Christopher Duncan

Abstract:

Galaxy morphologies and shape orientations are expected to correlate with their large-scale environments, since they grow by accreting matter from the cosmic web and are subject to interactions with other galaxies. We extracted cosmic filaments in projection from the Euclid Quick Data Release 1 (covering 63.1 deg 2 ) at 0.5 < z < 0.9 in tomographic slices of 170 comoving h −1 Mpc using photometric redshifts. Galaxy morphologies are accurately retrieved thanks to the excellent imaging resolution of the visible camera (VIS) of Euclid . The distribution of massive galaxies ( M * > 10 10 M ⊙ ) in the projected cosmic web is analysed as a function of morphology measured from VIS data. Specifically, the 2D alignment of galaxy shapes with large-scale filaments is quantified as a function of Sérsic indices and masses. We find the known trend that more massive galaxies are closer to filament spines. At fixed stellar masses, morphologies correlate both with densities and distances to large-scale filaments. In addition, the large volume of this data set allows us to detect a signal indicating that there is a preferential alignment of the major axis of massive early-type galaxies along projected cosmic filaments. Overall, these results demonstrate our capabilities to carry out detailed studies of galaxy environments with Euclid , which will be extended to higher redshift and lower stellar masses with the future Euclid Deep Survey.

Abstract:

The star-forming main sequence (SFMS) is a tight relation observed between stellar masses and star formation rates (SFR) in a population of galaxies. This relation is observed at different redshifts, in various morphological, and environmental domains, and is key to understanding the underlying relations between a galaxy budget of cold gas and its stellar content. Euclid Quick Data Release 1 (Q1) gives us the opportunity to investigate this fundamental relation in galaxy formation and evolution. We complement the Euclid release with public IRAC observations of the Euclid Deep Fields, improving the quality of recovered photometric redshifts, stellar masses, and SFRs, as is shown both with simulations and a comparison with available spectroscopic redshifts. From Q1 data alone, we recover more than ∼30 k galaxies with log 10 ( M * / M ⊙ ) > 11, giving a precise constraint of the SFMS at the high-mass end. We investigated the SFMS, in a redshift interval between 0.2 and 3.0, comparing our results with the existing literature and fitting them with a parameterisation taking into account the presence of a bending of the relation at the high-mass end, depending on the bending mass, M 0 . We find good agreement with previous results in terms of M 0 values, and an increasing trend for the relation scatter at higher stellar masses. We also investigate the distribution of physical (e.g. dust absorption, A V , and formation age) and morphological properties (e.g., Sérsic index and radius) in the SFR–stellar mass plane, and their relation with the SFMS. These results highlight the potential of Euclid in studying the fundamental scaling relations that regulate galaxy formation and evolution in anticipation of the forthcoming Data Release 1.

Abstract:

The first survey data release by the Euclid mission covers approximately 63 deg 2 in the Euclid Deep Fields to the same depth as the Euclid Wide Survey. This paper showcases, for the first time, the performance of cluster finders on Euclid data and presents examples of validated clusters in the Quick Release 1 (Q1) imaging data. We identify clusters using two algorithms ( AMICO and PZWav ) implemented in the Euclid cluster-detection pipeline. We explore the internal consistency of detections from the two codes, and cross-match detections with known clusters from other surveys using external multi-wavelength and spectroscopic data sets. This enables assessment of the Euclid photometric redshift accuracy and also of systematics such as mis-centring between the optical cluster centre and centres based on X-ray and/or Sunyaev–Zeldovich observations. We report 426 joint PZWav and AMICO -detected clusters with high signal-to-noise ratios over the full Q1 area in the redshift range 0.2 ≤ z ≤ 1.5. The chosen redshift and signal-to-noise thresholds are motivated by the photometric quality of the early Euclid data. We provide richness estimates for each of the Euclid -detected clusters and show its correlation with various external cluster mass proxies. Due to the limited area and evolving data quality, the sample is not intended to serve as a reference for cosmological applications, but to verify and validate the cluster workflow. Out of the full sample, 77 systems are potentially new to the literature. Overall, the Q1 cluster catalogue demonstrates a successful validation of the workflow ahead of the Euclid Data Release 1, based on the consistency of internal and external properties of Euclid -detected clusters.

Abstract:

The Euclid Wide Survey (EWS) is expected to identify in the order of 100 000 galaxy-galaxy strong lenses across 14 000deg 2 . The Euclid Quick Data Release (Q1) of 63.1deg 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 ± 2% (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% and the best machine learning network in this work achieved 7.3% purity for the same completeness). 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 ∼300 million galaxies, and thus the time investment of both experts and citizens must be carefully managed.

Abstract:

We present a detailed visual morphology catalogue for the Euclid Quick Release 1 (Q1). Our catalogue includes galaxy features such as bars, spiral arms, and ongoing mergers, for the 378 000 bright ( I E < 20.5) or extended (area ≥700 pixels) galaxies in Q1. The catalogue was created by finetuning the Zoobot galaxy foundation models on annotations from an intensive one-month campaign by Galaxy Zoo volunteers. Our measurements are fully automated, and hence fully scaleable. This catalogue is the first 0.4% of the approximately 100 million galaxies where Euclid will ultimately resolve detailed morphology.