Improving Photometric Redshifts By Varying Activation Functions In Artificial Neural Networks - Pathi, Imdad Binti Mahmud (2024)
In recent years, the astronomical community has faced a data deluge of up to exabytes due to the advancement of telescope technology. In fact, the application of machine learning techniques has simplified the task of analysing data for astronomers. Photometric redshift estimation or photo-z is one of the relevant applications of machine learning and statistical methods in cosmology. Due to its significant contribution to cosmology, there is an increasing demand for precise photo-z for forthcoming astronomical surveys, including the Euclid mission and LSST. The accuracy and performance of the photo-z algorithm have been improved by adopting and modifying machine learning hyperparameters. The Artificial Neural Network Redshift (annz) algorithm is a fast and simple machine learning photometric redshift estimator. One of the hyperparameters in the artificial neural network (ANN) is the activation function. It acts as a decision-making unit, which introduces non-linearity into the model, leading to better differentiation capabilities and potentially boosting the ANN’s performance if optimally tuned. We test the performance of annz by varying the activation functions, replacing the original logistic sigmoid with tanh, Softplus, SiLU, ReLU, Leaky ReLU and Mish. The training is demonstrated on the Luminous Red Galaxy (LRG) sample of the Sloan Digital Sky Survey (SDSS), Stripe-82 survey, and Physics of the Accelerating Universe Survey (PAUS). We also tested the performances of these activation functions by varying the depth and width of the ANN architectures.