Figure 1
Schematic of the SMDET neural network architecture used to pre-select Cool Neighbors moving object candidates shown to volunteers via Zooniverse. SMDET analysis starts with pixel data (top left of schematic) for a set of 13 time-series unWISE W1 (3.4 micron) images and a corresponding set of 13 time-series unWISE W2 (4.6 micron) images covering the same 176 arcsecond × 176 arcsecond patch of sky. The neural network processes the pixel data with consecutive groups of 3-dimensional convolutional layers and long short-term memory layers. The output of each group is averaged into skip connections (bottom of schematic) to improve gradient calculation during backpropagation. SMDET outputs an “object mask” (top right of schematic) that models faint, high proper motion sources in the input pixel data, and a “segmentation mask” (middle right of schematic) that classifies which input pixels capture 1% or more of the flux from a faint, high proper motion source. ELU: Exponential Linear Unit, LSTM: Long Short Term Memory.
Figure 2
Cool Neighbors Zooniverse classification interface example (from Humphreys et al. 2022). The two top row telescope images display the visually perceptible motion of a dim brown dwarf (orange dot near the center of each panel). The telescope image at bottom left displays a spurious algorithmically selected brown dwarf candidate caused by an orange donut-shaped observational artifact. Adjacent to this bottom left image is the Zooniverse classification task interface, as is seen by a volunteer performing a classification.
Figure 3
(Left) Cumulative Backyard Worlds classifications since Backyard Worlds: Planet 9 launch in February 2017, including both Backyard Worlds: Planet 9 and Backyard Worlds: Cool Neighbors. Backyard Worlds volunteers have performed more than 10 million total classifications since early 2017, with ~9 million from Backyard Worlds: Planet 9 and ~2 million from Backyard Worlds: Cool Neighbors. We thus have very robust sample sizes for our analyses of classification data from both projects. (Right) Histograms of time per classification separately for Backyard Worlds: Planet 9 (blue histogram) and Backyard Worlds: Cool Neighbors (orange histogram). The legend lists the median and mean of each distribution. Backyard Worlds: Cool Neighbors classifications are typically performed ~3x faster than Backyard Worlds: Planet 9 classifications, both in terms of median time per classification and mean time per classification. The Backyard Worlds: Cool Neighbors distribution peaks at a lower abscissa value (lesser amount of time taken per classification) and has less of a tail toward longer (several minutes or more) classification times compared with Backyard Worlds: Planet 9.
Table 1
Various quantitative comparisons of Backyard Worlds: Planet 9 versus Backyard Worlds: Cool Neighbors.
| PROJECT NAME | LAUNCH DATE | CLASSIFICATIONS (ALL TIME) | CLASSIFICATIONS (FIRST 6 MONTHS) | REGISTERED USERS |
|---|---|---|---|---|
| Backyard Worlds: Planet 9 | 2/15/2017 | 8.8 million | 4.3 million | 79,970 |
| Backyard Worlds: Cool Neighbors | 6/27/2023 | 1.8 million | 1.6 million | 4,087 |
| PROJECT NAME | MEAN CLASSIFICATIONS PER REGISTERED USER (ALL TIME) | MEDIAN CLASSIFICATIONS PER REGISTERED USER (ALL TIME) | MEAN TIME PER CLASSIFICATION(FIRST 6 MONTHS) | MEDIAN TIME PER CLASSIFICATION(FIRST 6 MONTHS) |
| Backyard Worlds: Planet 9 | 87.6 | 15 | 38.3 seconds | 22.0 seconds |
| Backyard Worlds: Cool Neighbors | 423.2 | 31 | 13.7 seconds | 6.0 seconds |
| PROJECT NAME | ROBUST STANDARD DEVIATION OF TIME PER CLASSIFICATION (FIRST 6 MONTHS) | REGISTERED USERS (FIRST 6 MONTHS) | ||
| Backyard Worlds: Planet 9 | 26.5 seconds | 41,298 | ||
| Backyard Worlds: Cool Neighbors | 7 seconds | 3,641 |
Figure 4
Results of our anonymous online survey of Backyard Worlds advanced participants regarding their views about the usage of machine learning to pre-select the Cool Neighbors moving object candidates that subsequently get displayed/classified on Zooniverse. Each of the four panels arranged vertically is one of the four multiple-choice or selection box questions, with the prompt, selection options, and breakdown of results shown. The order of the sub-panels from top to bottom matches the order in which the questions were shown to respondents. The order in which each question’s selection options are shown here is the same as the order in which these options were listed for respondents. The second question (second panel from top) represents our main focus of this poll, with other questions being asked primarily for additional context/metadata. 49.1% of Backyard Worlds advanced participants say that Cool Neighbors’ machine learning pre-selection does not strongly influence their excitement level about participating in the project, 39.6% of respondents say that incorporating machine learning makes them more excited about participating, and only 11.3% of respondents say that Cool Neighbors’ use of machine learning makes them less excited to participate. All survey questions were marked non-optional for respondents and each question received 53 responses. ML: Machine Learning, AI: Artificial Intelligence.