In 2018, the near-Earth object (155140) 2005 UD (hereafter UD) experienced a close fly by of the Earth. We present results from an observational campaign involving photometric, spectroscopic, and polarimetric observations carried out across a wide range of phase angles (0.°7–88°). We also analyze archival NEOWISE observations. We report an absolute magnitude of H V = 17.51 ± 0.02 mag and an albedo of p V = 0.10 ± 0.02. UD has been dynamically linked to Phaethon due their similar orbital configurations. Assuming similar surface properties, we derived new estimates for the diameters of Phaethon and UD of D = 5.4 ± 0.5 km and D = 1.3 ± 0.1 km, respectively. Thermophysical modeling of NEOWISE data suggests a surface thermal inertia of and regolith grain size in the range of 0.9–10 mm for UD and grain sizes of 3–30 mm for Phaethon. The light curve of UD displays a symmetric shape with a reduced amplitude of Am(0) = 0.29 mag and increasing at a linear rate of 0.017 mag/° between phase angles of 0° and ∼25°. Little variation in light-curve morphology was observed throughout the apparition. Using light-curve inversion techniques, we obtained a sidereal rotation period P = 5.235 ± 0.005 hr. A search for rotational variation in spectroscopic and polarimetric properties yielded negative results within observational uncertainties of ∼10% μm−1 and ∼16%, respectively. In this work, we present new evidence that Phaethon and UD are similar in composition and surface properties, strengthening the arguments for a genetic relationship between these two objects.

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 of the Organisational Unit MER of the Euclid Science Ground Segment, we have conducted the Euclid Morphology Challenge, which we present in two papers. While the companion paper focusses 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, ProFitand SourceXtractor++ , on a sample of about 1.5 million simulated galaxies (350 000 above 5σ) resembling reduced observations with the Euclid VIS and NIR instruments. The simulations include analytic Sérsic 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 Sérsic simulations) down to an apparent magnitude of about I E = 23 in one component and I E = 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 official Euclid 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.

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&gt;3 in the $H_{\scriptscriptstyle \rm E}$-band; ii) the stellar mass within a factor of two ($\sim 0.3 \rm dex$) for 99.5${{\%}}$ of the considered galaxies; iii) the SFR within a factor of two ($\sim 0.3 \rm dex$) 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.

Euclid’s photometric galaxy cluster survey has the potential to be a very competitive cosmological probe. The main cosmological probe with observations of clusters is their number count, within which the halo mass function (HMF) is a key theoretical quantity. We present a new calibration of the analytic HMF, at the level of accuracy and precision required for the uncertainty in this quantity to be subdominant with respect to other sources of uncertainty in recovering cosmological parameters from Euclid cluster counts. Our model is calibrated against a suite of N-body simulations using a Bayesian approach taking into account systematic errors arising from numerical effects in the simulation. First, we test the convergence of HMF predictions from different N-body codes, by using initial conditions generated with different orders of Lagrangian Perturbation theory, and adopting different simulation box sizes and mass resolution. Then, we quantify the effect of using different halo finder algorithms, and how the resulting differences propagate to the cosmological constraints. In order to trace the violation of universality in the HMF, we also analyse simulations based on initial conditions characterised by scale-free power spectra with different spectral indexes, assuming both Einstein–de Sitter and standard ΛCDM expansion histories. Based on these results, we construct a fitting function for the HMF that we demonstrate to be sub-percent accurate in reproducing results from 9 different variants of the ΛCDM model including massive neutrinos cosmologies. The calibration systematic uncertainty is largely sub-dominant with respect to the expected precision of future mass–observation relations; with the only notable exception of the effect due to the halo finder, that could lead to biased cosmological inference.