scholarly journals Close galaxy pairs with accurate photometric redshifts

2020 ◽  
Vol 634 ◽  
pp. A123 ◽  
Author(s):  
Facundo Rodriguez ◽  
Elizabeth Johana Gonzalez ◽  
Ana Laura O’Mill ◽  
Enrique Gaztañaga ◽  
Pablo Fosalba ◽  
...  
Keyword(s):  
Test Sample ◽  
Dark Matter Halo ◽  
Accelerating Universe ◽  
Identification Algorithm ◽  
Photometric Redshifts ◽  
Galaxy Groups ◽  
Mass Distributions ◽  
Photometric Redshift ◽  
Formation And Evolution ◽  
Isolated Systems

Context. Studies of galaxy pairs can provide valuable information to jointly understand the formation and evolution of galaxies and galaxy groups. Consequently, taking the new high-precision photo-z surveys into account, it is important to have reliable and tested methods that allow us to properly identify these systems and estimate their total masses and other properties. Aims. In view of the forthcoming Physics of the Accelerating Universe Survey (PAUS), we propose and evaluate the performance of an identification algorithm of projected close isolated galaxy pairs. We expect that the photometrically selected systems can adequately reproduce the observational properties and the inferred lensing mass–luminosity relation of a pair of truly bound galaxies that are hosted by the same dark matter halo. Methods. We developed an identification algorithm that considers the projected distance between the galaxies, the projected velocity difference, and an isolation criterion in order to restrict the sample to isolated systems. We applied our identification algorithm using a mock galaxy catalog that mimics the features of PAUS. To evaluate the feasibility of our pair finder, we compared the identified photometric samples with a test sample that considers that both members are included in the same halo. Taking advantage of the lensing properties provided by the mock catalog, we also applied a weak-lensing analysis to determine the mass of the selected systems. Results. Photometrically selected samples tend to show high purity values, but tend to misidentify truly bounded pairs as the photometric redshift errors increase. Nevertheless, overall properties such as the luminosity and mass distributions are successfully reproduced. We also accurately reproduce the lensing mass–luminosity relation as expected for galaxy pairs located in the same halo.

scholarly journals Pre-processing of galaxies in cosmic filaments around AMASCFI clusters in the CFHTLS

2019 ◽  
Vol 632 ◽  
pp. A49 ◽  
Author(s):  
F. Sarron ◽  
C. Adami ◽  
F. Durret ◽  
C. Laigle
Keyword(s):  
Galaxy Clusters ◽  
Spectral Energy ◽  
Photometric Redshifts ◽  
Star Forming ◽  
Galaxy Groups ◽  
Photometric Redshift ◽  
Preferred Direction ◽  
The Galaxy ◽  
Strategic Program ◽  
Galaxy Populations

Context. Galaxy clusters and groups are thought to accrete material along the preferred direction of cosmic filaments. These structures have proven difficult to detect because their contrast is low, however, and only a few studies have focused on cluster infall regions. Aims. We detect cosmic filaments around galaxy clusters using photometric redshifts in the range 0.15 <  z <  0.7. We characterise galaxy populations in these structures to study the influence of pre-processing by cosmic filaments and galaxy groups on star formation quenching. Methods. We detected cosmic filaments in the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) T0007 data, focusing on regions around clusters of the AMASCFI CFHTLS cluster sample. The filaments were reconstructed with the discrete persistent structure extractor (DISPERSE) algorithm in photometric redshift slices. We show that this reconstruction is reliable for a CFHTLS-like survey at 0.15 <  z <  0.7 using a mock galaxy catalogue. We split our galaxy catalogue into two populations (passive and star forming) using the LePhare spectral energy density fitting algorithm and worked with two redshift bins (0.15 <  z ≤ 0.4 and 0.4 <  z <  0.7). Results. We showed that the AMASCFI cluster connectivity (i.e. the number of filaments that is connected to a cluster) increases with cluster mass M200. Filament galaxies outside R200 are found to be closer to clusters at low redshift, regardless of the galaxy type. Passive galaxies in filaments are closer to clusters than star-forming galaxies in the low redshift bin alone. The passive fraction of galaxies decreases with increasing clustercentric distance up to d ∼ 5 cMpc. Galaxy groups and clusters that are not located at nodes of our reconstruction are mainly found inside cosmic filaments. Conclusions. These results give clues for pre-processing in cosmic filaments that could be due to smaller galaxy groups. This trend could be further explored by applying this method to larger photometric surveys such as the Hyper Suprime-Cam Subaru Strategic Program (HSC-SPP) or Euclid.

scholarly journals MIFAL: fully automated Multiple-Image Finder ALgorithm for strong-lens modelling – proof of concept

2019 ◽  
Vol 491 (3) ◽  
pp. 3778-3792 ◽  
Author(s):  
Mauricio Carrasco ◽  
Adi Zitrin ◽  
Gregor Seidel
Keyword(s):  
Simple Procedure ◽  
Lens Model ◽  
Proof Of Concept ◽  
Photometric Redshifts ◽  
Multiple Images ◽  
Mass Distributions ◽  
Photometric Redshift ◽  
Multiple Image ◽  
Strong Lensing ◽  
Blind Manner

ABSTRACT We outline a simple procedure designed for automatically finding sets of multiple images in strong lensing (SL) clusters. We show that by combining (a) an arc-finding (or source extracting) program, (b) photometric redshift measurements, and (c) a preliminary light-traces-mass lens model, multiple-image systems can be identified in a fully automated (‘blind’) manner. The presented procedure yields an assessment of the likelihood of each arc to belong to one of the multiple-image systems, as well as the preferred redshift for the different systems. These could be then used to automatically constrain and refine the initial lens model for an accurate mass distribution. We apply this procedure to Cluster Lensing And Supernova with Hubble observations of three galaxy clusters, MACS J0329.6-0211, MACS J1720.2 + 3536, and MACS J1931.8-2635, comparing the results to published SL analyses where multiple images were verified by eye on a particular basis. In the first cluster all originally identified systems are recovered by the automated procedure, and in the second and third clusters about half are recovered. Other known systems are not picked up, in part due to a crude choice of parameters, ambiguous photometric redshifts, or inaccuracy of the initial lens model. On top of real systems recovered, some false images are also mistakenly identified by the procedure, depending on the thresholds used. While further improvements to the procedure and a more thorough scrutinization of its performance are warranted, the work constitutes another important step toward fully automatizing SL analyses for studying mass distributions of large cluster samples.

Supermassive Black Hole feedback in early type galaxies

2020 ◽  
Vol 15 (S359) ◽  
pp. 119-125
Author(s):  
W. Forman ◽  
C. Jones ◽  
A. Bogdan ◽  
R. Kraft ◽  
E. Churazov ◽  
...  
Keyword(s):  
Dark Matter Halo ◽  
Scaling Relations ◽  
Radio Sources ◽  
Early Type ◽  
X Ray ◽  
Galaxy Groups ◽  
Sufficient Energy ◽  
The Galaxy ◽  
Simple Scaling ◽  
Halo Masses

AbstractOptically luminous early type galaxies host X-ray luminous, hot atmospheres. These hot atmospheres, which we refer to as coronae, undergo the same cooling and feedback processes as are commonly found in their more massive cousins, the gas rich atmospheres of galaxy groups and galaxy clusters. In particular, the hot coronae around galaxies radiatively cool and show cavities in X-ray images that are filled with relativistic plasma originating from jets powered by supermassive black holes (SMBH) at the galaxy centers. We discuss the SMBH feedback using an X-ray survey of early type galaxies carried out using Chandra X-ray Observatory observations. Early type galaxies with coronae very commonly have weak X-ray active nuclei and have associated radio sources. Based on the enthalpy of observed cavities in the coronae, there is sufficient energy to “balance” the observed radiative cooling. There are a very few remarkable examples of optically faint galaxies that are 1) unusually X-ray luminous, 2) have large dark matter halo masses, and 3) have large SMBHs (e.g., NGC4342 and NGC4291). These properties suggest that, in some galaxies, star formation may have been truncated at early times, breaking the simple scaling relations.

scholarly journals Hierarchical Matching and Regression with Application to Photometric Redshift Estimation

2016 ◽  
Vol 12 (S325) ◽  
pp. 145-155
Author(s):  
Fionn Murtagh
Keyword(s):  
Information Structure ◽  
Data Analytics ◽  
A Priori ◽  
Sloan Digital Sky Survey ◽  
Photometric Redshifts ◽  
Merging Galaxies ◽  
Photometric Redshift ◽  
Sky Survey ◽  
Wide Range ◽  
Regression Problems

AbstractThis work emphasizes that heterogeneity, diversity, discontinuity, and discreteness in data is to be exploited in classification and regression problems. A global a priori model may not be desirable. For data analytics in cosmology, this is motivated by the variety of cosmological objects such as elliptical, spiral, active, and merging galaxies at a wide range of redshifts. Our aim is matching and similarity-based analytics that takes account of discrete relationships in the data. The information structure of the data is represented by a hierarchy or tree where the branch structure, rather than just the proximity, is important. The representation is related to p-adic number theory. The clustering or binning of the data values, related to the precision of the measurements, has a central role in this methodology. If used for regression, our approach is a method of cluster-wise regression, generalizing nearest neighbour regression. Both to exemplify this analytics approach, and to demonstrate computational benefits, we address the well-known photometric redshift or ‘photo-z’ problem, seeking to match Sloan Digital Sky Survey (SDSS) spectroscopic and photometric redshifts.

scholarly journals H0LiCOW – X. Spectroscopic/imaging survey and galaxy-group identification around the strong gravitational lens system WFI 2033−4723

2019 ◽  
Vol 490 (1) ◽  
pp. 613-633 ◽  
Author(s):  
D Sluse ◽  
C E Rusu ◽  
C D Fassnacht ◽  
A Sonnenfeld ◽  
J Richard ◽  
...  
Keyword(s):  
Group Identification ◽  
Companion Paper ◽  
Gravitational Lens ◽  
Lens System ◽  
Photometric Redshifts ◽  
Galaxy Groups ◽  
Dark Energy Survey ◽  
Lens Position ◽  
Integral Field Spectrograph ◽  
Stellar Masses

ABSTRACT Galaxies and galaxy groups located along the line of sight towards gravitationally lensed quasars produce high-order perturbations of the gravitational potential at the lens position. When these perturbation are too large, they can induce a systematic error on H0 of a few per cent if the lens system is used for cosmological inference and the perturbers are not explicitly accounted for in the lens model. In this work, we present a detailed characterization of the environment of the lens system WFI 2033−4723 ($z_{\rm src} =\,$1.662, $z_{\rm lens}=\,$0.6575), one of the core targets of the H0LiCOW project for which we present cosmological inferences in a companion paper. We use the Gemini and ESO-Very Large telescopes to measure the spectroscopic redshifts of the brightest galaxies towards the lens, and use the ESO-MUSE integral field spectrograph to measure the velocity-dispersion of the lens ($\sigma _{\rm {los}}= 250^{+15}_{-21}$  km s−1) and of several nearby galaxies. In addition, we measure photometric redshifts and stellar masses of all galaxies down to i < 23 mag, mainly based on Dark Energy Survey imaging (DR1). Our new catalogue, complemented with literature data, more than doubles the number of known galaxy spectroscopic redshifts in the direct vicinity of the lens, expanding to 116 (64) the number of spectroscopic redshifts for galaxies separated by less than 3 arcmin (2 arcmin ) from the lens. Using the flexion-shift as a measure of the amplitude of the gravitational perturbation, we identify two galaxy groups and three galaxies that require specific attention in the lens models. The ESO MUSE data enable us to measure the velocity-dispersions of three of these galaxies. These results are essential for the cosmological inference analysis presented in Rusu et al.

scholarly journals An ALMA survey of the brightest sub-millimetre sources in the SCUBA-2–COSMOS field

2020 ◽  
Vol 495 (3) ◽  
pp. 3409-3430 ◽  
Author(s):  
J M Simpson ◽  
Ian Smail ◽  
U Dudzevičiūtė ◽  
Y Matsuda ◽  
B-C Hsieh ◽  
...  
Keyword(s):  
Pilot Study ◽  
Flux Density ◽  
Luminosity Function ◽  
Spectral Energy Distribution ◽  
Line Emission ◽  
Spectral Energy ◽  
Complete Sample ◽  
Photometric Redshifts ◽  
Photometric Redshift ◽  
Number Counts

ABSTRACT We present an ALMA study of the ∼180 brightest sources in the SCUBA-2 850-μm map of the COSMOS field from the S2COSMOS survey, as a pilot study for AS2COSMOS – a full survey of the ∼1000 sources in this field. In this pilot study, we have obtained 870-μm continuum maps of an essentially complete sample of the brightest 182 sub-millimetre sources ($S_{850\, \mu \rm m}\gt $ 6.2 mJy) in COSMOS. Our ALMA maps detect 260 sub-millimetre galaxies (SMGs) spanning a range in flux density of $S_{870\, \mu \rm m}$ = 0.7–19.2 mJy. We detect more than one SMG counterpart in 34 ± 2 per cent of sub-millimetre sources, increasing to 53 ± 8 per cent for SCUBA-2 sources brighter than $S_{850\, \mu \rm m}\gt $ 12 mJy. We estimate that approximately one-third of these SMG–SMG pairs are physically associated (with a higher rate for the brighter secondary SMGs, $S_{870\, \mu \rm m}\gtrsim$ 3 mJy), and illustrate this with the serendipitous detection of bright [C ii] 157.74-μm line emission in two SMGs, AS2COS 0001.1 and 0001.2 at z = 4.63, associated with the highest significance single-dish source. Using our source catalogue, we construct the interferometric 870-μm number counts at $S_{870\, \mu \rm m}\gt $ 6.2 mJy. We use the extensive archival data of this field to construct the multiwavelength spectral energy distribution of each AS2COSMOS SMG, and subsequently model this emission with magphys to estimate their photometric redshifts. We find a median photometric redshift for the $S_{870\, \mu \rm m}\gt $ 6.2 mJy AS2COSMOS sample of z = 2.87 ± 0.08, and clear evidence for an increase in the median redshift with 870-μm flux density suggesting strong evolution in the bright end of the 870-μm luminosity function.

scholarly journals Revisiting Abell 2744: a powerful synergy of GLASS spectroscopy and HFF photometry

2015 ◽  
Vol 11 (A29B) ◽  
pp. 776-778
Author(s):  
Xin Wang ◽  
Keyword(s):  
Mass Distribution ◽  
Galaxy Cluster ◽  
Adaptive Mesh ◽  
Field Of View ◽  
Relative Distribution ◽  
Lens Model ◽  
Photometric Redshifts ◽  
Screening Process ◽  
Photometric Redshift ◽  
Extensive Analysis

AbstractWe present new emission line identifications and improve the lensing reconstruction of the mass distribution of galaxy cluster Abell 2744 using the Grism Lens-Amplified Survey from Space (GLASS) spectroscopy and the Hubble Frontier Fields (HFF) imaging. We performed blind and targeted searches for faint line emitters on all objects, including the arc sample, within the field of view (FoV) of GLASS prime pointings. We report 55 high quality spectroscopic redshifts, 5 of which are for arc images. We also present an extensive analysis based on the HFF photometry, measuring the colors and photometric redshifts of all objects within the FoV, and comparing the spectroscopic and photometric redshift estimates. In order to improve the lens model of Abell 2744, we develop a rigorous algorithm to screen arc images, based on their colors and morphology, and selecting the most reliable ones to use. As a result, 25 systems (corresponding to 72 images) pass the screening process and are used to reconstruct the gravitational potential of the cluster pixellated on an adaptive mesh. The resulting total mass distribution is compared with a stellar mass map obtained from the Spitzer Frontier Fields data in order to study the relative distribution of stars and dark matter in the cluster.

scholarly journals Morpho-photometric redshifts

2019 ◽  
Vol 489 (4) ◽  
pp. 4802-4808 ◽  
Author(s):  
Kristen Menou
Keyword(s):  
Poor Performance ◽  
Sloan Digital Sky Survey ◽  
Gradient Boosting ◽  
Learning Tools ◽  
Multi Layer Perceptron ◽  
Comparable Data ◽  
Photometric Redshifts ◽  
Data Set ◽  
Photometric Redshift ◽  
Sky Survey

ABSTRACT Machine learning (ML) is one of two standard approaches (together with SED fitting) for estimating the redshifts of galaxies when only photometric information is available. ML photo-z solutions have traditionally ignored the morphological information available in galaxy images or partly included it in the form of hand-crafted features, with mixed results. We train a morphology-aware photometric redshift machine using modern deep learning tools. It uses a custom architecture that jointly trains on galaxy fluxes, colours, and images. Galaxy-integrated quantities are fed to a Multi-Layer Perceptron (MLP) branch, while images are fed to a convolutional (convnet) branch that can learn relevant morphological features. This split MLP-convnet architecture, which aims to disentangle strong photometric features from comparatively weak morphological ones, proves important for strong performance: a regular convnet-only architecture, while exposed to all available photometric information in images, delivers comparatively poor performance. We present a cross-validated MLP-convnet model trained on 130 000 SDSS-DR12 (Sloan Digital Sky Survey – Data Release 12) galaxies that outperforms a hyperoptimized Gradient Boosting solution (hyperopt+XGBoost), as well as the equivalent MLP-only architecture, on the redshift bias metric. The fourfold cross-validated MLP-convnet model achieves a bias δz/(1 + z) = −0.70 ± 1 × 10−3, approaching the performance of a reference ANNZ2 ensemble of 100 distinct models trained on a comparable data set. The relative performance of the morphology-aware and morphology-blind models indicates that galaxy morphology does improve ML-based photometric redshift estimation.

scholarly journals Deep ugrizY imaging and DEEP2/3 spectroscopy: a photometric redshift testbed for LSST and public release of data from the DEEP3 Galaxy Redshift Survey

2019 ◽  
Vol 488 (4) ◽  
pp. 4565-4584 ◽  
Author(s):  
Rongpu Zhou ◽  
Michael C Cooper ◽  
Jeffrey A Newman ◽  
Matthew L N Ashby ◽  
James Aird ◽  
...  
Keyword(s):  
Object Size ◽  
Random Forest Regression ◽  
Photometric Redshifts ◽  
Size Information ◽  
Photometric Redshift ◽  
Point Spread ◽  
Spread Function ◽  
Redshift Survey ◽  
Galaxy Redshift ◽  
Empirical Tests

ABSTRACT We present catalogues of calibrated photometry and spectroscopic redshifts in the Extended Groth Strip, intended for studies of photometric redshifts (photo-z’s). The data includes ugriz photometry from Canada–France–Hawaii Telescope Legacy Survey (CFHTLS) and Y-band photometry from the Subaru Suprime camera, as well as spectroscopic redshifts from the DEEP2, DEEP3, and 3D-HST surveys. These catalogues incorporate corrections to produce effectively matched-aperture photometry across all bands, based upon object size information available in the catalogue and Moffat profile point spread function fits. We test this catalogue with a simple machine learning-based photometric redshift algorithm based upon Random Forest regression, and find that the corrected aperture photometry leads to significant improvement in photo-z accuracy compared to the original SExtractor catalogues from CFHTLS and Subaru. The deep ugrizY photometry and spectroscopic redshifts are well suited for empirical tests of photometric redshift algorithms for LSST. The resulting catalogues are publicly available at http://d-scholarship.pitt.edu/36064/. We include a basic summary of the strategy of the DEEP3 Galaxy Redshift Survey to accompany the recent public release of DEEP3 data.

scholarly journals ANNz2 - Photometric redshift and probability density function estimation using machine-learning

2014 ◽  
Vol 10 (S306) ◽  
pp. 316-318
Author(s):  
Iftach Sadeh
Keyword(s):  
Machine Learning ◽  
Probability Density ◽  
Function Estimation ◽  
Photometric Redshifts ◽  
Galaxy Surveys ◽  
The Public ◽  
Photometric Redshift ◽  
Machine Learning Methods ◽  
Density Function Estimation ◽  
Full Probability

AbstractLarge photometric galaxy surveys allow the study of questions at the forefront of science, such as the nature of dark energy. The success of such surveys depends on the ability to measure the photometric redshifts of objects (photo-zs), based on limited spectral data. A new major version of the public photo-z estimation software, ANNz, is presented here. The new code incorporates several machine-learning methods, such as artificial neural networks and boosted decision/regression trees, which are all used in concert. The objective of the algorithm is to dynamically optimize the performance of the photo-z estimation, and to properly derive the associated uncertainties. In addition to single-value solutions, the new code also generates full probability density functions in two independent ways.

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