A Reanalysis of Public Galactic Bulge Gravitational Microlensing Events from OGLE-III and -IV

Abstract

Modern surveys of gravitational microlensing events have progressed to detecting thousands per year, and surveys are capable of probing Galactic structure, stellar evolution, lens populations, black hole physics, and the nature of dark matter. One of the key avenues for doing this is the microlensing Einstein radius crossing time (t E) distribution. However, systematics in individual light curves as well as oversimplistic modeling can lead to biased results. To address this, we developed a model to simultaneously handle the microlensing parallax due to Earth's motion, systematic instrumental effects, and unlensed stellar variability with a Gaussian process model. We used light curves for nearly 10,000 OGLE-III and -IV Milky Way bulge microlensing events and fit each with our model. We also developed a forward model approach to infer the t E distribution by forward modeling from the data rather than using point estimates from individual events. We find that modeling the variability in the baseline removes a source of significant bias in individual events, and the previous analyses overestimated the number of t E > 100 day events due to their oversimplistic model ignoring parallax effects. We use our fits to identify the hundreds filling a regime in the microlensing parameter space that are 50% pure of black holes. Finally, we have released the largest-ever catalog of Markov Chain Monte Carlo parameter estimates for microlensing events.

Probing the Milky Way Stellar and Brown Dwarf Initial Mass Function with Modern Microlensing Observations

“…This is no longer true for long-time events. Such a reanalysis of the OGLE-III and OGLE-IV data was recently conducted by Golovich et al (2022). As shown by these authors, the Earth's parallax correction decreases t E for the long-time events, yielding a distribution similar to the one displayed in our Figure 2 for f s > 0.2 (see their Figure 12).…”

Supplement: “An Isolated Mass-gap Black Hole or Neutron Star Detected with Astrometric Microlensing” (2022, ApJL, 933, L23)

“…The Gaussian process (GP) model is plotted on top of the residual. We emphasize that the residual is not independently fit by the GP but is simultaneously fit with the model parameters; this is purely to visualize the data (also see Golovich et al 2022). See Section 5 for more details about the fitting procedure.…”

“…If the errors are not correlated, K α is diagonal and the likelihood reduces to the familiar form where σ i is the uncertainty on data point y i and χ 2 is the "goodness of fit." Following Golovich et al (2022), a damped driven simple harmonic oscillator (DDSHO) kernel κ DDSHO added to a Matérn-3/2 kernel κ M3/2 is used to model the correlated noise in the photometric microlensing survey light curves. Both these kernels are stationary, as they are a function of the differences of the times only:…”

“…The ground-based photometry includes correlated noise we fit. We follow a very similar parameterization to Golovich et al (2022) for the GP priors. The main difference is that fit in magnitude space instead of flux space, and so our priors are also in magnitudes instead of fluxes.…”

The Impact of Initial–Final Mass Relations on Black Hole Microlensing

“…An analysis of the distributions of π E and t E can be used to statistically constrain the Milky Way BH population (Golovich et al 2022). The difference in the compact object mass distribution between the IFMRs is reflected in the difference between the distributions for Einstein crossing time and microlensing parallax, as shown in Figures 7 and 8.…”