skip to main content


Title: The Milky Way Project second data release: bubbles and bow shocks
ABSTRACT Citizen science has helped astronomers comb through large data sets to identify patterns and objects that are not easily found through automated processes. The Milky Way Project (MWP), a citizen science initiative on the Zooniverse platform, presents internet users with infrared (IR) images from Spitzer Space Telescope Galactic plane surveys. MWP volunteers make classification drawings on the images to identify targeted classes of astronomical objects. We present the MWP second data release (DR2) and an updated data reduction pipeline written in python. We aggregate ∼3 million classifications made by MWP volunteers during the years 2012–2017 to produce the DR2 catalogue, which contains 2600 IR bubbles and 599 candidate bow shock driving stars. The reliability of bubble identifications, as assessed by comparison to visual identifications by trained experts and scoring by a machine-learning algorithm, is found to be a significant improvement over DR1. We assess the reliability of IR bow shocks via comparison to expert identifications and the colours of candidate bow shock driving stars in the 2MASS point-source catalogue. We hence identify highly reliable subsets of 1394 DR2 bubbles and 453 bow shock driving stars. Uncertainties on object coordinates and bubble size/shape parameters are included in the DR2 catalogue. Compared with DR1, the DR2 bubbles catalogue provides more accurate shapes and sizes. The DR2 catalogue identifies 311 new bow shock driving star candidates, including three associated with the giant H ii regions NGC 3603 and RCW 49.  more » « less
Award ID(s):
1812747
NSF-PAR ID:
10166119
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Monthly Notices of the Royal Astronomical Society
Volume:
488
Issue:
1
ISSN:
0035-8711
Page Range / eLocation ID:
1141 to 1165
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract We announce the second data release (DR2) of the NOIRLab Source Catalog (NSC), using 412,116 public images from CTIO-4 m+DECam, the KPNO-4 m+Mosaic3, and the Bok-2.3 m+90Prime. NSC DR2 contains over 3.9 billion unique objects, 68 billion individual source measurements, covers ≈35,000 square degrees of the sky, has depths of ≈23 mag in most broadband filters with ≈1%–2% photometric precision, and astrometric accuracy of ≈7 mas. Approximately 1.9 billion objects within ≈30,000 square degrees of sky have photometry in three or more bands. There are several improvements over NSC DR1. DR2 includes 156,662 (61%) more exposures extending over 2 more years than in DR1. The southern photometric zero-points in griz are more accurate by using the Skymapper DR1 and ATLAS-Ref2 catalogs, and improved extinction corrections were used for high-extinction regions. In addition, the astrometric accuracy is improved by taking advantage of Gaia DR2 proper motions when calibrating the astrometry of individual images. This improves the NSC proper motions to ∼2.5 mas yr −1 (precision) and ∼0.2 mas yr −1 (accuracy). The combination of sources into unique objects is performed using a DBSCAN algorithm and mean parameters per object (such as mean magnitudes, proper motion, etc.) are calculated more robustly with outlier rejection. Finally, eight multi-band photometric variability indices are calculated for each object and variable objects are flagged (23 million objects). NSC DR2 will be useful for exploring solar system objects, stellar streams, dwarf satellite galaxies, quasi-stellar objects, variable stars, high proper-motion stars, and transients. Several examples of these science use cases are presented. The NSC DR2 catalog is publicly available via the NOIRLab’s Astro Data Lab science platform. 
    more » « less
  2. Abstract

    We present the Citizen Science program Active Asteroids and describe discoveries stemming from our ongoing project. Our NASA Partner program is hosted on the Zooniverse online platform and launched on 2021 August 31, with the goal of engaging the community in the search for active asteroids—asteroids with comet-like tails or comae. We also set out to identify other unusual active solar system objects, such as active Centaurs, active quasi-Hilda asteroids (QHAs), and Jupiter-family comets (JFCs). Active objects are rare in large part because they are difficult to identify, so we ask volunteers to assist us in searching for active bodies in our collection of millions of images of known minor planets. We produced these cutout images with our project pipeline that makes use of publicly available Dark Energy Camera data. Since the project launch, roughly 8300 volunteers have scrutinized some 430,000 images to great effect, which we describe in this work. In total, we have identified previously unknown activity on 15 asteroids, plus one Centaur, that were thought to be asteroidal (i.e., inactive). Of the asteroids, we classify four as active QHAs, seven as JFCs, and four as active asteroids, consisting of one main-belt comet (MBC) and three MBC candidates. We also include our findings concerning known active objects that our program facilitated, an unanticipated avenue of scientific discovery. These include discovering activity occurring during an orbital epoch for which objects were not known to be active, and the reclassification of objects based on our dynamical analyses.

     
    more » « less
  3. Public participation in scientific activities, often called citizen science, offers a possibility to collect and analyze an unprecedentedly large amount of data. However, diversity of volunteers poses a challenge to obtain accurate information when these data are aggregated. To overcome this problem, we propose a classification algorithm using Bayesian inference that harnesses diversity of volunteers to improve data accuracy. In the algorithm, each volunteer is grouped into a distinct class based on a survey regarding either their level of education or motivation to citizen science. We obtained the behavior of each class through a training set, which was then used as a prior information to estimate performance of new volunteers. By applying this approach to an existing citizen science dataset to classify images into categories, we demonstrate improvement in data accuracy, compared to the traditional majority voting. Our algorithm offers a simple, yet powerful, way to improve data accuracy under limited effort of volunteers by predicting the behavior of a class of individuals, rather than attempting at a granular description of each of them.

     
    more » « less
  4. ABSTRACT

    The Transiting Exoplanet Survey Satellite (TESS) has already begun to discover what will ultimately be thousands of exoplanets around nearby cool bright stars. These potential host stars must be well understood to accurately characterize exoplanets at the individual and population levels. We present a catalogue of the chemo-kinematic properties of 2218 434 stars in the TESS Candidate Target List using survey data from Gaia DR2, APOGEE, GALAH, RAVE, LAMOST, and photometrically derived stellar properties from SkyMapper. We compute kinematic thin disc, thick disc, and halo membership probabilities for these stars and find that though the majority of TESS targets are in the thin disc, 4 per cent of them reside in the thick disc and <1 per cent of them are in the halo. The TESS Objects of Interest in our sample also display similar contributions from the thin disc, thick disc, and halo with a majority of them being in the thin disc. We also explore metallicity and [α/Fe] distributions for each Galactic component and show that each cross-matched survey exhibits metallicity and [α/Fe] distribution functions that peak from higher to lower metallicity and lower to higher [α/Fe] from the thin disc to the halo. This catalogue will be useful to explore planet occurrence rates, among other things, with respect to kinematics, component membership, metallicity, or [α/Fe].

     
    more » « less
  5. Abstract We present the first results from Citizen ASAS-SN, a citizen science project for the All-Sky Automated Survey for Supernovae (ASAS-SN) hosted on the Zooniverse platform. Citizen ASAS-SN utilizes the newer, deeper, higher cadence ASAS-SN g -band data and tasks volunteers to classify periodic variable star candidates based on their phased light curves. We started from 40,640 new variable candidates from an input list of ∼7.4 million stars with δ < −60° and the volunteers identified 10,420 new discoveries which they classified as 4234 pulsating variables, 3132 rotational variables, 2923 eclipsing binaries, and 131 variables flagged as Unknown. They classified known variable stars with an accuracy of 89% for pulsating variables, 81% for eclipsing binaries, and 49% for rotational variables. We examine user performance, agreement between users, and compare the citizen science classifications with our machine learning classifier updated for the g -band light curves. In general, user activity correlates with higher classification accuracy and higher user agreement. We used the user’s “Junk” classifications to develop an effective machine learning classifier to separate real from false variables, and there is a clear path for using this “Junk” training set to significantly improve our primary machine learning classifier. We also illustrate the value of Citizen ASAS-SN for identifying unusual variables with several examples. 
    more » « less