scholarly journals Photometric redshifts for the Pan-STARRS1 survey

2020 ◽  
Vol 642 ◽  
pp. A102 ◽  
Author(s):  
P. Tarrío ◽  
S. Zarattini
Keyword(s):  
Standard Deviation ◽  
Photometric Data ◽  
Local Linear Regression ◽  
Training Set ◽  
Colour Space ◽  
Photometric Redshifts ◽  
Additional Advantage ◽  
Robust Approach ◽  
Photometric Redshift ◽  
Data Release

We present a robust approach to estimating the redshift of galaxies using Pan-STARRS1 photometric data. Our approach is an application of the algorithm proposed for the SDSS Data Release 12. It uses a training set of 2 313 724 galaxies for which the spectroscopic redshift is obtained from SDSS, and magnitudes and colours are obtained from the Pan-STARRS1 Data Release 2 survey. The photometric redshift of a galaxy is then estimated by means of a local linear regression in a 5D magnitude and colour space. Our approach achieves an average bias of Δ̅z̅n̅o̅r̅m̅ = −1.92 × 10−4, a standard deviation of σ(Δznorm) = 0.0299, and an outlier rate of Po = 4.30% when cross-validating the training set. Even though the relation between each of the Pan-STARRS1 colours and the spectroscopic redshifts is noisier than for SDSS colours, the results obtained by our approach are very close to those yielded by SDSS data. The proposed approach has the additional advantage of allowing the estimation of photometric redshifts on a larger portion of the sky (∼3/4 vs ∼1/3). The training set and the code implementing this approach are publicly available at the project website.

scholarly journals ELM-KNN for photometric redshift estimation of quasars

2016 ◽  
Vol 12 (S325) ◽  
pp. 225-228
Author(s):  
Yanxia Zhang ◽  
Yang Tu ◽  
Yongheng Zhao ◽  
Haijun Tian
Keyword(s):  
Extreme Learning Machine ◽  
Nearest Neighbor ◽  
Good Choice ◽  
Photometric Data ◽  
Complex Data ◽  
Photometric Redshifts ◽  
Photometric Redshift ◽  
Three Samples ◽  
The Ensemble Method ◽  
Learning Machine

AbstractWe explore photometric redshift estimation of quasars with the SDSS DR12 quasar sample. Firstly the quasar sample is separated into three parts according to different redshift ranges. Then three classifiers based on Extreme Learning Machine (ELM) are created in the three redshift ranges. Finally k-Nearest Neighbor (kNN) approach is applied on the three samples to predict photometric redshifts of quasars with multiwavelength photometric data. We compare the performance with different input patterns by ELM-KNN with that only by kNN. The experimental results show that ELM-KNN is feasible and superior to kNN (e.g. rms is 0.0751 vs. 0.2626 for SDSS sample), in other words, the ensemble method has the potential to increase regressor performance beyond the level reached by an individual regressor alone and will be a good choice when facing much more complex data.

scholarly journals Improved photometric redshifts with colour-constrained galaxy templates for future wide-area surveys

2020 ◽  
Vol 497 (2) ◽  
pp. 1935-1945
Author(s):  
Bomee Lee ◽  
Ranga-Ram Chary
Keyword(s):  
Galaxy Evolution ◽  
Training Sample ◽  
Spectral Energy ◽  
Colour Space ◽  
Photometric Redshifts ◽  
Mid Infrared ◽  
Photometric Redshift ◽  
Template Library ◽  
Galaxy Sample ◽  
Best Fitting

ABSTRACT Cosmology and galaxy evolution studies with LSST, Euclid, and Roman, will require accurate redshifts for the detected galaxies. In this study, we present improved photometric redshift estimates for galaxies using a template library that populates three-colour space and is constrained by HST/CANDELS photometry. For the training sample, we use a sample of galaxies having photometric redshifts that allows us to train on a large, unbiased galaxy sample having deep, unconfused photometry at optical-to-mid infrared wavelengths. Galaxies in the training sample are assigned to cubes in 3D colour space, V − H, I − J, and z − H. We then derive the best-fitting spectral energy distributions of the training sample at the fixed CANDELS median photometric redshifts to construct the new template library for each individual colour cube (i.e. colour-cube-based template library). We derive photometric redshifts (photo-z) of our target galaxies using our new colour-cube-based template library and with photometry in only a limited set of bands, as expected for the aforementioned surveys. As a result, our method yields σNMAD of 0.026 and an outlier fraction of 6 per cent using only photometry in the LSST and Euclid/Roman bands. This is an improvement of ∼10 per cent on σNMAD and a reduction in outlier fraction of ∼13 per cent compared to other techniques. In particular, we improve the photo-z precision by about 30 per cent at 2 < z < 3. We also assess photo-z improvements by including K or mid-infrared bands to the ugrizYJH photometry. Our colour-cube-based template library is a powerful tool to constrain photometric redshifts for future large surveys.

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 SuperCLASS – I. The super cluster assisted shear survey: Project overview and data release 1

2020 ◽  
Vol 495 (2) ◽  
pp. 1706-1723 ◽  
Author(s):  
Richard A Battye ◽  
Michael L Brown ◽  
Caitlin M Casey ◽  
Ian Harrison ◽  
Neal J Jackson ◽  
...  
Keyword(s):  
Cross Correlation ◽  
Near Infrared ◽  
Correlation Technique ◽  
Large Array ◽  
Photometric Redshifts ◽  
Very Large Array ◽  
Infrared Observations ◽  
Data Release ◽  
L Band ◽  
Cross Correlation Technique

ABSTRACT The SuperCLuster Assisted Shear Survey (SuperCLASS) is a legacy programme using the e-MERLIN interferometric array. The aim is to observe the sky at L-band (1.4 GHz) to a r.m.s. of $7\, \mu {\rm Jy}\,$beam−1 over an area of $\sim 1\, {\rm deg}^2$ centred on the Abell 981 supercluster. The main scientific objectives of the project are: (i) to detect the effects of weak lensing in the radio in preparation for similar measurements with the Square Kilometre Array (SKA); (ii) an extinction free census of star formation and AGN activity out to z ∼ 1. In this paper we give an overview of the project including the science goals and multiwavelength coverage before presenting the first data release. We have analysed around 400 h of e-MERLIN data allowing us to create a Data Release 1 (DR1) mosaic of $\sim 0.26\, {\rm deg}^2$ to the full depth. These observations have been supplemented with complementary radio observations from the Karl G. Jansky Very Large Array (VLA) and optical/near infrared observations taken with the Subaru, Canada-France-Hawaii, and Spitzer Telescopes. The main data product is a catalogue of 887 sources detected by the VLA, of which 395 are detected by e-MERLIN and 197 of these are resolved. We have investigated the size, flux, and spectral index properties of these sources finding them compatible with previous studies. Preliminary photometric redshifts, and an assessment of galaxy shapes measured in the radio data, combined with a radio-optical cross-correlation technique probing cosmic shear in a supercluster environment, are presented in companion papers.

Gaia Photometric Catalogue: the calibration of the DR2 photometry

2018 ◽  
Vol 14 (A30) ◽  
pp. 466-470
Author(s):  
D. W. Evans ◽  
M. Riello ◽  
F. De Angeli ◽  
J. M. Carrasco ◽  
P. Montegriffo ◽  
...  
Keyword(s):  
Scientific Community ◽  
Photometric Data ◽  
Photometric System ◽  
Gaia Dr1 ◽  
System A ◽  
Data Release ◽  
Definition Of ◽  
High Level ◽  
Crucial Part ◽  
Gaia Dr2

AbstractGaia DR2 was released in April 2018 and contains a photometric catalogue of more than 1 billion sources. This release contains colour information in the form of integrated BP and RP photometry in addition to the latest G-band photometry. The level of uncertainty can be as good as 2 mmag with some residual systematics at the 10 mmag level. The addition of colour information greatly enhances the value of the photometric data for the scientific community. A high level overview of the photometric processing, with a focus on the improvements with respect to Gaia DR1, was given. The definition of the Gaia photometric system, a crucial part of the calibration of the photometry, was also explained. Finally, some of the photometric improvements expected for the next data release were described.

scholarly journals Timing the earliest quenching events with a robust sample of massive quiescent galaxies at 2 < z < 5

2020 ◽  
Vol 496 (1) ◽  
pp. 695-707 ◽  
Author(s):  
A C Carnall ◽  
S Walker ◽  
R J McLure ◽  
J S Dunlop ◽  
D J McLeod ◽  
...  
Keyword(s):  
Energy Distribution ◽  
Selection Criteria ◽  
Spectral Energy Distribution ◽  
Photometric Data ◽  
Spectral Energy ◽  
Distribution Fitting ◽  
Star Forming ◽  
Photometric Redshift ◽  
The Times ◽  
Lower Redshift

ABSTRACT We present a sample of 151 massive (M* &gt; 1010 M⊙) quiescent galaxies at 2 &lt; z &lt; 5, based on a sophisticated Bayesian spectral energy distribution fitting analysis of the CANDELS UDS and GOODS-South fields. Our sample includes a robust sub-sample of 61 objects for which we confidently exclude low-redshift and star-forming solutions. We identify 10 robust objects at z &gt; 3, of which 2 are at z &gt; 4. We report formation redshifts, demonstrating that the oldest objects formed at z &gt; 6; however, individual ages from our photometric data have significant uncertainties, typically ∼0.5 Gyr. We demonstrate that the UVJ colours of the quiescent population evolve with redshift at z &gt; 3, becoming bluer and more similar to post-starburst galaxies at lower redshift. Based upon this, we construct a model for the time evolution of quiescent galaxy UVJ colours, concluding that the oldest objects are consistent with forming the bulk of their stellar mass at z ∼ 6–7 and quenching at z ∼ 5. We report spectroscopic redshifts for two of our objects at z = 3.440 and 3.396, which exhibit extremely weak Ly α emission in ultra-deep VANDELS spectra. We calculate star formation rates based on these line fluxes, finding that these galaxies are consistent with our quiescent selection criteria, provided their Ly α escape fractions are &gt;3 and &gt;10 per cent, respectively. We finally report that our highest redshift robust object exhibits a continuum break at λ ∼ 7000 Å in a spectrum from VUDS, consistent with our photometric redshift of $z_\mathrm{phot}=4.72^{+0.06}_{-0.04}$. If confirmed as quiescent, this object would be the highest redshift known quiescent galaxy. To obtain stronger constraints on the times of the earliest quenching events, high-SNR spectroscopy must be extended to z ≳ 3 quiescent objects.

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.

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