  Angle-resolved photoemission spectroscopy (ARPES) is a powerful experimental
technique to determine the electronic structure of solids. Advances in light
sources for ARPES experiments are currently leading to a vast increase of data
acquisition rates and data quantity. On the other hand, access time to the most
advanced ARPES instruments remains strictly limited, calling for fast,
effective, and on-the-fly data analysis tools to exploit this time. In response
to this need, we introduce ARPESNet, a versatile autoencoder network that
efficiently summmarises and compresses ARPES datasets. We train ARPESNet on a
large and varied dataset of 2-dimensional ARPES data extracted by cutting
standard 3-dimensional ARPES datasets along random directions in $\mathbf{k}$.
To test the data representation capacity of ARPESNet, we compare $k$-means
clustering quality between data compressed by ARPESNet, data compressed by
discrete cosine transform, and raw data, at different noise levels. ARPESNet
data excels in clustering quality despite its high compression ratio.
