Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Outlier Detection in Heavy-Ion Collisions   We present different methods of unsupervised learning which can be used for
outlier detection in high energy nuclear collisions. The UrQMD model is used to
generate the bulk background of events as well as different variants of outlier
events which may result from misidentified centrality or detector malfunctions.
The methods presented here can be generalized to different and novel physics
effects. To detect the outliers, dimensional reduction algorithms are
implemented, specifically the Principle Component Analysis (PCA) and
Autoencoders (AEN). We find that mainly the reconstruction error is a good
measure to distinguish outliers from background. The performance of the
algorithms is compared using a ROC curve. It is shown that the number of
reduced (encoded) dimensions to describe a single event contributes
significantly to the performance of the outlier detection task. We find that
the model which is best suited to separate outlier events requires a good
performance in reconstructing events and at the same time a small number of
parameters.

33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex