A real OGLE-IV case study · Galactic bulge

One star became 3.37× brighter.
Was gravity the lens?

Microlensing does not photograph the foreground object. It reveals it when gravity bends a background star’s light into a smooth, temporary brightening. Here, 360 public measurements are tested against that prediction.

Event
OGLE-2023-BLG-0100
Peak date
26 Feb 2023
Best-fit timescale
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OGLE I-band finding chart of the two arcminute field around OGLE-2023-BLG-0100, with the target marked by crosshairs
Real survey image OGLE I-band finding chart · 2′ × 2′ · north up, east left
01 / PHYSICS

The alignment is invisible.
The light curve is not.

A foreground lens passes near our line of sight. Its gravity makes multiple unresolved images of the source star, increasing the total light. For one isolated lens moving steadily, the brightening should be smooth and nearly symmetric.

observer
lens
source

02 / EVIDENCE

Test the signature, one question at a time.

Loading the public OGLE photometry…

03 / MODEL AUDIT

Three explanations enter.
Only one follows the peak.

A constant star is the null hypothesis. A point-source/point-lens curve is fitted both with and without extra unresolved blend light. Lower BIC is preferred because it rewards fit quality but penalises unnecessary parameters.

ExplanationParametersReduced χ²BICReading

WHAT THE FIT ACTUALLY MEANS

Three numbers describe the shape—not the identity of the lens.

04 / RESEARCH BOUNDARY

The light curve detects lensing.
It does not weigh the lens by itself.

Supported here

  • A transient, symmetric brightening
  • A PSPL event timescale and impact parameter
  • Model residuals and information criteria

Still degenerate

  • Unique lens mass and distance
  • Transverse velocity
  • A planetary companion

Fitted relation

F(t) = FsA[u(t)] + Fb

The data mildly prefer no additional blend parameter: ΔBIC is shown above.

REPRODUCIBILITY

From survey file to browser figure.

  1. Acquire360 I-band epochs from the official OGLE EWS file.
  2. TransformMagnitude to relative flux with propagated reported errors.
  3. FitBounded least squares for t₀, tE, u₀, source flux and blend flux.
  4. AuditResiduals, χ², BIC, source hash and a regenerating Python script.