Galaxy formation and evolution

Abstract:

We present a systematic statistical analysis of an informal astrophysical phenomenon known as Renzo’s rule (or Sancisi’s law), which states that ‘for any feature in a galaxy’s luminosity profile, there is a corresponding feature in the rotation curve, and vice versa’. This is often posed as a challenge for the standard Λ cold dark matter (CDM) model while supporting alternative theories such as modified Newtonian dynamics (MOND). Indeed, we identify clear features in the dwarf spiral NGC 1560 – a prime example for Renzo’s rule – and find correlation statistics which support Renzo’s rule with a slight preference for MOND over CDM halo fits. However, a broader analysis on galaxies in the Spitzer Photometry & Accurate Rotation Curves (SPARC) data base reveals an excess of features in rotation curves that lack clear baryonic counterparts, with correlation statistics deviating up to on average from that predicted by both MOND and CDM haloes, challenging the validity of Renzo’s rule. Thus we do not find clear evidence for Renzo’s rule in present galaxy data overall. We additionally perform mock tests, which show that a definitive test of Renzo’s rule is primarily limited by the lack of clearly resolved baryonic features in current galaxy data.

Abstract:

Abstract We use deep NIRCam and MIRI imaging from the JWST PRIMER survey to study the properties of ALMA detected (sub)mm sources in the COSMOS field, with the aim of defining the cosmic history of dust-enshrouded star formation. The wealth of ALMA data in this field enabled us to isolate a robust sample of 128 (sub)mm sources within the 175 arcmin2 PRIMER COSMOS survey footprint, spanning two decades in (sub)mm flux density. The JWST imaging is deep and red enough to reveal secure galaxy counterparts for all of these sources. This 100% identification completeness is accompanied by a high level of redshift completeness: 52% of the sources have spectroscopic redshifts, and this has enabled us to refine the photometric redshifts for the remaining galaxies. Armed with robust redshift information, we calculate the star-formation rates (SFR) and stellar masses (M*) of all 128 ALMA-detected galaxies, and place them in the context of other galaxies in the field. We find that the vast majority of star formation is dust-enshrouded in all of the ALMA-detected galaxies, with SFR ranging from ≃ 1000 M⊙ yr−1 down to ≃ 20 M⊙ yr−1. We also find that virtually all (126/128) have high stellar masses, M* > 1010 M⊙, independent of redshift. The unusually high quality of our sample enables us to make a robust estimate of the contribution of the ALMA-detected galaxies to cosmic star-formation rate density, ρSFR. The existing ALMA imaging only covers <20% of the PRIMER COSMOS area, but based on our knowledge of all other massive galaxies in the field, we produce a completeness-corrected estimate of dust-enshrouded ρSFR. This confirms that UV-visible star formation dominates ρSFR at z > 4, but also indicates that dust-enshrouded star formation still makes a contribution of ≃ 20% at z ≃ 8, and ≃ 5% at z ≃ 10.

Abstract:

Abstract The peculiar velocity field of the local Universe provides direct insights into its matter distribution and the underlying theory of gravity, and is essential in cosmological analyses for modelling deviations from the Hubble flow. Numerous methods have been developed to reconstruct the density and velocity fields at z ≲ 0.05, typically constrained by redshift-space galaxy positions or by direct distance tracers such as the Tully–Fisher relation, the fundamental plane, or Type Ia supernovae. We introduce a validation framework to evaluate the accuracy of these reconstructions against catalogues of direct distance tracers. Our framework assesses the goodness-of-fit of each reconstruction using Bayesian evidence, residual redshift discrepancies, velocity scaling, and the need for external bulk flows. Applying this framework to a suite of reconstructions—including those derived from the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm and from linear theory—we find that the non-linear BORG reconstruction consistently outperforms others. We highlight the utility of such a comparative approach for supernova or gravitational wave cosmological studies, where selecting an optimal peculiar velocity model is essential. Additionally, we present calibrated bulk flow curves predicted by the reconstructions and perform a density–velocity cross-correlation using a linear theory reconstruction to constrain the growth factor, yielding S8 = 0.793 ± 0.035. The result is in good agreement with both weak lensing and Planck, but is in strong disagreement with some peculiar velocity studies.

Abstract:

In this study, we examine over 14 000 radio galaxies finely selected from Radio Galaxy Zoo (RGZ) project and provide classifications for approximately 5900 FRIs and 8100 FRIIs. We present an analysis of these predicted radio galaxy morphologies for the RGZ catalogue, classified using a pre-trained radio galaxy foundation model that has been fine-tuned to predict Fanaroff–Riley (FR) morphology. As seen in previous studies, our results show overlap between morphologically classified FRI and FRII luminosity–size distributions and we find that the model’s confidence in its predictions is lowest in this overlap region, suggesting that source morphologies are more ambiguous. We identify the presence of low-luminosity FRII sources, the proportion of which, with respect to the total number of FRIIs, is consistent with previous studies. However, a comparison of the low-luminosity FRII sources found in this work with those identified by previous studies reveals differences that may indicate their selection is influenced by the choice of classification methodology. We investigate the impacts of both pre-training and fine-tuning data selection on model performance for the downstream classification task, and show that while different pre-training data choices affect model confidence they do not appear to cause systematic generalization biases for the range of physical and observational characteristics considered in this work; however, we note that the same is not necessarily true for fine-tuning. As automated approaches to astronomical source identification and classification become increasingly prevalent, we highlight training data choices that can affect the model outputs and propagate into downstream analyses.

Abstract:

We present a comprehensive study of the star-forming main sequence (SFMS) and its scatter at redshifts , using Near Infrared Camera (NIRCam) photometry from the JADES (JWST Advanced Deep Extragalactic Survey) survey in the Great Observatories Origins Deep Survey (GOODS) South (GOODS-S) and North (GOODS-N) fields. Our analysis is based on a sample of galaxies that is stellar mass complete down to . The redshift evolution of the SFMS at an averaging time-scale of 10 Myr follows a relation, quantified by the specific star formation rates (sSFR), of with , in good agreement with theoretical predictions and the specific mass accretion rate of dark matter haloes. We find that the SFMS normalization varies in a complex way with the SFR averaging time-scale, reflecting the combined effects of bursty star formation and rising star formation histories (SFHs). We quantify the scatter of the SFMS, revealing that it decreases with longer SFR averaging time-scales, from at 10 Myr to at 100 Myr, indicating that shorter term fluctuations dominate the scatter, although long-term variations in star formation activity are also present. Our findings suggest that bursty SFHs are more pronounced at lower stellar masses. Furthermore, we explore the implications of our results for the observed overabundance of UV-bright galaxies at , concluding that additional mechanisms, such as top-heavy initial mass functions, increased star formation efficiencies, or increased burstiness in star formation are needed to explain these observations. Finally, we emphasize the importance of accurate stellar mass completeness limits when fitting the SFMS, especially for galaxies with bursty SFHs.