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Abstract We present high angular resolution imaging that detects the MOA-2008-BLG-379L exoplanet host star using Keck adaptive optics and the Hubble Space Telescope. These observations reveal host star and planet masses ofM_host= 0.434 ± 0.065M_⊙andm_p= 2.44 ± 0.49M_Jupiter. They are located at a distance ofD_L= 3.44 ± 0.53 kpc, with a projected separation of 2.70 ± 0.42 au. These results contribute to our determination of exoplanet host star masses for the Suzuki et al. statistical sample, which will determine the dependence of the planet occurrence rate on the mass and distance of the host stars. We also present a detailed discussion of the image-constrained modeling version of theeesunhonglight-curve modeling code that applies high angular resolution image constraints to the light-curve modeling process. This code increases modeling efficiency by a large factor by excluding models that are inconsistent with the high angular resolution images. The analysis of this and other events from the Suzuki et al. statistical sample reveals the importance of including higher-order effects, such as microlensing parallax and planetary orbital motion, even when these features are not required to fit the light-curve data. The inclusion of these effects may be needed to obtain accurate estimates of the uncertainty of other microlensing parameters that affect the inferred properties of exoplanet microlens systems. This will be important for the exoplanet microlensing survey of the Roman Space Telescope, which will use both light-curve photometry and high angular resolution imaging to characterize planetary microlens systems. 

Abstract The Transiting Exoplanet Survey Satellite (TESS) has surveyed nearly the entire sky in full-frame image mode with a time resolution of 200 s to 30 minutes and a temporal baseline of at least 27 days. In addition to the primary goal of discovering new exoplanets, TESS is exceptionally capable at detecting variable stars, and in particular short-period eclipsing binaries, which are relatively common, making up a few percent of all stars, and represent powerful astrophysical laboratories for deep investigations of stellar formation and evolution. We combed Sectors 1–82 of the TESS full-frame image data searching for eclipsing binary stars using a neural network that identified ∼1.2 million stars with eclipse-like features. Of these, we have performed an in-depth analysis on ∼60,000 targets using automated methods and manual inspection by citizen scientists. Here we present a catalog of 10,001 uniformly vetted and validated eclipsing binary stars that passed all our ephemeris and photocenter tests, as well as complementary visual inspection. Of these, 7936 are new eclipsing binaries while the remaining 2065 are known systems for which we update the published ephemerides. We outline the detection and analysis of the targets, discuss the properties of the sample, and highlight potentially interesting systems. Finally, we also provide a list of ∼900,000 unvetted and unvalidated targets for which the neural network found eclipse-like features with a score higher than 0.9, and for which there are no known eclipsing binaries within a sky-projected separation of a TESS pixel (≈21″). 

Gatherings of thousands to millions of people frequently occur for an enormous variety of educational, social, sporting, and political events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for training deep neural networks may not be the most effective one. We propose an alternative inverse k-nearest neighbor (i[Formula: see text]NN) map mechanism that, even when used directly in existing state-of-the-art network structures, shows superior performance. We also provide new network architecture mechanisms that we demonstrate in our own MUD-i[Formula: see text]NN network architecture, which uses multi-scale drop-in replacement upsampling via transposed convolutions to take full advantage of the provided i[Formula: see text]NN labeling. This upsampling combined with the i[Formula: see text]NN maps further improves crowd counting accuracy. We further analyze several variations of the i[Formula: see text]NN labeling mechanism, which apply transformations on the [Formula: see text]NN measure before generating the map, in order to consider the impact of camera perspective views, image resolutions, and the changing rates of the mapping functions. To alleviate the effects of crowd density changes in each image, we also introduce an attenuation mechanism in the i[Formula: see text]NN mapping. Experimentally, we show that inverse square root [Formula: see text]NN map variation (iR[Formula: see text]NN) provides the best performance. Discussions are provided on computational complexity, label resolutions, the gains in mapping and upsampling, and details of critical cases such as various crowd counts, uneven crowd densities, and crowd occlusions. 

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. 

Aims. The light curves of the microlensing events MOA-2022-BLG-091 and KMT-2024-BLG-1209 exhibit anomalies with very similar features. These anomalies appear near the peaks of the light curves, where the magnifications are moderately high, and are distinguished by weak caustic-crossing features with minimal distortion while the source remains inside the caustic. To achieve a deeper understanding of these anomalies, we conducted a comprehensive analysis of the lensing events. Methods. We carried out binary-lens modeling with a thorough exploration of the parameter space. This analysis revealed that the anomalies in both events are of planetary origin, although their exact interpretation is complicated by different types of degeneracy. In the case of MOA-2022-BLG-091, the main difficulty in the interpretation of the anomaly arises from a newly identified degeneracy related to the uncertain angle at which the source trajectory intersects the planet–host axis. For KMT-2024-BLG-1209, the interpretation is affected by the previously known inner-outer degeneracy, which leads to ambiguity between solutions in which the source passes through either the inner or outer caustic region relative to the planet host. Results. Bayesian analysis indicates that the planets in both lens systems are giant planets with masses about two to four times that of Jupiter, orbiting early K-type main-sequence stars. Both systems are likely located in the Galactic disk at a distance of around 4 kiloparsecs. The degeneracy in KMT-2024-BLG-1209 is challenging to resolve because it stems from intrinsic similarities in the caustic structures of the degenerate solutions. In contrast, the degeneracy in MOA-2022-BLG-091, which occurs by chance rather than from inherent characteristics, is expected to be resolved by the future space based Roman RGES microlensing survey. 

Aims. We examined the anomalies in the light curves of the lensing events MOA-2022-BLG-033, KMT-2023-BLG-0119, and KMT- 2023-BLG-1896. These anomalies share similar traits: they occur near the peak of moderately to highly magnified events and display a distinct short-term dip feature. Methods. We conducted detailed modeling of the light curves to uncover the nature of the anomalies. This modeling revealed that all signals originated from planetary companions to the primary lens. The planet-to-host mass ratios are very low:q~ 7.5 × 10⁻⁵for MOA-2022-BLG-033,q~ 3.6 × 10⁻⁴for KMT-2023-BLG-0119, andq~ 6.9 × 10⁻⁵for KMT-2023-BLG-1896. The anomalies occurred as the source passed through the negative deviation region behind the central caustic along the planet-host axis. The solutions are subject to a common inner-outer degeneracy, which results in varying estimations of the projected planet-host separation. For KMT-2023-BLG-1896, although the planetary scenario provides the best explanation for the anomaly, the binary companion scenario is possible. Results. We estimated the physical parameters of the planetary systems through Bayesian analyses based on the lensing observables. While the event timescale was measured for all events, the angular Einstein radius was not measured for any. Additionally, the microlens parallax was measured for MOA-2022-BLG-033. The analysis identifies MOA-2022-BLG-033L as a planetary system with an ice giant with a mass of approximately 12 times that of Earth orbiting an early M dwarf star. The companion of KMT-2023-BLG-1896L is also an ice giant, with a mass of around 16 Earth masses, orbiting a mid-K-type main-sequence star. The companion of KMT-2023-BLG- 0119L, which has a mass around that of Saturn, orbits a mid-K-type dwarf star. The lens for MOA-2022-BLG-033 is highly likely to be located in the disk, whereas for the other events the probabilities of the lens being in the disk or the bulge are roughly equal. 

This paper describes the interface and testing of an indoor navigation app - ASSIST - that guides blind & visually impaired (BVI) individuals through an indoor environment with high accuracy while augmenting their understanding of the surrounding environment. ASSIST features personalized interfaces by considering the unique experiences that BVI individuals have in indoor wayfinding and offers multiple levels of multimodal feedback. After an overview of the technical approach and implementation of the first prototype of the ASSIST system, the results of two pilot studies performed with BVI individuals are presented – a performance study to collect data on mobility (walking speed, collisions, and navigation errors) while using the app, and a usability study to collect user evaluation data on the perceived helpfulness, safety, ease-of-use, and overall experience while using the app. Our studies show that ASSIST is useful in providing users with navigational guidance, improving their efficiency and (more significantly) their safety and accuracy in wayfinding indoors. Findings and user feed-back from the studies confirm some of the previous results, while also providing some new insights into the creation of such an app, including the use of customized user interfaces and expanding the types of information provided. 

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. 

We present the analysis of the microlensing event OGLE-2015-BLG-0845, which was affected by both the microlensing parallax and xallarap effects. The former was detected via the simultaneous observations from the ground and Spitzer, and the latter was caused by the orbital motion of the source star in a relatively close binary. The combination of these two effects led to a mass measurement of the lens object, revealing a low-mass ($$0.14 \pm 0.05 \, \mathrm{ M}_{\odot }$$) M dwarf at the bulge distance ($$7.6 \pm 1.0$$ kpc). The source binary consists of a late F-type subgiant and a K-type dwarf of $$\sim 1.2$$ and $$\sim 0.9 \mathrm{ M}_{\odot }$$, respectively, and the orbital period is $$70 \pm 10$$ d. OGLE-2015-BLG-0845 is the first single-lens event in which the lens mass is measured via the binarity of the source. Given the abundance of binary systems as potential microlensing sources, the xallarap effect may not be a rare phenomenon. Our work thus highlights the application of the xallarap effect in the mass determination of microlenses, and the same method can be used to identify isolated dark lenses. 

Gatherings of thousands to millions of people frequently occur for an enormous variety of events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for training deep neural networks is less effective than our alternative inverse k-nearest neighbor (i$$k$$NN) maps, even when used directly in existing state-of-the-art network structures. We also provide a new network architecture MUD-i$$k$$NN, which uses multi-scale upsampling via transposed convolutions to take full advantage of the provided i$$k$$NN labeling. This upsampling combined with the i$$k$$NN maps further improves crowd counting accuracy. Our new network architecture performs favorably in comparison with the state-of-the-art. However, our labeling and upsampling techniques are generally applicable to existing crowd counting architectures.