scholarly journals A reassessment of strong line metallicity conversions in the machine learning era

2021 ◽  
Vol 503 (1) ◽  
pp. 1082-1095
Author(s):  
Hossen Teimoorinia ◽  
Mansoureh Jalilkhany ◽  
Jillian M Scudder ◽  
Jaclyn Jensen ◽  
Sara L Ellison
Keyword(s):  
Machine Learning ◽  
Sloan Digital Sky Survey ◽  
Strong Line ◽  
H Ii Regions ◽  
Polynomial Coefficients ◽  
Sky Survey ◽  
Wide Range ◽  
Machine Learning Approach ◽  
Data Release ◽  
Linear Behaviour

ABSTRACT Strong line metallicity calibrations are widely used to determine the gas phase metallicities of individual H ii regions and entire galaxies. Over a decade ago, based on the Sloan Digital Sky Survey Data Release 4, Kewley & Ellison published the coefficients of third-order polynomials that can be used to convert between different strong line metallicity calibrations for global galaxy spectra. Here, we update the work of Kewley & Ellison in three ways. First, by using a newer data release, we approximately double the number of galaxies used in polynomial fits, providing statistically improved polynomial coefficients. Second, we include in the calibration suite five additional metallicity diagnostics that have been proposed in the last decade and were not included by Kewley & Ellison. Finally, we develop a new machine learning approach for converting between metallicity calibrations. The random forest (RF) algorithm is non-parametric and therefore more flexible than polynomial conversions, due to its ability to capture non-linear behaviour in the data. The RF method yields the same accuracy as the (updated) polynomial conversions, but has the significant advantage that a single model can be applied over a wide range of metallicities, without the need to distinguish upper and lower branches in R23 calibrations. The trained RF is made publicly available for use in the community.

scholarly journals Prediction of galaxy halo masses in SDSS DR7 via a machine learning approach

2019 ◽  
Vol 490 (2) ◽  
pp. 2367-2379 ◽  
Author(s):  
Victor F Calderon ◽  
Andreas A Berlind
Keyword(s):  
Machine Learning ◽  
Dark Matter ◽  
Sloan Digital Sky Survey ◽  
Training Data ◽  
Dark Matter Halo ◽  
Joint Distributions ◽  
Sky Survey ◽  
Machine Learning Approach ◽  
Improved Performance ◽  
Halo Masses

ABSTRACT We present a machine learning (ML) approach for the prediction of galaxies’ dark matter halo masses which achieves an improved performance over conventional methods. We train three ML algorithms (XGBoost, random forests, and neural network) to predict halo masses using a set of synthetic galaxy catalogues that are built by populating dark matter haloes in N-body simulations with galaxies and that match both the clustering and the joint distributions of properties of galaxies in the Sloan Digital Sky Survey (SDSS). We explore the correlation of different galaxy- and group-related properties with halo mass, and extract the set of nine features that contribute the most to the prediction of halo mass. We find that mass predictions from the ML algorithms are more accurate than those from halo abundance matching (HAM) or dynamical mass estimates (DYN). Since the danger of this approach is that our training data might not accurately represent the real Universe, we explore the effect of testing the model on synthetic catalogues built with different assumptions than the ones used in the training phase. We test a variety of models with different ways of populating dark matter haloes, such as adding velocity bias for satellite galaxies. We determine that, though training and testing on different data can lead to systematic errors in predicted masses, the ML approach still yields substantially better masses than either HAM or DYN. Finally, we apply the trained model to a galaxy and group catalogue from the SDSS DR7 and present the resulting halo masses.

scholarly journals Assessing the Relation between Mud Components and Rheology for Loss Circulation Prevention Using Polymeric Gels: A Machine Learning Approach

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1377
Author(s):  
Musaab I. Magzoub ◽  
Raj Kiran ◽  
Saeed Salehi ◽  
Ibnelwaleed A. Hussein ◽  
Mustafa S. Nasser
Keyword(s):  
Machine Learning ◽  
Rheological Properties ◽  
Nearest Neighbor ◽  
Drilling Fluid ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Wide Range ◽  
Machine Learning Approach ◽  
Drilling Operations

The traditional way to mitigate loss circulation in drilling operations is to use preventative and curative materials. However, it is difficult to quantify the amount of materials from every possible combination to produce customized rheological properties. In this study, machine learning (ML) is used to develop a framework to identify material composition for loss circulation applications based on the desired rheological characteristics. The relation between the rheological properties and the mud components for polyacrylamide/polyethyleneimine (PAM/PEI)-based mud is assessed experimentally. Four different ML algorithms were implemented to model the rheological data for various mud components at different concentrations and testing conditions. These four algorithms include (a) k-Nearest Neighbor, (b) Random Forest, (c) Gradient Boosting, and (d) AdaBoosting. The Gradient Boosting model showed the highest accuracy (91 and 74% for plastic and apparent viscosity, respectively), which can be further used for hydraulic calculations. Overall, the experimental study presented in this paper, together with the proposed ML-based framework, adds valuable information to the design of PAM/PEI-based mud. The ML models allowed a wide range of rheology assessments for various drilling fluid formulations with a mean accuracy of up to 91%. The case study has shown that with the appropriate combination of materials, reasonable rheological properties could be achieved to prevent loss circulation by managing the equivalent circulating density (ECD).

scholarly journals A New Catalog of Isolated Galaxies

2009 ◽  
Vol 5 (S267) ◽  
pp. 464-464
Author(s):  
J. A. Vázquez-Mata ◽  
H. M. Hernández-Toledo ◽  
Changbom Park ◽  
Yun-Young Choi
Keyword(s):  
Galaxy Formation ◽  
Environmental Effects ◽  
Comparative Studies ◽  
Sloan Digital Sky Survey ◽  
Selection Algorithm ◽  
Isolated Galaxies ◽  
Galaxy Formation And Evolution ◽  
Sky Survey ◽  
Data Release ◽  
Formation And Evolution

We present a new catalog of isolated galaxies (coined as UNAM–KIAS) obtained through an automated systematic search. The 1520 isolated galaxies were found in ~ 1.4 steradians of the sky in the Sloan Digital Sky Survey Data Release 5 (SDSS DR5) photometry. The selection algorithm was implemented from a variation of the criteria developed by Karachentseva (1973), with full redshift information. This new catalog is aimed to carry out comparative studies of environmental effects and constraining the currently competing scenarios of galaxy formation and evolution.

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 Cross-correlation of the 2XMMi catalogue with Data Release 7 of the Sloan Digital Sky Survey

2011 ◽  
Vol 527 ◽  
pp. A126 ◽  
Author(s):  
F.-X. Pineau ◽  
C. Motch ◽  
F. Carrera ◽  
R. Della Ceca ◽  
S. Derrière ◽  
...  
Keyword(s):  
Cross Correlation ◽  
Sloan Digital Sky Survey ◽  
Sky Survey ◽  
Data Release

scholarly journals Nitrogen-Enriched Quasars in the Sloan Digital Sky Survey First Data Release

2004 ◽  
Vol 128 (2) ◽  
pp. 561-568 ◽  
Author(s):  
Misty C. Bentz ◽  
Patrick B. Hall ◽  
Patrick S. Osmer
Keyword(s):  
Sloan Digital Sky Survey ◽  
Sky Survey ◽  
Data Release

scholarly journals White Dwarf Stars

2017 ◽  
Vol 45 ◽  
pp. 1760023
Author(s):  
S. O. Kepler ◽  
Alejandra Daniela Romero ◽  
Ingrid Pelisoli ◽  
Gustavo Ourique
Keyword(s):  
White Dwarf ◽  
Final Stage ◽  
White Dwarfs ◽  
Sloan Digital Sky Survey ◽  
Dwarf Stars ◽  
Multiple Systems ◽  
White Dwarf Stars ◽  
Sky Survey ◽  
Data Release ◽  
Low Mass

White dwarf stars are the final stage of most stars, born single or in multiple systems. We discuss the identification, magnetic fields, and mass distribution for white dwarfs detected from spectra obtained by the Sloan Digital Sky Survey up to Data Release 13 in 2016, which lead to the increase in the number of spectroscopically identified white dwarf stars from 5[Formula: see text]000 to 39[Formula: see text]000. This number includes only white dwarf stars with [Formula: see text], i.e., excluding the Extremely Low Mass white dwarfs, which are necessarily the byproduct of stellar interaction.

scholarly journals Influences of Galaxy Interactions on AGN Activity

2013 ◽  
Vol 30 ◽  
Author(s):  
Xin-Fa Deng ◽  
Guisheng Yu ◽  
Peng Jiang
Keyword(s):  
Survey Data ◽  
Strong Evidence ◽  
Sloan Digital Sky Survey ◽  
Limited Sample ◽  
Galaxy Interactions ◽  
Opposite Trend ◽  
Main Galaxy ◽  
Isolated Galaxies ◽  
Sky Survey ◽  
Data Release

AbstractUsing two volume-limited Main galaxy samples of the Sloan Digital Sky Survey Data Release 7 , we explore influences of galaxy interactions on AGN activity. It is found that in the faint volume-limited sample, paired galaxies have a slightly higher AGN fraction than isolated galaxies, whereas in the luminous volume-limited sample, an opposite trend can be observed. The significance is <1σ. Thus, we do not observe strong evidence that interactions or mergers likely trigger the AGN activity.

The Mass Function of Void Galaxies in the Sloan Digital Sky Survey Data Release 2

2005 ◽  
Vol 621 (2) ◽  
pp. 643-650 ◽  
Author(s):  
David M. Goldberg ◽  
Timothy D. Jones ◽  
Fiona Hoyle ◽  
Randall R. Rojas ◽  
Michael S. Vogeley ◽  
...  
Keyword(s):  
Survey Data ◽  
Mass Function ◽  
Sloan Digital Sky Survey ◽  
Sky Survey ◽  
Void Galaxies ◽  
Data Release
Sign in / Sign up

Export Citation Format

Share Document