Roman and Rubin microlensing simulations

Roman and Rubin simulations and analisys.

The repository contains simulations and analyses to study the impact of combining observations of Roman and Rubin microlensing events.

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Analysis Results

The all_results directory contains analysis results for a set of events corresponding to Free Floating Planets (FFP), Black Holes (BH), and Bound Planets (PB).

File Descriptions

• true.csv: Contains the true simulation parameters.
• fit_rr.csv: Contains the estimated parameters and uncertainties from the fit using data from both the Roman and Rubin observatories.
• fit_roman.csv: Contains similar information as fit_rr.csv but using only Roman data.

Column Descriptions

Each of these files includes the following columns:

• Event Identifiers:

  • Source: Identification number of the event.
  • Set: Set identifier, as events are generated in different sets.

• Microlensing Parameters:

  • t0: Time of maximum magnification.
  • u0: Impact parameter.
  • te: Einstein timescale.
  • rho: Ratio of the source’s angular radius to the Einstein angular radius.
  • s: Separation between lenses, in units of Einstein radius (θE).
  • q: Mass ratio of the lenses.
  • alpha: Angle between the lens axis and line of sight.
  • piEN: North component of the parallax.
  • piEE: East component of the parallax.

• Uncertainties for Each Parameter:

  • t0_err: Uncertainty in t0.
  • u0_err: Uncertainty in u0.
  • te_err: Uncertainty in te.
  • rho_err: Uncertainty in rho.
  • s_err: Uncertainty in s.
  • q_err: Uncertainty in q.
  • alpha_err: Uncertainty in alpha.
  • piEN_err: Uncertainty in piEN.
  • piEE_err: Uncertainty in piEE.

• Additional Parameters:

  • piE: Total parallax magnitude.
  • piE_err: Uncertainty in the total parallax.
  • piE_err_MC: Monte Carlo-derived uncertainty in the total parallax.

• Mass-Related Parameters:

  • mass_thetaE: Mass estimate derived from θE.
  • mass_mu: Mass estimate derived from proper motion.
  • mass_thetaS: Mass estimate derived from the source’s angular radius.
  • err_mass_thetaE_NotMC: Non-Monte Carlo uncertainty in mass_thetaE.
  • mass_err_thetaE: Uncertainty in mass_thetaE.
  • mass_err_mu: Uncertainty in mass_mu.
  • mass_err_thetaS: Uncertainty in mass_thetaS.

• Fit Quality Metrics:

  • chichi: Fit quality parameter.
  • dof: Degrees of freedom for the fit.
  • chi2: Chi-squared value of the fit.

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Notebooks with metrics

The notebooks in the notebooks directory contains three notebooks

• Binary_Lens_results.ipynb
• FFP_results.ipynb
• BH_results.ipynb these notebooks contain the plot of the metrics

, ,

Parallax uncertainty propagation

In the results you can find two propagation of uncertainty one using the error propagation formulae for a set of functions which all depend on the n random variables , thus

The second is using a montecarlo aproach by generating samples using the covariance matrix in a multinormal distribution, the covariance matrix is provided by the TRF routine in pyLIMA.

Mass estimation

We run three test for the mass estimation using

.

• Assuming known . We use only the information about the estimation of and its uncertainty.
• Assuming known . We use the information about the estimation of and its uncertainty and the estimation of and its uncertainty to compute and propagate its uncertainty.
• Assuming known . We use the information about the estimation of and its uncertainty and the estimation of and its uncertainty to compute and propagate its uncertainty.

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Fit and simulation

The code functions_roman_rubin.py contains the fit routine and the simulation using pyLIMA and rubin_sim.

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Functions

1. tel_roman_rubin

Purpose:
Simulates telescope observations for Rubin Observatory and Roman Space Telescope, creating synthetic light curves for microlensing events.

Inputs:

• path_ephemerides: Path to ephemerides file for spacecraft positions.
• path_dataslice: Path to Rubin data slice file.

Outputs:

• A microlensing event object with telescope data.

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2. deviation_from_constant

Purpose:
Checks if there are at least four data points within [t0 - tE, t0 + tE] that deviate from the constant flux baseline by more than 3σ.

Inputs:

• pyLIMA_parameters: Parameters describing the microlensing model.
• pyLIMA_telescopes: Telescope data objects with light curves.

Outputs:

• A boolean indicating whether the deviation condition is satisfied.

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3. filter5points

Purpose:
Ensures that at least one light curve contains at least five data points within the range [t0 - tE, t0 + tE].

Inputs:

• pyLIMA_parameters: Microlensing model parameters.
• pyLIMA_telescopes: Telescope data objects with light curves.

Outputs:

• A boolean indicating whether the condition is met.

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4. mag

Purpose:
Converts flux measurements into magnitudes.

Inputs:

• zp: Zero-point magnitude.
• Flux: Light curve flux values.

Outputs:

• Magnitudes corresponding to the input flux values.

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5. filter_band

Purpose:
Filters light curve data based on magnitude limits and 5σ depth criteria, ensuring that the curve contains sufficient points for analysis.

Inputs:

• mjd: Modified Julian Dates.
• mag: Magnitudes.
• magerr: Magnitude errors.
• m5: 5σ limiting magnitudes.
• fil: Filter name.

Outputs:

• Filtered light curve data points and a boolean indicating significant detections.

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6. has_consecutive_numbers

Purpose:
Checks if there are at least three consecutive numbers in a list.

Inputs:

• lst: List of integers.

Outputs:

• A boolean indicating if the condition is met.

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7. set_photometric_parameters

Purpose:
Configures photometric parameters, including exposure time and read noise.

Inputs:

• exptime: Exposure time.
• nexp: Number of exposures.
• readnoise: (Optional) Read noise in electrons per pixel.

Outputs:

• A photometric parameters object.

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8. fit_rubin_roman

Purpose:
Performs model fitting for Rubin and Roman telescope data using various microlensing models (e.g., FSPL, USBL, PSPL).

Inputs:

• Parameters for the event, model type, algorithm, and light curves for Rubin and Roman data.

Outputs:

• Fit results and associated event data.

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9. save

Purpose:
Saves processed event data, light curves, and model parameters to an HDF5 file.

Inputs:

• Event index, paths to save location, and model parameters.

Outputs:

• HDF5 file containing the saved data.

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10. read_data

Purpose:
Reads event data (simulated) for further processing.