Estimating microlensing parameters from observables and stellar isochrones with pyLIMASS
We present pyLIMASS, a novel algorithm for estimating the physical properties of the lensing system in microlensing events. The main idea of pyLIMASS is to combine all available information regarding the microlensing event, defined as observables, and to estimate the parameter distributions of the s...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
July
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| In: |
The astronomical journal
Year: 2024, Volume: 168, Pages: 1-16 |
| ISSN: | 1538-3881 |
| DOI: | 10.3847/1538-3881/ad4862 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.3847/1538-3881/ad4862 Verlag, kostenfrei, Volltext: https://dx.doi.org/10.3847/1538-3881/ad4862 
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| Author Notes: | E. Bachelet, M. Hundertmark, and S. Calchi Novati |
MARC
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| 520 | |a We present pyLIMASS, a novel algorithm for estimating the physical properties of the lensing system in microlensing events. The main idea of pyLIMASS is to combine all available information regarding the microlensing event, defined as observables, and to estimate the parameter distributions of the system, such as the lens mass and distance. The algorithm is based on isochrones for the stars model and combines the observables using a Gaussian mixture approach. After describing the mathematical formalism and its implementation, we discuss the algorithm’s performance on simulated and published events. Generally, the pyLIMASS estimations are in good agreement (i.e., within 1σ) with the results of the selected published events, making it an effective tool to estimate the lens properties and their distribution. The applicability of the method was tested by using a catalog of realistically simulated events that could be observed by the future Galactic Bulge Time Domain Survey of the Nancy Grace Roman Space Telescope. By solely using constraints from the Roman lightcurves and images, pyLIMASS estimates the masses of the lens of the Roman catalog with a median precision of 20% with almost no bias. | ||
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