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Abstract The mass function (MF) of isolated objects measured by microlensing consists of both a stellar and a planetary component. We compare the microlensing MFs of A. Gould et al. and T. Sumi et al. to other measurements of the MF. The abundance of brown dwarfs from the tail of the T. Sumi et al. stellar MF is consistent with measurements from the local solar neighborhood. Microlensing free-floating planets (μFFPs) may be free-floating or orbit host stars with semimajor axesa ≳  10 au and therefore can constrain the populations of both free-floating and wide-orbit planets. Comparisons to radial velocity and direct imaging low-mass companion populations suggest that either most of theμFFP population with masses  > 1M_Jupis bound to hosts more massive than M dwarfs, or some fraction of the observed companion population 1M_Jup < m_p <  0.08M_⊙actually comes from the low-mass tail of the stellar MF. TheμFFP population also places strong constraints on planets inferred from debris disks and gaps in protoplanetary disks observed by the Atacama Large Millimeter/submillimeter Array. 

Abstract To exhume the buried signatures of free-floating planets (FFPs) with small angular Einstein radiusθ_E, we build a new full-frame difference image pipeline for the Korean Microlensing Telescope Network (KMTNet) survey based on the newly optimized pySIS package. We introduce the detailed processes of the new pipeline, including frame registration, difference image analysis, and light curve extraction. To test this pipeline, we extract one-year light curves for 483,068 stars withI ≲ 17 and conduct a model-independent search for microlensing events. The search finds 36 microlensing events, including five new events and six events discovered by other collaborations but missed by previous KMTNet searches. We find that the light curves from the new pipeline are precise enough to be sensitive to FFPs withθ_E ∼ 1μas. Using the new pipeline, a complete FFP search on the eight-year KMTNet images can be finished within six months and then yield the FFP mass function. The new pipeline can be used for a new KMTNet AlertFinder system, with significantly reduced false positives. 

Abstract To conduct a comprehensive demographic study of microlensing planets, it is essential to detect all planetary signals that exceed a predefined threshold through a detailed analysis of survey data. We reanalyzed previous data from the Korea Microlensing Telescope Network survey to search for weak planetary signals in lensing events involving faint source stars. For events with potential short-term anomalies identified in the initial search, we validated the signals using rereduced data and conducted detailed modeling of the anomalous events. This process led to the discovery of four planetary events: KMT-2017-BLG-2197, KMT-2022-BLG-1790, KMT-2022-BLG-2076, and KMT-2023-BLG-2209. For all these events, the modeling resulted in two solutions due to the well-known inner–outer degeneracy. The estimated masses of the planets and their hosts are approximately (M_p/M_J, M_h/M_⊙) ∼ (0.36, 7.9) for KMT-2017-BLG-2197L, ∼(0.6, 1.7) for KMT-2022-BLG-1790L, ∼(0.67, 0.9) for KMT-2022-BLG-2076L, and ∼(0.73, 0.8) for KMT-2023-BLG-2209L. The planetary systems KMT-2017-BLG-2197L and KMT-2022-BLG-1790L are likely located in the Galactic bulge, while KMT-2022-BLG-2076L and KMT-2023-BLG-2209L are more likely situated in the disk. 

Abstract In this work, we continue to apply the updated KMTNet tender-love care photometric pipeline to historical microlensing events. We apply the pipeline to a subsample of events from the KMTNet database, which we refer to as the giant source sample. Leveraging the improved photometric data, we conduct a systematic search for anomalies within this sample. The search successfully uncovers four new planet-like anomalies and recovers two previously known planetary signals. After detailed analysis, two of the newly discovered anomalies are confirmed as clear planets: KMT-2019-BLG-0578 and KMT-2021-BLG-0736. Their planet-to-host mass ratios areq ∼ 4 × 10⁻³andq ∼ 1 × 10⁻⁴, respectively. Another event, OGLE-2018-BLG-0421 (KMT-2018-BLG-0831), remains ambiguous. Both a stellar companion and a giant planet in the lens system could potentially explain the observed anomaly. The anomaly signal of the last event, MOA-2022-BLG-038 (KMT-2022-BLG-2342), is attributed to an extra source star. Within this sample, our procedure doubles the number of confirmed planets, demonstrating a significant enhancement in the survey sensitivity. 

Abstract Traditional microlensing event vetting methods require highly trained human experts, and the process is both complex and time consuming. This reliance on manual inspection often leads to inefficiencies and constrains the ability to scale for widespread exoplanet detection, ultimately hindering discovery rates. To address the limits of traditional microlensing event vetting, we have developed LensNet, a machine learning pipeline specifically designed to distinguish legitimate microlensing events from false positives caused by instrumental artifacts, such as pixel bleed trails and diffraction spikes. Our system operates in conjunction with a preliminary algorithm that detects increasing trends in flux. These flagged instances are then passed to LensNet for further classification, allowing for timely alerts and follow-up observations. Tailored for the multiobservatory setup of the Korea Microlensing Telescope Network and trained on a rich data set of manually classified events, LensNet is optimized for early detection and warning of microlensing occurrences, enabling astronomers to organize follow-up observations promptly. The internal model of the pipeline employs a multibranch Recurrent Neural Network architecture that evaluates time-series flux data with contextual information, including sky background, the full width at half-maximum of the target star, flux errors, point-spread function quality flags, and air mass for each observation. We demonstrate a classification accuracy above 87.5% and anticipate further improvements as we expand our training set and continue to refine the algorithm. 

Abstract The gravitational microlensing method of discovering exoplanets and multi-star systems can produce degenerate solutions, some of which require in-depth analysis to uncover. We propose a new parameter space that can be used to sample potential solutions more efficiently and is more robust at finding all degenerate solutions for the “central-resonant” caustic degeneracy. We identified two new parameters,kandh, that can be sampled in place of the mass ratios and separations of the systems under analysis to identify degenerate solutions. The parameterkis related to the size of the central caustic, Δξ_c, whilehis related to the distance of a point along thekcontour from log(s) = 0, wheresis the projected planet-host separation. In this work, we present the characteristics of these parameters and the tests we conducted to prove their efficacy. 

Abstract Interferometric observations of gravitational microlensing events offer an opportunity for precise, efficient, and direct mass and distance measurements of lensing objects, especially those of isolated neutron stars and black holes. However, such observations have previously been possible for only a handful of extremely bright events. The recent development of a dual-field interferometer, GRAVITY Wide, has made it possible to reach out to significantly fainter objects and increase the pool of microlensing events amenable to interferometric observations by 2 orders of magnitude. Here, we present the first successful observation of a microlensing event with GRAVITY Wide and the resolution of microlensed images in the event OGLE-2023-BLG-0061/KMT-2023-BLG-0496. We measure the angular Einstein radius of the lens with subpercent precision,θ_E = 1.280 ± 0.009 mas. Combined with the microlensing parallax detected from the event light curve, the mass and distance to the lens are found to be 0.472 ± 0.012M_⊙and 1.81  ±  0.05 kpc, respectively. We present the procedure for the selection of targets for interferometric observations and discuss possible systematic effects affecting GRAVITY Wide data. This detection demonstrates the capabilities of the new instrument, and it opens up completely new possibilities for the follow-up of microlensing events and future routine discoveries of isolated neutron stars and black holes. 

Abstract We present the analysis of a microlensing event KMT-2022-BLG-0086 of which the overall light curve is not described by a binary-lens single-source (2L1S) model, which suggests the existence of an extra lens or an extra source. We found that the event is best explained by the binary-lens binary-source (2L2S) model, but the 2L2S model is only favored over the triple-lens single-source (3L1S) model by Δχ² ≃ 9. Although the event has noticeable anomalies around the peak of the light curve, they are not enough covered to constrain the angular Einstein radiusθ_E, thus we only measure the minimum angular Einstein radius ${\theta }_{\mathrm{E},\min }$. From the Bayesian analysis, it is found that that the binary lens system is a binary star with masses of $(m_1,m_2)=(0.46_{-0.25}^{+0.35}{M}_{\odot },0.75_{-0.55}^{+0.67}{M}_{\odot })$at a distance of $D_{\mathrm{L}}=5.87_{-1.79}^{+1.21}$kpc, while the triple lens system is a brown dwarf or a massive giant planet in a low-mass binary-star system with masses of $(m_1,m_2,m_3)=(0.43_{-0.35}^{+0.41}{M}_{\odot },0.056_{-0.047}^{+0.055}{M}_{\odot }$, $20.84_{-17.04}^{+20.20}{M}_{\mathrm{J}})$at a distance of $D_{\mathrm{L}}=4.06_{-3.28}^{+1.39}$kpc, indicating a disk lens system. The 2L2S model yields the relative lens-source proper motion ofμ_rel ≥ 4.6 mas yr⁻¹that is consistent with the Bayesian result, whereas the 3L1S model yieldsμ_rel ≥ 18.9 mas yr⁻¹, which is more than three times larger than that of a typical disk object of ∼6 mas yr⁻¹and thus is not consistent with the Bayesian result. This suggests that the event is likely caused by the binary-lens binary-source model. 

Abstract We report a free-floating planet (FFP) candidate identified from the analysis of the microlensing event KMT-2023-BLG-2669. The lensing light curve is characterized by a short duration (≲3 days) and a small amplitude (≲0.7 mag). From the analysis, we find an Einstein timescale oft_E⋍ 0.33 days and an Einstein radius ofθ_E⋍ 4.41μas. These measurements enable us to infer the lens mass as $M=8M_{\oplus }{\left({\pi }_{\mathrm{rel}}/0.1\mathrm{mas}\right)}^{-1}$, whereπ_relis the relative lens–source parallax. The inference implies that the lens is a sub-Neptune- to Saturn-mass object, depending on its unknown distance. This is the ninth isolated planetary mass microlens withθ_E< 10μas, which is a useful threshold for an FFP candidate. We conduct extensive searches for possible signals of a host star in the light curve, but find no strong evidence for the host. We investigate the possibility of using late-time high-resolution imaging to probe for possible hosts. In particular, we discuss the case of finite-source point-lens FFP candidates, for which it would be possible to search for very-wide-separation hosts immediately, although such searches are “high risk, high reward.” 

Abstract We report the analysis of four unambiguous planets and one possible planet from the subprime fields (Γ ≤ 1 hr⁻¹) of the 2017 Korea Microlensing Telescope Network (KMTNet) microlensing survey, to complete the KMTNet AnomalyFinder planetary sample for the 2017 subprime fields. They are KMT-2017-BLG-0849, KMT-2017-BLG-1057, OGLE-2017-BLG-0364, and KMT-2017-BLG-2331 (unambiguous), as well as KMT-2017-BLG-0958 (possible). For the four unambiguous planets, the mean planet–host mass ratios,q, are (1.0, 1.2, 4.6, 13) × 10⁻⁴, the median planetary masses are (6.4, 24, 76, 171)M_⊕, and the median host masses are (0.19, 0.57, 0.49, 0.40)M_⊙, respectively, found from a Bayesian analysis. We have completed the Anomaly Finder planetary sample from the first 4 yr of KMTNet data (2016–2019), with 112 unambiguous planets in total, which nearly tripled the microlensing planetary sample. The “sub-Saturn desert” ( $\log q=\left(-3.6,-3.0\right)$) found in the 2018 and 2019 KMTNet samples is confirmed by the 2016 and 2017 KMTNet samples.