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RTModel

RTModel is a package for modeling and interpretation of microlensing events (here is an introduction for novices). It uses photometric and/or astrometric time series collected from ground and/or space telescopes to propose one or more possible models among the following:

  • Single-lens-single-source microlensing (i.e. Paczynski)
  • Single-lens-binary-source microlensing (with or without xallarap)
  • Binary-lens-single-source microlensing (including planetary microlensing, parallax and orbital motion)
  • Triple-lens-single-source microlensing (including parallax and circular orbital motion)

All models include the finite-size of the source(s).

The modeling strategy is based on a grid search in the parameter space for single-lens models, whereas a template library for binary-lens models is used including all possible geometries of the source trajectory with respect to the caustics. In addition to this global search, planets are searched where maximal deviations from a Paczynski model occurs. Triple-lens models are searched as small perturbations to binary-lens models.

The library is in the form of a standard Python package that launches specific subprocesses for different tasks. Model fitting is executed in parallel exploiting available processors in the machine. The full modeling may take from one to three hours depending on the event and on the machine speed. The results of modeling are given in the form of a text assessment file; in addition, final models are made available with their parameters and covariance matrices.

RTModel also includes a subpackage RTModel.plotmodel that allows an immediate visualization of models and the possibility to review each individual fitting process as an animated gif.

A second subpackage RTModel.templates helps the user in the visualization and customization of the template library.

Attribution

RTModel has been created by Valerio Bozza (University of Salerno) as a product of many years of direct experience on microlensing modeling (see RTModel webpage).

Any scientific use of RTModel should be acknowledged by citing the paper V.Bozza, A&A 688 (2024) 83, describing all the algorithms behind the code.

We are grateful to Greg Olmschenk, who revised the package installation in order to make it as cross-platform as possible. Antonio Consiglio collaborated to the development of the anomaly detection code. We also thank all the users who are providing suggestions, reporting bugs or failures: Etienne Bachelet, David Bennett, Jonathan Brashear, Sophie Budzik, Paolo Rota, Laura Salmeri, Stela Ishitani Silva, Yiannis Tsapras, Sigfried Vanaverbeke, Keto Zhang.

Installation

The easiest way to install RTModel is through pip install.

pip install RTModel

In alternative, you may clone this repository. Then go to the repository directory and type

pip install .

Currently, RTModel works on Linux, Windows and MacOS, requiring Python >= 3.8. A C++ compiler compatible with C++17 standard is needed for installation. RTModel uses VBMicrolensing for all calculations. You are encouraged to cite the relevant papers listed in that repository as well.

Documentation

Full documentation for the use of RTModel is available.

In the directory events we provide some microlensing data on which you may practise with RTModel.

A Jupyter notebook for quick start-up is also available in the jupyter folder.

License

RTModel is freely available to the community under the GNU Lesser General Public License Version 3 included in this repository.

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Microlensing modeling: fast and efficient exploration of the parameter space

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Readme
LGPL-3.0 license
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RTModel
RTModel is a package for modeling and interpretation of microlensing events (here is an introduction for novices). It uses photometric and/or astrometric time series collected from ground and/or space telescopes to propose one or more possible models among the following:
  • Single-lens-single-source microlensing (i.e. Paczynski)
  • Single-lens-binary-source microlensing (with or without xallarap)
  • Binary-lens-single-source microlensing (including planetary microlensing, parallax and orbital motion)
  • Triple-lens-single-source microlensing (including parallax and circular orbital motion)
All models include the finite-size of the source(s).
The modeling strategy is based on a grid search in the parameter space for single-lens models, whereas a template library for binary-lens models is used including all possible geometries of the source trajectory with respect to the caustics. In addition to this global search, planets are searched where maximal deviations from a Paczynski model occurs. Triple-lens models are searched as small perturbations to binary-lens models.
The library is in the form of a standard Python package that launches specific subprocesses for different tasks. Model fitting is executed in parallel exploiting available processors in the machine. The full modeling may take from one to three hours depending on the event and on the machine speed. The results of modeling are given in the form of a text assessment file; in addition, final models are made available with their parameters and covariance matrices.
RTModel also includes a subpackage RTModel.plotmodel that allows an immediate visualization of models and the possibility to review each individual fitting process as an animated gif.
A second subpackage RTModel.templates helps the user in the visualization and customization of the template library.
Attribution
RTModel has been created by Valerio Bozza (University of Salerno) as a product of many years of direct experience on microlensing modeling (see RTModel webpage).
Any scientific use of RTModel should be acknowledged by citing the paper V.Bozza, A&A 688 (2024) 83, describing all the algorithms behind the code.
We are grateful to Greg Olmschenk, who revised the package installation in order to make it as cross-platform as possible. Antonio Consiglio collaborated to the development of the anomaly detection code. We also thank all the users who are providing suggestions, reporting bugs or failures: Etienne Bachelet, David Bennett, Jonathan Brashear, Sophie Budzik, Paolo Rota, Laura Salmeri, Stela Ishitani Silva, Yiannis Tsapras, Sigfried Vanaverbeke, Keto Zhang.
Installation
The easiest way to install RTModel is through pip install.
pip install RTModel

In alternative, you may clone this repository. Then go to the repository directory and type
pip install .

Currently, RTModel works on Linux, Windows and MacOS, requiring Python >= 3.8.
A C++ compiler compatible with C++17 standard is needed for installation.
RTModel uses VBMicrolensing for all calculations. You are encouraged to cite the relevant papers listed in that repository as well.
Documentation
Full documentation for the use of RTModel is available.
In the directory events we provide some microlensing data on which you may practise with RTModel.
A Jupyter notebook for quick start-up is also available in the jupyter folder.
License
RTModel is freely available to the community under the
GNU Lesser General Public License Version 3 included in this repository.
About
Microlensing modeling: fast and efficient exploration of the parameter space
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LGPL-3.0 license
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RTModel
RTModel is a package for modeling and interpretation of microlensing events (here is an introduction for novices). It uses photometric and/or astrometric time series collected from ground and/or space telescopes to propose one or more possible models among the following:
  • Single-lens-single-source microlensing (i.e. Paczynski)
  • Single-lens-binary-source microlensing (with or without xallarap)
  • Binary-lens-single-source microlensing (including planetary microlensing, parallax and orbital motion)
  • Triple-lens-single-source microlensing (including parallax and circular orbital motion)
All models include the finite-size of the source(s).
The modeling strategy is based on a grid search in the parameter space for single-lens models, whereas a template library for binary-lens models is used including all possible geometries of the source trajectory with respect to the caustics. In addition to this global search, planets are searched where maximal deviations from a Paczynski model occurs. Triple-lens models are searched as small perturbations to binary-lens models.
The library is in the form of a standard Python package that launches specific subprocesses for different tasks. Model fitting is executed in parallel exploiting available processors in the machine. The full modeling may take from one to three hours depending on the event and on the machine speed. The results of modeling are given in the form of a text assessment file; in addition, final models are made available with their parameters and covariance matrices.
RTModel also includes a subpackage RTModel.plotmodel that allows an immediate visualization of models and the possibility to review each individual fitting process as an animated gif.
A second subpackage RTModel.templates helps the user in the visualization and customization of the template library.
Attribution
RTModel has been created by Valerio Bozza (University of Salerno) as a product of many years of direct experience on microlensing modeling (see RTModel webpage).
Any scientific use of RTModel should be acknowledged by citing the paper V.Bozza, A&A 688 (2024) 83, describing all the algorithms behind the code.
We are grateful to Greg Olmschenk, who revised the package installation in order to make it as cross-platform as possible. Antonio Consiglio collaborated to the development of the anomaly detection code. We also thank all the users who are providing suggestions, reporting bugs or failures: Etienne Bachelet, David Bennett, Jonathan Brashear, Sophie Budzik, Paolo Rota, Laura Salmeri, Stela Ishitani Silva, Yiannis Tsapras, Sigfried Vanaverbeke, Keto Zhang.
Installation
The easiest way to install RTModel is through pip install.
pip install RTModel

In alternative, you may clone this repository. Then go to the repository directory and type
pip install .

Currently, RTModel works on Linux, Windows and MacOS, requiring Python >= 3.8.
A C++ compiler compatible with C++17 standard is needed for installation.
RTModel uses VBMicrolensing for all calculations. You are encouraged to cite the relevant papers listed in that repository as well.
Documentation
Full documentation for the use of RTModel is available.
In the directory events we provide some microlensing data on which you may practise with RTModel.
A Jupyter notebook for quick start-up is also available in the jupyter folder.
License
RTModel is freely available to the community under the
GNU Lesser General Public License Version 3 included in this repository.
About
Microlensing modeling: fast and efficient exploration of the parameter space
Resources
Readme
LGPL-3.0 license
Activity
Stars
16 stars
Watchers
4 watching
Forks
8 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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RTModel
RTModel is a package for modeling and interpretation of microlensing events (here is an introduction for novices). It uses photometric and/or astrometric time series collected from ground and/or space telescopes to propose one or more possible models among the following:
  • Single-lens-single-source microlensing (i.e. Paczynski)
  • Single-lens-binary-source microlensing (with or without xallarap)
  • Binary-lens-single-source microlensing (including planetary microlensing, parallax and orbital motion)
  • Triple-lens-single-source microlensing (including parallax and circular orbital motion)
All models include the finite-size of the source(s).
The modeling strategy is based on a grid search in the parameter space for single-lens models, whereas a template library for binary-lens models is used including all possible geometries of the source trajectory with respect to the caustics. In addition to this global search, planets are searched where maximal deviations from a Paczynski model occurs. Triple-lens models are searched as small perturbations to binary-lens models.
The library is in the form of a standard Python package that launches specific subprocesses for different tasks. Model fitting is executed in parallel exploiting available processors in the machine. The full modeling may take from one to three hours depending on the event and on the machine speed. The results of modeling are given in the form of a text assessment file; in addition, final models are made available with their parameters and covariance matrices.
RTModel also includes a subpackage RTModel.plotmodel that allows an immediate visualization of models and the possibility to review each individual fitting process as an animated gif.
A second subpackage RTModel.templates helps the user in the visualization and customization of the template library.
Attribution
RTModel has been created by Valerio Bozza (University of Salerno) as a product of many years of direct experience on microlensing modeling (see RTModel webpage).
Any scientific use of RTModel should be acknowledged by citing the paper V.Bozza, A&A 688 (2024) 83, describing all the algorithms behind the code.
We are grateful to Greg Olmschenk, who revised the package installation in order to make it as cross-platform as possible. Antonio Consiglio collaborated to the development of the anomaly detection code. We also thank all the users who are providing suggestions, reporting bugs or failures: Etienne Bachelet, David Bennett, Jonathan Brashear, Sophie Budzik, Paolo Rota, Laura Salmeri, Stela Ishitani Silva, Yiannis Tsapras, Sigfried Vanaverbeke, Keto Zhang.
Installation
The easiest way to install RTModel is through pip install.
pip install RTModel

In alternative, you may clone this repository. Then go to the repository directory and type
pip install .

Currently, RTModel works on Linux, Windows and MacOS, requiring Python >= 3.8.
A C++ compiler compatible with C++17 standard is needed for installation.
RTModel uses VBMicrolensing for all calculations. You are encouraged to cite the relevant papers listed in that repository as well.
Documentation
Full documentation for the use of RTModel is available.
In the directory events we provide some microlensing data on which you may practise with RTModel.
A Jupyter notebook for quick start-up is also available in the jupyter folder.
License
RTModel is freely available to the community under the
GNU Lesser General Public License Version 3 included in this repository.
About
Microlensing modeling: fast and efficient exploration of the parameter space
Resources
Readme
LGPL-3.0 license
Activity
Stars
16 stars
Watchers
4 watching
Forks
8 forks
Report repository
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Used by
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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RTModel
RTModel is a package for modeling and interpretation of microlensing events (here is an introduction for novices). It uses photometric and/or astrometric time series collected from ground and/or space telescopes to propose one or more possible models among the following:
  • Single-lens-single-source microlensing (i.e. Paczynski)
  • Single-lens-binary-source microlensing (with or without xallarap)
  • Binary-lens-single-source microlensing (including planetary microlensing, parallax and orbital motion)
  • Triple-lens-single-source microlensing (including parallax and circular orbital motion)
All models include the finite-size of the source(s).
The modeling strategy is based on a grid search in the parameter space for single-lens models, whereas a template library for binary-lens models is used including all possible geometries of the source trajectory with respect to the caustics. In addition to this global search, planets are searched where maximal deviations from a Paczynski model occurs. Triple-lens models are searched as small perturbations to binary-lens models.
The library is in the form of a standard Python package that launches specific subprocesses for different tasks. Model fitting is executed in parallel exploiting available processors in the machine. The full modeling may take from one to three hours depending on the event and on the machine speed. The results of modeling are given in the form of a text assessment file; in addition, final models are made available with their parameters and covariance matrices.
RTModel also includes a subpackage RTModel.plotmodel that allows an immediate visualization of models and the possibility to review each individual fitting process as an animated gif.
A second subpackage RTModel.templates helps the user in the visualization and customization of the template library.
Attribution
RTModel has been created by Valerio Bozza (University of Salerno) as a product of many years of direct experience on microlensing modeling (see RTModel webpage).
Any scientific use of RTModel should be acknowledged by citing the paper V.Bozza, A&A 688 (2024) 83, describing all the algorithms behind the code.
We are grateful to Greg Olmschenk, who revised the package installation in order to make it as cross-platform as possible. Antonio Consiglio collaborated to the development of the anomaly detection code. We also thank all the users who are providing suggestions, reporting bugs or failures: Etienne Bachelet, David Bennett, Jonathan Brashear, Sophie Budzik, Paolo Rota, Laura Salmeri, Stela Ishitani Silva, Yiannis Tsapras, Sigfried Vanaverbeke, Keto Zhang.
Installation
The easiest way to install RTModel is through pip install.
pip install RTModel

In alternative, you may clone this repository. Then go to the repository directory and type
pip install .

Currently, RTModel works on Linux, Windows and MacOS, requiring Python >= 3.8.
A C++ compiler compatible with C++17 standard is needed for installation.
RTModel uses VBMicrolensing for all calculations. You are encouraged to cite the relevant papers listed in that repository as well.
Documentation
Full documentation for the use of RTModel is available.
In the directory events we provide some microlensing data on which you may practise with RTModel.
A Jupyter notebook for quick start-up is also available in the jupyter folder.
License
RTModel is freely available to the community under the
GNU Lesser General Public License Version 3 included in this repository.
About
Microlensing modeling: fast and efficient exploration of the parameter space
Resources
Readme
LGPL-3.0 license
Activity
Stars
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Watchers
4 watching
Forks
8 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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RTModel
RTModel is a package for modeling and interpretation of microlensing events (here is an introduction for novices). It uses photometric and/or astrometric time series collected from ground and/or space telescopes to propose one or more possible models among the following:
  • Single-lens-single-source microlensing (i.e. Paczynski)
  • Single-lens-binary-source microlensing (with or without xallarap)
  • Binary-lens-single-source microlensing (including planetary microlensing, parallax and orbital motion)
  • Triple-lens-single-source microlensing (including parallax and circular orbital motion)
All models include the finite-size of the source(s).
The modeling strategy is based on a grid search in the parameter space for single-lens models, whereas a template library for binary-lens models is used including all possible geometries of the source trajectory with respect to the caustics. In addition to this global search, planets are searched where maximal deviations from a Paczynski model occurs. Triple-lens models are searched as small perturbations to binary-lens models.
The library is in the form of a standard Python package that launches specific subprocesses for different tasks. Model fitting is executed in parallel exploiting available processors in the machine. The full modeling may take from one to three hours depending on the event and on the machine speed. The results of modeling are given in the form of a text assessment file; in addition, final models are made available with their parameters and covariance matrices.
RTModel also includes a subpackage RTModel.plotmodel that allows an immediate visualization of models and the possibility to review each individual fitting process as an animated gif.
A second subpackage RTModel.templates helps the user in the visualization and customization of the template library.
Attribution
RTModel has been created by Valerio Bozza (University of Salerno) as a product of many years of direct experience on microlensing modeling (see RTModel webpage).
Any scientific use of RTModel should be acknowledged by citing the paper V.Bozza, A&A 688 (2024) 83, describing all the algorithms behind the code.
We are grateful to Greg Olmschenk, who revised the package installation in order to make it as cross-platform as possible. Antonio Consiglio collaborated to the development of the anomaly detection code. We also thank all the users who are providing suggestions, reporting bugs or failures: Etienne Bachelet, David Bennett, Jonathan Brashear, Sophie Budzik, Paolo Rota, Laura Salmeri, Stela Ishitani Silva, Yiannis Tsapras, Sigfried Vanaverbeke, Keto Zhang.
Installation
The easiest way to install RTModel is through pip install.
pip install RTModel

In alternative, you may clone this repository. Then go to the repository directory and type
pip install .

Currently, RTModel works on Linux, Windows and MacOS, requiring Python >= 3.8.
A C++ compiler compatible with C++17 standard is needed for installation.
RTModel uses VBMicrolensing for all calculations. You are encouraged to cite the relevant papers listed in that repository as well.
Documentation
Full documentation for the use of RTModel is available.
In the directory events we provide some microlensing data on which you may practise with RTModel.
A Jupyter notebook for quick start-up is also available in the jupyter folder.
License
RTModel is freely available to the community under the
GNU Lesser General Public License Version 3 included in this repository.
About
Microlensing modeling: fast and efficient exploration of the parameter space
Resources
Readme
LGPL-3.0 license
Activity
Stars
16 stars
Watchers
4 watching
Forks
8 forks
Report repository
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Used by
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RTModel
RTModel is a package for modeling and interpretation of microlensing events (here is an introduction for novices). It uses photometric and/or astrometric time series collected from ground and/or space telescopes to propose one or more possible models among the following:
  • Single-lens-single-source microlensing (i.e. Paczynski)
  • Single-lens-binary-source microlensing (with or without xallarap)
  • Binary-lens-single-source microlensing (including planetary microlensing, parallax and orbital motion)
  • Triple-lens-single-source microlensing (including parallax and circular orbital motion)
All models include the finite-size of the source(s).
The modeling strategy is based on a grid search in the parameter space for single-lens models, whereas a template library for binary-lens models is used including all possible geometries of the source trajectory with respect to the caustics. In addition to this global search, planets are searched where maximal deviations from a Paczynski model occurs. Triple-lens models are searched as small perturbations to binary-lens models.
The library is in the form of a standard Python package that launches specific subprocesses for different tasks. Model fitting is executed in parallel exploiting available processors in the machine. The full modeling may take from one to three hours depending on the event and on the machine speed. The results of modeling are given in the form of a text assessment file; in addition, final models are made available with their parameters and covariance matrices.
RTModel also includes a subpackage RTModel.plotmodel that allows an immediate visualization of models and the possibility to review each individual fitting process as an animated gif.
A second subpackage RTModel.templates helps the user in the visualization and customization of the template library.
Attribution
RTModel has been created by Valerio Bozza (University of Salerno) as a product of many years of direct experience on microlensing modeling (see RTModel webpage).
Any scientific use of RTModel should be acknowledged by citing the paper V.Bozza, A&A 688 (2024) 83, describing all the algorithms behind the code.
We are grateful to Greg Olmschenk, who revised the package installation in order to make it as cross-platform as possible. Antonio Consiglio collaborated to the development of the anomaly detection code. We also thank all the users who are providing suggestions, reporting bugs or failures: Etienne Bachelet, David Bennett, Jonathan Brashear, Sophie Budzik, Paolo Rota, Laura Salmeri, Stela Ishitani Silva, Yiannis Tsapras, Sigfried Vanaverbeke, Keto Zhang.
Installation
The easiest way to install RTModel is through pip install.
pip install RTModel

In alternative, you may clone this repository. Then go to the repository directory and type
pip install .

Currently, RTModel works on Linux, Windows and MacOS, requiring Python >= 3.8.
A C++ compiler compatible with C++17 standard is needed for installation.
RTModel uses VBMicrolensing for all calculations. You are encouraged to cite the relevant papers listed in that repository as well.
Documentation
Full documentation for the use of RTModel is available.
In the directory events we provide some microlensing data on which you may practise with RTModel.
A Jupyter notebook for quick start-up is also available in the jupyter folder.
License
RTModel is freely available to the community under the
GNU Lesser General Public License Version 3 included in this repository.
About
Microlensing modeling: fast and efficient exploration of the parameter space
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RTModel
RTModel is a package for modeling and interpretation of microlensing events (here is an introduction for novices). It uses photometric and/or astrometric time series collected from ground and/or space telescopes to propose one or more possible models among the following:
  • Single-lens-single-source microlensing (i.e. Paczynski)
  • Single-lens-binary-source microlensing (with or without xallarap)
  • Binary-lens-single-source microlensing (including planetary microlensing, parallax and orbital motion)
  • Triple-lens-single-source microlensing (including parallax and circular orbital motion)
All models include the finite-size of the source(s).
The modeling strategy is based on a grid search in the parameter space for single-lens models, whereas a template library for binary-lens models is used including all possible geometries of the source trajectory with respect to the caustics. In addition to this global search, planets are searched where maximal deviations from a Paczynski model occurs. Triple-lens models are searched as small perturbations to binary-lens models.
The library is in the form of a standard Python package that launches specific subprocesses for different tasks. Model fitting is executed in parallel exploiting available processors in the machine. The full modeling may take from one to three hours depending on the event and on the machine speed. The results of modeling are given in the form of a text assessment file; in addition, final models are made available with their parameters and covariance matrices.
RTModel also includes a subpackage RTModel.plotmodel that allows an immediate visualization of models and the possibility to review each individual fitting process as an animated gif.
A second subpackage RTModel.templates helps the user in the visualization and customization of the template library.
Attribution
RTModel has been created by Valerio Bozza (University of Salerno) as a product of many years of direct experience on microlensing modeling (see RTModel webpage).
Any scientific use of RTModel should be acknowledged by citing the paper V.Bozza, A&A 688 (2024) 83, describing all the algorithms behind the code.
We are grateful to Greg Olmschenk, who revised the package installation in order to make it as cross-platform as possible. Antonio Consiglio collaborated to the development of the anomaly detection code. We also thank all the users who are providing suggestions, reporting bugs or failures: Etienne Bachelet, David Bennett, Jonathan Brashear, Sophie Budzik, Paolo Rota, Laura Salmeri, Stela Ishitani Silva, Yiannis Tsapras, Sigfried Vanaverbeke, Keto Zhang.
Installation
The easiest way to install RTModel is through pip install.
pip install RTModel

In alternative, you may clone this repository. Then go to the repository directory and type
pip install .

Currently, RTModel works on Linux, Windows and MacOS, requiring Python >= 3.8.
A C++ compiler compatible with C++17 standard is needed for installation.
RTModel uses VBMicrolensing for all calculations. You are encouraged to cite the relevant papers listed in that repository as well.
Documentation
Full documentation for the use of RTModel is available.
In the directory events we provide some microlensing data on which you may practise with RTModel.
A Jupyter notebook for quick start-up is also available in the jupyter folder.
License
RTModel is freely available to the community under the
GNU Lesser General Public License Version 3 included in this repository.
About
Microlensing modeling: fast and efficient exploration of the parameter space
Resources
Readme
LGPL-3.0 license
Activity
Stars
16 stars
Watchers
4 watching
Forks
8 forks
Report repository
Releases
Packages
Used by
Contributors
Languages