Microlensing Parallax Signals

• Microlensing parallax signals are deviations in light curves caused by Earth's orbital motion or dual-observatory setups, allowing determination of lens mass, distance, and velocity.
• They are measured through annual, satellite, terrestrial, or astrometric methods that require high cadence and photometric precision to capture subtle deviations.
• Robust parallax measurements break the mass–distance–velocity degeneracy, enhancing lens classification and enabling a census of dark objects like free-floating planets and black holes.

Microlensing parallax signals are deviations in gravitational microlensing light curves arising from the apparent shift in the observer’s position, typically due to the orbital motion of Earth or by utilizing simultaneous observations from two well-separated locations (e.g., ground and space-based telescopes). These signals encode crucial information required to break the degeneracy between lens mass, distance, and relative velocity in microlensing events, thus enabling the determination of physical properties—most notably, the masses and distances of otherwise unseen astrophysical objects such as planets, brown dwarfs, black holes, and free-floating planets.

1. Fundamental Principles and Mathematical Formalism

The core observable in microlensing parallax is the microlens parallax vector $\boldsymbol\pi_E$, defined as

$$\boldsymbol{\pi}_E = \frac{\pi_{\rm rel}}{\theta_E}\,\hat{\boldsymbol\mu}$$

where $\pi_{\rm rel} = \mathrm{AU}(1/D_L - 1/D_S)$ is the lens–source relative parallax, $D_L$ and $D_S$ are the lens and source distances, $\theta_E = \sqrt{\kappa M \pi_{\rm rel}}$ is the angular Einstein radius, $$\kappa = 4G/(c^2\,\mathrm{AU}) \simeq 8.14\,\mathrm{mas}/M_\odot$$, $M$ is the lens mass, and $\hat{\boldsymbol\mu}$ is the direction of lens–source relative proper motion.

The magnitude $|\boldsymbol\pi_E| = \pi_{\rm rel} / \theta_E$ encapsulates the normalized scale of the Earth's projected orbit relative to the Einstein radius, and thus is dimensionless. The mass–parallax relation is then

$M = \frac{\theta_E}{\kappa\,\pi_E}$

and, using $\pi_{\rm rel} = \pi_E\,\theta_E$, the lens distance is

$D_L = \frac{\mathrm{AU}}{\pi_E\,\theta_E + \pi_S}$

with $\pi_S = \mathrm{AU}/D_S$.

The instantaneous lens–source separation in the presence of parallax is

$$\mathbf{u}(t) = \mathbf{u}_0 + \frac{t-t_0}{t_E}\,\hat{\boldsymbol\mu} + \boldsymbol{\delta u}(t; \boldsymbol{\pi}_E)$$

where $\boldsymbol{\delta u}$ parametrizes the parallax-induced trajectory distortion, which depends on the observing configuration (annual, terrestrial, or space-based parallax).

2. Parallax Signal Origins: Observational Geometries

Microlensing parallax signals are generated in several distinct observing frameworks:

• Annual Parallax: The most common ground-based configuration, arising from the Earth's orbit around the Sun, imparts an asymmetric modulation (a “tilt”) to the microlensing light curve, most evident in long-duration events with $t_E \gtrsim 30$ days. The effect is primarily sensitive to the north and east components $(\pi_{E,N}, \pi_{E,E})$ of the parallax vector and scales as $\pi_E$ (Shin et al., 2017).
• Satellite–Earth Parallax (Space-Based): When an observatory in solar orbit (e.g., Kepler, Spitzer, Roman at L2) observes simultaneously with Earth, the projected displacement $D_\perp$ modifies the apparent peak time $t_0$ and impact parameter $u_0$. To first order,

$$\boldsymbol{\pi}_E = \frac{\mathrm{AU}}{D_\perp}(\Delta\tau, \Delta\beta)$$

where $\Delta\tau = (t_{0,\rm sat} - t_{0,\oplus})/t_E$ and $\Delta\beta = u_{0,\rm sat} - u_{0,\oplus}$ (Gould et al., 2013, Gould et al., 2013).

• Terrestrial Parallax: For high-magnification events or those observed by widely separated locations on Earth, minute shifts in $t_0$ and $u_0$ can be detected, though the baseline is much smaller than for space-based configurations (Shin et al., 2021).
• Astrometric Parallax: For objects with substantial projected Einstein radii (e.g., stellar-mass black holes), astrometric shifts in the centroid of the lensed source (sampled by high-precision instruments) provide alternative access to $\pi_E$, with the parallax amplitude in astrometry scaling as $\pi_{\rm rel}$ rather than $\pi_E$ (Sajadian et al., 2023).

3. Physical Scaling and Signal Amplitudes

Microlens parallax amplitude $\pi_E$ and detectability depend sensitively on lens properties:

• $\pi_E \propto M^{-1/2}$: High-mass lenses (e.g., black holes) have small parallax amplitudes, making photometric detection challenging; typical values for stellar lenses are $\pi_E \sim 0.1$, while for GW-mass black holes $\pi_E \sim 0.01-0.02$ (Toki et al., 2021, Karolinski et al., 2020).
• For short-timescale events (i.e., free-floating planets (FFPs), $t_E \lesssim 10$ days), $\pi_E$ can be very large (even $\pi_E \sim 10^2-10^3$), causing significant deviations in event shapes if parallax is neglected (Sangtarash et al., 24 Mar 2024).
• The observable offset scales in physical units as $\Delta u \simeq D_\perp / \tilde r_E$, where $\tilde r_E = \mathrm{AU}/\pi_E$ is the projected Einstein radius (Gould et al., 2013).

4. Simulation Results and Biases: Case Study of FFP Events

Extensive simulations of FFP microlensing with $t_E < 10$ days using Roman-like cadence illustrate the significant distortion induced by "invisible" parallax:

• In $\sim 46$% of simulated Roman FFP events, the unmodeled parallax introduces substantial lightcurve deformation ($\Delta\chi^2 > 100$), systematically biasing fitted parameters (Sangtarash et al., 24 Mar 2024).
• Dimensionless deviations exceed 0.1 for event timescale ($\delta t_E$) and normalized source size ($\delta\rho_\star$) in $\sim$27% and $\sim$69% of parallax-affected events, respectively.
• Neglecting parallax leads to over- or underestimates in $t_E$, $\rho_\star$, $u_0$, and blending fraction $f_{\mathrm{bl}}$, but the time of maximum $t_0$ is largely unaffected.
• For Roman’s projected yield of $\sim 897$ FFP events, this suggests $\sim 46$ will have both $\delta t_E > 0.1$ and $\delta \rho_\star > 0.1$ due to unaccounted parallax deformation.
• Events most susceptible to parallax distortion have closer lenses (higher $\pi_{\rm rel}$), longer $t_E$, higher blending, and smaller $\rho_\star$.

5. Strategies for Robust Parallax Measurement

Given the degeneracies and potential biases, precise and robust recovery of $\pi_E$ requires optimized observational strategies:

• Two-Site Observations: Simultaneous, dense lightcurve sampling by Roman and a well-separated secondary platform (Euclid, ground-based telescope, or low-Earth/sun-synchronous orbit satellite) recovers both components of $\boldsymbol{\pi}_E$ and breaks degeneracies (Penny et al., 2019, Yan et al., 2021).
• Cadence and Photometric Requirements: For short-$t_E$ events, high cadence ($<15$ min) and per-point photometric precision at $<$1% are necessary to resolve subtle time and amplitude offsets induced by parallax.
• Astrometric Follow-up: For massive lenses with small photometric parallax signatures, precise astrometric centroid measurements (e.g., with ELT or Roman) dramatically increase parallax measurement efficiency (Sajadian et al., 2023).
• Accounting for Finite-Source and Blending Effects: Accurate modeling of finite-source effects (source size $\rho_\star$) and blending is required to avoid further parameter entanglement, especially for high-magnification and/or short-duration events.

6. Scientific Implications and Future Prospects

The scientific rewards from secure parallax measurements are extensive:

• Lens Mass and Distance: Combined measurements of $\theta_E$ (from finite-source or astrometric effects) and $\pi_E$ yield lens mass to $\sim10-20$% precision for most planetary events and allow the mapping of planet frequencies as a function of host mass, separation, and Galactic location (Gould et al., 2013, Shin et al., 2017).
• Census of Non-Luminous and Dark Objects: Systematic application enables identification and quantification of the population of FFPs, brown dwarfs, compact remnants, and isolated black holes (Kaczmarek et al., 2022, Karolinski et al., 2020).
• Removal of Mass–Distance–Velocity Degeneracy: Correct parallax modeling (including degeneracy treatment via the “Rich Argument”) is essential for unbiased lens classification (Gould, 2020).
• Survey Optimization: Missions including Roman, Euclid, ground-based wide-field surveys, and low-Earth orbit telescopes should coordinate observing windows, optimize cadence, and synchronize alert systems to maximize parallax yields (Bachelet et al., 2019).

Inadequate accounting for parallax leads to nontrivial biases in lens parameter inference—including misclassification of mass and distance, with downstream effects on empirical mass functions and Galactic structure models. As such, future microlensing surveys targeting FFPs and other short-lived events must make multi-site, high-cadence parallax monitoring standard practice to realize the full potential of microlensing as a probe of Galactic populations (Sangtarash et al., 24 Mar 2024).