scholarly journals Z-Sequence: photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours

2021 ◽  
Vol 503 (4) ◽  
pp. 6078-6097
Author(s):  
Matthew C Chan ◽  
John P Stott
Keyword(s):  
Galaxy Clusters ◽  
Prediction Error ◽  
Sloan Digital Sky Survey ◽  
Selection Strategy ◽  
Proof Of Concept ◽  
Photometric Redshift ◽  
Nearest Neighbours ◽  
Sky Survey ◽  
Photometric Measurements ◽  
Fine Tune

ABSTRACT We introduce Z-Sequence, a novel empirical model that utilizes photometric measurements of observed galaxies within a specified search radius to estimate the photometric redshift of galaxy clusters. Z-Sequence itself is composed of a machine learning ensemble based on the k-nearest neighbours algorithm. We implement an automated feature selection strategy that iteratively determines appropriate combinations of filters and colours to minimize photometric redshift prediction error. We intend for Z-Sequence to be a standalone technique but it can be combined with cluster finders that do not intrinsically predict redshift, such as our own DEEP-CEE. In this proof-of-concept study, we train, fine-tune, and test Z-Sequence on publicly available cluster catalogues derived from the Sloan Digital Sky Survey. We determine the photometric redshift prediction error of Z-Sequence via the median value of |Δ$z$|/(1 + $z$) (across a photometric redshift range of 0.05 ≤ $z$ ≤ 0.6) to be ∼0.01 when applying a small search radius. The photometric redshift prediction error for test samples increases by 30–50 per cent when the search radius is enlarged, likely due to line-of-sight interloping galaxies. Eventually, we aim to apply Z-Sequence to upcoming imaging surveys such as the Legacy Survey of Space and Time to provide photometric redshift estimates for large samples of as yet undiscovered and distant clusters.

scholarly journals Searching for Mg ii absorbers in and around galaxy clusters

2021 ◽  
Vol 503 (3) ◽  
pp. 4309-4319
Author(s):  
Jong Chul Lee ◽  
Ho Seong Hwang ◽  
Hyunmi Song
Keyword(s):  
Galaxy Clusters ◽  
Detection Rate ◽  
Massive Star ◽  
Sloan Digital Sky Survey ◽  
Number Density ◽  
Cluster Galaxies ◽  
Star Forming ◽  
Quasar Spectra ◽  
Sky Survey ◽  
The Galaxy

ABSTRACT To study environmental effects on the circumgalactic medium (CGM), we use the samples of redMaPPer galaxy clusters, background quasars, and cluster galaxies from the Sloan Digital Sky Survey (SDSS). With ∼82 000 quasar spectra, we detect 197 Mg ii absorbers in and around the clusters. The detection rate per quasar is 2.7 ± 0.7 times higher inside the clusters than outside the clusters, indicating that Mg ii absorbers are relatively abundant in clusters. However, when considering the galaxy number density, the absorber-to-galaxy ratio is rather low inside the clusters. If we assume that Mg ii absorbers are mainly contributed by the CGM of massive star-forming galaxies, a typical halo size of cluster galaxies is smaller than that of field galaxies by 30 ± 10 per cent. This finding supports that galaxy haloes can be truncated by interaction with the host cluster.

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 Redshift measurement through star formation

2019 ◽  
Vol 629 ◽  
pp. A7
Author(s):  
Mikkel O. Lindholmer ◽  
Kevin A. Pimbblet
Keyword(s):  
Star Formation ◽  
Galaxy Clusters ◽  
Main Sequence ◽  
Formation Rate ◽  
Sloan Digital Sky Survey ◽  
X Ray ◽  
Star Forming ◽  
Sky Survey ◽  
The Galaxy ◽  
Galaxy Population

In this work we use the property that, on average, star formation rate increases with redshift for objects with the same mass – the so called galaxy main sequence – to measure the redshift of galaxy clusters. We use the fact that the general galaxy population forms both a quenched and a star-forming sequence, and we locate these ridges in the SFR–M⋆ plane with galaxies taken from the Sloan Digital Sky Survey in discrete redshift bins. We fitted the evolution of the galaxy main sequence with redshift using a new method and then subsequently apply our method to a suite of X-ray selected galaxy clusters in an attempt to create a new distance measurement to clusters based on their galaxy main sequence. We demonstrate that although it is possible in several galaxy clusters to measure the main sequences, the derived distance and redshift from our galaxy main sequence fitting technique has an accuracy of σz = ±0.017 ⋅ (z + 1) and is only accurate up to z ≈ 0.2.

scholarly journals THE SLOAN DIGITAL SKY SURVEY CO-ADD: A GALAXY PHOTOMETRIC REDSHIFT CATALOG

2012 ◽  
Vol 747 (1) ◽  
pp. 59 ◽  
Author(s):  
Ribamar R. R. Reis ◽  
Marcelle Soares-Santos ◽  
James Annis ◽  
Scott Dodelson ◽  
Jiangang Hao ◽  
...  
Keyword(s):  
Sloan Digital Sky Survey ◽  
Photometric Redshift ◽  
Sky Survey

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 Characterization of a subsample of the Planck SZ source cluster catalogues using optical SDSS DR12 data

2018 ◽  
Vol 617 ◽  
pp. A71 ◽  
Author(s):  
A. Streblyanska ◽  
R. Barrena ◽  
J. A. Rubiño-Martín ◽  
R. F. J. van der Burg ◽  
N. Aghanim ◽  
...  
Keyword(s):  
Galaxy Clusters ◽  
Sloan Digital Sky Survey ◽  
Cluster Galaxy ◽  
Robust Identification ◽  
Optical Images ◽  
Sky Survey ◽  
Spectroscopic Information ◽  
Source Sample ◽  
Available Information

Aims. The Planck catalogues of Sunyaev –Zeldovich (SZ) sources, PSZ1 and PSZ2, are the largest catalogues of galaxy clusters selected through their SZ signature in the full sky. In 2013, we started a long-term observational programme at Canary Island observatories with the aim of validating ∼500 unconfirmed SZ sources. In this work we present results of the initial pre-screening of possible cluster counterparts using photometric and spectroscopic data of the Sloan Digital Sky Survey DR12. Our main aim is to identify previously unconfirmed PSZ2 cluster candidates and to contribute in the determination of the actual purity and completeness of Planck SZ source sample. Methods. Using the latest version of the PSZ2 catalogue, we selected all sources overlapping with the SDSS DR12 footprint and without redshift information. We validated these cluster fields following optical criteria (mainly distance with respect to the Planck pointing, magnitude of the brightest cluster galaxy, and cluster richness), and combined these criteria with the profiles of the Planck Compton y-maps. This combined procedure allows for a more robust identification of optical counterparts compared to simply cross-matching with existing SDSS cluster catalogues that have been constructed from earlier SDSS data releases. Results. The sample contains new redshifts for 37 Planck galaxy clusters that were not included in the original release of PSZ2 Planck catalogue. We detect three cases as possible multiple counterparts. We show that a combination of all available information (optical images and profile of SZ signal) can provide correct associations between the observed Planck SZ source and the optically identified cluster. We also show that Planck SZ detection is very sensitive even to high-z (z > 0.5) clusters. In addition, we also present updated spectroscopic information for 34 Planck PSZ1 sources (33 previously photometrically confirmed and 1 new identification).

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