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Abstract We present Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) maps of the Cosmic Microwave Background temperature and polarization anisotropy at arcminute resolution over three frequency bands centered on 98, 150 and 220 GHz. The maps are based on data collected with the AdvancedACT camera over the period 2017–2022 and cover 19,000 square degrees with a median combined depth of 10 μK arcmin. We describe the instrument, mapmaking and map properties and illustrate them with a number of figures and tables. The ACT DR6 maps and derived products are available on LAMBDA athttps://lambda.gsfc.nasa.gov/product/act/actadv_prod_table.html. We also provide an interactive web atlas athttps://phy-act1.princeton.edu/public/snaess/actpol/dr6/atlasand HiPS data sets in Aladin (e.g.https://alasky.cds.unistra.fr/ACT/DR4DR6/color_CMB).
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Applications of physics informed neural operators
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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- Award ID(s):
- 1931561
- PAR ID:
- 10414382
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 4
- Issue:
- 2
- ISSN:
- 2632-2153
- Page Range / eLocation ID:
- Article No. 025022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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