English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Using X-ray morphological parameters to strengthen galaxy cluster mass estimates via machine learning

Green, S. B., Ntampaka, M., Nagai, D., Lovisari, L., Dolag, K., Eckert, D., et al. (2019). Using X-ray morphological parameters to strengthen galaxy cluster mass estimates via machine learning. The Astrophysical Journal, 884(1): 33. doi:10.3847/1538-4357/ab426f.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0005-8605-E Version Permalink: http://hdl.handle.net/21.11116/0000-0005-8606-D
Genre: Journal Article

Files

show Files
hide Files
:
Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning.pdf (Any fulltext), 2MB
 
File Permalink:
-
Name:
Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning.pdf
Description:
-
Visibility:
Private
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Green, Sheridan B., Author
Ntampaka, Michelle, Author
Nagai, Daisuke, Author
Lovisari, Lorenzo, Author
Dolag, Klaus, Author
Eckert, Dominique1, Author              
ZuHone, John A., Author
Affiliations:
1High Energy Astrophysics, MPI for Extraterrestrial Physics, Max Planck Society, ou_159890              

Content

show
hide
Free keywords: -
 Abstract: We present a machine-learning approach for estimating galaxy cluster masses, trained using both Chandra and eROSITA mock X-ray observations of 2041 clusters from the Magneticum simulations. We train a random forest (RF) regressor, an ensemble learning method based on decision tree regression, to predict cluster masses using an input feature set. The feature set uses core-excised X-ray luminosity and a variety of morphological parameters, including surface brightness concentration, smoothness, asymmetry, power ratios, and ellipticity. The regressor is cross-validated and calibrated on a training sample of 1615 clusters (80% of sample), and then results are reported as applied to a test sample of 426 clusters (20% of sample). This procedure is performed for two different mock observation series in an effort to bracket the potential enhancement in mass predictions that can be made possible by including dynamical state information. The first series is computed from idealized Chandra-like mock cluster observations, with high spatial resolution, long exposure time (1 Ms), and the absence of background. The second series is computed from realistic-condition eROSITA mocks with lower spatial resolution, short exposures (2 ks), instrument effects, and background photons modeled. We report a 20% reduction in the mass estimation scatter when either series is used in our RF model compared to a standard regression model that only employs core-excised luminosity. The morphological parameters that hold the highest feature importance are smoothness, asymmetry, and surface brightness concentration. Hence these parameters, which encode the dynamical state of the cluster, can be used to make more accurate predictions of cluster masses in upcoming surveys, offering a crucial step forward for cosmological analyses.

Details

show
hide
Language(s):
 Dates: 2019-10-11
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.3847/1538-4357/ab426f
Other: LOCALID: 3188323
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: The Astrophysical Journal
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Bristol; Vienna : IOP Publishing; IAEA
Pages: - Volume / Issue: 884 (1) Sequence Number: 33 Start / End Page: - Identifier: ISSN: 0004-637X
CoNE: https://pure.mpg.de/cone/journals/resource/954922828215_3