Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detecting composite orders in layered models via machine learning   Determining the phase diagram of systems consisting of smaller subsystems
'connected' via a tunable coupling is a challenging task relevant for a variety
of physical settings. A general question is whether new phases, not present in
the uncoupled limit, may arise. We use machine learning to study layered spin
models, in which the spin variables constituting each of the uncoupled systems
(to which we refer as layers) are coupled to each other via an interlayer
coupling. In such systems, in general, composite order parameters involving
spins of different layers may emerge as a consequence of the interlayer
coupling. We focus on the layered Ising and Ashkin-Teller models as a
paradigmatic case study, determining their phase diagram via the application of
a machine learning algorithm to the Monte Carlo data. Remarkably our technique
is able to correctly characterize all the system phases also in the case of
hidden order parameters, i.e., order parameters whose expression in terms of
the microscopic configurations would require additional preprocessing of the
data fed to the algorithm. We correctly retrieve the three known phases of the
Ashkin-Teller model with ferromagnetic couplings, including the phase described
by a composite order parameter. For the bilayer and trilayer Ising models the
phases we find are only the ferromagnetic and the paramagnetic ones. Within the
approach we introduce, owing to the construction of convolutional neural
networks, naturally suitable for layered image-like data with arbitrary number
of layers, no preprocessing of the Monte Carlo data is needed, also with regard
to its spatial structure. The physical meaning of our results is discussed and
compared with analytical data, where available. Yet, the method can be used
without any a priori knowledge of the phases one seeks to find and can be
applied to other models and structures.

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The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
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The categories of this paper are: stat
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
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The categories of this paper are: funct-an