We present a search for single photometric exocomet transits using a magnitude-limited sample of stars observed by the TESS primary mission. These events are asymmetric, with a sharp ingress and more gradual egress expected because the comet tail trails behind the coma. Our goals are to estimate the occurrence rate of exocomet transits, and given sufficient numbers comment on whether the host stars are biased towards being A/F spectral types, as suggested by a previous survey with Kepler data. We recovered the previously identified exocomet transit with TESS around 3 Pic (TIC 270577175) and identified three additional main-sequence systems with exocomet-like transits (TIC 280832588, TIC 73149665, and TIC 143152957). We also identified one exocomet candidate around a giant star (TIC 229790952) and one around a probable supergiant (TIC 110969638). We find a total occurrence rate of 2 . 64 x 10(-4 )star(-1) yr(-1), much higher than Kepler's rate of 6 . 7 x 10(-6 )star(-1) yr(-1). Some of this difference may be because our rate includes a correction for detection efficiency, where the Kepler search did not. However, with only a handful of detections in each survey, the rates are also very uncertain. In contrast to the Kepler search, we find two candidate hosts that may be G types, but the spectral types would be better supported with spectroscopic follow-up. Primarily, we conclude that exocomet-like transits are very rare at 0.1 per cent to 1 per cent transit depth levels, and that higher precision photometry to detect and characterize shallower transits effectively is the most likely path to more detections and stronger statistical conclusions.

The SETI Ellipsoid is a strategy for technosignature candidate selection that assumes that extraterrestrial civilizations who have observed a galactic-scale event-such as supernova 1987A-may use it as a Schelling point to broadcast synchronized signals indicating their presence. Continuous wide-field surveys of the sky offer a powerful new opportunity to look for these signals, compensating for the uncertainty in their estimated time of arrival. We explore sources in the TESS continuous viewing zone, which corresponds to 5% of all TESS data, observed during the first 3 yr of the mission. Using improved 3D locations for stars from Gaia Early Data Release 3, we identified 32 SN 1987A SETI Ellipsoid targets in the TESS continuous viewing zone with uncertainties better than 0.5 lt-yr. We examined the TESS light curves of these stars during the Ellipsoid crossing event and found no anomalous signatures. We discuss ways to expand this methodology to other surveys, more targets, and different potential signal types.

The search for extraterrestrial intelligence (SETI) Ellipsoid is a geometric method for prioritizing technosignature observations based on the strategy of receiving signals synchronized to conspicuous astronomical events. Precise distances to nearby stars from Gaia makes constraining Ellipsoid crossing times possible. Here we explore the utility of using the Gaia Catalog of Nearby Stars to select targets on the SN 1987A SETI Ellipsoid, as well as the Ellipsoids defined by 278 classical novae. Less than 8% of stars within the 100 pc sample are inside the SN 1987A SETI Ellipsoid, meaning the vast majority of nearby stars are still viable targets for monitoring over time. We find an average of 734 stars per year within the 100 pc volume will intersect the Ellipsoid from SN 1987A, with ∼10% of those having distance uncertainties from Gaia better than 0.1 lyr.

Our understanding of the Universe has profited from deliberate, targeted studies of known phenomena, as well as from serendipitous, unexpected discoveries, such as the discovery of a complex variability pattern in the direction of KIC 8462852 (Boyajian's star). Upcoming surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will explore the parameter space of astrophysical transients at all time scales, and offer the opportunity to discover even more extreme examples of unexpected phenomena. We investigate strategies to identify novel objects and to contextualize them within large time-series data sets in order to facilitate the discovery of new classes of objects, as well as the physical interpretation of their anomalous nature. We develop a method that combines tree-based and manifold-learning algorithms for anomaly detection in order to perform two tasks: 1) identify and rank anomalous objects in a time-domain dataset; and 2) group those anomalies according to their similarity in order to identify analogs. We achieve the latter by combining an anomaly score from a tree-based method with a dimensionality manifold-learning reduction strategy. Clustering in the reduced space allows for the successful identification of anomalies and analogs. We also assess the impact of pre-processing and feature engineering schemes and investigate the astrophysical nature of the objects that our models identify as anomalous by augmenting the Kepler data with Gaia color and luminosity information. We find that multiple models, used in combination, are a promising strategy to identify novel light curves and light curve families.

Our understanding of the Universe has progressed through deliberate, targeted studies of known phenomena, like the supernova campaigns that enabled the discovery of the accelerated expansion of the Universe, as much as through serendipitous, unexpected discoveries. The discovery of the Jovian moons, and of interstellar objects like 1I/'Oumuamua forced us to rethink the framework through which we explain the Universe and develop new theories. Recent surveys, like the Catalina Realtime-Transient Survey and the Zwicky Transient Facility, and upcoming ones, like the Rubin Legacy Survey of Space and Time, explore the parameter space of astrophysical transients at all time scales, from hours to years, and offer the opportunity to discover new, unexpected phenomena. In this paper, we investigate strategies to identify novel objects and to contextualize them within large time-series data sets to facilitate the discovery of new objects, new classes of objects, and the physical interpretation of their anomalous nature. We compare tree-based and manifold-learning algorithms for anomaly detection as they are applied to a data set of light curves from the Kepler observatory that include the bona fide anomalous Boyajian's star. We assess the impact of pre-processing and feature engineering schemes and investigate the astrophysical nature of the objects that our models identify as anomalous by augmenting the Kepler data with \emph{Gaia} color and luminosity information. We find that multiple models, used in combination, are a promising strategy to not only identify novel time series but also to find objects that share phenomenological and astrophysical characteristics with them, facilitating the interpretation of their anomalous characteristics.

Magnetic activity in stars manifests as dark spots on their surfaces that modulate the brightness observed by telescopes. These light curves contain important information on stellar rotation. However, the accurate estimation of rotation periods is computationally expensive due to scarce ground truth information, noisy data, and large parameter spaces that lead to degenerate solutions. We harness the power of deep learning and successfully apply Convolutional Neural Networks to regress stellar rotation periods from Kepler light curves. Geometry-preserving time-series to image transformations of the light curves serve as inputs to a ResNet-18 based architecture which is trained through transfer learning. The McQuillan catalog of published rotation periods is used as ansatz to groundtruth. We benchmark the performance of our method against a random forest regressor, a 1D CNN, and the Auto-Correlation Function (ACF) - the current standard to estimate rotation periods. Despite limiting our input to fewer data points (1k), our model yields more accurate results and runs 350 times faster than ACF runs on the same number of data points and 10,000 times faster than ACF runs on 65k data points. With only minimal feature engineering our approach has impressive accuracy, motivating the application of deep learning to regress stellar parameters on an even larger scale

ABSTRACT In the present era of large-scale surveys, big data present new challenges to the discovery process for anomalous data. Such data can be indicative of systematic errors, extreme (or rare) forms of known phenomena, or most interestingly, truly novel phenomena that exhibit as-of-yet unobserved behaviours. In this work, we present an outlier scoring methodology to identify and characterize the most promising unusual sources to facilitate discoveries of such anomalous data. We have developed a data mining method based on k-nearest neighbour distance in feature space to efficiently identify the most anomalous light curves. We test variations of this method including using principal components of the feature space, removing select features, the effect of the choice of k, and scoring to subset samples. We evaluate the performance of our scoring on known object classes and find that our scoring consistently scores rare (<1000) object classes higher than common classes. We have applied scoring to all long cadence light curves of Quarters 1–17 of Kepler’s prime mission and present outlier scores for all 2.8 million light curves for the roughly 200k objects.

Advances in astronomy are often driven by serendipitous discoveries. As survey astronomy continues to grow, the size and complexity of astronomical data bases will increase, and the ability of astronomers to manually scour data and make such discoveries decreases. In this work, we introduce a machine learning-based method to identify anomalies in large data sets to facilitate such discoveries, and apply this method to long cadence light curves from NASA's Kepler Mission. Our method clusters data based on density, identifying anomalies as data that lie outside of dense regions. This work serves as a proof-of-concept case study and we test our method on four quarters of the Kepler long cadence light curves. We use Kepler's most notorious anomaly, Boyajian's star (KIC 8462852), as a rare 'ground truth' for testing outlier identification to verify that objects of genuine scientific interest are included among the identified anomalies. We evaluate the method's ability to identify known anomalies by identifying unusual behaviour in Boyajian's star; we report the full list of identified anomalies for these quarters, and present a sample subset of identified outliers that includes unusual phenomena, objects that are rare in the Kepler field, and data artefacts. By identifying <4 per cent of each quarter as outlying data, we demonstrate that this anomaly detection method can create a more targeted approach in searching for rare and novel phenomena.