We present new deep ALMA and Hubble Space Telescope (HST)/WFC3 observations of MASOSA and VR7, two luminous Ly emitters (LAEs) at z = 6.5, for which the UV continuum levels differ by a factor of ...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 M1500 = −20.9, compact size, and very high Ly , is undetected in C ii to a limit of LC ii < 2.2 × 107 L , implying a metallicity Z 0.07 Z . Intriguingly, our HST data indicate a red UV slope β = −1.1 0.7, at odds with the low dust content. VR7, which is a bright (M1500 = −22.4) galaxy with moderate color (β = −1.4 0.3) and Ly EW0 = 34 , is clearly detected in C ii emission (S/N = 15). VR7's rest-frame UV morphology can be described by two components separated by 1.5 kpc and is globally more compact than the C ii emission. The global C ii/UV ratio indicates Z 0.2 Z , but there are large variations in the UV/C ii ratio on kiloparsec scales. We also identify diffuse, possibly outflowing, C ii-emitting gas at 100 km s−1 with respect to the peak. VR7 appears to be assembling its components at a slightly more evolved stage than other luminous LAEs, with outflows already shaping its direct environment at z ∼ 7. Our results further indicate that the global C ii−UV relation steepens at SFR < 30 M yr−1, naturally explaining why the C ii/UV ratio is anticorrelated with Ly EW in many, but not all, observed LAEs.
ABSTRACT
We explore deep rest-frame UV to FIR data in the COSMOS field to measure the individual spectral energy distributions (SED) of the ∼4000 SC4K (Sobral et al.) Lyman α (Ly α) emitters (LAEs) ...at z ∼ 2–6. We find typical stellar masses of 109.3 ± 0.6 M⊙ and star formation rates (SFR) of SFR$_{\rm SED}=4.4^{+10.5}_{-2.4}$ M⊙ yr−1 and SFR$_{\rm Ly\,\alpha }=5.9^{+6.3}_{-2.6}$ M⊙ yr−1, combined with very blue UV slopes of $\beta =-2.1^{+0.5}_{-0.4}$, but with significant variations within the population. MUV and β are correlated in a similar way to UV-selected sources, but LAEs are consistently bluer. This suggests that LAEs are the youngest and/or most dust-poor subset of the UV-selected population. We also study the Ly α rest-frame equivalent width (EW0) and find 45 ‘extreme’ LAEs with EW0 > 240 Å (3σ), implying a low number density of (7 ± 1) × 10−7 Mpc−3. Overall, we measure little to no evolution of the Ly α EW0 and scale length parameter (w0), which are consistently high (EW$_0=140^{+280}_{-70}$ Å, $w_0=129^{+11}_{-11}$ Å) from z ∼ 6 to z ∼ 2 and below. However, w0 is anticorrelated with MUV and stellar mass. Our results imply that sources selected as LAEs have a high Ly α escape fraction (fesc,Ly α) irrespective of cosmic time, but fesc,Ly α is still higher for UV-fainter and lower mass LAEs. The least massive LAEs (<109.5 M⊙) are typically located above the star formation ‘main sequence’ (MS), but the offset from the MS decreases towards z ∼ 6 and towards 1010 M⊙. Our results imply a lack of evolution in the properties of LAEs across time and reveals the increasing overlap in properties of LAEs and UV-continuum selected galaxies as typical star-forming galaxies at high redshift effectively become LAEs.
ABSTRACT
We investigate the clustering and halo properties of ∼5000 Ly α-selected emission-line galaxies (LAEs) from the Slicing COSMOS 4K (SC4K) and from archival NB497 imaging of SA22 split in 15 ...discrete redshift slices between z ∼ 2.5 and 6. We measure clustering lengths of r0 ∼ 3–6 h−1 Mpc and typical halo masses of ∼1011 M⊙ for our narrowband-selected LAEs with typical LLy α ∼ 1042–43 erg s−1. The intermediate-band-selected LAEs are observed to have r0 ∼ 3.5–15 h−1 Mpc with typical halo masses of ∼1011–12 M⊙ and typical LLy α ∼ 1043–43.6 erg s−1. We find a strong, redshift-independent correlation between halo mass and Ly α luminosity normalized by the characteristic Ly α luminosity, L⋆(z). The faintest LAEs (L ∼ 0.1 L⋆(z)) typically identified by deep narrowband surveys are found in 1010 M⊙ haloes and the brightest LAEs (L ∼ 7 L⋆(z)) are found in ∼5 × 1012 M⊙ haloes. A dependency on the rest-frame 1500 Å UV luminosity, MUV, is also observed where the halo masses increase from 1011 to 1013 M⊙ for MUV ∼ −19 to −23.5 mag. Halo mass is also observed to increase from 109.8 to 1012 M⊙ for dust-corrected UV star formation rates from ∼0.6 to 10 M⊙ yr−1 and continues to increase up to 1013 M⊙ in halo mass, where the majority of those sources are active galactic nuclei. All the trends we observe are found to be redshift independent. Our results reveal that LAEs are the likely progenitors of a wide range of galaxies depending on their luminosity, from dwarf-like, to Milky Way-type, to bright cluster galaxies. LAEs therefore provide unique insight into the early formation and evolution of the galaxies we observe in the local Universe.
ABSTRACT
We measure the evolution of the rest-frame UV luminosity function (LF) and the stellar mass function (SMF) of Lyman-α (Ly α) emitters (LAEs) from z ∼ 2 to z ∼ 6 by exploring ∼4000 LAEs from ...the SC4K sample. We find a correlation between Ly α luminosity (LLy α) and rest-frame UV (MUV), with best fit M$_{\rm UV}=-1.6_{-0.3}^{+0.2}\log _{10} (\rm L_{Ly\,\alpha }/erg\, s^{-1})+47_{-11}^{+12}$ and a shallower relation between LLy α and stellar mass (M⋆), with best fit $\log _{10} (\rm M_\star /{\rm M}_\odot)=0.9_{-0.1}^{+0.1}\log _{10} (\rm L_{Ly\,\alpha }/erg\, s^{-1})-28_{-3.8}^{+4.0}$. An increasing LLy α cut predominantly lowers the number density of faint MUV and low M⋆ LAEs. We estimate a proxy for the full UV LFs and SMFs of LAEs with simple assumptions of the faint end slope. For the UV LF, we find a brightening of the characteristic UV luminosity (M$_{\rm UV}^*$) with increasing redshift and a decrease of the characteristic number density (Φ*). For the SMF, we measure a characteristic stellar mass (${\rm M_\star ^*/{\rm M}_\odot }$) increase with increasing redshift, and a Φ* decline. However, if we apply a uniform luminosity cut of $\log _{10} (\rm L_{Ly\,\alpha }/erg\, s^{-1}) \ge 43.0$, we find much milder to no evolution in the UV and SMF of LAEs. The UV luminosity density (ρUV) of the full sample of LAEs shows moderate evolution and the stellar mass density (ρM) decreases, with both being always lower than the total ρUV and ρM of more typical galaxies but slowly approaching them with increasing redshift. Overall, our results indicate that both ρUV and ρM of LAEs slowly approach the measurements of continuum-selected galaxies at z > 6, which suggests a key role of LAEs in the epoch of reionization.
ABSTRACT
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 per cent in metrics of absolute error. We also find similar improvements in the photometric redshift catastrophic outlier fraction. We argue that the pseudo-labelling 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.
We present very long baseline interferometry (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 (RL NLS1) characterized by a steep radio spectrum. The source, compact at Very Large Array resolution, is resolved on the milliarcsec scale, showing a central region plus two extended structures. The relatively high brightness temperature of all components (5 x 10 super( 6)-1.3 x 10 super( 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 (CSS) sources. However, the linear size of the source (~0.5 kpc) measured from the EVN map is lower than the value predicted using the turnover/size relation valid for CSS sources (~6 kpc). 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 RL NLS1 and favours the hypothesis of a direct link between RL NLS1 and compact, possibly young, radio galaxies.
We present SDSS J143244.91+301435.3, a new case of a radio-loud narrow-line Seyfert 1 (RL NLS1) with a relatively high radio power (P
1.4 GHz = 2.1 × 1025 W Hz−1) and large radio-loudness parameter ...(R
1.4 = 600 ± 100). The radio source is compact with a linear size below ∼1.4 kpc but, in contrast to most of the RL NLS1 discovered so far with such a high R
1.4, its radio spectrum is very steep (α = 0.93, Sν ∝ ν−α) and does not support a ‘blazar-like’ nature. Both the small mass of the central supermassive black hole and the high accretion rate relative to the Eddington limit estimated for this object (3.2 × 107 M⊙ and 0.27, respectively, with a formal error of ∼0.4 dex for both quantities) are typical of the NLS1 class. Through modelling the spectral energy distribution of the source, we have found that the galaxy hosting SDSS J143244.91+301435.3 is undergoing quite intense star formation (SFR = 50 M⊙ yr−1), which, however, is expected to contribute only marginally (∼1 per cent) to the observed radio emission. The radio properties of SDSS J143244.91+301435.3 are remarkably similar to those of compact steep-spectrum (CSS) radio sources, a class of active galactic nuclei (AGN) mostly composed of young radio galaxies. This may suggest a direct link between these two classes of AGN, with CSS sources possibly representing the misaligned version (the so-called ‘parent population’) of RL NLS1 showing blazar characteristics.
A sub-population of AGNs where the central engine is obscured are known as type II quasars (QSO2s). These luminous AGNs have a thick and dusty torus that obscures the accretion disc from our line of ...sight. Thus, their special orientation allows for detailed studies of the AGN-host co-evolution. Increasing the sample size of QSO2 sources in critical redshift ranges is crucial for understanding the interplay of AGN feedback, the AGN-host relationship, and the evolution of active galaxies. We aim to identify QSO2 candidates in the `redshift desert' using optical and infrared photometry. At this intermediate redshift range (i.e. $1 z 2$), most of the prominent optical emission lines in QSO2 sources (e.g. CIV$ 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 $1 z 2$ 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 emitters at $z 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. Our results demonstrate the potential of few-shot learning applied to small data sets of rare objects, in particular QSO2s, and confirms that optical-IR information can be further explored to search for obscured AGNs. We present a new sample of candidates to be further studied and validated using multi-wavelength observations.
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 as part of the AMUSING survey. Here, we present a study of the systematic errors and bias on the host stellar mass with increasing redshift, which 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 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 ∼0.004 (6% lower). The dark energy equation of state parameter
w
changes by Δ
w
∼ 0.006 (0.6% higher) and the value of Ω
m
increases at most by 0.001 (∼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 systematic error that needs to be corrected for as we enter a new era of precision cosmology.
Context.
The study of active galactic nuclei (AGNs) is fundamental to discern the formation and growth of supermassive black holes (SMBHs) and their connection with star formation and galaxy ...evolution. Due to the significant kinetic and radiative energy emitted by powerful AGNs, they are prime candidates to observe the interplay between SMBH and stellar growth in galaxies.
Aims.
We aim to develop a method to predict the AGN nature of a source, its radio detectability, and redshift purely based on photometry. The use of such a method will increase the number of radio AGNs, allowing us to improve our knowledge of accretion power into an SMBH, the origin and triggers of radio emission, and its impact on galaxy evolution.
Methods.
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.
Results.
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
NMAD
= 0.07 for HETDEX (
σ
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.
Conclusions.
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).
![Resolved UV and [C ii] Stru...](http://api.summon.serialssolutions.com/2.0.0/image/issn/XR4PS5YY4K/0004-637X/small "Resolved UV and [C ii] Structures of Luminous Galaxies within the Epoch of Reionization")
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