Microlensify: a Transformer Based Machine Learning Classifier for Microlensing Events Trained on TESS Light Curves

Atousa Kalantari; Somayeh Khakpash; Sedighe Sajadian; Hosein Haghi; Willow Fox Fortino; Rosanne Di Stefano

astro-ph.IMAug 19, 2026

Microlensify: a Transformer Based Machine Learning Classifier for Microlensing Events Trained on TESS Light Curves

Authors: Atousa Kalantari, Somayeh Khakpash, Sedighe Sajadian, Hosein Haghi, Willow Fox Fortino, Rosanne Di Stefano

Organizations: Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran · Department of Physics, Lehigh University, 16 Memorial Drive East, Bethlehem, PA 18015, USA · Department of Astronomy, University of Virginia, 530 McCormick Road, Charlottesville, VA 22904, USA · Department of Physics, Isfahan University of Technology, Isfahan 84156-83111, Iran · University of Delaware, Department of Physics and Astronomy, 217 Sharp Lab, Newark, DE 19716 USA · Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, 02138, USA

Abstract

Microlensing can reveal populations of faint compact objects that are otherwise difficult to detect. Depending on their design, all-sky surveys have the potential to search for these objects across the sky. The Transiting Exoplanet Survey Satellite (TESS), primarily designed to detect transiting exoplanets, also provides near all-sky coverage with high cadence. In this work, we use TESS data to search for microlensing candidates using both traditional and machine-learning methods and to identify associated false positives in high-cadence surveys. Microlensify is a physics-informed, transformer-based variational autoencoder trained on simulated single-lens microlensing light curves and real TESS Sector 12 data. The model classifies events, reconstructs light curves, and estimates microlensing event durations. Applied to $\sim 5.6$ million TESS light curves, it identified between $0.036\%$ and $1.89\%$ as microlensing candidates across different TESS pipelines. After applying microlensing detection metrics and cross-matching with SIMBAD, we obtained a final list of candidates and identified false positives including long-period variables, Mira variables, cataclysmic variables, red giants, and transients. We also found Gaussian-like peaks caused by asteroid crossings, a potential source of false positives in high-cadence microlensing surveys. The model also predicts event duration with an accuracy of $R^2 = 0.97$ . The model was further tested on published events from different ground-based microlensing surveys, confirming 92.7% as microlensing, demonstrating its applicability across surveys with different cadences.

Figures & tables

Figure 1: Distribution of microlensing events from KMTNet, MOA, and OGLE, overlaid with the TESS observing fields in its first two years, plotted in ecliptic coordinates.

Figure 2: Relationship between $\log_{10}(\mathrm{err}/\mathrm{flux})$ and $\log_{10}(\mathrm{flux})$ for all data points from the 12,000 selected light curves extracted with the Eleanor pipeline, shown as green dots. The pink dashed curve shows the fitted two-dimensional polynomial function.

Figure 3: Simulated TESS microlensing light curves with the corresponding best-fit KMTNet PSPL models overplotted as pink dashed lines. Panels (a)–(c) are rejected by our quality cuts: (a) due to insufficient baseline coverage on one side of the event and low SNR; (b) and (c) due to low SNR, low $\Delta\chi^{2}_{\mathrm{KMTNet}}$ . Panels (d)–(f) satisfy all selection criteria.

Figure 4: Parameter distributions of the full microlensing simulation sample (blue) and the K-means-selected training subset of 17,000 light curves (pink). Panels show blending ratio, $t_{0}$ (0-–27.4 days as TESS observation time), $t_{E}$ (days), $u_{0}$ , $\log_{10}\rho$ , and baseline magnitude of the flat light curve. The clustered subset preserves the original distributions.

Figure 5: Architecture of Microlensify, a physics-informed transformer-based conditional variational autoencoder trained on $\sim$ 34,500 TESS (Eleanor, Sector 12) light curves. The model takes a normalized flux time series (length 940) and five statistical features as input, simultaneously classifies microlensing vs. non-microlensing candidates, reconstructs the input light curve, and predicts event duration via a physics-informed latent dimension. The example light curve shown in green is the known microlensing event Gaia20fnr (found in TESS with TIC ID 123793270); the model reconstruction is in pink produced by the decoder. For details on this event see subsection VI.2 .

Figure 6: Three true-positive microlensing light curves from the test set. Green points show the original simulation light curve, and the dashed pink line shows Microlensify’s reconstruction. For each event, the true duration and the duration predicted by Microlensify, in days, are indicated.

Figure 7: Predicted versus true microlensing event duration for all true-positive events in the test set. Microlensify achieves $R^{2}=0.97$ , MSE = 7.84, and MAE = 1.22. The teal dashed line represents the 1:1 relation, and the gray dash-dot lines indicate $\pm 5\%$ deviation.

	Eleanor Sector12	SPOC Sector12	QLP Sector12	QLP Sector33
Light curves fed to model	1,500,000	133,329	2,857,972	1,130,307
Predicted with probability=1.0	545	108	54,091	11,770
Remaining after threshold cleaning	363	86	24,907	2,383
Remaining after visual cleaning	328	69	3,554	1,532
SIMBAD Object Types in Final Clean Sample
Star	6	19	317	238

Table 1: Classification summary for TESS pipelines (Eleanor Sector 12, TESS-SPOC Sector 12, QLP Sector 12, and QLP Sector 33). Shown are the initial number of light curves processed, the number of sources assigned classification probability

P=1.0

by the Microlensify model, and the final counts after quality cleaning (

\Delta\chi^{2}_{\rm KMTNet}>400

, peak SNR

>30

, and position inside the skewness-–Von Neumann boundary with 0.1 tolerance). The lower panel lists SIMBAD object types found in the cleaned high-confidence samples. Note that the object types are not deterministic, and the model prediction is based on microlensing-like peaks, which may be caused by microlensing false positives, blending with nearby sources, systematics, or asteroid crossings.

Figure 8: Example light curves from the final high-confidence sample in each pipeline: Eleanor (Sector 12), SPOC (Sector 12), QLP (Sector 12), and QLP (Sector 33). Each panel is labeled with its TIC ID. Plotted flux is raw flux for Eleanor and SPOC pipelines, and normalized raw flux for QLP pipeline. The x-axis shows time in days (BJD $-$ 2457000). Corresponding sky coordinates (R.A., Dec), best-fit KMTNet parameters, SNR, model-predicted 4FWHM values, and SIMBAD object types are reported in Table 2 .

TIC ID	Mission	RA [ ∘ ]	DEC [ ∘ ]	$f_{0}$	$f_{1}$	$t_{\rm eff}$ [days]	$t_{0}$ (days)	$\Delta\chi^{2}$	SNR	$\mathrm{4FWHM}_{\rm pred}$ [days]	Obj Type
17115525	Eleanor 12	247.8453	-53.7273	22837.4	4288.4	14.25	1639.22	990.9	103.4	95.8	LPV* Cand
13594596	Eleanor 12	247.4081	-53.4260	6283.6	4149.7	14.25	1639.22	1055.9	38.5	52.8	LPV* Cand
10954658	Eleanor 12	247.2055	-52.9932	7591.2	1817.9	6.012	1639.001	1053.5	38.0	30.5	LPV* Cand
10949358	Eleanor 12	247.1984	-52.2926	4892.5	1160.3	14.250	1639.224	975.6	79.2	86.1	Mira
1311842998	Eleanor 12	259.6461	-63.9124	18875.4	15232.7	14.250	1639.223	1025.8	35.5	67.4	Not Found
125958042	SPOC 12	256.8225	-46.7657	43881.7	19999.7	25.333	1645.488	912.5	65.7	77.0	C*

Table 2: Parameters for the example light curves shown in Figure 8 . Listed are sky coordinates (R.A., Dec), best-fit KMTNet model parameters (

f_{0}

f_{1}

t_{\rm eff}

t_{0}

\Delta\chi^{2}_{\rm KMTNet}

), SNR, model-predicted 4FWHM values, and SIMBAD object types including long-period variables (LPV* and candidates), Mira variables, carbon stars (C*), EclBin (Eclipsing Binary), cataclysmic variables (CV*), asymptotic giant branch stars (AGB*), spectroscopic binaries (SB*), and regular stars; a fraction of sources have no counterpart in SIMBAD ("Not Found"). The flux parameters

f_{0}

and

f_{1}

are given in units of

e^{-}\,\mathrm{s}^{-1}

for Eleanor and SPOC light curves and are dimensionless for QLP light curves.

Figure 9: Light curves of three microlensing candidates (all extracted from the QLP pipeline). (a) TIC 123793270, cross-matched with Gaia20fnr, observed in TESS Sector 33; Microlensify predicts probability 1.0. (b) TIC 378615385, cross-matched with Gaia23bra, observed in Sector 64; a clear binary microlensing event well extracted by QLP but not classified as microlensing by our model. (c) Free-floating planet candidate reported by Kunimoto et al. [32] in Sector 61; Microlensify successfully recovers it as a microlensing candidate.

Figure 10: Three examples from TESS Sector 12 processed by all three light curve extraction pipelines (Eleanor, QLP, and SPOC). TIC 35302284 (SIMBAD type: AGB star): Microlensify probability 1.0 in QLP and SPOC, poor-quality Eleanor light curve. TIC 30716342 (SIMBAD type: star): clear normalization offset across the sector gap in QLP, cleanly extracted by SPOC, poor Eleanor extraction; not selected as a microlensing candidate in any pipeline. TIC 25133286 (SIMBAD type: cataclysmic variable): good-quality extraction and probability 1.0 in all three pipelines.

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Abstract

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