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  1. Abstract We report the discovery of MAGAZ3NE J095924+022537, a spectroscopically confirmed protocluster at z = 3.3665 − 0.0012 + 0.0009 around a spectroscopically confirmed UVJ -quiescent ultramassive galaxy (UMG; M ⋆ = 2.34 − 0.34 + 0.23 × 10 11 M ⊙ ) in the COSMOS UltraVISTA field. We present a total of 38 protocluster members (14 spectroscopic and 24 photometric), including the UMG. Notably, and in marked contrast to protoclusters previously reported at this epoch that have been found to contain predominantly star-forming members, we measure an elevated fraction of quiescent galaxies relative to the coeval field ( 73.3more »− 16.9 + 26.7 % versus 11.6 − 4.9 + 7.1 % for galaxies with stellar mass M ⋆ ≥ 10 11 M ⊙ ). This high quenched fraction provides a striking and important counterexample to the seeming ubiquitousness of star-forming galaxies in protoclusters at z > 2 and suggests, rather, that protoclusters exist in a diversity of evolutionary states in the early universe. We discuss the possibility that we might be observing either “early mass quenching” or nonclassical “environmental quenching.” We also present the discovery of MAGAZ3NE J100028+023349, a second spectroscopically confirmed protocluster, at a very similar redshift of z = 3.3801 − 0.0281 + 0.0213 . We present a total of 20 protocluster members, 12 of which are photometric and eight spectroscopic including a poststarburst UMG ( M ⋆ = 2.95 − 0.20 + 0.21 × 10 11 M ⊙ ). Protoclusters MAGAZ3NE J0959 and MAGAZ3NE J1000 are separated by 18′ on the sky (35 comoving Mpc), in good agreement with predictions from simulations for the size of “Coma”-type cluster progenitors at this epoch. It is highly likely that the two UMGs are the progenitors of Brightest Cluster Galaxies seen in massive virialized clusters at lower redshift.« less
    Free, publicly-accessible full text available February 1, 2023
  2. This study evaluates the level of service of shared transportation facilities through mining geotagged data from social media and analyzing the perceptions of road users. An algorithm is developed adopting a text classification approach with contextual understanding to filter out relevant information related to users’ perceptions toward active mobility. Using a heuristic-based keyword matching approach produces about 75% tweets that are out of context, so that approach is deemed unsuitable for information extraction from Twitter. This study implements six different text classification models and compares the performance of these models for tweet classification. The model is applied to real-world datamore »to filter out relevant information, and content analysis is performed to check the distribution of keywords within the filtered data. The text classification model “term frequency-inverse document frequency” vectorizer-based logistic regression model performed best at classifying the tweets. To select the best model, the performances of the models are compared based on precision, recall, F1 score (geometric mean of precision and recall), and accuracy metrics. The findings from the analysis show that the proposed method can help produce more relevant information on walking and biking facilities as well as safety concerns. By analyzing the sentiments of the filtered data, the existing condition of biking and walking facilities in the DC area can be inferred. This method can be a critical part of the decision support system to understand the qualitative level of service of existing transportation facilities.« less