scholarly journals The miniJPAS survey: Photometric redshift catalogue

Author(s):  
A. Hernán-Caballero ◽  
J. Valera ◽  
C. Lopez-Sanjuan ◽  
D. Muniesa ◽  
T. Civera ◽  
...  
Keyword(s):  
2020 ◽  
Vol 15 (S359) ◽  
pp. 40-41
Author(s):  
L. M. Izuti Nakazono ◽  
C. Mendes de Oliveira ◽  
N. S. T. Hirata ◽  
S. Jeram ◽  
A. Gonzalez ◽  
...  

AbstractWe present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.


2016 ◽  
Vol 16 (5) ◽  
pp. 005 ◽  
Author(s):  
Bo Han ◽  
Hong-Peng Ding ◽  
Yan-Xia Zhang ◽  
Yong-Heng Zhao
Keyword(s):  

2016 ◽  
Vol 12 (S325) ◽  
pp. 145-155
Author(s):  
Fionn Murtagh

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.


2016 ◽  
Vol 463 (4) ◽  
pp. 3737-3754 ◽  
Author(s):  
A. Choi ◽  
C. Heymans ◽  
C. Blake ◽  
H. Hildebrandt ◽  
C. A. J. Duncan ◽  
...  

Galaxies ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. 64 ◽  
Author(s):  
Nicholas Paul ◽  
Nicholas Virag ◽  
Lior Shamir
Keyword(s):  

2016 ◽  
Vol 12 (S325) ◽  
pp. 39-45 ◽  
Author(s):  
Maria Süveges ◽  
Sotiria Fotopoulou ◽  
Jean Coupon ◽  
Stéphane Paltani ◽  
Laurent Eyer ◽  
...  

AbstractThroughout the processing and analysis of survey data, a ubiquitous issue nowadays is that we are spoilt for choice when we need to select a methodology for some of its steps. The alternative methods usually fail and excel in different data regions, and have various advantages and drawbacks, so a combination that unites the strengths of all while suppressing the weaknesses is desirable. We propose to use a two-level hierarchy of learners. Its first level consists of training and applying the possible base methods on the first part of a known set. At the second level, we feed the output probability distributions from all base methods to a second learner trained on the remaining known objects. Using classification of variable stars and photometric redshift estimation as examples, we show that the hierarchical combination is capable of achieving general improvement over averaging-type combination methods, correcting systematics present in all base methods, is easy to train and apply, and thus, it is a promising tool in the astronomical “Big Data” era.


2002 ◽  
Vol 330 (4) ◽  
pp. 889-894 ◽  
Author(s):  
A. Fernndez-Soto ◽  
K. M. Lanzetta ◽  
H.-W. Chen ◽  
B. Levine ◽  
N. Yahata

2011 ◽  
Vol 106 (24) ◽  
Author(s):  
Shaun A. Thomas ◽  
Filipe B. Abdalla ◽  
Ofer Lahav

2020 ◽  
Vol 499 (3) ◽  
pp. 3884-3908 ◽  
Author(s):  
A Molino ◽  
M V Costa-Duarte ◽  
L Sampedro ◽  
F R Herpich ◽  
L Sodré ◽  
...  

ABSTRACT In this paper we present a thorough discussion about the photometric redshift (photo-z) performance of the Southern Photometric Local Universe Survey (S-PLUS). This survey combines a seven narrow +5 broad passband filter system, with a typical photometric-depth of r ∼ 21 AB. For this exercise, we utilize the Data Release 1 (DR1), corresponding to 336 deg2 from the Stripe-82 region. We rely on the BPZ2 code to compute our estimates, using a new library of SED models, which includes additional templates for quiescent galaxies. When compared to a spectroscopic redshift control sample of ∼100 k galaxies, we find a precision of σz <0.8 per cent, <2.0 per cent, or <3.0 per cent for galaxies with magnitudes r < 17, <19, and <21, respectively. A precision of 0.6 per cent is attained for galaxies with the highest Odds values. These estimates have a negligible bias and a fraction of catastrophic outliers inferior to 1 per cent. We identify a redshift window (i.e. 0.26 < z < 0.32) where our estimates double their precision, due to the simultaneous detection of two emission lines in two distinct narrow bands; representing a window opportunity to conduct statistical studies such as luminosity functions. We forecast a total of ∼2 M, ∼16 M and ∼32 M galaxies in the S-PLUS survey with a photo-z precision of σz <1.0 per cent, <2.0 per cent, and <2.5 per cent after observing 8000 deg2. We also derive redshift probability density functions, proving their reliability encoding redshift uncertainties and their potential recovering the n(z) of galaxies at z < 0.4, with an unprecedented precision for a photometric survey in the Southern hemisphere.


Sign in / Sign up

Export Citation Format

Share Document