Southern Oregon University

Accurate redshift estimates are a vital component in understanding galaxy evolution and precision cosmology. In this paper, we explore approaches to increase the applicability of machine learning models for photometric redshift estimation on a broader range of galaxy types. Typical models are trained with ground-truth redshifts from spectroscopy. We test the utility and effectiveness of two approaches for combining spectroscopic redshifts and redshifts derived from multiband (∼35 filters) photometry, which sample different types of galaxies compared to spectroscopic surveys. The two approaches are (1) training on a composite dataset, and (2) transfer learning from one dataset to another. We compile photometric redshifts from the COSMOS2020 catalog (TransferZ) to complement an established spectroscopic redshift dataset (GalaxiesML). We used two architectures, deterministic neural networks (NN) and Bayesian neural networks (BNN), to examine and evaluate their performance with respect to the Legacy Survey of Space and Time (LSST) photo- z science requirements. We also use split conformal prediction for calibrating uncertainty estimates and producing prediction intervals for the BNN and NN, respectively. We find that a NN trained on a composite dataset predicts photo- z 's 4.5 times less biased within the redshift range 0.3 < z < 1.5, 1.1 times less scattered, and has a 1.4 times lower outlier rate than a model trained on only spectroscopic ground truths. We also find that BNNs produce reliable uncertainty estimates, but are sensitive to the different ground truths. This investigation leverages different sources of ground truths to develop models that can accurately predict photo- z 's for a broader galaxy population, which is crucial for surveys such as Euclid and LSST.

In this work, we demonstrate how Low-Rank Adaptation (LoRA) can be used to combine different galaxy imaging datasets to improve redshift estimation with CNN models for cosmology. LoRA is an established technique for large language models that adds adapter networks to adjust model weights and biases to efficiently fine-tune large base models without retraining. We train a base model using a photometric redshift ground truth dataset, which contains broad galaxy types but is less accurate. We then fine-tune using LoRA on a spectroscopic redshift ground truth dataset. These redshifts are more accurate but limited to bright galaxies and take orders of magnitude more time to obtain, so are less available for large surveys. Ideally, the combination of the two datasets would yield more accurate models that generalize well. The LoRA model performs better than a traditional transfer learning method, with ∼2.5× less bias and ∼2.2× less scatter. Retraining the model on a combined dataset yields a model that generalizes better than LoRA but at a cost of greater computation time. Our work shows that LoRA is useful for fine-tuning regression models in astrophysics by providing a middle ground between full retraining and no retraining. LoRA shows potential in allowing us to leverage existing pretrained astrophysical models, especially for data sparse tasks.

This article examines the advancements that food, energy, and water systems (FEWS) researchers have made in proposing and implementing solutions to FEWS issues. We examined 483 FEWS articles published between 2015 and 2023 to determine whether solutions were proposed or implemented and the factors leading to solution proposal and implementation. Our research suggests that only 18 of the articles led to solutions. Factors that contributed to finding solutions included the integration of stakeholders into the research project, the inclusion of governmental stakeholders, and the inclusion of diverse stakeholders. Although most manuscripts included computational or statistical models, our research suggests that they do not lead to the proposal or implementation of solutions, even when stakeholders are included. We call for greater incorporation of stakeholders in FEWS projects in order to more effectively address the environmental issues that arise in these systems.

Los pájaros carpinteros de Lewis (Melanerpes lewis) actúan como forrajeadores oportunistas conocidos por su propensión a capturar insectos en vuelo. Aquí describo observaciones de un grupo de carpinteros que forrajeaba activamente sobre invertebrados acuáticos a lo largo de una barra de cantos rodados en el tramo superior del río Rogue, Oregón. Las aves se posaban sobre los cantos expuestos y exploraban el agua somera que fluía entre ellos; durante este comportamiento capturaban y consumían presas invertebradas. En este hábitat de aguas poco profundas habitaba un conjunto abundante y diverso de invertebrados, y entre los taxones consumidos identifiqué caracoles y ninfas grandes de plecópteros.