Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Nuclear liquid-gas phase transition with machine learning   The machine-learning techniques have shown their capability for studying
phase transitions in condensed matter physics. Here, we employ the
machine-learning techniques to study the nuclear liquid-gas phase transition.
We adopt an unsupervised learning and classify the liquid and gas phases of
nuclei directly from the final state raw experimental data of heavy-ion
reactions. Based on a confusion scheme which combines the supervised and
unsupervised learning, we obtain the limiting temperature of the nuclear
liquid-gas phase transition. Its value $9.24\pm0.04~\rm MeV$ is consistent with
that obtained by the traditional caloric curve method. Our study explores the
paradigm of combining the machine-learning techniques with heavy-ion
experimental data, and it is also instructive for studying the phase transition
of other uncontrollable systems, like QCD matter.

1
The categories of this paper are: cs
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci