arXiv subjects

A. Paulino-Afonso

Publications and source records attributed to A. Paulino-Afonso.

At least 19 recordsLinked to original sources

arXiv2026

Euclid Quick Data Release (Q1) Euclid spectroscopy of quasars. 2. Physical properties from spectral fitting

A substantial volume of spectroscopic data has become available with the Euclid Data Release (Q1), which represents a unique opportunity to study quasars (QSOs) in the Euclid Deep Fields, provide spectroscopic measurements, and estimates of their physical properties. We present results from a spectroscopic analysis of QSOs with HE < = 22.5 using Q1. We use external catalogues from surveys such as the Dark Energy Spectroscopic Instrument (DESI), Gaia, Wide-field Infrared Survey Explorer (WISE), and the QUasars as BRIght beacons for Cosmology in the Southern hemisphere (QUBRICS) for the QSO selection and redshift determination, totaling 5489 QSOs. We provide measurements of the line fluxes, full widths at half-maximum, and respective uncertainties for the emission lines of Euclid spectra based on fitting statistica and signal-to-noise ratio (S/N). Of the total 5489 QSOs, 5387 are successfully fitted. We find that 45% of the total number of successfully fitted spectra are free of problematic artefacts allowing for trustworthy measurements. Our final Euclid QSO sample spans a redshift range of 0.01 < = z < = 4.8 and shows a median spectral index of αλ = - 1.17. Finally, we estimate black hole masses with single epoch methods for 1213 sources, using the He I λ10830, Pa β, H α, H β, and Mg II emission lines, obtainning a mean logarithmc mass of log10 (MBH / Msolar) = 8.7 and mean Edigton ratio of λEdd = 0.34. The catalogue with the physical properties of this sample together with the software developed for the analysis is available online for scientific exploitation.

astro-ph.GA↗

arXiv2025

Moonlit sky polarization patterns from Cerro Paranal

We investigate the polarization patterns from the moonlit sky as observed from the European Southern Observatory at Cerro Paranal. The moonlit sky background can be significant in astronomical observations and thus be a source of contamination in polarimetric studies. Based on sky observations during full Moon with FORS2 in imaging polarimetric mode, we measure the polarization degree and intensity at different wavelengths and scattering angles from the Moon, and compare them to theoretical and phenomenological single and multiple scattering models. Single scattering Rayleigh models are able to reproduce the wavelength dependence of the polarization as long as strong depolarization factors that increase with wavelength are introduced. Intensity data, however, require the inclusion of single Mie scattering from larger aerosol particles. The best models that simultaneously fit polarization and intensity data, are a combination of both single scattering processes, Rayleigh and Mie, plus an unpolarized multiple scattering component. Both Mie and multiple scattering become more dominant at longer wavelengths. Other factors like cloud depolarization and the sunlight contribution during the twilight were also investigated. The present study underscores the importance of accounting for moonlight scattering to enhance the accuracy of polarimetric observations of astronomical targets.

astro-ph.IM↗

arXiv2025

Euclid Quick Data Release (Q1). Active galactic nuclei identification using diffusion-based inpainting of Euclid VIS images

Light emission from galaxies exhibit diverse brightness profiles, influenced by factors such as galaxy type, structural features and interactions with other galaxies. Elliptical galaxies feature more uniform light distributions, while spiral and irregular galaxies have complex, varied light profiles due to their structural heterogeneity and star-forming activity. In addition, galaxies with an active galactic nucleus (AGN) feature intense, concentrated emission from gas accretion around supermassive black holes, superimposed on regular galactic light, while quasi-stellar objects (QSO) are the extreme case of the AGN emission dominating the galaxy. The challenge of identifying AGN and QSO has been discussed many times in the literature, often requiring multi-wavelength observations. This paper introduces a novel approach to identify AGN and QSO from a single image. Diffusion models have been recently developed in the machine-learning literature to generate realistic-looking images of everyday objects. Utilising the spatial resolving power of the Euclid VIS images, we created a diffusion model trained on one million sources, without using any source pre-selection or labels. The model learns to reconstruct light distributions of normal galaxies, since the population is dominated by them. We condition the prediction of the central light distribution by masking the central few pixels of each source and reconstruct the light according to the diffusion model. We further use this prediction to identify sources that deviate from this profile by examining the reconstruction error of the few central pixels regenerated in each source's core. Our approach, solely using VIS imaging, features high completeness compared to traditional methods of AGN and QSO selection, including optical, near-infrared, mid-infrared, and X-rays.

astro-ph.GA↗

arXiv2025

A gradient boosting and broadband approach to finding Lyman-α emitting galaxies beyond narrowband surveys

In this work, we test whether gradient-boosting algorithms, trained on broadband photometric data from traditional Lyman-$α$ emitting (LAE) surveys, can efficiently and accurately identify LAE candidates from typical star-forming galaxies at similar redshifts and brightness levels. Using galaxy samples at $z \in [2,6]$ derived from the COSMOS2020 and SC4K catalogs, we trained gradient-boosting machine-learning algorithms (LGBM, XGBoost, and CatBoost) using optical and near-infrared broadband photometry. To ensure balanced performance, the models were trained on carefully selected datasets with similar redshift and i-band magnitude distributions. Additionally, the models were tested for robustness by perturbing the photometric data using the associated observational uncertainties. Our classification models achieved F1-scores of $\sim 87\%$ and successfully identified about $7,000$ objects with an unanimous agreement across all models. This more than doubles the number of LAEs identified in the COSMOS field compared with the SC4K dataset. We managed to spectroscopically confirm 60 of these LAE candidates using the publicly available catalogs in the COSMOS field. These results highlight the potential of machine learning in efficiently identifying LAEs candidates. This lays the foundations for applications to larger photometric surveys, such as Euclid and LSST. By complementing traditional approaches and providing robust preselection capabilities, our models facilitate the analysis of these objects. This is crucial to increase our knowledge of the overall LAE population.

astro-ph.GA↗

arXiv2025

Euclid Quick Data Release (Q1). Exploring galaxy morphology across cosmic time through Sersic fits

We present the results of the single-component Sérsic profile fitting for the magnitude-limited sample of \IE$<23$ galaxies within the 63.1 deg$^2$ area of the Euclid Quick Data Release (Q1). The associated morphological catalogue includes two sets of structural parameters fitted using \texttt{SourceXtractor++}: one for VIS \IE images and one for a combination of three NISP images in \YE, \JE and \HE bands. We compare the resulting Sérsic parameters to other morphological measurements provided in the Q1 data release, and to the equivalent parameters based on higher-resolution \HST imaging. These comparisons confirm the consistency and the reliability of the fits to Q1 data. Our analysis of colour gradients shows that NISP profiles have systematically smaller effective radii ($R_{\rm e}$) and larger Sérsic indices ($n$) than in VIS. In addition, we highlight trends in NISP-to-VIS parameter ratios with both magnitude and $n_{\rm VIS}$. From the 2D bimodality of the $(u-r)$ colour-$\log(n)$ plane, we define a $(u-r)_{\rm lim}(n)$ that separates early- and late-type galaxies (ETGs and LTGs). We use the two subpopulations to examine the variations of $n$ across well-known scaling relations at $z<1$. ETGs display a steeper size--stellar mass relation than LTGs, indicating a difference in the main drivers of their mass assembly. Similarly, LTGs and ETGs occupy different parts of the stellar mass--star-formation rate plane, with ETGs at higher masses than LTGs, and further down below the Main Sequence of star-forming galaxies. This clear separation highlights the link known between the shutdown of star formation and morphological transformations in the Euclid imaging data set. In conclusion, our analysis demonstrates both the robustness of the Sérsic fits available in the Q1 morphological catalogue and the wealth of information they provide for studies of galaxy evolution with Euclid.

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arXiv2025

Euclid: The Early Release Observations Lens Search Experiment

We investigated the ability of the Euclid telescope to detect galaxy-scale gravitational lenses. To do so, we performed a systematic visual inspection of the $0.7\,\rm{deg}^2$ Euclid Early Release Observations data towards the Perseus cluster using both the high-resolution $I_{\scriptscriptstyle\rm E}$ band and the lower-resolution $Y_{\scriptscriptstyle\rm E}$, $J_{\scriptscriptstyle\rm E}$, $H_{\scriptscriptstyle\rm E}$ bands. Each extended source brighter than magnitude 23 in $I_{\scriptscriptstyle\rm E}$ was inspected by 41 expert human classifiers. This amounts to $12\,086$ stamps of $10^{\prime\prime}\,\times\,10^{\prime\prime}$. We found $3$ grade A and $13$ grade B candidates. We assessed the validity of these $16$ candidates by modelling them and checking that they are consistent with a single source lensed by a plausible mass distribution. Five of the candidates pass this check, five others are rejected by the modelling, and six are inconclusive. Extrapolating from the five successfully modelled candidates, we infer that the full $14\,000\,{\rm deg}^2$ of the Euclid Wide Survey should contain $100\,000^{+70\,000}_{-30\,000}$ galaxy-galaxy lenses that are both discoverable through visual inspection and have valid lens models. This is consistent with theoretical forecasts of $170\,000$ discoverable galaxy-galaxy lenses in Euclid. Our five modelled lenses have Einstein radii in the range $0.\!\!^{\prime\prime}68\,<\,θ_\mathrm{E}\,<1.\!\!^{\prime\prime}24$, but their Einstein radius distribution is on the higher side when compared to theoretical forecasts. This suggests that our methodology is likely missing small-Einstein-radius systems. Whilst it is implausible to visually inspect the full Euclid dataset, our results corroborate the promise that Euclid will ultimately deliver a sample of around $10^5$ galaxy-scale lenses.

astro-ph.GA↗

arXiv2025

Exploring the physical properties of Type II Quasar candidates at intermediate redshifts with CIGALE

Active Galactic Nuclei (AGN) significantly influence galaxy evolution. Specific sources such as obscured AGNs, especially Type II quasars (QSO2), still remain understudied. We characterise 366 QSO2 candidates in the redshift desert (median z~1.1) identified via machine learning from SDSS/WISE photometry, analysing their spectral energy distributions (SEDs) and deriving their physical properties. Using CIGALE, we estimated star formation rate (SFR), stellar mass (M), AGN luminosity, and AGN fraction. We compared these with SPRITZ simulations and the literature, placing results in the galaxy evolution context. Our QSO2 candidates show diverse evolutionary stages. The SFR-M diagram reveals high-SFR sources above the main sequence, linking AGN activity to enhanced star formation. Quenched galaxies may indicate obscured star formation or AGN feedback. Additionally, the physical properties align with SPRITZ composite systems and AGN2, endorsing our obscured AGN classification. This study validates machine learning for identifying AGN-host galaxies, beyond traditional colour-colour selections. Diverse candidate properties highlight this method's ability to identify complex AGN systems. This advances our understanding of AGN-driven galaxy evolution with new target selection.

astro-ph.GA↗

arXiv2024

Euclid: Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field

The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg^2 of the sky. Detecting this many lenses by visual inspection with professional astronomers and citizen scientists alone is infeasible. Machine learning algorithms, particularly convolutional neural networks (CNNs), have been used as an automated method of detecting strong lenses, and have proven fruitful in finding galaxy-galaxy strong lens candidates. We identify the major challenge to be the automatic detection of galaxy-galaxy strong lenses while simultaneously maintaining a low false positive rate. One aim of this research is to have a quantified starting point on the achieved purity and completeness with our current version of CNN-based detection pipelines for the VIS images of EWS. We select all sources with VIS IE < 23 mag from the Euclid Early Release Observation imaging of the Perseus field. We apply a range of CNN architectures to detect strong lenses in these cutouts. All our networks perform extremely well on simulated data sets and their respective validation sets. However, when applied to real Euclid imaging, the highest lens purity is just 11%. Among all our networks, the false positives are typically identifiable by human volunteers as, for example, spiral galaxies, multiple sources, and artefacts, implying that improvements are still possible, perhaps via a second, more interpretable lens selection filtering stage. There is currently no alternative to human classification of CNN-selected lens candidates. Given the expected 10^5 lensing systems in Euclid, this implies 10^6 objects for human classification, which while very large is not in principle intractable and not without precedent.

astro-ph.IM↗

arXiv2024

Identifying type II quasars at intermediate redshift with few-shot learning photometric classification

We aim to identify QSO2 candidates in the redshift desert using optical and infrared photometry. At this intermediate redshift range, most of the prominent optical emission lines in QSO2 sources (e.g. CIV1549; [OIII]4959,5008) fall either outside the wavelength range of the SDSS optical spectra or in particularly noisy wavelength ranges, making QSO2 identification challenging. Therefore, we adopted a semi-supervised machine learning approach to select candidates in the SDSS galaxy sample. Recent applications of machine learning in astronomy focus on problems involving large data sets, with small data sets often being overlooked. We developed a few-shot learning approach for the identification and classification of rare-object classes using limited training data (200 sources). The new AMELIA pipeline uses a transfer-learning based approach with decision trees, distance-based, and deep learning methods to build a classifier capable of identifying rare objects on the basis of an observational training data set. We validated the performance of AMELIA by addressing the problem of identifying QSO2s at 1 $\leq$ z $\leq$ 2 using SDSS and WISE photometry, obtaining an F1-score above 0.8 in a supervised approach. We then used AMELIA to select new QSO2 candidates in the redshift desert and examined the nature of the candidates using SDSS spectra, when available. In particular, we identified a sub-population of [NeV]3426 emitters at z $\sim$ 1.1, which are highly likely to contain obscured AGNs. We used X-ray and radio cross-matching to validate our classification and investigated the performance of photometric criteria from the literature showing that our candidates have an inherent dusty nature. Finally, we derived physical properties for our QSO2 sample using photoionisation models and verified the AGN classification using an SED fitting.

astro-ph.IM↗

arXiv2023

A Sample of Dust Attenuation Laws for DES Supernova Host Galaxies

Type Ia supernovae (SNe Ia) are useful distance indicators in cosmology, provided their luminosity is standardized by applying empirical corrections based on light-curve properties. One factor behind these corrections is dust extinction, accounted for in the color-luminosity relation of the standardization. This relation is usually assumed to be universal, which could potentially introduce systematics into the standardization. The ``mass-step'' observed for SNe Ia Hubble residuals has been suggested as one such systematic. We seek to obtain a completer view of dust attenuation properties for a sample of 162 SN Ia host galaxies and to probe their link to the ``mass-step''. We infer attenuation laws towards hosts from both global and local (4 kpc) Dark Energy Survey photometry and Composite Stellar Population model fits. We recover a optical depth/attenuation slope relation, best explained by differing star/dust geometry for different galaxy orientations, which is significantly different from the optical depth/extinction slope relation observed directly for SNe. We obtain a large variation of attenuation slopes and confirm these change with host properties, like stellar mass and age, meaning a universal SN Ia correction should ideally not be assumed. Analyzing the cosmological standardization, we find evidence for a ``mass-step'' and a two dimensional ``dust-step'', both more pronounced for red SNe. Although comparable, the two steps are found no to be completely analogous. We conclude that host galaxy dust data cannot fully account for the ``mass-step'', using either an alternative SN standardization with extinction proxied by host attenuation or a ``dust-step'' approach.

astro-ph.GA↗

arXiv2023

Selection of powerful radio galaxies with machine learning

We developed and trained a pipeline of three machine learning (ML) models than can predict which sources are more likely to be an AGN and to be detected in specific radio surveys. Also, it can estimate redshift values for predicted radio-detectable AGNs. These models, which combine predictions from tree-based and gradient-boosting algorithms, have been trained with multi-wavelength data from near-infrared-selected sources in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) Spring field. Training, testing, calibration, and validation were carried out in the HETDEX field. Further validation was performed on near-infrared-selected sources in the Stripe 82 field. In the HETDEX validation subset, our pipeline recovers 96% of the initially labelled AGNs and, from AGNs candidates, we recover 50% of previously detected radio sources. For Stripe 82, these numbers are 94% and 55%. Compared to random selection, these rates are two and four times better for HETDEX, and 1.2 and 12 times better for Stripe 82. The pipeline can also recover the redshift distribution of these sources with $σ_{\mathrm{NMAD}}$ = 0.07 for HETDEX ($σ_{\mathrm{NMAD}}$ = 0.09 for Stripe 82) and an outlier fraction of 19% (25% for Stripe 82), compatible with previous results based on broad-band photometry. Feature importance analysis stresses the relevance of near- and mid-infrared colours to select AGNs and identify their radio and redshift nature. Combining different algorithms in ML models shows an improvement in the prediction power of our pipeline over a random selection of sources. Tree-based ML models (in contrast to deep learning techniques) facilitate the analysis of the impact that features have on the predictions. This prediction can give insight into the potential physical interplay between the properties of radio AGNs (e.g. mass of black hole and accretion rate).

astro-ph.GA↗

arXiv2022

Improving machine learning-derived photometric redshifts and physical property estimates using unlabelled observations

In the era of huge astronomical surveys, machine learning offers promising solutions for the efficient estimation of galaxy properties. The traditional, `supervised' paradigm for the application of machine learning involves training a model on labelled data, and using this model to predict the labels of previously unlabelled data. The semi-supervised `pseudo-labelling' technique offers an alternative paradigm, allowing the model training algorithm to learn from both labelled data and as-yet unlabelled data. We test the pseudo-labelling method on the problems of estimating redshift, stellar mass, and star formation rate, using COSMOS2015 broad band photometry and one of several publicly available machine learning algorithms, and we obtain significant improvements compared to purely supervised learning. We find that the gradient-boosting tree methods CatBoost, XGBoost, and LightGBM benefit the most, with reductions of up to ~15% in metrics of absolute error. We also find similar improvements in the photometric redshift catastrophic outlier fraction. We argue that the pseudo-labellng technique will be useful for the estimation of redshift and physical properties of galaxies in upcoming large imaging surveys such as Euclid and LSST, which will provide photometric data for billions of sources.

astro-ph.GA↗

arXiv2022

z~2-9 Galaxies magnified by the Hubble Frontier Field Clusters I: Source Selection and Surface Density-Magnification Constraints from >2500 galaxies

We assemble a large comprehensive sample of 2534 z~2, 3, 4, 5, 6, 7, 8, and 9 galaxies lensed by the six clusters from the Hubble Frontier Fields (HFF) program. Making use of the availability of multiple independent magnification models for each of the HFF clusters and alternatively treating one of the models as the "truth," we show that the median magnification factors from the v4 parametric models are typically reliable to values of 30 to 50, and in one case to 100. Using the median magnification factor from the latest v4 models, we estimate the UV luminosities of the 2534 lensed z~2-9 galaxies, finding sources as faint as -12.4 mag at z~3 and -12.9 mag at z~7. We explicitly demonstrate the power of the surface density-magnification relations Sigma(z) vs. mu in the HFF clusters to constrain both distant galaxy properties and cluster lensing properties. Based on the Sigma(z) vs. mu relations, we show that the median magnification estimates from existing public models must be reliable predictors of the true magnification mu to mu<15 (95% confidence). We also use the observed Sigma(z) vs. mu relations to derive constraints on the evolution of the luminosity function faint-end slope from z~7 to z~2, showing that faint-end slope results can be consistent with blank-field studies if, and only if, the selection efficiency shows no strong dependence on the magnification factor mu. This can only be the case if very low luminosity galaxies are very small, being unresolved in deep lensing probes.

astro-ph.GA↗

arXiv2022

Systematic errors on optical-SED stellar mass estimates for galaxies across cosmic time and their impact on cosmology

Studying galaxies at different cosmic epochs entails several observational effects that need to be taken into account to compare populations across a large time span in a consistent manner. We use a sample of 166 nearby galaxies that hosted type Ia supernovae (SNe Ia) and have been observed with the integral field spectrograph MUSE through the AMUSING survey. Here, we present a study of the systematic errors and bias in the host stellar mass with increasing redshifts that are generally overlooked in SNe Ia cosmological analyses. We simulate observations at different redshifts (0.1<z<2.0) using four photometric bands (griz, similar to the Dark Energy Survey-SN program) to then estimate the host galaxy properties across cosmic time. We find that stellar masses are systematically underestimated as we move towards higher redshifts, due mostly to different rest-frame wavelength coverage, with differences reaching 0.3 dex at z~1. We have used the newly derived corrections as a function of redshift to correct the stellar masses of a known sample of SN Ia hosts and derive cosmological parameters. We show that these corrections have a small impact on the derived cosmological parameters. The most affected is the value of the mass step $Δ_M$, which is reduced by $\sim$0.004 (6% lower). The dark energy equation of state parameter $w$ changes by $Δw \sim $0.006 (0.6% higher) and the value of $Ω_m$ increases at most by 0.001 ($\sim$0.3%), all within the derived uncertainties of the model. While the systematic error found in the estimate of the host stellar mass does not significantly affect the derived cosmological parameters, it is an important source of a systematic error that one should correct for as we enter a new era of precision cosmology.

astro-ph.CO↗

arXiv2020

The VIMOS Ultra-Deep Survey: the Ly$α$ emission line morphology at $2 < z < 6$

The Lyman-$α$ (Ly$α$) emission line has been ubiquitously used to confirm and study high redshift galaxies. We report on the line morphology as seen in the 2D spectra from the VIMOS Ultra Deep Survey in a sample of 914 Ly$α$ emitters from a parent sample of 4192 star-forming galaxies at $2<z_\mathrm{spec}\lesssim6$. The study of the spatial extent of Ly$α$ emission provides insight into the escape of Ly$α$ photons from galaxies. We classify the line emission as either non-existent, coincident, projected spatial offset, or extended with respect to the observed 2D UV continuum emission. The line emitters in our sample are classified as ~45% coincident, ~24% extended and ~11% offset emitters. For galaxies with detected UV continuum, we show that extended Ly$α$ emitters (LAEs) correspond to the highest equivalent width galaxies (with an average $W_\mathrm{Lyα}\sim-22$Å). This means that this class of objects is the most common in narrow-band selected samples, which usually select high equivalent width LAEs, $<-20$Å. Extended Ly$α$ emitters are found to be less massive, less star-forming, with lower dust content, and smaller UV continuum sizes ($r_{50}\sim0.9$kpc) of all the classes considered here. We also find that galaxies with larger UV-sizes have lower fractions of Ly$α$ emitters. By stacking the spectra per emitter class we find that the weaker Ly$α$ emitters have stronger low ionization inter-stellar medium (ISM) absorption lines. Interestingly, we find that galaxies with Ly$α$ offset emission (median separation of $1.1_{-0.8}^{+1.3}$kpc from UV continuum) show similar velocity offsets in the ISM as those with no visible emission (and different from other Ly$α$ emitting classes). This class of objects may hint at episodes of gas accretion, bright offset clumps, or on-going merging activity into the larger galaxies.

astro-ph.GA↗

arXiv2019

Resolved UV and [CII] structures of luminous galaxies within the epoch of reionisation

We present new deep ALMA and HST/WFC3 observations of MASOSA and VR7, two luminous Ly$α$ emitters (LAEs) at $z=6.5$, for which the UV continuum level differ by a factor four. No IR dust continuum emission is detected in either, indicating little amounts of obscured star formation and/or high dust temperatures. MASOSA, with a UV luminosity M$_{1500}=-20.9$, compact size and very high Ly$α$ EW$_{0}\approx145$ A, is undetected in [CII] to a limit of L$_{\rm [CII]}<2.2\times10^7$ L$_{\odot}$ implying a metallicity $Z\lesssim0.07 Z_{\odot}$. Intriguingly, our HST data indicates a red UV slope $β=-1.1\pm0.7$, at odds with the low dust content. VR7, which is a bright (M$_{1500}=-22.4$) galaxy with moderate color ($β=-1.4\pm0.3$) and Ly$α$ EW$_0 = 34$ A, is clearly detected in [CII] emission (S/N=15). VR7's rest-frame UV morphology can be described by two components separated by $\approx1.5$ kpc and is globally more compact than the [CII] emission. The global [CII]-UV ratio indicates $Z\approx0.2 Z_{\odot}$, but there are large variations in the UV-[CII] ratio on kpc scales. We also identify diffuse, possibly outflowing, [CII]-emitting gas at $\approx 100$ km s$^{-1}$ with respect to the peak. VR7 appears assembling its components at a slightly more evolved stage than other luminous LAEs, with outflows already shaping its direct environment at $z\sim7$. Our results further indicate that the global [CII]-UV relation steepens at SFR $<30$ M$_{\odot}$ yr$^{-1}$, naturally explaining why the [CII]-UV ratio is anti-correlated with Ly$α$ EW in many, but not all, observed LAEs.

astro-ph.GA↗

arXiv2017

Bulgeless galaxies in the COSMOS field: environment and star formation evolution at $z < 1$

Combining the catalogue of galaxy morphologies in the COSMOS field and the sample of H$α$ emitters at redshifts $z=0.4$ and $z=0.84$ of the HiZELS survey, we selected $\sim$ 220 star-forming bulgeless systems (Sérsic index $n \leq 1.5$) at both epochs. We present their star formation properties and we investigate their contribution to the star formation rate function (SFRF) and global star formation rate density (SFRD) at $z < 1$. For comparison, we also analyse H$α$ emitters with more structurally evolved morphologies that we split into two classes according to their Sérsic index $n$: intermediate ($ 1.5 < n \leq 3 $) and bulge-dominated ($n > 3$). At both redshifts the SFRF is dominated by the contribution of bulgeless galaxies and we show that they account for more than 60% of the cosmic SFRD at $z < 1$. The decrease of the SFRD with redshift is common to the three morphological types but it is stronger for bulge-dominated systems. Star-forming bulgeless systems are mostly located in regions of low to intermediate galaxy densities ($Σ\sim 1 - 4$ Mpc$^{-2}$) typical of field-like and filament-like environments and their specific star formation rates (sSFRs) do not appear to vary strongly with local galaxy density. Only few bulgeless galaxies in our sample have high (sSFR $>$ 10$^{-9}$ yr$^{-1}$) and these are mainly low-mass systems. Above $M_* \sim 10^{10}$ M$_{\odot}$ bulgeless are evolving at a "normal" rate (10$^{-9}$ yr$^{-1} <$ sSFR $<$10$^{-10}$ yr$^{-1}$) and in the absence of an external trigger (i.e. mergers/strong interactions) they might not be able to develop a central classical bulge.

astro-ph.GA↗

arXiv2016

SDSSJ143244.91+301435.3 at VLBI: a compact radio galaxy in a narrow-line Seyfert 1

We present VLBI observations, carried out with the European Very Long Baseline Interferometry Network (EVN), of SDSSJ143244.91+301435.3, a radio-loud narrow-line Seyfert 1 (RLNLS1) characterized by a steep radio spectrum. The source, compact at Very Large Array (VLA) resolution, is resolved on the milliarcsec scale, showing a central region plus two extended structures. The relatively high brightness temperature of all components (5x10^6-1.3x10^8 K) supports the hypothesis that the radio emission is non-thermal and likely produced by a relativistic jet and/or small radio lobes. The observed radio morphology, the lack of a significant core and the presence of a low frequency (230 MHz) spectral turnover are reminiscent of the Compact Steep Spectrum sources (CSS). However, the linear size of the source (~0.5kpc) measured from the EVN map is lower than the value predicted using the turnover/size relation valid for CSS sources (~6kpc). This discrepancy can be explained by an additional component not detected in our observations, accounting for about a quarter of the total source flux density, combined to projection effects. The low core-dominance of the source (CD<0.29) confirms that SDSSJ143244.91+301435.3 is not a blazar, i.e. the relativistic jet is not pointing towards the observer. This supports the idea that SDSSJ143244.91+301435.3 may belong to the "parent population" of flat-spectrum RLNLS1 and favours the hypothesis of a direct link between RLNLS1 and compact, possibly young, radio galaxies.

astro-ph.HE↗