The Reproduction and Evaluation of Star Fields with the Milky Way in a Planetarium

In this study, we investigated the physical factors required to accurately reproduce the Milky Way in star fields in a planetarium using three evaluation indices: faithfulness, preference, and depth feeling. Psychometric experiments were conducted by manipulating three different physical factors (transmittance, representation size and star density) of the stars projected on a dome screen as experimental stimuli. The three evaluation indices were rated by observers for 12 different reproductions of the Milky Way. By analyzing the experimental results, we developed a common model to estimate the scores for each evaluation index by changing the coefficients of the three physical factors. Our proposed model has good accuracy, and each evaluation index can be represented by transmittance, representation size and star density. The weighting values indicate that density reproduction was the pivotal factor for the majority of observers. In contrast, the observers were not affected by the size of the stars in the projected Milky Way.

Deriving star cluster parameters with convolutional neural networks

Context. Convolutional neural networks (CNNs) have been proven to perform fast classification and detection on natural images and have the potential to infer astrophysical parameters on the exponentially increasing amount of sky-survey imaging data. The inference pipeline can be trained either from real human-annotated data or simulated mock observations. Until now, star cluster analysis was based on integral or individual resolved stellar photometry. This limits the amount of information that can be extracted from cluster images. Aims. We aim to develop a CNN-based algorithm capable of simultaneously deriving ages, masses, and sizes of star clusters directly from multi-band images. We also aim to demonstrate CNN capabilities on low-mass semi-resolved star clusters in a low-signal-to-noise-ratio regime. Methods. A CNN was constructed based on the deep residual network (ResNet) architecture and trained on simulated images of star clusters with various ages, masses, and sizes. To provide realistic backgrounds, M 31 star fields taken from The Panchromatic Hubble Andromeda Treasury (PHAT) survey were added to the mock cluster images. Results. The proposed CNN was verified on mock images of artificial clusters and has demonstrated high precision and no significant bias for clusters of ages ≲3 Gyr and masses between 250 and 4000 M⊙. The pipeline is end-to-end, starting from input images all the way to the inferred parameters; no hand-coded steps have to be performed: estimates of parameters are provided by the neural network in one inferential step from raw images.

Digital Archive of the Astrograph Plates Stored at the Inasan Zvenigorod Observatory

The plate collection of the Zvenigorod 40-cm Carl Zeiss astrograph, obtained in 1972-2003, contains direct photographs of star fields, comets, asteroids, Pluto, and Mars. The electronic library of images from photographic plates was created from scanning the astronomical negatives. We present information on programs scheduled at the telescope and the structure and maintenance of the plate stacks. We also list the plates with images of asteroids and comets. Access to all our plate lists is provided at the web sites of the Institute of Astronomy (INASAN) and WFPDB. It is possible to select plates by the date of observation, by the coordinates of the sky area, by the object type. Preview images can be inspected.