Catalog of quasars from the Kilo-Degree Survey Data Release 3

We present a catalog of quasars selected from broad-band photometric ugri data of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The QSOs are identified by the random forest (RF) supervised machine learning model, trained on SDSS DR14 spectroscopic data. We first cleaned the input KiDS data from entries with excessively noisy, missing or otherwise problematic measurements. Applying a feature importance analysis, we then tune the algorithm and identify in the KiDS multiband catalog the 17 most useful features for the classification, namely magnitudes, colors, magnitude ratios, and the stellarity index. We used the t-SNE algorithm to map the multi-dimensional photometric data onto 2D planes and compare the coverage of the training and inference sets. We limited the inference set to r<22 to avoid extrapolation beyond the feature space covered by training, as the SDSS spectroscopic sample is considerably shallower than KiDS. This gives 3.4 million objects in the final inference sample, from which the random forest identified 190,000 quasar candidates. Accuracy of 97%, purity of 91%, and completeness of 87%, as derived from a test set extracted from SDSS and not used in the training, are confirmed by comparison with external spectroscopic and photometric QSO catalogs overlapping with the KiDS footprint. The robustness of our results is strengthened by number counts of the quasar candidates in the r band, as well as by their mid-infrared colors available from WISE. An analysis of parallaxes and proper motions of our QSO candidates found also in Gaia DR2 suggests that a probability cut of p(QSO)>0.8 is optimal for purity, whereas p(QSO)>0.7 is preferable for better completeness. Our study presents the first comprehensive quasar selection from deep high-quality KiDS data and will serve as the basis for versatile studies of the QSO population detected by this survey.Comment: Data available from the KiDS website at http://kids.strw.leidenuniv.nl/DR3/quasarcatalog.php and the source code from https://github.com/snakoneczny/kids-quasar

From Data to Software to Science with the Rubin Observatory LSST

editorial reviewedThe Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science. To facilitate such opportunities, a community workshop entitled "From Data to Software to Science with the Rubin Observatory LSST" was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science

The emergence of language as a function of brain-hemispheric feedback

This text posits the emergence of language as a function of brain-hemispheric feedback, where “emergence” refers to the generation of complex patterns from relatively simple interactions, “language” refers to an abstraction-based and representational-recombinatorial-recursive mapping-signaling system, “function” refers to an input-output relationship described by fractal algorithms, “brain-hemispheric” refers to complementary (approach-abstraction / avoidance-gestalt) cognitive modules, and “feedback” refers to self-regulation driven by neural inhibition and recruitment. The origin of language marks the dawn of human self-awareness and culture, and is thus a matter of fundamental and cross-disciplinary interest. This text is a synthesized research essay that constructs its argument by drawing diverse scholarly voices into a critical, cross-disciplinary intertextual narrative. While it does not report any original empirical findings, it harnesses those made by others to offer a tentative, partial solution—one that can later be altered and expanded—to a problem that has occupied thinkers for centuries. The research contained within this text is preceded by an introductory Section 1 that contextualizes the problem of the origin of language. Section 2 details the potential of evolutionary theory for addressing the problem, and the reasons for the century-long failure of linguistics to take advantage of that potential. Section 3 reviews the history of the discovery of brain lateralization, as well as its behavioral and structural characteristics. Section 4 discusses evolutionary evidence and mechanisms in terms of increasing adaptive complexity and intelligence, in general, and tool use, in particular. Section 5 combines chaos theory, brain science, and semiotics to propose that, after the neotenic acquisition of contingency-based abstraction, language emerged as a feedback interaction between the left-hemisphere abstract word and the right-hemisphere gestalt image. I conclude that the model proposed here might be a valuable tool for understanding, organizing, and relating data and ideas concerning human evolution, language, culture, and psychology. I recommend, of course, that I present this text to the scholarly community for criticism, and that I continue to gather and collate relevant data and ideas, in order to prepare its next iteration