligned with ours. In particular, we report the number densities (calculated as number counts divided by the area) from Wang et al. (2016), where the selection was first introduced, as well as from Barrufet et al. (2023) and Gottumukkala et al. (2024), who used the colour criteria F160W − F444W > 2.0 and F150W − F444W > 2.1, respectively.

As shown in Fig. 4, the different estimates do not always agree. As expected, JWST is able to probe much fainter 4.4 μm magnitudes. It is worth noting that the error bars on the y axis, which is plotted on a logarithmic scale, are computed assuming Poissonian noise. We did not consider other source of errors, so the true uncertainties may be underestimated. Small differences in the selection criteria also contribute to the lack of full agreement between the estimates.

Thumbnail: Fig. 4. Refer to the following caption and surrounding text. Fig. 4.

Number counts normalised by area as a function of IRAC2 magnitude. Different colours and symbols represent results from different studies.

Our values typically lie about one order of magnitude below those reported by Wang et al. (2016), while they appear more consistent with Gottumukkala et al. (2024) in the overlapping magnitude bins. A direct comparison, however, is challenging due to the different cleaning procedures applied to the samples in the cited studies. As expected, we have prioritised reliability over completeness; therefore, even after applying the statistical correction, our number densities are likely to be underestimated. For instance, during the visual inspection (see Sect. 3.2), we discarded numerous objects affected by defects or masks in the Euclid images. Although these candidates could be genuine HIEROs, they did not satisfy the requirements of our final selection criteria.

Another important consideration is that, by construction, our sample excludes the HE-undetected sources (see Sect. 2.2), which are included in the samples of the other studies. For instance, in the sample presented by Wang et al. (2016), HE-undetected objects constitute approximately 6.3% of the total. In our case, this fraction would likely be even higher due to the shallower data. This limitation will be addressed in future work, which will take advantage of the de-blended Spitzer/IRAC catalogues available for the EDFs. Nevertheless, this plot highlights the true power of Euclid: thanks to its unmatched field of view, we are able recover IRAC2 magnitudes up to 15 mag, extending our current knowledge towards thebright end.

4.2. Derived galaxy properties

Despite removing sources with the fewest photometric detections, we stress that a fraction of our sample still relies on spectral energy distribution (SED) fits constrained by only three or four detections with S/N >  3. Given these limitations, we invite the reader to interpret the derived photometric redshifts and galaxy properties with caution. This uncertainty is reflected in the non-negligible errors, reported in each plot. Future observations of the EDFs will be crucial for these objects, because they will benefit from the increased depth of upcoming surveys.

We first present the M* versus z distribution in Fig. 5. The left panel displays all data points for sources with three or more detections, using values derived from CIGALE. The grey-shaded region represents the prohibited area according to the ΛCDM cosmological model, where baryonic conversion efficiency (ϵ) exceeds 100%. Approximately 9.7% of the sample falls within this region. The vertical dotted red line marks z = 3.5, with sources to the right of this line represented by red dots; we refer to these as high-z sources from this point onwards. Given the limited photometric information and the relatively shallow depth of this data release (as reported in Table 1), we do not place full confidence in the SED-fitting results for these objects.

Thumbnail: Fig. 5. Refer to the following caption and surrounding text. Fig. 5.

Stellar mass versus redshift distribution of the HIERO sample. The y-axis is on a logarithmic scale. The vertical dotted red line corresponds to z = 3.5, above which we do not trust the solutions found by the fit. These high-z sources are displayed as red dots. The grey-shaded area represents the region forbidden by the ΛCDM model. The black lines show the limit for different values of ϵ. The median error for both the quantities is reported in the top-right corner of the plot. Left panel: Results from the CIGALE run described in Sect. 3.3. Right panel: Results from the low-z run for the high-z objects, which are still displayed as red dots.

We also took into account the findings of Forrest et al. (2024), whose empirical study suggests that SED-fitting estimates yielding M* > 1011.7 M⊙ at 3 < zphot < 4 are not supported by spectroscopic observations. Specifically, they selected a sample of red objects with such estimated properties and obtained spectroscopic data. Upon comparison, none of the objects satisfied zspec − zphot < 0.5, underscoring the difficulty in accurately characterising such sources. Their results, based on very high S/N observations and a broader set of observed bands, rely on more robust photometry. Given this distinction, we opted not to impose a stellar mass cut but instead to focus solely on redshift considerations.

This decision is further supported by an analysis of the total redshift distribution of the EDFs, normalised by total counts and shown in Fig. 6. We find that our number counts at z > 3.5 exceed those reported by Gottumukkala et al. (2024) in the Cosmic Evolution Early Release Science (CEERS) field. This mismatch is more clearly quantified by comparing the median redshift values. In fact, we find zmedian = 3.79, whereas their analysis reports zmedian = 3.13. The latter study utilises JWST observations covering a significantly smaller area (about 80 arcmin2) but reaching considerably greater depth than the current Q1 data release. This discrepancy raises concerns about the reliability of our photometric redshift estimates for the high-z sub-sample. To address this, we performed a second CIGALE run, constraining the redshift to z < 3.5. The right panel of Fig. 5 presents the same distribution, but incorporates the results from this second run for high-z sources, which are still shown as red dots to illustrate their redistribution. We observe that these empirical constraints align well with the theoretical limit, reducing the fraction of points in the prohibited area to approximately 0.17%. Given this refined distribution, we did not blindly trust the SED-fitting tool’s results, but instead integrated both empirical and theoretical insights.

Thumbnail: Fig. 6. Refer to the following caption and surrounding text. Fig. 6.

Redshift distributions for the total HIERO sample in the EDFs (green) compared to the distribution found by Gottumukkala et al. (2024) in the CEERS field applying a similar selection, (red). The vertical line corresponds to z = 3.5.

Preferring a lower-z solution for this sub-sample implies prioritising a low-z interpretation over a high-z one. In fact, in the preliminary work of Girardi et al. (in prep.), we found that nearly all HIEROs exhibit a bimodal or multi-peak probability distribution function for redshift. This is due to the degeneracy between a high-z, less dusty solution and a lower-z, dustier one. Statistically, some objects are likely to be genuine high-z sources; however, given our inability to discern which cases reflect true high-z objects versus those with overestimated photometric redshifts, we adopted a conservative approach. The presence of high-z sources could be further tested in a future work with longer wavelength data, such as sub-millimetre observations from Herschel/SPIRE. Based on all these considerations, we proceed by presenting and discussing results derived from the distribution shown in the right panel of Fig. 5.

Figure 7 shows the distribution of the stellar masses. The counts are proportional to the different numbers of candidates in the different fields. We find a mean value of ⟨M*⟩ = 1010.6 M⊙, a value that confirms the expected massive nature of these sources.

Thumbnail: Fig. 7. Refer to the following caption and surrounding text. Fig. 7.

Stellar mass distributions for EDF-F (blue), EDF-N (orange), and EDF-S (purple). The counts of the distributions are normalised to the total number of objects in each field.

Studying this type of source, we are interested in looking at the dust attenuation values since we expect them to be very dusty. We find a mean value of ⟨AV⟩ = 2.3, confirming the expectation. Looking at the AV versus M* distribution, shown in Fig. 8, we see that our points are spread across quite a broad range, covering AV from about 0 up to 6. For reference, the expected relation for normal galaxies in McLure et al. (2018) is also shown as a solid dark teal line. Our sample roughly follows this relation, revealing, however, a possible population of extremely obscured objects at M* > 1010 M⊙. This proves the power of Euclid in providing statistical samples of candidates of such rare objects for subsequent follow-up observations using other facilities, in order to confirm their redshifts and nature. For comparison, we include in this plot recent results from the literature of samples selected with similar criteria (Pampliega et al. 2019; Pérez-González et al. 2023; Gentile et al. 2024), including much fainter JWST dark objects.

Thumbnail: Fig. 8. Refer to the following caption and surrounding text. Fig. 8.

Dust attenuation versus stellar mass of the HIERO sample. The x-axis is on a logarithmic scale. The solid dark teal line reports the relation from McLure et al. (2018), while the purple line delimits the area that identifies the so-called Highly Extincted Low-Mass (HELM) galaxies (Bisigello et al. 2025). The median error for both quantities is reported in the top-left corner of the plot. Different symbols report values from previous studies, as indicated in the legend.

5. Conclusions

In this study we exploited the Q1 Euclid data release to characterise HIERO galaxies, a dusty and massive population exhibiting extremely red colours. Starting from the official Euclid photometric catalogue, we applied a series of selection cuts (see Sect. 3.1) to isolate candidates that meet the HIERO colour criteria. This process resulted in a clean sample of 26 906 sources.

To ensure the robustness of our sample, we performed a rigorous visual inspection, retaining only candidates with reliable photometry (i.e. free from defects, masked regions, or blending or contamination issues in the IRAC bands; see Sect. 3.2). This refinement led to a final sample of 3870 sources.

We used an SED-fitting code to derive the physical properties of these objects (see Sect. 3.3). To enhance the robustness of our sample, we included only sources with at least three photometric detections (S/N >  3). However, the results should be interpreted with caution as in some cases the SED fitting relies on only three or four photometric points. Nevertheless, we believe these findings are valuable, both for anticipating the potential of future Euclid data releases and as a foundation for further investigations.

The wide area of the EDFs enabled us to obtain meaningful statistics, even after applying all the selection cuts to define the final sample. This is particularly crucial for these rare objects, which would otherwise be challenging to characterise. The next step is to leverage future data releases to accurately determine the HIERO contribution to the stellar mass function across different epochs, a task that will be addressed in a forthcoming paper.

This pilot analysis highlights the need for further studies to fully understand this population. Our results show that these objects span a broad range of parameters, leaving their nature uncertain. Obtaining spectroscopic data will be crucial to better constraining their properties. Euclid’s slitless spectroscopy will help disentangle the degeneracy between redshift and dust attenuation and in turn help determine whether the identified high-z contaminants are genuine or instead represent more dust-obscured sources at lower redshifts.

Overall, Euclid has demonstrated its potential as a pivotal instrument for studying this massive and dusty population. Even more exciting results are expected with the DR1 Euclid data release, which will cover approximately 1900 deg2 of sky.

Acknowledgments

The research activities described in this paper were carried out with contribution of the Next Generation EU funds within the National Recovery and Resilience Plan (PNRR), Mission 4–Education and Research, Component 2–From Research to Business (M4C2), Investment Line 3.1–Strengthening and creation of Research Infrastructures, Project IR0000034–“STILES–Strengthening the Italian Leadership in ELT and SKA”. This work has made use of the Euclid Quick Release Q1 data from the Euclid/mission of the European Space Agency (ESA), 2025, https://doi.org/10.57780/esa-2853f3b. The Euclid Consortium acknowledges the European Space Agency and a number of agencies and institutes that have supported the development of Euclid, in particular the Agenzia Spaziale Italiana, the Austrian Forschungsförderungsgesellschaft funded through BMK, the Belgian Science Policy, the Canadian Euclid Consortium, the Deutsches Zentrum für Luft- und Raumfahrt, the DTU Space and the Niels Bohr Institute in Denmark, the French Centre National d’Etudes Spatiales, the Fundação para a Ciência e a Tecnologia, the Hungarian Academy of Sciences, the Ministerio de Ciencia, Innovación y Universidades, the National Aeronautics and Space Administration, the National Astronomical Observatory of Japan, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Research Council of Finland, the Romanian Space Agency, the State Secretariat for Education, Research, and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency. A complete and detailed list is available on the Euclid web site (www.euclid-ec.org). This research makes use of ESA Datalabs (datalabs.esa.int), an initiative by ESA’s Data Science and Archives Division in the Science and Operations Department, Directorate of Science.

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1

Retrieved at this page: https://exchg.calet.org/Spitzer/linear/

All Tables

Table 1.

All the available bands in the Q1 data release, differentiated by the three fields.

In the text
Table 2.

Total number of objects in the merged Euclid + Spitzer catalogue and the remaining number of objects after each cleaning step, shown for the three fields.

In the text
Table 3.

Input models and main parameters for the CIGALE code.

In the text

All Figures

Thumbnail: Fig. 1. Refer to the following caption and surrounding text. Fig. 1.

Colour-magnitude plot of the clean sample. All the objects above the red line, representing HE−IRAC2 > 2.25, i.e. the HIERO colour selection (Wang et al. 2016), are included in our HIERO sample. The diagonal cut is due to the magnitude limit in the HE band.
In the text
Thumbnail: Fig. 2. Refer to the following caption and surrounding text. Fig. 2.

Examples of two HIEROs that passed our visual check and that were kept in our final catalogue. From left to right: IE, YE, JE, HE, IRAC1, and IRAC2. Each cutout has a size of 5″ × 5″.
In the text
Thumbnail: Fig. 3. Refer to the following caption and surrounding text. Fig. 3.

Examples of three HIEROs that did not pass our visual check and were discarded from our final catalogue. From left to right: IE, YE, JE, HE, IRAC1, and IRAC2. Each cutout has a size of 5″  ×  5″.
In the text
Thumbnail: Fig. 4. Refer to the following caption and surrounding text. Fig. 4.

Number counts normalised by area as a function of IRAC2 magnitude. Different colours and symbols represent results from different studies.
In the text
Thumbnail: Fig. 5. Refer to the following caption and surrounding text. Fig. 5.

Stellar mass versus redshift distribution of the HIERO sample. The y-axis is on a logarithmic scale. The vertical dotted red line corresponds to z = 3.5, above which we do not trust the solutions found by the fit. These high-z sources are displayed as red dots. The grey-shaded area represents the region forbidden by the ΛCDM model. The black lines show the limit for different values of ϵ. The median error for both the quantities is reported in the top-right corner of the plot. Left panel: Results from the CIGALE run described in Sect. 3.3. Right panel: Results from the low-z run for the high-z objects, which are still displayed as red dots.
In the text
Thumbnail: Fig. 6. Refer to the following caption and surrounding text. Fig. 6.

Redshift distributions for the total HIERO sample in the EDFs (green) compared to the distribution found by Gottumukkala et al. (2024) in the CEERS field applying a similar selection, (red). The vertical line corresponds to z = 3.5.
In the text
Thumbnail: Fig. 7. Refer to the following caption and surrounding text. Fig. 7.

Stellar mass distributions for EDF-F (blue), EDF-N (orange), and EDF-S (purple). The counts of the distributions are normalised to the total number of objects in each field.
In the text
Thumbnail: Fig. 8. Refer to the following caption and surrounding text. Fig. 8.

Dust attenuation versus stellar mass of the HIERO sample. The x-axis is on a logarithmic scale. The solid dark teal line reports the relation from McLure et al. (2018), while the purple line delimits the area that identifies the so-called Highly Extincted Low-Mass (HELM) galaxies (Bisigello et al. 2025). The median error for both quantities is reported in the top-left corner of the plot. Different symbols report values from previous studies, as indicated in the legend.
In the text

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