We present a catalogue of more than 5000 new ultracool dwarf (UCD) candidates in the three Euclid Deep Fields in the Q1 data release. They range from late M to late T dwarfs, and include 1200 L and T dwarfs. More than 100 of them have been spectroscopically confirmed, with seven of them being T dwarfs. Our UCD selection criteria are based only on colour ($I_\mathrm{E}-Y_\mathrm{E}>2.5$). The combined requirement for optical detection and stringent signal-to-noise ratio threshold ensure a high purity of the sample, but at the expense of completeness, especially for T dwarfs. The detections range from magnitudes 19 and 24 in the near-infrared bands, and extend down to 26 in the optical band. The average surface density of detected UCDs on the sky is approximately 100 objects per $\mathrm{deg}^2$, including 20 L and T dwarfs per $\mathrm{deg}^2$. This leads to an expectation of at least 1.4 million ultracool dwarfs in the final data release of the Euclid Wide Survey, including at least 300,000 L dwarfs, and more than 2,600 T dwarfs, using the strict selection criteria from this work. We provide empirical Euclid colours as a function of spectral type, and a probability that an object with a given colour has a certain spectral type.
Ultracool dwarfs (UCDs) encompass the lowest mass stars and brown dwarfs, defining the stellar substellar boundary. They have significant potential for advancing the understanding of substellar physics; however, these objects are challenging to detect due to their low luminosity. The wide coverage and deep sensitivity of the Euclid survey will increase the number of confirmed and well characterised UCDs by several orders of magnitude. In this study, we take advantage of the Euclid Quick Data Release (Q1) and in particular we look in detail at the known and new UCDs in the Euclid Deep Field North (22.9 deg2 down to JE = 24.5 mag), to understand the advantages of using the slitless Euclid spectroscopy. We compile a comparison sample of known UCDs and use their spectra to demonstrate the capability of Euclid to derive spectral types using a template matching method. This method is then applied to the spectra of the newly identified candidates. We confirm that 33 of these candidates are new UCDs, with spectral types ranging from M7 to T1 and JE = 17 to 21 mag. We look at their locus in colour colour diagrams and compare them with the expected colours of QSOs. A machine readable catalogue is provided for further study, containing both the comparison sample and the newly identified UCDs, along with their spectral classifications where the Q1 spectra quality allows for confident determination
Recent analyses of cosmological hydrodynamic simulations from CAMELS have shown that machine learning models can predict the parameter describing the total matter content of the universe, $\Omega_{\rm m}$, from the features of a single galaxy. We investigate the statistical properties of two of these simulation suites, IllustrisTNG and ASTRID, confirming that $\Omega_{\rm m}$ induces a strong displacement on the distribution of galaxy features. We also observe that most other parameters have little to no effect on the distribution, except for the stellar-feedback parameter $A_{SN1}$, which introduces some near-degeneracies that can be broken with specific features. These two properties explain the predictability of $\Omega_{\rm m}$. We use Optimal Transport to further measure the effect of parameters on the distribution of galaxy properties, which is found to be consistent with physical expectations. However, we observe discrepancies between the two simulation suites, both in the effect of $\Omega_{\rm m}$ on the galaxy properties and in the distributions themselves at identical parameter values. Thus, although $\Omega_{\rm m}$'s signature can be easily detected within a given simulation suite using just a single galaxy, applying this result to real observational data may prove significantly more challenging.