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  1. Subseasonal to seasonal forecasts are likely to improve from better sea surface temperature (SST) predictions, as SST is the bottom boundary condition for the marine atmosphere. We present research that extends the analysis and prediction of SST to include variability of upper ocean mixing to explore how the variability of the ocean mixed layer affects the intraseasonal statistics of SST and its covariance with tropical intraseasonal atmospheric variability. We present a conceptual framework to identify the contribution of fast (hourly to daily) co-variations in ocean mixed layer depth and atmospheric fluxes to seasonal to sub-seasonal sea surface temperature prediction. First, metrics from this framework will be analyzed from data collected throughout the tropical and subtropical oceans from moored platforms and profiling instruments to demonstrate how diurnal solar warming, fast wind gusts and rain showers, and daily variable clouds and winds rectify into longer timescale intraseasonal SST variability. We will then focus the pre-monsoon season in the Arabian Sea using observations of the upper ocean collected during the 2023 ASTRraL/EKAMSAT field program, highlighting the role of the diurnal warm layer variability on mean SST. 
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    Free, publicly-accessible full text available February 23, 2025
  2. ABSTRACT

    Young stellar objects (YSOs) are the gold standard for tracing star formation in galaxies but have been unobservable beyond the Milky Way and Magellanic Clouds. But that all changed when the JWST was launched, which we use to identify YSOs in the Local Group galaxy M33, marking the first time that individual YSOs have been identified at these large distances. We present Mid-Infrared Instrument (MIRI) imaging mosaics at 5.6 and 21 $\mu$m that cover a significant portion of one of M33’s spiral arms that has existing panchromatic imaging from the Hubble Space Telescope and deep Atacama Large Millimeter/submillimeter Array CO measurements. Using these MIRI and Hubble Space Telescope images, we identify point sources using the new dolphot MIRI module. We identify 793 candidate YSOs from cuts based on colour, proximity to giant molecular clouds (GMCs), and visual inspection. Similar to Milky Way GMCs, we find that higher mass GMCs contain more YSOs and YSO emission, which further show YSOs identify star formation better than most tracers that cannot capture this relationship at cloud scales. We find evidence of enhanced star formation efficiency in the southern spiral arm by comparing the YSOs to the molecular gas mass.

     
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  3. Abstract

    Hierarchical probability models are being used more often than non-hierarchical deterministic process models in environmental prediction and forecasting, and Bayesian approaches to fitting such models are becoming increasingly popular. In particular, models describing ecosystem dynamics with multiple states that are autoregressive at each step in time can be treated as statistical state space models (SSMs). In this paper, we examine this subset of ecosystem models, embed a process-based ecosystem model into an SSM, and give closed form Gibbs sampling updates for latent states and process precision parameters when process and observation errors are normally distributed. Here, we use simulated data from an example model (DALECev) and study the effects changing the temporal resolution of observations on the states (observation data gaps), the temporal resolution of the state process (model time step), and the level of aggregation of observations on fluxes (measurements of transfer rates on the state process). We show that parameter estimates become unreliable as temporal gaps between observed state data increase. To improve parameter estimates, we introduce a method of tuning the time resolution of the latent states while still using higher-frequency driver information and show that this helps to improve estimates. Further, we show that data cloning is a suitable method for assessing parameter identifiability in this class of models. Overall, our study helps inform the application of state space models to ecological forecasting applications where (1) data are not available for all states and transfers at the operational time step for the ecosystem model and (2) process uncertainty estimation is desired.

     
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  4. Abstract

    We present NIRCam and NIRISS modules for DOLPHOT, a widely used crowded-field stellar photometry package. We describe details of the modules including pixel masking, astrometric alignment, star finding, photometry, catalog creation, and artificial star tests. We tested these modules using NIRCam and NIRISS images of M92 (a Milky Way globular cluster), Draco II (an ultrafaint dwarf galaxy), and Wolf–Lundmark–Mellote (a star-forming dwarf galaxy). DOLPHOT’s photometry is highly precise, and the color–magnitude diagrams are deeper and have better definition than anticipated during original program design in 2017. The primary systematic uncertainties in DOLPHOT’s photometry arise from mismatches in the model and observed point-spread functions (PSFs) and aperture corrections, each contributing ≲0.01 mag to the photometric error budget. Version 1.2 of WebbPSF models, which include charge diffusion and interpixel capacitance effects, significantly reduced PSF-related uncertainties. We also observed minor (≲0.05 mag) chip-to-chip variations in NIRCam’s zero-points, which will be addressed by the JWST flux calibration program. Globular cluster observations are crucial for photometric calibration. Temporal variations in the photometry are generally ≲0.01 mag, although rare large misalignment events can introduce errors up to 0.08 mag. We provide recommended DOLPHOT parameters, guidelines for photometric reduction, and advice for improved observing strategies. Our Early Release Science DOLPHOT data products are available on MAST, complemented by comprehensive online documentation and tutorials for using DOLPHOT with JWST imaging data.

     
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    Free, publicly-accessible full text available March 27, 2025
  5. ABSTRACT

    We use young clusters and giant molecular clouds (GMCs) in the galaxies M33 and M31 to constrain temporal and spatial scales in the star formation process. In M33, we compare the Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER) catalogue of 1214 clusters with ages measured via colour–magnitude diagram (CMD) fitting to 444 GMCs identified from a new 35 pc resolution Atacama Large Millimeter/submillimeter Array (ALMA) 12CO(2–1) survey. In M31, we compare the Panchromatic Hubble Andromeda Treasury (PHAT) catalogue of 1249 clusters to 251 GMCs measured from a Combined Array for Research in Millimeter-wave Astronomy (CARMA) 12CO(1–0) survey with 20 pc resolution. Through two-point correlation analysis, we find that young clusters have a high probability of being near other young clusters, but correlation between GMCs is suppressed by the cloud identification algorithm. By comparing the positions, we find that younger clusters are closer to GMCs than older clusters. Through cross-correlation analysis of the M33 cluster data, we find that clusters are statistically associated when they are ≤10 Myr old. Utilizing the high precision ages of the clusters, we find that clusters older than ≈18 Myr are uncorrelated with the molecular interstellar medium (ISM). Using the spatial coincidence of the youngest clusters and GMCs in M33, we estimate that clusters spend ≈4–6 Myr inside their parent GMC. Through similar analysis, we find that the GMCs in M33 have a total lifetime of ≈11–15 Myr. We also develop a drift model and show that the above correlations can be explained if the clusters in M33 have a 5–10 km s−1 velocity dispersion relative to the molecular ISM.

     
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  6. This work describes a novel approach to patterning Indium Tin Oxide (ITO) on Polyvinylidene Fluoride (PVDF) using a laser cut Kapton® tape mask for rapid prototyping. Measurements taken before and after experimentation conclude a non-significant change in sheet resistance while using this method to pattern with a p-value of 0.2947 for a two-tailed paired t-test for significance. 
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  7. Abstract

    We present analysis using a citizen science campaign to improve the cosmological measures from the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX). The goal of HETDEX is to measure the Hubble expansion rate,H(z), and angular diameter distance,DA(z), atz= 2.4, each to percent-level accuracy. This accuracy is determined primarily from the total number of detected Lyαemitters (LAEs), the false positive rate due to noise, and the contamination due to [Oii] emitting galaxies. This paper presents the citizen science project, Dark Energy Explorers (https://www.zooniverse.org/projects/erinmc/dark-energy-explorers), with the goal of increasing the number of LAEs and decreasing the number of false positives due to noise and the [Oii] galaxies. Initial analysis shows that citizen science is an efficient and effective tool for classification most accurately done by the human eye, especially in combination with unsupervised machine learning. Three aspects from the citizen science campaign that have the most impact are (1) identifying individual problems with detections, (2) providing a clean sample with 100% visual identification above a signal-to-noise cut, and (3) providing labels for machine-learning efforts. Since the end of 2022, Dark Energy Explorers has collected over three and a half million classifications by 11,000 volunteers in over 85 different countries around the world. By incorporating the results of the Dark Energy Explorers, we expect to improve the accuracy on theDA(z) andH(z) parameters atz= 2.″4 by 10%–30%. While the primary goal is to improve on HETDEX, Dark Energy Explorers has already proven to be a uniquely powerful tool for science advancement and increasing accessibility to science worldwide.

     
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  8. We demonstrate cryogenic operation of a silicon-organic hybrid (SOH) Mach-Zehnder modu- lator. The device is based on a dedicated material formulation and allows for 50 Gbps on-off-keying (OOK) at 4 K - a record-high line rate generated by an MZM at this temperature. 
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  9. Abstract We construct a catalog of star clusters from Hubble Space Telescope images of the inner disk of the Triangulum Galaxy (M33) using image classifications collected by the Local Group Cluster Search, a citizen science project hosted on the Zooniverse platform. We identify 1214 star clusters within the Hubble Space Telescope imaging footprint of the Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER) survey. Comparing this catalog to existing compilations in the literature, 68% of the clusters are newly identified. The final catalog includes multiband aperture photometry and fits for cluster properties via integrated light spectral energy distribution fitting. The cluster catalog’s 50% completeness limit is ∼1500 M ☉ at an age of 100 Myr, as derived from comprehensive synthetic cluster tests. 
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  10. Abstract Background The Learning Assistant (LA) model with its subsequent support and training has evidenced significant gains for undergraduate STEM learning and persistence, especially in high-stakes courses like Calculus. Yet, when a swift and unexpected transition occurs from face-to-face to online, remote learning of the LA environment, it is unknown how LAs are able to maintain their motivation (competence, autonomy, and relatedness), adapt to these new challenges, and sustain their student-centered efforts. This study used Self-Determination Theory (SDT) to model theoretical aspects of LAs’ motivations (persistence and performance) both before and after changes were made in delivery of a Calculus II course at Texas Tech University due to COVID-19 interruptions. Results Analysis of weekly written reflections, a focus group session, and a post-course questionnaire of 13 Calculus II LAs throughout Spring semester of 2020 showed that LAs’ reports of competence proportionally decreased when they transitioned online, which was followed by a moderate proportional increase in reports of autonomy (actions they took to adapt to distance instruction) and a dramatic proportional increase in reports of relatedness (to build structures for maintaining communication and building community with undergraduate students). Conclusions Relatedness emerged as the most salient factor from SDT to maintain LA self-determination due to the COVID-19 facilitated interruption to course delivery in a high-stakes undergraduate STEM course. Given that online learning continues during the pandemic and is likely to continue after, this research provides an understanding to how LAs responded to this event and the mounting importance of relatedness when LAs are working with undergraduate STEM learners. Programmatic recommendations are given for enhancing LA preparation including selecting LAs for autonomy and relatedness factors (in addition to competence), modeling mentoring for remote learners, and coaching in best practices for online instruction. 
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