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We present the photometric redshift characterization and calibration for the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. The redshifts are estimated from a combination of wide-field photometry, deep-field photometry with associated redshift estimates, and a transfer function between the wide field and deep field that is estimated using a source injection catalog. We construct four tomographic bins for the galaxy catalog, and estimate the redshift distribution, $n\left(z\right)$, within each one using the Self-organizing Map Photo-Z (SOMPZ) methodology. Our estimates include the contributions from sample variance, zeropoint calibration uncertainties, and redshift biases, as quantified for the deep-field dataset. The total uncertainties on the mean redshifts are ${\sigma }_{⟨z⟩}\approx 0.01$. The SOMPZ estimates are then compared to those from the clustering redshift method, obtained by cross-correlating our source galaxies with galaxies in spectroscopic surveys, and are shown to be consistent with each other. 

We present the pipeline for the cosmic shear analysis of the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog consisting of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. The catalog derives from a large number of disparate observing programs and is therefore more inhomogeneous across the sky compared to existing lensing surveys. First, we use simulated data-vectors to show the sensitivity of our constraints to different analysis choices in our inference pipeline, including sensitivity to residual systematics. Next we use simulations to validate our covariance modeling for inhomogeneous datasets. Finally, we show that our choices in the end-to-end cosmic shear pipeline are robust against inhomogeneities in the survey, by extracting relative shifts in the cosmology constraints across different subsets of the footprint/catalog and showing they are all consistent within $1\sigma$to $2\sigma$. This is done for forty-six subsets of the data and is carried out in a fully consistent manner: for each subset of the data, we re-derive the photometric redshift estimates, shear calibrations, survey transfer functions, the data vector, measurement covariance, and finally, the cosmological constraints. Our results show that existing analysis methods for weak lensing cosmology can be fairly resilient towards inhomogeneous datasets. This also motivates exploring a wider range of image data for pursuing such cosmological constraints. 

We present the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. This catalog was assembled from public DECam data including survey and standard observing programs. These data were consistently processed with the Dark Energy Survey Data Management pipeline as part of the DECADE campaign and serve as the basis of the DECam Local Volume Exploration survey (DELVE) Early Data Release 3 (EDR3). We apply the Metacalibration measurement algorithm to generate and calibrate galaxy shapes. After cuts, the resulting cosmology-ready galaxy shape catalog covers a region of 5,412 deg2 with an effective number density of 4.59 arcmin−2. The coadd images used to derive this data have a median limiting magnitude of r=23.6, i=23.2, and z=22.6, estimated at S/N=10 in a 2 arcsecond aperture. We present a suite of detailed studies to characterize the catalog, measure any residual systematic biases, and verify that the catalog is suitable for cosmology analyses. In parallel, we build an image simulation pipeline to characterize the remaining multiplicative shear bias in this catalog, which we measure to be m=(−2.454±0.124)×10−2 for the full sample. Despite the significantly inhomogeneous nature of the data set, due to it being an amalgamation of various observing programs, we find the resulting catalog has sufficient quality to yield competitive cosmological constraints. 

We present cosmological constraints from the Dark Energy Camera All Data Everywhere (DECADE) cosmic shear analysis. This work uses shape measurements for 107 million galaxies measured through Dark Energy Camera (DECam) imaging of $5,412$deg ${}^2$of sky that is outside the Dark Energy Survey (DES) footprint. We derive constraints on the cosmological parameters $S_8={0.791}_{-0.032}^{+0.027}$and for the $\Lambda$CDM model, which are consistent with those from other weak lensing surveys and from the cosmic microwave background. We combine our results with cosmic shear results from DES Y3 at the likelihood level, since the two datasets span independent areas on the sky. The combined measurements, which cover $\approx 10,000$deg ${}^2$, prefer $S_8=0.791\pm 0.023$and under the $\Lambda$CDM model. These results are the culmination of a series of rigorous studies that characterize and validate the DECADE dataset and the associated analysis methodologies (Anbajagane et. al 2025a,b,c). Overall, the DECADE project demonstrates that the cosmic shear analysis methods employed in Stage-III weak lensing surveys can provide robust cosmological constraints for fairly inhomogeneous datasets. This opens the possibility of using data that have been previously categorized as ``unusable’’ for cosmic shear analyses, thereby increasing the statistical power of upcoming weak lensing surveys. 

Neural networks are powerful tools. Applying them in computer systems—operating systems, databases, and networked systems—attracts much attention. However, neural networks are complicated black boxes that may produce unexpected results. To train networks with well-defined behaviors, we introduce ouroboros, a system that constructs verified neural networks. Verified neural networks are those that satisfy user-defined safety properties, known as specifications. Ouroboros builds verified networks by a training-verification loop that combines deep learning training and neural network verification. The system employs multiple techniques to fill the gap between today’s verification and the properties required for systems. Ouroboros also accelerates the training-verification loop by spec-aware learning. Our experiments show that ouroboros can train verified networks for five applications that we study and has a 2.8× speedup on average compared with the vanilla training-verification loop. 

We present Magellan/IMACS and Magellan/MIKE spectroscopy of the ultra-faint dwarf (UFD) galaxy Pictor~II (Pic~II) that is located only 12 kpc from the Large Magellanic Cloud (LMC). From the IMACS spectroscopy, we identify 13 member stars and measure a mean heliocentric velocity of , a velocity dispersion of , a mean metallicity of , and an upper limit on the metallicity dispersion of . We measure detailed elemental abundances for the brightest star, finding $\text{[Fe/H]}=-3.3$, high [ $\alpha$/Fe] ratios, and no detectable neutron capture elements, similar to stars in other UFDs. However, this star has an unusually high [Sc/Fe] ratio. The dynamical mass-to-light ratio ( $M/L={760}_{-420}^{+910}{M}_{\odot }{L}_{\odot }^{-1}$), size, and chemical abundances confirms that Pic~II is a dark matter-dominated dwarf galaxy. We perform detailed orbit modeling of Pic~II in a combined Milky Way (MW) and LMC potential and find that Pic~II is highly likely to be a long-term LMC satellite. Furthermore, we find that Pic II is likely still bound to the LMC today. Pic~II is the seventh LMC-associated UFD and among the most metal-poor UFDs known. We further update the morphological parameters with deeper Dark Energy Camera (DECam) photometry, compute the dark matter properties for dark matter indirect detection searches, verify the extremely low metallicity with narrowband CaHK imaging, and briefly discuss tidal influences of the LMC and MW. 

Abstract The Green Bank 820 MHz pulsar survey covers ≃173 deg²in the Cygnus X region of the Galaxy, centered onl= 84.°5 andb= 1.°5. Significant star formation is present in this region, and lines of sight pass through three arms of the Galaxy (Orion–Cygnus, Perseus, and an outer arm). Using the Green Bank Telescope, we recorded 200 MHz of bandwidth for 4.5 minutes at 81.92μs resolution for each of 3457 observed survey pointings during 2016 and 2017, covering about two-thirds of the total area. We searched these data for pulsars and report the discovery of six new pulsars—PSRs J2016+3820, J2016+4231, J2019+3810, J2035+3538, J2035+3655, and J2041+4551—and the codiscovery of PSR J2057+4701. PSR J2035+3655 is in a short (4.5 hr) binary orbit; we report the full binary solution and weakly constrain the mass of the pulsar via a marginal (2σ) detection of the Shapiro delay. We also searched the survey data for known pulsars to estimate the survey’s sensitivity and measured 820 MHz pulse widths and flux density for 20 detected sources. For sources that were also detected in the Green Bank North Celestial Cap survey at 350 MHz, we measure scattering parameters and compare to expectations for the region. With these results, we revisit the population estimates that motivated this survey and consider the impact of the survey’s yield on their underlying models. We note an apparent underestimate in dispersion measure predictions from typical Galactic electron density models in the survey region, and discuss future observation strategies. 

As the COVID-19 pandemic is disrupting life worldwide, related online communities are popping up. In particular, two “new” communities, /r/China flu and /r/Coronavirus, emerged on Reddit and have been dedicated to COVID- related discussions from the very beginning of this pandemic. With /r/Coronavirus promoted as the official community on Reddit, it remains an open question how users choose between these two highly-related communities. In this paper, we characterize user trajectories in these two communities from the beginning of COVID-19 to the end of September 2020. We show that new users of /r/China flu and /r/Coronavirus were similar from January to March. After that, their differences steadily increase, both in language distance and membership prediction, as the pandemic continues to unfold. Furthermore, users who started at /r/China flu from January to March were more likely to leave, while those who started in later months tend to remain highly “loyal”. To understand this difference, we develop a movement analysis framework to understand membership changes in these two communities and identify a significant proportion of /r/China flu members (around 50%) that moved to /r/Coronavirus in February. This movement turns out to be highly predictable based on other subreddits that users were previously active in. Our work demonstrates how two highly-related communities emerge and develop their own identity in a crisis, and highlights the important role of existing communities in understanding such an emergence. 

Inclusion in mathematics education is strongly tied to discourse rich classrooms, where students ideas play a central role. Talk moves are specific discursive practices that promote inclusive and equitable participation in classroom discussions. This paper describes the development of the TalkMoves application, which provides teachers with detailed feedback on their usage of talk moves based on accountable talk theory. Building on our recent work to automate the classification of teacher talk moves, we have expanded the application to also include feedback on a set of student talk moves. We present results from several deep learning models trained to classify student sentences into student talk moves with performance up to 73% F1. The classroom data used for training these models were collected from multiple sources that were pre-processed and annotated by highly reliable experts. We validated the performance of the model on an out-of-sample dataset which included 166 classroom transcripts collected from teachers piloting the application. 

TalkMoves is an innovative application designed to support K-12 mathematics teachers to reflect on, and continuously improve their instructional practices. This application combines state-of-the-art natural language processing capabilities with automated speech recognition to automatically analyze classroom recordings and provide teachers with personalized feedback on their use of specific types of discourse aimed at broadening and deepening classroom conversations about mathematics. These specific discourse strategies are referred to as “talk moves” within the mathematics education community and prior research has documented the ways in which systematic use of these discourse strategies can positively impact student engagement and learning. In this article, we describe the TalkMoves application’s cloud-based infrastructure for managing and processing classroom recordings, and its interface for providing teachers with feedback on their use of talk moves during individual teaching episodes. We present the series of model architectures we developed, and the studies we conducted, to develop our best-performing, transformer-based model (F1 = 79.3%). We also discuss several technical challenges that need to be addressed when working with real-world speech and language data from noisy K-12 classrooms.