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We present a new constraint on the Hubble constant ($H_0$) from the standard dark siren method using a sample of five well-covered gravitational wave (GW) alerts reported during the first part of the fourth observing run of the Laser Interferometer Gravitational-Wave Observatory (LIGO), the Virgo and Kamioka Gravitational Wave Detector (KAGRA) collaborations (LVK) and with three updated standard dark sirens from third observation run in combination with the previous constraints from the first three runs. Our methodology relies on the galaxy catalogue method alone. We use a deep learning method to derive the full probability density estimation of photometric redshifts using the Legacy Survey catalogues. We add the constraints from well localized binary black hole mergers to the sample of standard dark sirens analysed in our previous work. We combine the $H_0$ posterior for 5 new standard sirens with other 10 previous events (using the most recent available data for the five novel events and updated three previous posteriors from O3), finding $H_0 = 70.4^{+13.6}_{-11.7}~{\rm km~s^{-1}~Mpc^{-1}}$ (68 per cent confidence interval) with the catalogue method only. This result represents an improvement of $\sim 23~{{\ \rm per\ cent}}$ comparing the new 15 dark siren constraints with the previous 10 dark siren constraints and a reduction in uncertainty of $\sim 40~{{\ \rm per\ cent}}$ from the combination of 15 dark and bright sirens compared with the GW170817 bright siren alone. The combination of dark and bright siren GW170817 with recent jet constraints yields $H_0$ of $68.0^{+4.4}_{-3.8}~{\rm km~s^{-1}~Mpc^{-1}}$, a $\sim 6~{{\ \rm per\ cent}}$ precision from standard sirens, reducing the previous constraint uncertainty by $\sim 10~{{\ \rm per\ cent}}$.

Cosmological analyses using galaxy clusters in optical/near-infrared photometric surveys require robust characterization of their galaxy content. Precisely determining which galaxies belong to a cluster is crucial. In this paper, we present the COlor Probabilistic Assignment of Clusters And BAyesiaN Analysis (Copacabana) algorithm. Copacabana computes membership probabilities for all galaxies within an aperture centred on the cluster using photometric redshifts, colours, and projected radial probability density functions. We use simulations to validate Copacabana and we show that it achieves up to 89 per cent membership accuracy with a mild dependence on photometric redshift uncertainties and choice of aperture size. We find that the precision of the photometric redshifts has the largest impact on the determination of the membership probabilities followed by the choice of the cluster aperture size. We also quantify how much these uncertainties in the membership probabilities affect the stellar mass–cluster mass scaling relation, a relation that directly impacts cosmology. Using the sum of the stellar masses weighted by membership probabilities ($\rm \mu _{\star }$) as the observable, we find that Copacabana can reach an accuracy of 0.06 dex in the measurement of the scaling relation at low redshift for a Legacy Survey of Space and Time type survey. These results indicate the potential of Copacabana and $\rm \mu _{\star }$ to be used in cosmological analyses of optically selected clusters in the future.

Gravitational lensing magnification of Type Ia supernovae (SNe Ia) allows information to be obtained about the distribution of matter on small scales. In this paper, we derive limits on the fraction $\alpha$ of the total matter density in compact objects (which comprise stars, stellar remnants, small stellar groupings, and primordial black holes) of mass M > 0.03 ${\rm M}_{\odot }$ over cosmological distances. Using 1532 SNe Ia from the Dark Energy Survey Year 5 sample (DES-SN5YR) combined with a Bayesian prior for the absolute magnitude M, we obtain α < 0.12 at the 95 per cent confidence level after marginalization over cosmological parameters, lensing due to large-scale structure, and intrinsic non-Gaussianity. Similar results are obtained using priors from the cosmic microwave background, baryon acoustic oscillations, and galaxy weak lensing, indicating our results do not depend on the background cosmology. We argue our constraints are likely to be conservative (in the sense of the values we quote being higher than the truth), but discuss scenarios in which they could be weakened by systematics of the order of $\Delta \alpha \sim 0.04$.

High-ionization iron coronal lines (CLs) are a rare phenomenon observed in galaxy and quasi-stellar object spectra that are thought to be created by high-energy emission from active galactic nuclei and certain types of transients. In cases known as extreme coronal line emitting galaxies (ECLEs), these CLs are strong and fade away on a time-scale of years. The most likely progenitors of these variable CLs are tidal disruption events (TDEs), which produce sufficient high-energy emission to create and sustain the CLs over these time-scales. To test the possible connection between ECLEs and TDEs, we present the most complete variable ECLE rate calculation to date and compare the results to TDE rates from the literature. To achieve this, we search for ECLEs in the Sloan Digital Sky Survey (SDSS). We detect sufficiently strong CLs in 16 galaxies, more than doubling the number previously found in SDSS. Using follow-up spectra from the Dark Energy Spectroscopic Instrument and Gemini Multi-Object Spectrograph, Wide-field Infrared Survey Explorer mid-infrared observations, and Liverpool Telescope optical photometry, we find that none of the nine new ECLEs evolve in a manner consistent with that of the five previously discovered variable ECLEs. Using this sample of five variable ECLEs, we calculate the galaxy-normalized rate of variable ECLEs in SDSS to be $R_\mathrm{G}=3.6~^{+2.6}_{-1.8}~(\mathrm{statistical})~^{+5.1}_{-0.0}~(\mathrm{systematic})\times 10^{-6}~\mathrm{galaxy}^{-1}~\mathrm{yr}^{-1}$. The mass-normalized rate is $R_\mathrm{M}=3.1~^{+2.3}_{-1.5}~(\mathrm{statistical})~^{+4.4}_{-0.0}~(\mathrm{systematic})\times 10^{-17}~\mathrm{M_\odot ^{-1}}~\mathrm{yr}^{-1}$ and the volumetric rate is $R_\mathrm{V}=7~^{+20}_{-5}~(\mathrm{statistical})~^{+10}_{-0.0}~(\mathrm{systematic})\times 10^{-9}~\mathrm{Mpc}^{-3}~\mathrm{yr}^{-1}$. Our rates are one to two orders of magnitude lower than TDE rates from the literature, which suggests that only 10–40 per cent of all TDEs produce variable ECLEs. Additional uncertainties in the rates arising from the structure of the interstellar medium have yet to be included.

Low-surface-brightness galaxies (LSBGs) are excellent probes of quenching and other environmental processes near massive galaxies. We study an extensive sample of LSBGs near massive hosts in the local universe that are distributed across a diverse range of environments. The LSBGs with surface-brightness${\mu }_{\mathrm{eff},\mathit{g}}>24.2\mathrm{mag}{\mathrm{arcsec}}^{-2}$are drawn from the Dark Energy Survey Year 3 catalog while the hosts with masses$9.0<\log ({\mathcal{M}}_{\star }/M_{\odot })<11.0$comparable to the Milky Way and the Large Magellanic Cloud are selected from the z0MGS sample. We study the projected radial density profiles of LSBGs as a function of their color and surface brightness around hosts in both the rich Fornax–Eridanus cluster environment and the low-density field. We detect an overdensity with respect to the background density, out to 2.5 times the virial radius for both hosts in the cluster environment and the isolated field galaxies. When the LSBG sample is split byg−icolor or surface brightnessμ_eff,g, we find the LSBGs closer to their hosts are significantly redder and brighter, like their high-surface-brightness counterparts. The LSBGs form a clear “red sequence” in both the cluster and isolated environments that is visible beyond the virial radius of the hosts. This suggests preprocessing of infalling LSBGs and a quenched backsplash population around both host samples. More so, the relative prominence of the “blue cloud” feature implies that preprocessing is ongoing near the isolated hosts compared to the cluster environment where the LSBGs are already well processed.

Type Ia Supernovae (SNe Ia) are a critical tool in measuring the accelerating expansion of the universe. Recent efforts to improve these standard candles have focused on incorporating the effects of dust on distance measurements with SNe Ia. In this paper, we use the state-of-the-art Dark Energy Survey 5 year sample to evaluate two different families of dust models: empirical extinction models derived from SNe Ia data and physical attenuation models from the spectra of galaxies. In this work, we use realistic simulations of SNe Ia to forward-model different models of dust and compare summary statistics in order to test different assumptions and impacts on SNe Ia data. Among the SNe Ia-derived models, we find that a logistic function of the total-to-selective extinction $R_V$ best recreates the correlations between supernova distance measurements and host galaxy properties, though an additional 0.02 mag of grey scatter is needed to fully explain the scatter in SNIa brightness in all cases. These empirically derived extinction distributions are highly incompatible with the physical attenuation models from galactic spectral measurements. From these results, we conclude that SNe Ia must either preferentially select extreme ends of galactic dust distributions, or that the characterization of dust along the SNe Ia line-of-sight is incompatible with that of galactic dust distributions.

Current and future Type Ia Supernova (SN Ia) surveys will need to adopt new approaches to classifying SNe and obtaining their redshifts without spectra if they wish to reach their full potential. We present here a novel approach that uses only photometry to identify SNe Ia in the 5-yr Dark Energy Survey (DES) data set using the SuperNNova classifier. Our approach, which does not rely on any information from the SN host-galaxy, recovers SNe Ia that might otherwise be lost due to a lack of an identifiable host. We select $2{,}298$ high-quality SNe Ia from the DES 5-yr data set an almost complete sample of detected SNe Ia. More than 700 of these have no spectroscopic host redshift and are potentially new SNIa compared to the DES-SN5YR cosmology analysis. To analyse these SNe Ia, we derive their redshifts and properties using only their light curves with a modified version of the SALT2 light-curve fitter. Compared to other DES SN Ia samples with spectroscopic redshifts, our new sample has in average higher redshift, bluer and broader light curves, and fainter host-galaxies. Future surveys such as LSST will also face an additional challenge, the scarcity of spectroscopic resources for follow-up. When applying our novel method to DES data, we reduce the need for follow-up by a factor of four and three for host-galaxy and live SN, respectively, compared to earlier approaches. Our novel method thus leads to better optimization of spectroscopic resources for follow-up.

The current and next observation seasons will detect hundreds of gravitational waves (GWs) from compact binary systems coalescence at cosmological distances. When combined with independent electromagnetic measurements, the source redshift will be known, and we will be able to obtain precise measurements of the Hubble constant H0 via the distance–redshift relation. However, most observed mergers are not expected to have electromagnetic counterparts, which prevents a direct redshift measurement. In this scenario, one possibility is to use the dark sirens method that statistically marginalizes over all the potential host galaxies within the GW location volume to provide a probabilistic source redshift. Here we presented H0 measurements using two new dark sirens compared to previous analyses using DECam data: GW190924$\_$021846 and GW200202$\_$154313. The photometric redshifts of the possible host galaxies of these two events are acquired from the DECam Local Volume Exploration Survey (DELVE) carried out on the Blanco telescope at Cerro Tololo. The combination of the H0 posterior from GW190924$\_$021846 and GW200202$\_$154313 together with the bright siren GW170817 leads to $H_{0} = 68.84^{+15.51}_{-7.74}\, \rm {km\, s^{-1}\, Mpc^{-1}}$. Including these two dark sirens improves the 68  per cent confidence interval (CI) by 7  per cent over GW170817 alone. This demonstrates that the addition of well-localized dark sirens in such analysis improves the precision of cosmological measurements. Using a sample containing 10 well-localized dark sirens observed during the third LIGO/Virgo observation run, without the inclusion of GW170817, we determine a measurement of $H_{0} = 76.00^{+17.64}_{-13.45}\, \rm {km\, s^{-1}\, Mpc^{-1}}$.

Context.The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches is machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples since they are key factors in the estimation of reliable photo-zs.

Aims.The goal of this work is to calculate the photo-zsfor the Year 3 (Y3) Dark Energy Survey (DES) Deep Fields catalogue using the Directional Neighborhood Fitting (DNF) machine learning algorithm. Moreover, we want to develop techniques to assess the incompleteness of the training sample and metrics to study how incompleteness affects the quality of photometric redshifts. Finally, we are interested in comparing the performance obtained by DNF on the Y3 DES Deep Fields catalogue with that of the EAzY template fitting approach.

Methods.We emulated – at a brighter magnitude – the training incompleteness with a spectroscopic sample whose redshifts are known to have a measurable view of the problem. We used a principal component analysis to graphically assess the incompleteness and relate it with the performance parameters provided by DNF. Finally, we applied the results on the incompleteness to the photo-zcomputation on the Y3 DES Deep Fields with DNF and estimated its performance.

Results.The photo-zsof the galaxies in the DES deep fields were computed with the DNF algorithm and added to the Y3 DES Deep Fields catalogue. We have developed some techniques to evaluate the performance in the absence of “true” redshift and to assess the completeness. We have studied the tradeoff in the training sample between the highest spectroscopic redshift quality versus completeness. We found some advantages in relaxing the highest-quality spectroscopic redshift requirements at fainter magnitudes in favour of completeness. The results achieved by DNF on the Y3 Deep Fields are competitive with the ones provided by EAzY, showing notable stability at high redshifts. It should be noted that the good results obtained by DNF in the estimation of photo-zsin deep field catalogues make DNF suitable for the future Legacy Survey of Space and Time (LSST) andEucliddata, which will have similar depths to the Y3 DES Deep Fields.