Deriving physical parameters of unresolved star clusters. VII. Adaptive aperture photometry of the M31 PHAT star clusters

This work is the seventh study in a series dedicated to investigating degeneracies of simultaneous age, mass, extinction, and metallicity determinations of partially resolved or unresolved star clusters with Hubble Space Telescope broadband aperture photometry. In the sixth work (hereafter, Paper I), it was demonstrated that the adaptive aperture photometry, performed to avoid the majority of the projected foreground and background stars falling within the apertures, gives more consistent colour indices for star clusters. In this study, we aim to supplement the homogeneous multi-colour aperture photometry results published in Paper~I and provide a complete M31 Panchromatic Hubble Andromeda Treasury (PHAT) survey star cluster photometry catalogue for further analysis. Following Paper I, we used a two-aperture approach for photometry. The first aperture is the standard one used to measure total cluster fluxes. The second (smaller) aperture is introduced to avoid the bright foreground and background stars projecting onto the clusters. We selected the radii of smaller apertures to be larger than the half-light radii of the clusters. We present the second part of the star cluster aperture photometry catalogues for a sample of 1477 star clusters from the M31 PHAT survey not covered in Paper I. Compared to the M31 PHAT star cluster aperture photometry catalogue published by Johnson et al., adjustments were made to the cluster centre coordinates, aperture sizes, and sky background levels.Comment: 8 pages, 8 figures, accepted in A&

Deep learning as an alternative to global optimization in diffusion model for conflict tasks

To apply mathematical models of decision making in psychological research, researchers need ways to extract model parameters from behavioural studies. The expansion of the drift diffusion model to con ict tasks (DMC) (Ulrich, Schroter, Leuthold, & Birngruber, 2015) resulted in the model being non-differentiable, which means that the parameters of DMC can only be estimated. The current methods for recovering parameters from DMC rely on comparing reaction time (RT) distributions. Such methods will struggle to recover all DMC parameters well due to the of the solution space of DMC, which means that some parameters can be confused with others when RT distributions are compared. Following that, five global optimization algorithms from different optimization families were compared to create a benchmark for parameter recovery from DMC. The results revealed that differential evolution outperformed the other four optimization algorithms in recovery of parameters from both distributions with high and low trial numbers. Even though differential evolution is capable of recovering parameters well, it is very expensive in computational time, which means that researchers who do not have access to vast computational resources cannot apply DMC in their research. Due to this, deep learning was investigated in application of parameter recovery from DMC. The results showed that deep learning recovered all model parameters exceptionally well from RT distributions with large trial numbers, and as well as differential evolution from RT distributions with low trial numbers, which allows application of deep learning models in deployment pipelines that take seconds rather than months. Finally, deep learning models were applied in several experimental studies investigating the effects of speed-accuracy trade-off (SAT) in response inhibition and perceptual decision making tasks, and how the performance relates between the tasks and over two different testing sessions, and demonstrated the effects of SAT on DMC parameters in different tasks