English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Optimal machine-driven acquisition of future cosmological data

Kostić, A., Jasche, J., Ramanah, D. K., & Lavaux, G. (2022). Optimal machine-driven acquisition of future cosmological data. Astronomy and Astrophysics, 657: L17. doi:10.1051/0004-6361/202141706.

Item is

Files

show Files
hide Files
:
Optimal machine-driven acquisition of future cosmological data.pdf (Any fulltext), 4MB
 
File Permalink:
-
Name:
Optimal machine-driven acquisition of future cosmological data.pdf
Description:
-
OA-Status:
Visibility:
Private
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Kostić, Andrija1, Author           
Jasche, Jens, Author
Ramanah, Doogesh Kodi, Author
Lavaux, Guilhem, Author
Affiliations:
1MPI for Astrophysics, Max Planck Society, ou_159875              

Content

show
hide
Free keywords: -
 Abstract: We present a set of maps classifying regions of the sky according to their information gain potential as quantified by Fisher information. These maps can guide the optimal retrieval of relevant physical information with targeted cosmological searches. Specifically, we calculated the response of observed cosmic structures to perturbative changes in the cosmological model and we charted their respective contributions to Fisher information. Our physical forward-modeling machinery transcends the limitations of contemporary analyses based on statistical summaries to yield detailed characterizations of individual 3D structures. We demonstrate this advantage using galaxy counts data and we showcase the potential of our approach by studying the information gain of the Coma cluster. We find that regions in the vicinity of the filaments and cluster core, where mass accretion ensues from gravitational infall, are the most informative with regard to our physical model of structure formation in the Universe. Hence, collecting data in those regions would be most optimal for testing our model predictions. The results presented in this work are the first of their kind to elucidate the inhomogeneous distribution of cosmological information in the Universe. This study paves a new way forward for the performance of efficient targeted searches for the fundamental physics of the Universe, where search strategies are progressively refined with new cosmological data sets within an active learning framework.

Details

show
hide
Language(s): eng - English
 Dates: 2022-01-24
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1051/0004-6361/202141706
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Astronomy and Astrophysics
  Other : Astron. Astrophys.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: France : EDP Sciences S A
Pages: - Volume / Issue: 657 Sequence Number: L17 Start / End Page: - Identifier: ISSN: 1432-0746
CoNE: https://pure.mpg.de/cone/journals/resource/954922828219_1