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1. Preliminary Target Selection for the DESI Milky Way Survey (MWS)

   https://doi.org/10.3847/2515-5172/abc1dc  

   Prieto, Carlos Allende; Cooper, Andrew P; Dey, Arjun; Gänsicke, Boris T; Koposov, Sergey E; Li, Ting; Manser, Christopher; Nidever, David L; Rockosi, Constance; Wang, Mei-Yu; et al (October 2020, Research Notes of the AAS)

   Abstract The DESI Milky Way Survey (MWS) will observe ≥8 million stars between 16 < r < 19 mag, supplemented by observations of brighter targets under poor observing conditions. The survey will permit an accurate determination of stellar kinematics and population gradients, characterize diffuse substructure in the thick disk and stellar halo, enable the discovery of extremely metal-poor stars and other rare stellar types, and improve constraints on the Galaxy’s 3D dark matter distribution from halo star kinematics. MWS will also enable a detailed characterization of the stellar populations within 100 pc of the Sun, including a complete census of white dwarfs. The target catalog from the preliminary selection described here is public (Available at https://data.desi.lbl.gov/public/ets/target/catalogs/and detailed at  https://desidatamodel.readthedocs.io). 

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2. The Atacama Cosmology Telescope: DR6 power spectrum foreground model and validation

   https://doi.org/10.1088/1475-7516/2025/10/082  

   Beringue, Benjamin; Surrao, Kristen M; Hill, J Colin; Atkins, Zachary; Battaglia, Nicholas; Bolliet, Boris; Calabrese, Erminia; Choi, Steve K; Clark, Susan E; Duivenvoorden, Adriaan J; et al (October 2025, Journal of Cosmology and Astroparticle Physics)

   Abstract We discuss the model of astrophysical emission at millimeter wavelengths used to characterize foregrounds in the multi-frequency power spectra of the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6), expanding on Louis et al. (2025) (2503.14452). We detail several tests to validate the capability of the DR6 parametric foreground model to describe current observations and complex simulations, and show that cosmological parameter constraints are robust against model extensions and variations. We demonstrate consistency of the model with pre-DR6 ACT data and observations fromPlanckand the South Pole Telescope. We evaluate the implications of using different foreground templates and extending the model with new components and/or free parameters. In all scenarios, the DR6 ΛCDM and ΛCDM+N_effcosmological parameters shift by less than 0.5σrelative to the baseline constraints. Some foreground parameters shift more; we estimate their systematic uncertainties associated with modeling choices. From our constraint on the kinematic Sunyaev-Zel'dovich power, we obtain a conservative limit on the duration of reionization of Δz_rei< 4.4, assuming a reionization midpoint consistent with optical depth measurements and a minimal low-redshift contribution, with varying assumptions for this component leading to tighter limits. Finally, we analyze realistic non-Gaussian, correlated microwave sky simulations containing Galactic and extragalactic foreground fields, built independently of the DR6 parametric foreground model. Processing these simulations through the DR6 power spectrum and likelihood pipeline, we recover the input cosmological parameters of the underlying cosmic microwave background field, a new demonstration for small-scale CMB analysis. These tests validate the robustness of the ACT DR6 foreground model and cosmological parameter constraints. 

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3. STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping

   https://doi.org/10.1038/s41467-023-43915-7  

   Clifton, Kalen; Anant, Manjari; Aihara, Gohta; Atta, Lyla; Aimiuwu, Osagie K.; Kebschull, Justus M.; Miller, Michael I.; Tward, Daniel; Fan, Jean (December 2023, Nature Communications)

   Abstract Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we develop STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit athttps://github.com/JEFworks-Lab/STalignand as Supplementary Software with additional documentation and tutorials available athttps://jef.works/STalign. 

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4. Applications of physics informed neural operators

   https://doi.org/10.1088/2632-2153/acd168  

   Rosofsky, Shawn G.; Al Majed, Hani; Huerta, E. A. (May 2023, Machine Learning: Science and Technology)

   Abstract We present a critical analysis of physics-informed neural operators (PINOs) to solve partial differential equations (PDEs) that are ubiquitous in the study and modeling of physics phenomena using carefully curated datasets. Further, we provide a benchmarking suite which can be used to evaluate PINOs in solving such problems. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our PINOs to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled PDEs. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide thesource code, an interactivewebsiteto visualize the predictions of our PINOs, and a tutorial for their use at theData and Learning Hub for Science. 

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5. CMB/kSZ and Compton-y Maps from 2500 deg ² of SPT-SZ and Planck Survey Data

   https://doi.org/10.3847/1538-4365/ac35e9  

   Bleem, L. E.; Crawford, T. M.; Ansarinejad, B.; Benson, B. A.; Bocquet, S.; Carlstrom, J. E.; Chang, C. L.; Chown, R.; Crites, A. T.; Haan, T. de; et al (February 2022, The Astrophysical Journal Supplement Series)

   Abstract We present component-separated maps of the primary cosmic microwave background/kinematic Sunyaev–Zel’dovich (SZ) amplitude and the thermal SZ Compton-yparameter, created using data from the South Pole Telescope (SPT) and the Planck satellite. These maps, which cover the ∼2500 deg²of the southern sky imaged by the SPT-SZ survey, represent a significant improvement over previous such products available in this region by virtue of their higher angular resolution ( $1.\prime 25$for our highest-resolution Compton-ymaps) and lower noise at small angular scales. In this work we detail the construction of these maps using linear combination techniques, including our method for limiting the correlation of our lowest-noise Compton-ymap products with the cosmic infrared background. We perform a range of validation tests on these data products to test our sky modeling and combination algorithms, and we find good performance in all of these tests. Recognizing the potential utility of these data products for a wide range of astrophysical and cosmological analyses, including studies of the gas properties of galaxies, groups, and clusters, we make these products publicly available athttp://pole.uchicago.edu/public/data/sptsz_ymapand on the NASA/LAMBDA website. 

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