Skip to main content

Astrometric microlensing prediction with Gaia sources

Project description

GAML: Astrometric MicroLensing prediction using Gaia's data

This Python package searches for astrometric gravitational microlensing events given a list of lens-source pairs, and output quality assessments and astrometric and photometric microlensing effects of significant lensing events.

This project has evolved from Klüter's amlensing, with the following improvements:

  • major overhaul to standardize and generalize the codebase
  • substantial refactors to adapt for general lensing objects and background sources
    • E.g., allow setting their mass, mass error, and individual epochs
  • makes it easier to prepare the input data files, which was abcent in the original code
  • also a few bug fixes, which affects the result (most slightly)

For more detailed changes of this fork, see CHANGES.md and the commit log.

What does it do

  1. GAML will perform several filters to exclude lenses and sources with low quality.
  2. By predicting the motion over a specific time span, GAML determines the time and angular separation of lens-source closest approach.
  3. By sampling the angular Einstein ring radius and angular separation, it calculates the astrometric and photometric observables of the gravitational microlensing event.
    • Such as centroid shift, positive image shift, centroid shift with a luminous lens, and magnification.

Although there are quite some changes from the original codebase, but it is still recommended to read Kluter 2022 for theoretical details.

Documentation

For further documentations, see the docs folder

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

amlensing-0.1.2-py3-none-any.whl (40.6 kB view details)

Uploaded Python 3

File details

Details for the file amlensing-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: amlensing-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 40.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for amlensing-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 aaa3b2b5b35db82b1b2d4844384b1ef186cd59327c82b25512bfa84880a2a692
MD5 61fc15c848ff580a6721005c69ba4743
BLAKE2b-256 e65e976e551c7dda15b6755993cb458a9a28fa5053691a1f344210cab53cffa5

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page