I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
A deep learning approach to infer galaxy cluster masses fro..:
de Andres, Daniel
;
Cui, Weiguang
;
Ruppin, Florian
...
Nature Astronomy. 6 (2022) 11 - p. 1325-1331 , 2022
Link:
https://doi.org/10.1038/s41550-022-01784-y
RT Journal T1
A deep learning approach to infer galaxy cluster masses from Planck Compton-y parameter maps
UL https://suche.suub.uni-bremen.de/peid=cr-10.1038_s41550-022-01784-y&Exemplar=1&LAN=DE A1 de Andres, Daniel A1 Cui, Weiguang A1 Ruppin, Florian A1 De Petris, Marco A1 Yepes, Gustavo A1 Gianfagna, Giulia A1 Lahouli, Ichraf A1 Aversano, Gianmarco A1 Dupuis, Romain A1 Jarraya, Mahmoud A1 Vega-Ferrero, Jesús PB Springer Science and Business Media LLC YR 2022 SN 2397-3366 JF Nature Astronomy VO 6 IS 11 SP 1325 OP 1331 LK http://dx.doi.org/https://doi.org/10.1038/s41550-022-01784-y DO https://doi.org/10.1038/s41550-022-01784-y SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)