Photometry Of Galaxies Research Articles

Toward Discovery of Gravitationally Lensed Explosive Transients: The Brightest Galaxies in Massive Galaxy Clusters from Planck-SZ2

We combine the Planck-SZ2 galaxy cluster catalog with near-infrared photometry of galaxies from the VISTA Hemisphere Survey to identify candidate brightest cluster galaxies (BCGs) in 306 massive clusters in the Southern skies at redshifts of z > 0.1. We find that 91% of these clusters have at least one candidate BCG within the 95% confidence interval on the cluster centers quoted by the Planck collaboration, providing reassurance that our analyses are statistically compatible, and find 92% to be reasonable candidates following a manual inspection. We make our catalog publicly available to assist colleagues interested in multi-wavelength studies of cluster cores, and the search for gravitationally lensed explosive transients in upcoming surveys including the Legacy Survey of Space and Time by the Vera C. Rubin Observatory.

JWST-MIRI Synthetic Photometry Composition using 460 Spitzer-IRS Spectra of Nearby Galaxies

We use extragalactic Spitzer-InfraRed Spectrograph (IRS) spectroscopy to determine the polycyclic aromatic hydrocarbon band, emission line, and stellar and dust continuum contributions to the James Webb Space Telescope (JWST)-Mid-InfraRed Instrument (MIRI) photometric filters, F560W through F1280W, appropriate for z ∼ 0 star-forming galaxies. Our data set consists of 460 IRS-SL spectra from 5.25 to 14.8 μm. Of these, 340 are from large radial strips that sample a wide range of environments across three nearby galaxies. We also include 120 spectra from the Spitzer Infrared Nearby Galaxy Survey that are centered on the brightest star-forming regions in 46 nearby galaxies. This large compilation of extragalactic mid-infrared spectra facilitates the interpretation of future work with JWST-MIRI photometry of galaxies.

Euclidpreparation

TheEuclidSpace Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15 000deg2of the sky.Euclidis expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. In order to optimally exploit the expected very large dataset, appropriate methods and software tools need to be developed. Here we present a novel machine-learning-based methodology for the selection of quiescent galaxies using broadbandEuclid IE,YE,JE, andHEphotometry, in combination with multi-wavelength photometry from other large surveys (e.g. theRubinLSST). The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has been designed to have 'sparsity awareness', such that missing photometry values are informative for the classification. In addition, our pipeline is able to derive photometric redshifts for galaxies selected as quiescent, aided by the 'pseudo-labelling' semi-supervised method, and using an outlier detection algorithm to identify and reject likely catastrophic outliers. After the application of the outlier filter, our pipeline achieves a normalised mean absolute deviation of ≲0.03 and a fraction of catastrophic outliers of ≲0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i)EuclidDeep Survey photometry with ancillaryugriz,WISE, and radio data; (ii)EuclidWide Survey photometry with ancillaryugriz,WISE, and radio data; and (iii)EuclidWide Survey photometry only, with no foreknowledge of galaxy redshifts. In a like-for-like comparison, our classification pipeline outperformsUVJselection, in addition to theEuclid IE–YE,JE–HEandu–IE, IE–JEcolour-colour methods, with improvements in completeness and theF1-score (the harmonic mean of precision and recall) of up to a factor of 2.

DSPS: Differentiable stellar population synthesis

ABSTRACT Models of stellar population synthesis (SPS) are the fundamental tool that relates the physical properties of a galaxy to its spectral energy distribution (SED). In this paper, we present DSPS: a python package for SPS. All of the functionality in DSPS is implemented natively in the JAX library for automatic differentiation, and so our predictions for galaxy photometry are fully differentiable, and directly inherit the performance benefits of JAX, including portability onto GPUs. DSPS also implements several novel features, such as i) a flexible empirical model for stellar metallicity that incorporates correlations with stellar age, ii) support for the Diffstar model that provides a physically-motivated connection between the star formation history of a galaxy (SFH) and the mass assembly of its underlying dark matter halo. We detail a set of theoretical techniques for using autodiff to calculate gradients of predictions for galaxy SEDs with respect to SPS parameters that control a range of physical effects, including SFH, stellar metallicity, nebular emission, and dust attenuation. When forward modelling the colours of a synthetic galaxy population, we find that DSPS can provide a factor of 5 speed-up over standard SPS codes on a CPU, and a factor of 300-400 on a modern GPU. When coupled with gradient-based techniques for optimization and inference, DSPS makes it practical to conduct expansive likelihood analyses of simulation-based models of the galaxy–halo connection that fully forward model galaxy spectra and photometry.

Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST

The advent of the James Webb Space Telescope (JWST) signals a new era in exploring galaxies in the high-z universe. Current and upcoming JWST imaging will potentially detect galaxies at z ∼ 20, creating a new urgency in the quest to infer accurate photometric redshifts (photo-z) for individual galaxies from their spectral energy distributions, as well as masses, ages, and star formation rates. Here we illustrate the utility of informed priors encoding previous observations of galaxies across cosmic time in achieving these goals. We construct three joint priors encoding empirical constraints of redshifts, masses, and star formation histories in the galaxy population within the Prospector Bayesian inference framework. In contrast with uniform priors, our model breaks an age–mass–redshift degeneracy, and thus reduces the mean bias error in masses from 0.3 to 0.1 dex, and in ages from 0.6 to 0.2 dex in tests done on mock JWST observations. Notably, our model recovers redshifts at least as accurately as the state-of-the-art photo-z code EAzY in deep JWST fields, but with two advantages: tailoring a model based on a particular survey is rendered mostly unnecessary given well-motivated priors; obtaining joint posteriors describing stellar, active galactic nuclei, gas, and dust contributions becomes possible. We can now confidently use the joint distribution to propagate full non-Gaussian redshift uncertainties into inferred properties of the galaxy population. This model, “Prospector-β,” is intended for fitting galaxy photometry where the redshift is unknown, and will be instrumental in ensuring the maximum science return from forthcoming photometric surveys with JWST. The code is made publicly available online as a part of Prospector 9 9 The version used in this work corresponds to the state of the Git repository at commit https://github.com/bd-j/prospector/commit/820ad72363a1f9c22cf03610bfe6e361213385cd..

DIGS: deep inference of galaxy spectra with neural posterior estimation

With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of simulation-based inference (SBI) and amortized neural posterior estimation (NPE) has been successfully used to analyse simulated and real galaxy photometry both precisely and efficiently. In this work, we utilise this combination and build on existing literature to analyse simulated noisy galaxy spectra. Here, we demonstrate a proof-of-concept study of spectra that is (a) an efficient analysis of galaxy SEDs and inference of galaxy parameters with physically interpretable uncertainties; and (b) amortized calculations of posterior distributions of said galaxy parameters at the modest cost of a few galaxy fits with Markov chain Monte Carlo (MCMC) methods. We utilise the SED generator and inference framework Prospector to generate simulated spectra, and train a dataset of 2 × 106 spectra (corresponding to a five-parameter SED model) with NPE. We show that SBI—with its combination of fast and amortized posterior estimations—is capable of inferring accurate galaxy stellar masses and metallicities. Our uncertainty constraints are comparable to or moderately weaker than traditional inverse-modelling with Bayesian MCMC methods (e.g. 0.17 and 0.26 dex in stellar mass and metallicity for a given galaxy, respectively). We also find that our inference framework conducts rapid SED inference (0.9–1.2 × 105 galaxy spectra via SBI/NPE at the cost of 1 MCMC-based fit). With this work, we set the stage for further work that focuses of SED fitting of galaxy spectra with SBI, in the era of JWST galaxy survey programs and the wide-field Roman Space Telescope spectroscopic surveys.

The miniJPAS survey quasar selection – I. Mock catalogues for classification

ABSTRACT In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper, we develop a pipeline to compute synthetic photometry of quasars, galaxies, and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey. To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range 17.5 ≤ r < 24, we augment our sample of available spectra by shifting the original r-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modelling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys.

The effect of spiral arms on the Sérsic photometry of galaxies

Context. The Sérsic profile is a widely used model for describing the surface brightness distribution of galaxies. Spiral galaxies, however, are qualitatively different from a Sérsic model. Aims. The goal of this study is to assess how accurately the total flux and half-light radius of a galaxy with spiral arms can be recovered when fitted with a Sérsic profile. Methods. I selected a sample of bulge-dominated galaxies with spiral arms. Using photometric data from the Hyper Suprime-Cam survey, I estimated the contribution of the spiral arms to their total flux. Then I generated simulated images of galaxies with similar characteristics, fitted them with a Sérsic model, and quantified the error on the determination of the total flux and half-light radius. Results. Spiral arms can introduce biases on the photometry of galaxies in a way that depends on the underlying smooth surface brightness profile, the location of the arms, and the depth of the photometric data. A set of spiral arms accounting for 10% of the flux of a bulge-dominated galaxy typically causes the total flux and the half-light radius to be overestimated by 15% and 30%, respectively. This bias, however, is much smaller if the galaxy is disk-dominated. Conclusions. Galaxies with a prominent bulge and a non-zero contribution from spiral arms are the most susceptible to biases in the total flux and half-light radius when fitted with a Sérsic profile. If photometric measurements with high accuracy are required, then measurements over finite apertures are to be preferred over global estimates of the flux.

A composite likelihood approach for inference under photometric redshift uncertainty

ABSTRACTObtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.

A Local Baseline of the Black Hole Mass Scaling Relations for Active Galaxies. IV. Correlations Between M BH and Host Galaxy σ, Stellar Mass, and Luminosity

Abstract The tight correlations between the mass of supermassive black holes (M BH) and their host-galaxy properties have been of great interest to the astrophysical community, but a clear understanding of their origin and fundamental drivers still eludes us. The local relations for active galaxies are interesting in their own right and form the foundation for any evolutionary study over cosmic time. We present Hubble Space Telescope optical imaging of a sample of 66 local active galactic nuclei (AGNs); for 14 objects, we also obtained Gemini near-infrared images. We use state-of-the-art methods to perform surface photometry of the AGN host galaxies, decomposing them into spheroid, disk, and bar (when present), and inferring the luminosity and stellar mass of the components. We combine this information with spatially resolved kinematics obtained at the Keck Telescopes to study the correlations between M BH (determined from single-epoch virial estimators) and host galaxy properties. The correlations are uniformly tight for our AGN sample, with intrinsic scatter 0.2–0.4 dex, smaller than or equal to that of quiescent galaxies. We find no difference between pseudo and classical bulges or barred and nonbarred galaxies. We show that all the tight correlations can be simultaneously satisfied by AGN hosts in the 107–109 M ⊙ regime, with data of sufficient quality. The M BH–σ relation is also in agreement with that of AGN with M BH obtained from reverberation mapping, providing an indirect validation of single-epoch virial estimators of M BH.

The LSST-DESC 3x2pt Tomography Optimization Challenge

This paper presents the results of the Rubin Observatory Dark Energy Science Collaboration (DESC) 3x2pt tomography challenge, which served as a first step toward optimizing the tomographic binning strategy for the main DESC analysis. The task of choosing an optimal tomographic binning scheme for a photometric survey is made particularly delicate in the context of a metacalibrated lensing catalogue, as only the photometry from the bands included in the metacalibration process (usually riz and potentially g) can be used in sample definition. The goal of the challenge was to collect and compare bin assignment strategies under various metrics of a standard 3x2pt cosmology analysis in a highly idealized setting to establish a baseline for realistically complex follow-up studies; in this preliminary study, we used two sets of cosmological simulations of galaxy redshifts and photometry under a simple noise model neglecting photometric outliers and variation in observing conditions, and contributed algorithms were provided with a representative and complete training set. We review and evaluate the entries to the challenge, finding that even from this limited photometry information, multiple algorithms can separate tomographic bins reasonably well, reaching figures-of-merit scores close to the attainable maximum. We further find that adding the g band to riz photometry improves metric performance by ~15% and that the optimal bin assignment strategy depends strongly on the science case: which figure-of-merit is to be optimized, and which observables (clustering, lensing, or both) are included.

Preparing for LSST data

Aims.We study how the upcoming Legacy Survey of Space and Time (LSST) data from theVera C. RubinObservatory can be employed to constrain the physical properties of normal star-forming galaxies (main-sequence galaxies). Because the majority of the observed LSST objects will have no auxiliary data, we use simulated LSST data and existing real observations to test the reliability of estimates of the physical properties of galaxies, such as their star formation rate (SFR), stellar mass (Mstar), and dust luminosity (Ldust). We focus on normal star-forming galaxies because they form the majority of the galaxy population in the universe and are therefore more likely to be observed with the LSST.Methods.We performed a simulation of LSST observations and uncertainties of 50 385 real galaxies within the redshift range 0 < z < 2.5. In order to achieve this goal, we used the unique multi-wavelength data from theHerschelExtragalactic Legacy Project (HELP) survey. Our analysis focused on two fields, ELAIS N1 and COSMOS. To obtain the physical properties of the galaxies, we fit their spectral energy distributions (SEDs) using the Code Investigating GALaxy Emission. We simulated the LSST data by convolving the SEDs fitted by employing the multi-wavelength observations. We compared the main galaxy physical properties, such as SFR,Mstar, andLdustobtained from the fit of the observed multi-wavelength photometry of galaxies (from the UV to the far-IR) to those obtained from the simulated LSST optical measurements alone.Results.We present the catalogue of simulated LSST observations for 23 291 main-sequence galaxies in the ELAIS N1 field and for 9093 galaxies in the COSMOS field. It is available in the HELP virtual observatory. The stellar masses estimated based on the LSST measurements agree with the full UV to far-IR SED estimates because they mainly depend on the UV and optical emission, which is well covered by LSST in the considered redshift range. Instead, we obtain a clear overestimate of the dust-related properties (SFR,Ldust,Mstar) estimated with the LSST alone. They are highly correlated with redshift. We investigate the cause of this overestimate and conclude that it is related to an overestimate of the dust attenuation in both UV and near-IR. We find that it is necessary to employ auxiliary rest-frame mid-IR observations, simulated UV observations, or the far-UV attenuation (AFUV)-Mstarrelation to correct for the overestimate. We also deliver the correction formula log10(SFRLSST/SFRreal) = 0.26 ⋅ z2 − 0.94 ⋅ z + 0.87. It is based on the 32 384 MS galaxies detected withHerschel.

An ALMA view of 11 dusty star-forming galaxies at the peak of cosmic star formation history

ABSTRACT We present the ALMA view of 11 main-sequence dusty star-forming galaxies (DSFGs) (sub-)millimetre selected in the Great Observatories Origins Survey South (GOODS-S) field and spectroscopically confirmed to be at the peak of cosmic star formation history (z ∼ 2). Our study combines the analysis of galaxy spectral energy distribution with ALMA continuum and CO spectral emission by using ALMA Science Archive products at the highest spatial resolution currently available for our sample (Δθ ≲ 1 arcsec). We include galaxy multiband images and photometry (in the optical, radio, and X-rays) to investigate the interlink between dusty, gaseous, and stellar components and the eventual presence of AGN. We use multiband sizes and morphologies to gain an insight on the processes that lead galaxy evolution, e.g. gas condensation, star formation, AGN feedback. The 11 DSFGs are very compact in the (sub-)millimetre (median rALMA = 1.15 kpc), while the optical emission extends to larger radii (median rH/rALMA = 2.05). CO lines reveal the presence of a rotating disc of molecular gas, but we cannot exclude the presence of interactions and/or molecular outflows. Images at higher (spectral and spatial) resolution are needed to disentangle from the possible scenarios. Most of the galaxies are caught in the compaction phase, when gas cools and falls into galaxy centre, fuelling the dusty burst of star formation and the growing nucleus. We expect these DSFGs to be the high-z star-forming counterparts of massive quiescent galaxies. Some features of CO emission in three galaxies are suggestive of forthcoming/ongoing AGN feedback, which is thought to trigger the morphological transition from star-forming discs to early-type galaxies.

Herschel Photometric Observations of Little Things Dwarf Galaxies

Abstract We present here far-infrared photometry of galaxies in a sample that is relatively unexplored at these wavelengths: low-metallicity dwarf galaxies with moderate star formation rates. Four dwarf irregular galaxies from the Little Things survey are considered, with deep Herschel PACS and SPIRE observations at 100, 160, 250, 350, and 500 μm. Results from modified blackbody fits indicate that these galaxies have low dust masses and cooler dust temperatures than more actively star-forming dwarfs, occupying the lowest L TIR and M dust regimes seen among these samples. Dust-to-gas mass ratios of ∼10−5 are lower, overall, than in more massive and active galaxies but are roughly consistent with the broken power-law relation between the dust-to-gas ratio and metallicity found for other low-metallicity systems. Chemical evolution modeling suggests that these dwarf galaxies are likely forming very little dust via stars or grain growth and have very high dust destruction rates.

GALEX UV Catalog of Low-redshift Galaxies for Estimating Transient Rates

AbstractDetection of transients such as supernovae (SNe) and kilonovae (KNe) in early phase has recently become important for understanding the progenitor properties and multi-messenger astronomy. Predicting which galaxy has the higher probability of hosting the transient events would help detect the early phase of the events and get information on their progenitors. The SN and KN rates are known to be a function of star formation rate (SFR) and stellar mass of the host galaxy. The SFR of a galaxy can be estimated from ultraviolet (UV) luminosity. However, the UV magnitudes have been derived carefully only for a limited number of nearby galaxies. Here, we introduce GALEX galaxy catalog of all-sky UV brightness of low redshift galaxies. To do so, we derive the UV photometry of galaxies in the GLADE catalog using the GALEX AIS images, supplemented by GALEX NGS and MIS data. From the near-UV (NUV) and far-UV (FUV) magnitudes, we calculate the SFRs of the galaxies, which will further be useful for estimating the SN and KN rate. The results are compared with previous GALEX UV catalog of galaxies. There will be an updated catalog based on this catalog for calculating KN rate of the galaxies in the future work.

The PAU survey: estimating galaxy photometry with deep learning

ABSTRACT With the dramatic rise in high-quality galaxy data expected from Euclid and Vera C. Rubin Observatory, there will be increasing demand for fast high-precision methods for measuring galaxy fluxes. These will be essential for inferring the redshifts of the galaxies. In this paper, we introduce Lumos, a deep learning method to measure photometry from galaxy images. Lumos builds on BKGnet, an algorithm to predict the background and its associated error, and predicts the background-subtracted flux probability density function. We have developed Lumos for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using a 40 narrow-band filter camera (PAUCam). PAUCam images are affected by scattered light, displaying a background noise pattern that can be predicted and corrected for. On average, Lumos increases the SNR of the observations by a factor of 2 compared to an aperture photometry algorithm. It also incorporates other advantages like robustness towards distorting artefacts, e.g. cosmic rays or scattered light, the ability of deblending and less sensitivity to uncertainties in the galaxy profile parameters used to infer the photometry. Indeed, the number of flagged photometry outlier observations is reduced from 10 to 2 per cent, comparing to aperture photometry. Furthermore, with Lumos photometry, the photo-z scatter is reduced by ≈10 per cent with the Deepz machine-learning photo-z code and the photo-z outlier rate by 20 per cent. The photo-z improvement is lower than expected from the SNR increment, however, currently the photometric calibration and outliers in the photometry seem to be its limiting factor.

The dependence of subhalo abundance matching on galaxy photometry and selection criteria

ABSTRACT Subhalo abundance matching (SHAM) is a popular technique for assigning galaxy mass or luminosity to haloes produced in N-body simulations. The method works by matching the cumulative number functions of the galaxy and halo properties, and is therefore sensitive both to the precise definitions of those properties and to the selection criteria used to define the samples. Further dependence follows when SHAM parameters are calibrated with galaxy clustering, which is known to depend strongly on the manner in which galaxies are selected. In this paper we introduce a new parametrization for SHAM and derive the best-fitting SHAM parameters as a function of various properties of the selection of the galaxy sample and of the photometric definition, including Sérsic versus Petrosian magnitudes, stellar masses versus r-band magnitudes, and optical (Sloan Digital Sky Survey) versus $\mathrm{H}\, \small {\rm I}$ (ALFALFA) selection. In each case we calculate the models’ goodness-of-fit to measurements of the projected two-point galaxy correlation function. In the optically selected samples we find strong evidence that the scatter in the galaxy–halo connection increases towards the faint end, and that AM performs better with luminosity than stellar mass. The SHAM parameters of optically and $\mathrm{H}\, \small {\rm I}$-selected galaxies are mutually exclusive, with the latter suggesting the importance of properties beyond halo mass. We provide best-fitting parameters for the SHAM galaxy–halo connection as a function of each of our input choices, extending the domain of validity of the model while reducing potential systematic error in its use.

Photometric redshift estimation with a convolutional neural network: NetZ

Galaxy redshifts are a key characteristic for nearly all extragalactic studies. Since spectroscopic redshifts require additional telescope and human resources, millions of galaxies are known without spectroscopic redshifts. Therefore, it is crucial to have methods for estimating the redshift of a galaxy based on its photometric properties, the so-called photo-z. We have developed NetZ, a new method using a convolutional neural network (CNN) to predict the photo-z based on galaxy images, in contrast to previous methods that often used only the integrated photometry of galaxies without their images. We use data from the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) in five different filters as the training data. The network over the whole redshift range between 0 and 4 performs well overall and especially in the high-z range, where it fares better than other methods on the same data. We obtained a precision |zpred − zref| of σ = 0.12 (68% confidence interval) with a CNN working for all galaxy types averaged over all galaxies in the redshift range of 0 to ∼4. We carried out a comparison with a network trained on point-like sources, highlighting the importance of morphological information for our redshift estimation. By limiting the scope to smaller redshift ranges or to luminous red galaxies, we find a further notable improvement. We have published more than 34 million new photo-z values predicted with NetZ. This shows that the new method is very simple and swift in application, and, importantly, it covers a wide redshift range that is limited only by the available training data. It is broadly applicable, particularly with regard to upcoming surveys such as the Rubin Observatory Legacy Survey of Space and Time, which will provide images of billions of galaxies with similar image quality as HSC. Our HSC photo-z estimates are also beneficial to the Euclid survey, given the overlap in the footprints of the HSC and Euclid.

The local vertical density distribution of ultracool dwarfs M7 to L2.5 and their luminosity function

We investigate the form of the local vertical density profile of the stars in the Galactic disk, close to the Galactic plane. We use a homogeneous sample of 34000 ultracool dwarfs M7 to L2.5 that all lie within 350 pc of the plane. We fit a profile of the form sech$^\alpha$, where $\alpha=2$ is the theoretically preferred isothermal profile and $\alpha=0$ is the exponential function. Larger values of $\alpha$ correspond to greater flattening of the profile towards the plane. We employ a likelihood analysis that accounts in a direct way for unresolved binaries in the sample, as well as for the spread in absolute magnitude $M_J$ within each spectral sub-type (Malmquist bias). We measure $\alpha=0.29^{+0.12}_{-0.13}$. The $\alpha=1$ (sech) and flatter profiles are ruled out at high confidence for this sample, while $\alpha=0$ (exponential) is included in the 95% credible interval. Any flattening relative to exponential is modest, and is confined to within 50 pc of the plane. The measured value of $\alpha$ is consistent with the results of the recent analysis by Xiang et al. Our value for $\alpha$ is also similar to that determined for nearby spiral galaxies by de Grijs et al., measured from photometry of galaxies viewed edge on. The measured profile allows an accurate determination of the local space density of ultracool dwarfs M7 to L2.5, and we use this to make a new determination of the luminosity function at the bottom of the main sequence. Our results for the luminosity function are a factor two to three lower than the recent measurement by Bardalez Gagliuffi et al., that uses stars in the local 25 pc radius bubble, but agree well with the older study by Cruz et al.

Dark Energy Survey Year 3 results: redshift calibration of the weak lensing source galaxies

ABSTRACT Determining the distribution of redshifts of galaxies observed by wide-field photometric experiments like the Dark Energy Survey (DES) is an essential component to mapping the matter density field with gravitational lensing. In this work we describe the methods used to assign individual weak lensing source galaxies from the DES Year 3 Weak Lensing Source Catalogue to four tomographic bins and to estimate the redshift distributions in these bins. As the first application of these methods to data, we validate that the assumptions made apply to the DES Y3 weak lensing source galaxies and develop a full treatment of systematic uncertainties. Our method consists of combining information from three independent likelihood functions: self-organizing map p(z) (sompz), a method for constraining redshifts from galaxy photometry; clustering redshifts (WZ), constraints on redshifts from cross-correlations of galaxy density functions; and shear ratios (SRs), which provide constraints on redshifts from the ratios of the galaxy-shear correlation functions at small scales. Finally, we describe how these independent probes are combined to yield an ensemble of redshift distributions encapsulating our full uncertainty. We calibrate redshifts with combined effective uncertainties of σ〈z〉 ∼ 0.01 on the mean redshift in each tomographic bin.