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This work studies the generalization of semi-supervised generative adversarial networks (GANs) to regression tasks. A novel feature layer contrasting optimization function, in conjunction with a feature matching optimization, allows the adversarial network to learn from unannotated data and thereby reduce the number of labels required to train a predictive network. An analysis of simulated training conditions is performed to explore the capabilities and limitations of the method. In concert with the semi-supervised regression GANs, an improved label topology and upsampling technique for multi-target regression tasks are shown to reduce data requirements. Improvements are demonstrated on a wide variety of vision tasks, including dense crowd counting, age estimation, and automotive steering angle prediction. With training data limitations arguably being the most restrictive component of deep learning, methods which reduce data requirements hold immense value. The methods proposed here are general-purpose and can be incorporated into existing network architectures with little or no modifications to the existing structure.

In this work, we generalize semi-supervised generative adversarial networks (GANs) from classification problems to regression problems. In the last few years, the importance of improving the training of neural networks using semi-supervised training has been demonstrated for classification problems. We present a novel loss function, called feature contrasting, resulting in a discriminator which can distinguish between fake and real data based on feature statistics. This method avoids potential biases and limitations of alternative approaches. The generalization of semi-supervised GANs to the regime of regression problems of opens their use to countless applications as well as providing an avenue for a deeper understanding of how GANs function. We first demonstrate the capabilities of semi-supervised regression GANs on a toy dataset which allows for a detailed understanding of how they operate in various circumstances. This toy dataset is used to provide a theoretical basis of the semi-supervised regression GAN. We then apply the semi-supervised regression GANs to a number of real-world computer vision applications: age estimation, driving steering angle prediction, and crowd counting from single images. We perform extensive tests of what accuracy can be achieved with significantly reduced annotated data. Through the combination of the theoretical example and real-world scenarios, we demonstrate how semi-supervised GANs can be generalized to regression problems.

The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ~85% of the sky throughout its two-year primary mission, resulting in millions of TESS 30-minute cadence light curves to analyze in the search for transiting exoplanets. To search this vast dataset, we aim to provide an approach that is both computationally efficient, produces highly performant predictions, and minimizes the required human search effort. We present a convolutional neural network that we train to identify short period variables. To make a prediction for a given light curve, our network requires no prior target parameters identified using other methods. Our network performs inference on a TESS 30-minute cadence light curve in ~5ms on a single GPU, enabling large scale archival searches. We present a collection of 14156 short-period variables identified by our network. The majority of our identified variables fall into two prominent populations, one of short-period main sequence binaries and another of Delta Scuti stars. Our neural network model and related code is additionally provided as open-source code for public use and extension.

We report the discovery with TESS of a remarkable quadruple star system with a 2+1+1 configuration. The two unique characteristics of this system are that (i) the inner eclipsing binary (stars Aa and Ab) eclipses the star in the outermost orbit (star C), and (ii) these outer 4th body eclipses last for \\(\\sim\\)12 days, the longest of any such system known. The three orbital periods are \\(\\sim\\)3.3 days, \\(\\sim\\)51 days, and \\(\\sim\\)2100 days. The extremely long duration of the outer eclipses is due to the fact that star B slows binary A down on the sky relative to star C. We combine TESS photometric data, ground-based photometric observations, eclipse timing points, radial velocity measurements, the composite spectral energy distribution, and stellar isochones in a spectro-photodynamical analysis to deduce all of the basic properties of the four stars (mass, radius, \\(T_{\\rm eff}\\), and age), as well as the orbital parameters for all three orbits. The four masses are \\(M_{\\rm Aa} =0.382\\)M\\(_\\odot\\), \\(M_{\\rm Ab} =0.300\\)M\\(_\\odot\\), \\(M_{\\rm B} =0.540\\)M\\(_\\odot\\) and \\(M_{\\rm C} =0.615\\)M\\(_\\odot\\), with a typical uncertainty of 0.015 M\\(_\\odot\\).

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.

The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ~75% of the sky throughout its two year primary mission, resulting in millions of TESS 30-minute cadence light curves to analyze in the search for transiting exoplanets. To search this vast data trove for transit signals, we aim to provide an approach that is both computationally efficient and produces highly performant predictions. This approach minimizes the required human search effort. We present a convolutional neural network, which we train to identify planetary transit signals and dismiss false positives. To make a prediction for a given light curve, our network requires no prior transit parameters identified using other methods. Our network performs inference on a TESS 30-minute cadence light curve in ~5ms on a single GPU, enabling large scale archival searches. We present 181 new planet candidates identified by our network, which pass subsequent human vetting designed to rule out false positives. Our neural network model is additionally provided as open-source code for public use and extension.

We present a catalog of 97 uniformly-vetted candidates for quadruple star systems. The candidates were identified in TESS Full Frame Image data from Sectors 1 through 42 through a combination of machine learning techniques and visual examination, with major contributions from a dedicated group of citizen scientists. All targets exhibit two sets of eclipses with two different periods, both of which pass photocenter tests confirming that the eclipses are on-target. This catalog outlines the statistical properties of the sample, nearly doubles the number of known multiply-eclipsing quadruple systems, and provides the basis for detailed future studies of individual systems. Several important discoveries have already resulted from this effort, including the first sextuply-eclipsing sextuple stellar system and the first transiting circumbinary planet detected from one sector of TESS data.

Recently, there have been reports of various types of degeneracies in the interpretation of planetary signals induced by planetary caustics. In this work, we check whether such degeneracies persist in the case of well-covered signals by analyzing the lensing event KMT-2021-BLG-1150, for which the light curve exhibits a densely and continuously covered short-term anomaly. In order to identify degenerate solutions, we thoroughly investigate the parameter space by conducting dense grid searches for the lensing parameters. We then check the severity of the degeneracy among the identified solutions. We identify a pair of planetary solutions resulting from the well-known inner-outer degeneracy, and find that interpreting the anomaly is not subject to any degeneracy other than the inner-outer degeneracy. The measured parameters of the planet separation (normalized to the Einstein radius) and mass ratio between the lens components are \\((s, q)_{\\rm in}\\sim (1.297, 1.10\\times 10^{-3})\\) for the inner solution and \\((s, q)_{\\rm out}\\sim (1.242, 1.15\\times 10^{-3})\\) for the outer solution. According to a Bayesian estimation, the lens is a planetary system consisting of a planet with a mass \\(M_{\\rm p}=0.88^{+0.38}_{-0.36}~M_{\\rm J}\\) and its host with a mass \\(M_{\\rm h}=0.73^{+0.32}_{-0.30}~M_\\odot\\) lying toward the Galactic center at a distance \\(D_{\\rm L} =3.8^{+1.3}_{-1.2}\\)~kpc. By conducting analyses using mock data sets prepared to mimic those obtained with data gaps and under various observational cadences, it is found that gaps in data can result in various degenerate solutions, while the observational cadence does not pose a serious degeneracy problem as long as the anomaly feature can be delineated.

We inspect the microlensing data of the KMTNet survey collected during the 2018--2020 seasons in order to find lensing events produced by binaries with brown-dwarf companions. In order to pick out binary-lens events with candidate BD lens companions, we conduct systematic analyses of all anomalous lensing events observed during the seasons. By applying the selection criterion with mass ratio between the lens components of \\(0.03\\lesssim q\\lesssim 0.1\\), we identify four binary-lens events with candidate BD companions, including KMT-2018-BLG-0321, KMT-2018-BLG-0885, KMT-2019-BLG-0297, and KMT-2019-BLG-0335. For the individual events, we present the interpretations of the lens systems and measure the observables that can constrain the physical lens parameters. The masses of the lens companions estimated from the Bayesian analyses based on the measured observables indicate that the probabilities for the lens companions to be in the brown-dwarf mass regime are high: 59\\%, 68\\%, 66\\%, and 66\\% for the four events respectively.

We investigate the data collected by the high-cadence microlensing surveys during the 2022 season in search for planetary signals appearing in the light curves of microlensing events. From this search, we find that the lensing event MOA-2022-BLG-249 exhibits a brief positive anomaly that lasted for about 1 day with a maximum deviation of \\(\\sim 0.2\\)~mag from a single-source single-lens model. We analyze the light curve under the two interpretations of the anomaly: one originated by a low-mass companion to the lens (planetary model) and the other originated by a faint companion to the source (binary-source model). It is found that the anomaly is better explained by the planetary model than the binary-source model. We identify two solutions rooted in the inner--outer degeneracy, for both of which the estimated planet-to-host mass ratio, \\(q\\sim 8\\times 10^{-5}\\), is very small. With the constraints provided by the microlens parallax and the lower limit on the Einstein radius, as well as the blend-flux constraint, we find that the lens is a planetary system, in which a super-Earth planet, with a mass \\((4.83\\pm 1.44)~M_\\oplus\\), orbits a low-mass host star, with a mass \\((0.18\\pm 0.05)~M_\\odot\\), lying in the Galactic disk at a distance \\((2.00\\pm 0.42)\\)~kpc. The planet detection demonstrates the elevated microlensing sensitivity of the current high-cadence lensing surveys to low-mass planets.