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1. Second Data Release of the All-sky NOIRLab Source Catalog

   https://doi.org/10.3847/1538-3881/abd6e1  

   Nidever, David L.; Dey, Arjun; Fasbender, Katie; Juneau, Stéphanie; Meisner, Aaron M.; Wishart, Joseph; Scott, Adam; Matt, Kyle; Nikutta, Robert; Pucha, Ragadeepika (March 2021, The Astronomical Journal)

   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. 

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2. The Milky Way Project MOBStIRS: Parameterizing Infrared Stellar-wind Bow Shock Morphologies with Citizen Science

   https://doi.org/10.3847/1538-3881/adcd72  

   Whisnant, Angelica S; Povich, Matthew S; Patten, Nikhil; Kobulnicky, Henry A (May 2025, The Astronomical Journal)

   Abstract Mass-loss influences stellar evolution, especially for massive stars with strong winds. Stellar wind bow shock nebulae driven by Galactic OB stars can be used to measure mass-loss rates ( $\dot{M}$). The standoff distance (R₀) between the star and the bow shock is set by momentum flux balance between the stellar wind and the surrounding interstellar medium (ISM). We created the Milky Way Project: mass-loss rates for OB Stars driving infrared bow shocks (MOBStIRS) using the online Zooniverse citizen science platform. We enlisted several hundred students to measureR₀and two other projected shape parameters for 764 cataloged infrared bow shocks. MOBStIRS incorporated 1528 JPEG cutout images produced from Spitzer GLIMPSE and MIPSGAL survey data. Measurements were aggregated to compute shape parameters for each bow shock image deemed high quality by participants. The average statistical uncertainty onR₀is 12.5% but varies from <5% to ∼40% among individual bow shocks, contributing significantly to the total error budget of $\dot{M}$. The derived nebular morphologies agree well with (magneto) hydrodynamic simulations of bow shocks driven by the winds of OB stars moving atV_a = 10–40 km s⁻¹with respect to the ambient ISM. A systematic correction toR₀to account for viewing angle appears unnecessary for computing $\dot{M}$. Slightly more than half of MOBStIRS bow shocks are asymmetric, which could indicate anisotropic stellar winds, ISM clumping on sub-pc scales, time-dependent instabilities, and/or misalignments between the local ISM magnetic field and the star-bow-shock axis. 

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3. Crowdsourcing star-formation research and the power of participatory science

   https://doi.org/10.1007/s10509-025-04471-2  

   Wolf-Chase, Grace; Kerton, Charles; Devine, Kathryn; Larose, Nicholas; Coleman, Maya (September 2025, Astrophysics and Space Science)

   Abstract We review participatory science programs that have contributed to the understanding of star formation. The Milky Way Project (MWP), one of the earliest participatory science projects launched on the Zooniverse platform, produced the largest catalog of “bubbles” associated with feedback from hot young stars to date, and enabled the identification of a new class of compact star-forming regions (SFRs) known as “yellowballs” (YBs). The analysis of YBs through their infrared colors and catalog cross-matching led to discovering that YBs are compact photodissociation regions generated by intermediate- and high-mass young stellar objects embedded in clumps that range in mass from 10 - 10⁴M_⊙and luminosity from 10 - 10⁶L_⊙. The MIRION catalog, assembled from 6176 YBs identified by citizen scientists, increases the number of candidate intermediate-mass SFRs by nearly two orders of magnitude. Ongoing work utilizing data from theSpitzer,HerschelandWISEmissions involves analyzing infrared color trends to predict physical properties and ages of YB environments. Methods include applying summary statistics to histograms and color-color plots as well as SED fitting. Students in introductory astronomy classes contribute toward continued efforts refining photometric measurements of YBs while learning fundamental concepts in astronomy through a classroom-based participatory science experience, the PERYSCOPE project. We also describe an initiative that engaged seminaries, family groups, and interfaith communities in a wide variety of science projects on the Zooniverse platform. This initiative produced important guidance on attracting audiences that are underserved, underrepresented, or apprehensive about science. 

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4. Through the Citizen Scientists’ Eyes: Insights into Using Citizen Science with Machine Learning for Effective Identification of Unknown-Unknowns in Big Data

   https://doi.org/10.5334/cstp.740  

   Mantha, Kameswara Bharadwaj; Roberts, Hayley; Fortson, Lucy; Lintott, Chris; Dickinson, Hugh; Keel, William; Sankar, Ramanakumar; Krawczyk, Coleman; Simmons, Brooke; Walmsley, Mike; et al (December 2024, Citizen Science: Theory and Practice)

   Fortson, Lucy; Crowston, Kevin; Kloetzer, Laure; Ponti, Marisa (Ed.)

   In the era of rapidly growing astronomical data, the gap between data collection and analysis is a significant barrier, especially for teams searching for rare scientific objects. Although machine learning (ML) can quickly parse large data sets, it struggles to robustly identify scientifically interesting objects, a task at which humans excel. Human-in-the-loop (HITL) strategies that combine the strengths of citizen science (CS) and ML offer a promising solution, but first, we need to better understand the relationship between human- and machine-identified samples. In this work, we present a case study from the Galaxy Zoo: Weird & Wonderful project, where volunteers inspected ~200,000 astronomical images—processed by an ML-based anomaly detection model—to identify those with unusual or interesting characteristics. Volunteer-selected images with common astrophysical characteristics had higher consensus, while rarer or more complex ones had lower consensus. This suggests low-consensus choices shouldn’t be dismissed in further explorations. Additionally, volunteers were better at filtering out uninteresting anomalies, such as image artifacts, which the machine struggled with. We also found that a higher ML-generated anomaly score that indicates images’ low-level feature anomalousness was a better predictor of the volunteers’ consensus choice. Combining a locus of high volunteer-consensus images within the ML learnt feature space and anomaly score, we demonstrated a decision boundary that can effectively isolate images with unusual and potentially scientifically interesting characteristics. Using this case study, we lay important guidelines for future research studies looking to adapt and operationalize human-machine collaborative frameworks for efficient anomaly detection in big data. 

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5. The Active Asteroids Citizen Science Program: Overview and First Results

   https://doi.org/10.3847/1538-3881/ad1de2  

   Chandler, Colin Orion; Trujillo, Chadwick A.; Oldroyd, William J.; Kueny, Jay K.; Burris, William A.; Hsieh, Henry H.; DeSpain, Jarod A.; Sedaghat, Nima; Sheppard, Scott S.; Farrell, Kennedy A.; et al (March 2024, The Astronomical Journal)

   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. 

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