  Astronomical outliers, such as unusual, rare or unknown types of astronomical
objects or phenomena, constantly lead to the discovery of genuinely unforeseen
knowledge in astronomy. More unpredictable outliers will be uncovered in
principle with the increment of the coverage and quality of upcoming survey
data. However, it is a severe challenge to mine rare and unexpected targets
from enormous data with human inspection due to a significant workload.
Supervised learning is also unsuitable for this purpose since designing proper
training sets for unanticipated signals is unworkable. Motivated by these
challenges, we adopt unsupervised machine learning approaches to identify
outliers in the data of galaxy images to explore the paths for detecting
astronomical outliers. For comparison, we construct three methods, which are
built upon the k-nearest neighbors (KNN), Convolutional Auto-Encoder (CAE)+
KNN, and CAE + KNN + Attention Mechanism (attCAE KNN) separately. Testing sets
are created based on the Galaxy Zoo image data published online to evaluate the
performance of the above methods. Results show that attCAE KNN achieves the
best recall (78%), which is 53% higher than the classical KNN method and 22%
higher than CAE+KNN. The efficiency of attCAE KNN (10 minutes) is also superior
to KNN (4 hours) and equal to CAE+KNN(10 minutes) for accomplishing the same
task. Thus, we believe it is feasible to detect astronomical outliers in the
data of galaxy images in an unsupervised manner. Next, we will apply attCAE KNN
to available survey datasets to assess its applicability and reliability.
