scholarly journals Can Machine Learning Provide Understanding? How Cosmologists Use Machine Learning to Understand Observations of the Universe

Erkenntnis ◽  
2021 ◽  
Author(s):  
Helen Meskhidze

AbstractThe increasing precision of observations of the large-scale structure of the universe has created a problem for simulators: running the simulations necessary to interpret these observations has become impractical. Simulators have thus turned to machine learning (ML) algorithms instead. Though ML decreases computational expense, one might be worried about the use of ML for scientific investigations: How can algorithms that have repeatedly been described as black-boxes deliver scientific understanding? In this paper, I investigate how cosmologists employ ML, arguing that in this context, ML algorithms should not be considered black-boxes and can deliver genuine scientific understanding. Accordingly, understanding the methodological role of ML algorithms is crucial to understanding the types of questions they are capable of, and ought to be responsible for, answering.

1992 ◽  
Vol 01 (02) ◽  
pp. 303-333 ◽  
Author(s):  
MAREK DEMIAŃSKI ◽  
ANDREJ G. DOROSHKEVICH

We review different theories on the formation of the large scale structure of the Universe. Special emphasis is put on the theory of inertial instability. We show that, for a large class of initial spectra, the resulting two point correlation functions are similar. We also discuss the adhesion theory which uses the Burgers equation, Navier-Stokes equation or coagulation process. We review the Zeldovich theory of gravitational instability and discuss the internal structure of pancakes. Finally, we discuss the role of the velocity potential in determining the global characteristics of large scale structure (distribution of caustics, scale of voids, etc.). In the last section, we list the main unsolved problems and the main successes of the theory of formation of large scale structure.


2011 ◽  
Vol 54 (10) ◽  
pp. 983-1005 ◽  
Author(s):  
Vladimir N Lukash ◽  
Elena V Mikheeva ◽  
A M Malinovsky

Physics Today ◽  
1981 ◽  
Vol 34 (8) ◽  
pp. 62-63 ◽  
Author(s):  
P. J. E. Peebles ◽  
Simon D. M. White

1978 ◽  
Vol 79 ◽  
pp. 409-421 ◽  
Author(s):  
Ya B. Zeldovich

The God-father of psychoanalysis Professor Sigmund Freud taught us that the behaviour of adults depends on their early childhood experiences. in the same spirit, the problem of cosmological analysis is to derive the observed present day situation and structure of the Universe from certain plausible assumptions about its early behaviour. Perhaps the most important single statement about the large scale structure is that there is no structure at all on the largest scale − 1000 Mpc and more. On this scale the Universe is rather uniform, structureless and isotropically expanding - just according to the simplified pictures of Einstein-Friedmann……. Humason, Hubble…. Robertson, Walker. On the other hand there is a lot of structure on the scale of 100 or 50 Mpc and less. There are clusters and superclusters of galaxies.


1987 ◽  
Vol 124 ◽  
pp. 335-348
Author(s):  
Neta A. Bahcall

The evidence for the existence of very large scale structures, ∼ 100h−1Mpc in size, as derived from the spatial distribution of clusters of galaxies is summarized. Detection of a ∼ 2000 kms−1 elongation in the redshift direction in the distribution of the clusters is also described. Possible causes of the effect are peculiar velocities of clusters on scales of 10–100h−1Mpc and geometrical elongation of superclusters. If the effect is entirely due to the peculiar velocities of clusters, then superclusters have masses of order 1016.5M⊙ and may contain a larger amount of dark matter than previously anticipated.


1990 ◽  
Vol 43 (2) ◽  
pp. 159
Author(s):  
E Saar

Implications of the observed large scale structure on the physics of the early universe are described. A short review of Soviet work on the subject is given, and the present status of the fractal model of the large scale structure is discussed.


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