Thousands Of Galaxies Research Articles

Less is less: Photometry alone cannot predict the observed spectral indices of z ~ 1 galaxies from the LEGA-C spectroscopic survey

Aims. We test whether we can predict optical spectra from deep-field photometry of distant galaxies. Our goal is to perform a comparison in data space, highlighting the differences between predicted and observed spectra. Methods. The Large Early Galaxy Astrophysics Census (LEGA-C) provides high-quality optical spectra of thousands of galaxies at redshift 0.6 < z < 1. Broadband photometry of the same galaxies, drawn from the recent COSMOS2020 catalog, is used to predict the optical spectra with the spectral energy distribution (SED) fitting code Prospector and the MILES stellar library. The observed and predicted spectra are compared in terms of two age and metallicity-sensitive absorption features (HδA and Fe4383). Results. The global bimodality of star-forming and quiescent galaxies in photometric space is recovered with the model spectra. However, the presence of a systematic offset in the Fe4383 line strength and the weak correlation between the observed and modeled line strength imply that accurate age or metallicity determinations cannot be inferred from photometry alone. Conclusions. For now we caution that photometry-based estimates of stellar population properties are determined mostly by the modeling approach and not the physical properties of galaxies, even when using the highest-quality photometric datasets and state-of-the-art fitting techniques. When exploring a new physical parameter space (i.e. redshift or galaxy mass) high-quality spectroscopy is always needed to inform the analysis of photometry.

On the correlations of galaxy peculiar velocities and their covariance

ABSTRACT Measurements of the peculiar velocities of large samples of galaxies enable new tests of the standard cosmological model, including determination of the growth rate of cosmic structure that encodes gravitational physics. With the size of such samples now approaching hundreds of thousands of galaxies, complex statistical analysis techniques and models are required to extract cosmological information. In this paper, we summarize how correlation functions between galaxy velocities, and with the surrounding large-scale structure, may be utilized to test cosmological models. We present new determinations of the analytical covariance between such correlation functions, which may be useful for cosmological likelihood analyses. The statistical model we use to determine these covariances includes the sample selection functions, observational noise, curved-sky effects, and redshift-space distortions. By comparing these covariance determinations with corresponding estimates from large suites of cosmological simulations, we demonstrate that these analytical models recover the key features of the covariance between different statistics and separations, and produce similar measurements of the growth rate of structure.

Stellar metallicity from optical and UV spectral indices: Test case for WEAVE-StePS

Context. The upcoming generation of optical spectrographs on four meter-class telescopes, with their huge multiplexing capabilities, excellent spectral resolution, and unprecedented wavelength coverage, will provide high-quality spectra for thousands of galaxies. These data will allow us to examine of the stellar population properties at intermediate redshift, an epoch that remains unexplored by large and deep surveys. Aims. We assess our capability to retrieve the mean stellar metallicity in galaxies at different redshifts and signal-to-noise ratios (S/N), while simultaneously exploiting the ultraviolet (UV) and optical rest-frame wavelength coverage. Methods. The work is based on a comprehensive library of spectral templates of stellar populations, covering a wide range of age and metallicity values and built assuming various star formation histories, to cover an observable parameter space with diverse chemical enrichment histories and dust attenuation. We took into account possible observational errors, simulating realistic observations of a large sample of galaxies carried out with WEAVE at the William Herschel Telescope at different redshifts and S/N values. We measured all the available and reliable indices on the simulated spectra and on the comparison library. We then adopted a Bayesian approach to compare the two sets of measurements in order to obtain the probability distribution of stellar metallicity with an accurate estimate of the uncertainties. Results. The analysis of the spectral indices has shown how some mid-UV indices, such as BL3580 and Fe3619, can provide reliable constraints on stellar metallicity, along with optical indicators. The analysis of the mock observations has shown that even at S/N = 10, the metallicity can be derived within 0.3 dex, in particular, for stellar populations older than 2 Gyr. The S/N value plays a crucial role in the uncertainty of the estimated metallicity and so, the differences between S/N = 10 and S/N = 30 are quite large, with uncertainties of ~0.15 dex in the latter case. On the contrary, moving from S/N = 30 to S/N = 50, the improvement on the uncertainty of the metallicity measurements is almost negligible. Our results are in good agreement with other theoretical and observational works in the literature and show how the UV indicators, coupled with classic optical ones, can be advantageous in constraining metallicities. Conclusions. We demonstrate that a good accuracy can be reached on the spectroscopic measurements of the stellar metallicity of galaxies at intermediate redshift, even at low S/N, when a large number of indices can be employed, including some UV indices. This is very promising for the upcoming surveys carried out with new, highly multiplexed, large-field spectrographs, such as StePS at the WEAVE and 4MOST, which will provide spectra of thousands of galaxies covering large spectral ranges (between 3600 and 9000 Å in the observed frame) at relatively high S/N (>10 Å−1).

COSMOS-Web: An Overview of the JWST Cosmic Origins Survey

We present the survey design, implementation, and outlook for COSMOS-Web, a 255 hr treasury program conducted by the James Webb Space Telescope in its first cycle of observations. COSMOS-Web is a contiguous 0.54 deg2 NIRCam imaging survey in four filters (F115W, F150W, F277W, and F444W) that will reach 5σ point-source depths ranging ∼27.5–28.2 mag. In parallel, we will obtain 0.19 deg2 of MIRI imaging in one filter (F770W) reaching 5σ point-source depths of ∼25.3–26.0 mag. COSMOS-Web will build on the rich heritage of multiwavelength observations and data products available in the COSMOS field. The design of COSMOS-Web is motivated by three primary science goals: (1) to discover thousands of galaxies in the Epoch of Reionization (6 ≲ z ≲ 11) and map reionization’s spatial distribution, environments, and drivers on scales sufficiently large to mitigate cosmic variance, (2) to identify hundreds of rare quiescent galaxies at z > 4 and place constraints on the formation of the universe’s most-massive galaxies (M ⋆ > 1010 M ⊙), and (3) directly measure the evolution of the stellar-mass-to-halo-mass relation using weak gravitational lensing out to z ∼ 2.5 and measure its variance with galaxies’ star formation histories and morphologies. In addition, we anticipate COSMOS-Web’s legacy value to reach far beyond these scientific goals, touching many other areas of astrophysics, such as the identification of the first direct collapse black hole candidates, ultracool subdwarf stars in the Galactic halo, and possibly the identification of z > 10 pair-instability supernovae. In this paper we provide an overview of the survey’s key measurements, specifications, goals, and prospects for new discovery.

The probability of identifying the cosmic web environment of galaxies around clusters motivated by the Weave Wide Field Cluster Survey

ABSTRACT Upcoming wide-field spectroscopic surveys will observe galaxies in a range of cosmic web environments in and around galaxy clusters. In this paper, we test and quantify how successfully we will be able to identify the environment of individual galaxies in the vicinity of massive galaxy clusters, reaching out to ∼5R200 into the clusters’ infall region. We focus on the WEAVE Wide Field Cluster Survey (WWFCS), but the methods we develop can be easily generalized to any similar spectroscopic survey. Using numerical simulations of a large sample of massive galaxy clusters from thethreehundred project, we produce mock observations that take into account the selection effects and observational constraints imposed by the WWFCS. We then compare the ‘true’ environment of each galaxy derived from the simulations (cluster core, filament, and neither core nor filament, ‘NCF’) with the one derived from the observational data, where only galaxy sky positions and spectroscopic redshifts will be available. We find that, while cluster core galaxy samples can be built with a high level of completeness and moderate contamination, the filament and NCF galaxy samples will be significantly contaminated and incomplete due to projection effects exacerbated by the galaxies’ peculiar velocities. We conclude that, in the infall regions surrounding massive galaxy clusters, associating galaxies with the correct cosmic web environment is highly uncertain. However, with large enough spectroscopic samples like the ones the WWFCS will provide (thousands of galaxies per cluster, out to 5R200), and the correct statistical treatment that takes into account the probabilities we provide here, we expect we will be able to extract robust and well-quantified conclusions on the relationship between galaxy properties and their environment.

Forming intracluster gas in a galaxy protocluster at a redshift of 2.16

Galaxy clusters are the most massive gravitationally bound structures in the Universe, comprising thousands of galaxies and pervaded by a diffuse, hot intracluster medium (ICM) that dominates the baryonic content of these systems. The formation and evolution of the ICM across cosmic time1 is thought to be driven by the continuous accretion of matter from the large-scale filamentary surroundings and energetic merger events with other clusters or groups. Until now, however, direct observations of the intracluster gas have been limited only to mature clusters in the later three-quarters of the history of the Universe, and we have been lacking a direct view of the hot, thermalized cluster atmosphere at the epoch when the first massive clusters formed. Here we report the detection (about 6σ) of the thermal Sunyaev–Zeldovich (SZ) effect2 in the direction of a protocluster. In fact, the SZ signal reveals the ICM thermal energy in a way that is insensitive to cosmological dimming, making it ideal for tracing the thermal history of cosmic structures3. This result indicates the presence of a nascent ICM within the Spiderweb protocluster at redshift z = 2.156, around 10 billion years ago. The amplitude and morphology of the detected signal show that the SZ effect from the protocluster is lower than expected from dynamical considerations and comparable with that of lower-redshift group-scale systems, consistent with expectations for a dynamically active progenitor of a local galaxy cluster.

Cosmological Simulations of the Intergalactic Medium Evolution. III. SPH Simulations

We have developed a new numerical algorithm to study the joint evolution of galaxies and the intergalactic medium (IGM) in a cosmological context, with the specific goal of studying the deposition and dispersion of metals in the IGM. This algorithm combines a standard gasdynamical algorithm to simulate the evolution of the IGM, a semi-analytical model to describe the evolution of galaxies, and prescriptions for galaxy formation, accretion, mergers, and tidal disruption. The main goal in designing this algorithm was performance. In its current version, the algorithm can simulate the evolution of cosmological volumes containing thousands of galaxies in a few days, using between 12 and 32 processors. This algorithm is particularly suited for parameter surveys (both numerical parameters and physical parameters) since a large number of simulations can be completed in a fairly short amount of time. Furthermore, the algorithm provides a platform for the development and testing of new treatments of subgrid physics, which could then be implemented into other algorithms. In this paper, we describe the algorithm and present, for illustration, two simulations of the evolution of a (20 Mpc)3 cosmological volume containing ∼1200 galaxies at z = 0.

The dynamical mass of the Coma cluster from deep learning

In 1933, Fritz Zwicky's famous investigations of the mass of the Coma cluster led him to infer the existence of dark matter \cite{1933AcHPh...6..110Z}. His fundamental discoveries have proven to be foundational to modern cosmology; as we now know such dark matter makes up 85\% of the matter and 25\% of the mass-energy content in the universe. Galaxy clusters like Coma are massive, complex systems of dark matter in addition to hot ionized gas and thousands of galaxies, and serve as excellent probes of the dark matter distribution. However, empirical studies show that the total mass of such systems remains elusive and difficult to precisely constrain. Here, we present new estimates for the dynamical mass of the Coma cluster based on Bayesian deep learning methodologies developed in recent years. Using our novel data-driven approach, we predict Coma's $\mthc$ mass to be $10^{15.10 \pm 0.15}\ \hmsun$ within a radius of $1.78 \pm 0.03\ h^{-1}\mathrm{Mpc}$ of its center. We show that our predictions are rigorous across multiple training datasets and statistically consistent with historical estimates of Coma's mass. This measurement reinforces our understanding of the dynamical state of the Coma cluster and advances rigorous analyses and verification methods for empirical applications of machine learning in astronomy.

Detectability of a spatial correlation between stellar mass black hole mergers and active galactic nuclei in the local Universe

ABSTRACT The origin of the binary black hole (BBH) mergers detected through gravitational waves (GWs) by the LIGO-Virgo-KAGRA (LVK) collaboration remains debated. One fundamental reason is our ignorance of their host environment, as the typical size of an event’s localization volume can easily contain thousands of galaxies. A strategy around this is to exploit statistical approaches to assess the spatial correlation between these mergers and astrophysically motivated host galaxy types, such as active galactic nuclei (AGNs). We use a likelihood ratio method to infer the degree of GW–AGN connection out to z = 0.2. We simulate BBH mergers whose components’ masses are sampled from a realistic distribution of the underlying population of black holes (BHs). Localization volumes for these events are calculated assuming two different interferometric network configurations. These correspond to the configuration of the third (O3) and of the upcoming fourth (O4) LVK observing runs. We conclude that the 13 BBH mergers detected during the third observing run at z ≤ 0.2 are not enough to reject with a 3σ significance the hypothesis according to which there is no connection between GW and AGNs more luminous than $\approx 10^{44.3}\rm {erg}\ \rm {s}^{-1}$, that have number density higher than 10−4.75 Mpc−3. However, 13 detections are enough to reject this no-connection hypothesis when rarer categories of AGNs are considered, with bolometric luminosities greater than $\approx 10^{45.5}\rm {erg}\ \rm {s}^{-1}$. We estimate that O4 results will potentially allow us to test fractional contributions to the total BBH merger population from AGNs of any luminosity higher than $80{{\ \rm per\ cent}}$.

Cosmology with One Galaxy?

Galaxies can be characterized by many internal properties such as stellar mass, gas metallicity, and star formation rate. We quantify the amount of cosmological and astrophysical information that the internal properties of individual galaxies and their host dark matter halos contain. We train neural networks using hundreds of thousands of galaxies from 2000 state-of-the-art hydrodynamic simulations with different cosmologies and astrophysical models of the CAMELS project to perform likelihood-free inference on the value of the cosmological and astrophysical parameters. We find that knowing the internal properties of a single galaxy allows our models to infer the value of Ωm, at fixed Ωb, with a ∼10% precision, while no constraint can be placed on σ 8. Our results hold for any type of galaxy, central or satellite, massive or dwarf, at all considered redshifts, z ≤ 3, and they incorporate uncertainties in astrophysics as modeled in CAMELS. However, our models are not robust to changes in subgrid physics due to the large intrinsic differences the two considered models imprint on galaxy properties. We find that the stellar mass, stellar metallicity, and maximum circular velocity are among the most important galaxy properties to determine the value of Ωm. We believe that our results can be explained by considering that changes in the value of Ωm, or potentially Ωb/Ωm, affect the dark matter content of galaxies, which leaves a signature in galaxy properties distinct from the one induced by galactic processes. Our results suggest that the low-dimensional manifold hosting galaxy properties provides a tight direct link between cosmology and astrophysics.

Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning

Abstract Modern spectroscopic surveys of galaxies such as MaNGA consist of millions of diverse spectra covering different regions of thousands of galaxies. We propose and implement a deep unsupervised machine-learning method to summarize the entire diversity of MaNGA spectra onto a 15 × 15 map (DESOM-1), where neighboring points on the map represent similar spectra. We demonstrate our method as an alternative to conventional full spectral fitting for deriving physical quantities and full probability distributions much more efficiently than traditional resource-intensive Bayesian methods. Since spectra are grouped by similarity, the distribution of spectra onto the map for a single galaxy, i.e., its “fingerprint,” reveals the presence of distinct stellar populations within the galaxy, indicating smoother or episodic star formation histories. We further map the diversity of galaxy fingerprints onto a second map (DESOM-2). Using galaxy images and independent measures of galaxy morphology, we confirm that galaxies with similar fingerprints have similar morphologies and inclination angles. Since morphological information was not used in the mapping algorithm, relating galaxy morphology to the star formation histories encoded in the fingerprints is one example of how the DESOM maps can be used to make scientific inferences.

The nature of high [O iii]88 μ m/[C ii]158 μm galaxies in the epoch of reionization: Low carbon abundance and a top-heavy IMF?

ABSTRACT ALMA observations of z > 6 galaxies have revealed abnormally high [O iii]88 $\mu$m/[C ii]158 $\mu$m ratios and [C ii]158 $\mu$m deficits compared to local galaxies. The origin of this behaviour is unknown. Numerous solutions have been proposed including differences in C and O abundance ratios, observational bias, and differences in ISM properties, including ionization parameter, gas density, or photodissociation region (PDR) covering fraction. In order to elucidate the underlying physics that drives this high-redshift phenomenon, we employ sphinx20, a state-of-the-art, cosmological radiation–hydrodynamics simulation, that resolves detailed ISM properties of thousands of galaxies in the epoch of reionization which has been post-processed with cloudy to predict emission lines. We find that the observed z > 6 [O iii]88 $\mu$m–SFR and [C ii]158 $\mu$m–SFR relations can only be reproduced when the C/O abundance ratio is ∼8 × lower than Solar and the total metal production is ∼4 × higher than that of a Kroupa IMF. This implies that high-redshift galaxies are potentially primarily enriched by low-metallicity core–collapse supernovae with a more top-heavy IMF. As AGB stars and type-Ia supernova begin to contribute to the galaxy metallicity, both the [C ii]158 $\mu$m–SFR and [C ii]158 $\mu$m luminosity functions are predicted to converge to observed values at z ∼ 4.5. While we demonstrate that ionization parameter, LyC escape fraction, ISM gas density, and CMB attenuation all drive galaxies towards higher [O iii]88 $\mu$m/[C ii]158 $\mu$m, observed values at z > 6 can only be reproduced with substantially lower C/O abundances compared to Solar. The combination of [C ii]158 $\mu$m and [O iii]88 $\mu$m can be used to predict the values of ionization parameter, ISM gas density, and LyC escape fraction and we provide estimates of these quantities for nine observed z > 6 galaxies. Finally, we demonstrate that [O i]63 $\mu$m can be used as a replacement for [C ii]158 $\mu$ m in high-redshift galaxies where [C ii]158 $\mu$ m is unobserved and argue that more observation time should be used to target [O i]63 $\mu$m at z > 6. Future simulations will be needed to self-consistently address the numerous uncertainties surrounding a varying IMF at high redshift and the associated metal returns.

Optically detected galaxy cluster candidates in the AKARI North Ecliptic Pole field based on photometric redshift from the Subaru Hyper Suprime-Cam

ABSTRACT Galaxy clusters provide an excellent probe in various research fields in astrophysics and cosmology. However, the number of galaxy clusters detected so far in the AKARI North Ecliptic Pole (NEP) field is limited. In this work, we provide galaxy cluster candidates in the AKARI NEP field with the minimum requisites based only on the coordinates and photometric redshift (photo-z) of galaxies. We used galaxies detected in five optical bands (g, r, i, z, and Y) by the Subaru Hyper Suprime-Cam (HSC), with additional data from the u band obtained from the Canada-France-Hawaii Telescope (CFHT) MegaPrime/MegaCam, and from the IRAC1 and IRAC2 bands from the Spitzer space telescope for photo-z estimation. We calculated the local density around every galaxy using the 10th-nearest neighbourhood. Cluster candidates were determined by applying the friends-of-friends algorithm to over-densities. A total of 88 cluster candidates containing 4390 member galaxies below redshift 1.1 in 5.4 deg2 were identified. The reliability of our method was examined through false-detection tests, redshift-uncertainty tests, and applications on the Cosmic Evolution Survey (COSMOS) data, giving false-detection rates of 0.01 to 0.05 and a recovery rate of 0.9 at high richness. Three X-ray clusters previously observed by ROSAT and Chandra were recovered. The cluster galaxies show a higher stellar mass and lower star formation rate compared with the field galaxies in two-sample Z-tests. These cluster candidates are useful for environmental studies of galaxy evolution and future astronomical surveys in the NEP, where AKARI has performed unique nine-band mid-infrared photometry for tens of thousands of galaxies.

Chronos: A NIR spectroscopic galaxy survey to probe the most fundamental stages of galaxy evolution

We propose a dedicated, ultra-deep spectroscopic survey in the near infrared (NIR), that will target a mass-limited sample of galaxies during two of the most fundamental epochs of cosmic evolution: the formation of the first galaxies (at z gtrsim 6; cosmic dawn), and at the peak of galaxy formation activity (at redshift zsim 1–3; cosmic noon). By way of NIR observations (λ= 0.8–2μ m), it is possible to study the UV Lyman-α region in the former, and the optical rest-frame in the latter, allowing us to extract fundamental observables such as gas and stellar kinematics, chemical abundances, and ages, providing a unique legacy database covering these two crucial stages of cosmic evolution. The need to work in the NIR at extremely low flux levels makes a ground-based approach unfeasible due to atmospheric emission and absorption. Only with the largest facilities of the future (e.g. ELT) will be possible to observe a reduced set of targets, comprising at most of order thousands of galaxies. Likewise, from space, the small field of view of JWST and its use as a general purpose facility will yield a rather small set of high quality NIR spectra of distant galaxies (in the thousands, at best). Our project (codename Chronos) aims to produce sim 1 million high quality spectra, with a high S/N in the continuum, where information about the underlying stellar populations is encoded. The main science drivers are: The connection between the star formation history and the mass assembly history.The role of AGN and supernova feedback in shaping the formation histories of galaxies, with a quantitative estimate of quenching timescales.The formation of the first galaxies.The source of reionization.Evolution of the metallicity-mass relation, including [α/Fe] and individual abundances.Precision cosmology through detailed studies of the “baryon physics” of galaxy formation, probing the power spectrum over scales ksim 1 Mpc− 1.

Mid-IR cosmological spectrophotometric surveys from space: Measuring AGN and star formation at the cosmic noon with a SPICA-like mission

AbstractWe use the SPace Infrared telescope for Cosmology and Astrophysics (SPICA) project as a template to demonstrate how deep spectrophotometric surveys covering large cosmological volumes over extended fields (1–$15\, \rm{deg^2}$) with a mid-IR imaging spectrometer (17–$36\, \rm{\rm{\upmu m}}$) in conjunction with deep$70\, \rm{\rm{\upmu m}}$photometry with a far-IR camera, at wavelengths which are not affected by dust extinction can answer the most crucial questions in current galaxy evolution studies. A SPICA-like mission will be able for the first time to provide an unobscured three-dimensional (3D, i.e.x,y, and redshiftz) view of galaxy evolution back to an age of the universe of less than$\sim$2 Gyrs, in the mid-IR rest frame. This survey strategy will produce a full census of the Star Formation Rate (SFR) in the universe, using polycyclic aromatic hydrocarbons (PAH) bands and fine-structure ionic lines, reaching the characteristic knee of the galaxy luminosity function, where the bulk of the population is distributed, at any redshift up to$z \sim 3.5$. Deep follow-up pointed spectroscopic observations with grating spectrometers onboard the satellite, across the full IR spectral range (17–$210\, \rm{\rm{\upmu m}}$), would simultaneously measure Black Hole Accretion Rate (BHAR), from high-ionisation fine-structure lines, and SFR, from PAH and low- to mid-ionisation lines in thousands of galaxies from solar to low metallicities, down to the knee of their luminosity functions. The analysis of the resulting atlas of IR spectra will reveal the physical processes at play in evolving galaxies across cosmic time, especially its heavily dust-embedded phase during theactivity peakat the cosmic noon ($z \sim 1$–3), through IR emission lines and features that are insensitive to the dust obscuration.

Water megamaser emission in hard X-ray selected AGN

Context. Water megamaser emission at 22 GHz has proven to be a powerful tool for astrophysical studies of active galactic nuclei (AGN) because it allows an accurate determination of the mass of the central black hole and of the accretion disc geometry and dynamics. However, after searches among thousands of galaxies, only about 200 of them have shown such spectroscopic features, most of them of uncertain classification. In addition, the physical and geometrical conditions under which a maser activates are still unknown. Aims. We characterize the occurrence of water maser emission in an unbiased sample of AGN by investigating the relation with the X-ray properties and the possible favourable geometry that is required to detect water maser. Methods. We searched for 22 GHz maser emission in a hard X-ray selected sample of AGN, taken from the INTEGRAL/IBIS survey above 20 keV. Only half of the 380 sources in the sample have water maser data. We also considered a volume-limited sub-sample of 87 sources, for which we obtained new observations with the Green Bank and Effelsberg telescopes (for 35 sources). We detected one new maser and increased its radio coverage to 75%. Results. The detection rate of water maser emission in the total sample is 15 ± 3%. This fraction increases to 19 ± 5% for the complete sub-sample, especially when we consider type 2 (22 ± 5% and 31 ± 10% for the total and complete samples, respectively) and Compton-thick AGN (56 ± 18% and 50 ± 35% for the total and complete samples, respectively). No correlation is found between water maser and X-ray luminosity. We note that all types of masers (disc and jet) are associated with hard X-ray selected AGN. Conclusions. These results demonstrate that the hard X–ray selection may significantly enhance the maser detection efficiency over comparably large optical or infrared surveys. A possible decline in detection fraction with increasing luminosity might suggest that an extremely luminous nuclear environment does not favour maser emission. The large fraction of CT AGN with water maser emission could be explained in terms of geometrical effects. The maser medium would then be the very edge-on portion of the obscuring medium.

Galaxy classification: deep learning on the OTELO and COSMOS databases

Context.The accurate classification of hundreds of thousands of galaxies observed in modern deep surveys is imperative if we want to understand the universe and its evolution.Aims.Here, we report the use of machine learning techniques to classify early- and late-type galaxies in the OTELO and COSMOS databases using optical and infrared photometry and available shape parameters: either the Sérsic index or the concentration index.Methods.We used three classification methods for the OTELO database: (1)u − rcolor separation, (2) linear discriminant analysis usingu − rand a shape parameter classification, and (3) a deep neural network using thermagnitude, several colors, and a shape parameter. We analyzed the performance of each method by sample bootstrapping and tested the performance of our neural network architecture using COSMOS data.Results.The accuracy achieved by the deep neural network is greater than that of the other classification methods, and it can also operate with missing data. Our neural network architecture is able to classify both OTELO and COSMOS datasets regardless of small differences in the photometric bands used in each catalog.Conclusions.In this study we show that the use of deep neural networks is a robust method to mine the cataloged data.

OzDES multi-object fibre spectroscopy for the Dark Energy Survey: results and second data release

ABSTRACT We present a description of the Australian Dark Energy Survey (OzDES) and summarize the results from its 6 years of operations. Using the 2dF fibre positioner and AAOmega spectrograph on the 3.9-m Anglo-Australian Telescope, OzDES has monitored 771 active galactic nuclei, classified hundreds of supernovae, and obtained redshifts for thousands of galaxies that hosted a transient within the 10 deep fields of the Dark Energy Survey. We also present the second OzDES data release, containing the redshifts of almost 30 000 sources, some as faint as rAB = 24 mag, and 375 000 individual spectra. These data, in combination with the time-series photometry from the Dark Energy Survey, will be used to measure the expansion history of the Universe out to z ∼ 1.2 and the masses of hundreds of black holes out to z ∼ 4. OzDES is a template for future surveys that combine simultaneous monitoring of targets with wide-field imaging cameras and wide-field multi-object spectrographs.

Decadal Variability Survey in MACSJ1149

Abstract We present a long temporal baseline variability survey in the Frontier Field MACSJ1149. In this study, we identify active galactic nuclei (AGNs) and other transient sources via their variability using over a decade of Hubble Space Telescope (HST) images for thousands of galaxies in the cluster region and detect significant variability in galaxies extending down to an apparent nuclear magnitude of m i < 26.5. Our analysis utilizes HST images obtained in six different wavelengths from 435 nm to 1.6 microns and covers timescales 12 hr to 12 yr apart. We find that ∼2% of galaxies in these images are variable, with 49 AGN candidates and 4 new supernovae candidates detected. Half of the variables are in the cluster and these are primarily elliptical galaxies displaying variability only in the near-infrared bands. About 20% of the AGN candidates have morphologies and colors consistent with quasars, though most of the variables appear to be dominated by the host galaxy light. The structure function for these sources shows a greater amplitude of variability at shorter wavelengths, with slopes shallower than typical quasars. We also report a previously unknown Einstein cross identified in this field.

Predicting star formation properties of galaxies using deep learning

ABSTRACT Understanding the star formation properties of galaxies as a function of cosmic epoch is a critical exercise in studies of galaxy evolution. Traditionally, stellar population synthesis (SPS) models have been used to obtain best-fitting parameters that characterize star formation in galaxies. As multiband flux measurements become available for thousands of galaxies, an alternative approach to characterizing star formation using machine learning becomes feasible. In this work, we present the use of deep learning techniques to predict three important star formation properties – stellar mass, star formation rate, and dust luminosity. We characterize the performance of our deep learning models through comparisons with outputs from a standard SPS code.