Ground-based telescope simulations
Project description
Oh, maria blows the stars around / and sends the clouds a-flyin’
maria is a python-based package that simulates turbulent atmospheric emission using a auto-regressive Gaussian process framework, for applications in observational astronomy. Tutorials for installation and usage can be found in the documentation.
Background
Atmospheric modeling is an important step in both experiment design and subsequent data analysis for ground-based cosmological telescopes observing the cosmic microwave background (CMB). The next generation of ground-based CMB experiments will be marked by a huge increase in data acquisition: telescopes like AtLAST and CMB-S4 will consist of hundreds of thousands of superconducting polarization-sensitive bolometers sampling the sky. This necessitates new methods of efficiently modeling and simulating atmospheric emission at small angular resolutions, with algorithms than can keep up with the high throughput of modern telescopes.
maria simulates layers of turbulent atmospheric emission according to a statistical model derived from observations of the atmosphere in the Atacama Desert, from the Atacama Cosmology Telescope (ACT) and the Atacama B-Mode Search (ABS). It uses a sparse-precision auto-regressive Gaussian process algorithm that allows for both fast simulation of high-resolution atmosphere, as well as the ability to simulate arbitrarily long periods of atmospheric evolution.
Methodology
maria auto-regressively simulates an multi-layeed two-dimensional “integrated” atmospheric model that is much cheaper to compute than a three-dimensional model, which can effectively describe time-evolving atmospheric emission. maria can thus effectively simulate correlated atmospheric emission for in excess of 100,000 detectors observing the sky concurrently, at resolutions as fine as one arcminute. The atmospheric model used is detailed here.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file maria-1.0.0.tar.gz.
File metadata
- Download URL: maria-1.0.0.tar.gz
- Upload date:
- Size: 59.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.0.0 CPython/3.12.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 22673ab55be2308cc3a428d459d65f753ac82ff05baa953836a69a5382f5e887 |
|
| MD5 | a05d10ae0e262083b6f60d7ebcd6edcf |
|
| BLAKE2b-256 | ab5ae1b96cbe3f930a3fc9461a762fcca4a4110d5b762948acae2b97009ecbd1 |
Provenance
File details
Details for the file maria-1.0.0-py3-none-any.whl.
File metadata
- Download URL: maria-1.0.0-py3-none-any.whl
- Upload date:
- Size: 93.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.0.0 CPython/3.12.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 163284ce5e487df5fbf8ccc72f367bfaf120a1bc1ec8aaaf8c277cb8a4f714a7 |
|
| MD5 | f974399b4a5855fcf17735abb8fdcaf6 |
|
| BLAKE2b-256 | d35a0e911e5d4aeb46553350dfcdeb85170b3788ec74d6616bf16503c8058b11 |
