Prof Chris Lintott

Abstract:

With a new probabilistic technique for sampling interstellar object (ISO) orbits with high efficiency, we assess the observability of ISOs under a realistic cadence for the upcoming Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST). Using the Ōtautahi–Oxford population model, we show that there will be complex on-sky structure in the pattern of direction and velocity revealed by the detected ISO population, with the expected enhanced northern flux complicating efforts to derive population parameters from the LSST’s predominately southern footprint. For reasonable luminosity functions with slopes of 2.5 ≤ qs ≤ 4.0, the most discoverable ISOs have Hr ≃ 14.6−20.7. The slope of the luminosity function of ISOs will be relatively quickly constrained by the characteristics of the LSST detected population, such as the distributions of perihelia, velocity at infinity, and discovery circumstances. Discoveries are evenly split around their perihelion passage and are biased to lower velocities. After their discovery by LSST, it will be rare for ISOs to be visible for less than a month; most will have mr ≤ 23 for months, and the window for spectroscopic characterization could be as long as 2 yr. While these probabilistic assessments are robust against model or spatial density refinements that change the absolute numbers of ISO discoveries, our simulations predict a yield of 6–51 asteroidal ISOs, which is similar to previous works and demonstrates the validity of our new methods.

Abstract:

The discovery of the third interstellar object (ISO), 3I/ATLAS (“3I”), provides a rare chance to directly observe a small body from another solar system. Studying its chemistry and dynamics will add to our understanding of how the processes of planetesimal formation and evolution happen across the Milky Way’s disk, and how such objects respond to the Milky Way’s potential. In this Letter, we present a first assessment of 3I in the context of the Ōtautahi–Oxford model, which uses data from Gaia in conjunction with models of protoplanetary disk chemistry and Galactic dynamics to predict the properties of the ISO population. The model shows that both the velocity and radiant of 3I are within the expected range. Its velocity predicts an age of over 7.6 Gyr and a high water mass fraction, which may become observable shortly. We also conclude that it is very unlikely that 3I shares an origin with either of the previous two ISO detections.

Abstract:

Upcoming surveys are likely to discover a new sample of interstellar objects (ISOs) within the solar system, but questions remain about the origin and distribution of this population within the Galaxy. ISOs are ejected from their host systems with a range of velocities, spreading out into tidal streams—analogous to the stellar streams routinely observed from the disruption of star clusters and dwarf galaxies. We create a simulation of ISO streams orbiting in the Galaxy, deriving a simple model for their density distribution over time. We then construct a population model to predict the properties of the streams in which the Sun is currently embedded. We find that the number of streams encountered by the Sun is quite large, ∼106 or more. However, the wide range of stream properties means that for reasonable future samples of ISOs observed in the solar system, we may see ISOs from the same star (“siblings”), and we are likely to see ISOs from the same star cluster (“cousins”). We also find that ISOs are typically not traceable to their parent star, though this may be possible for ISO siblings. Any ISOs observed with a common origin will come from younger, dynamically colder streams.

Abstract:

We study the evolution of the bar fraction in disk galaxies between 0.5 < z < 4.0 using multiband colored images from JWST Cosmic Evolution Early Release Science Survey (CEERS). These images were classified by citizen scientists in a new phase of the Galaxy Zoo (GZ) project called GZ CEERS. Citizen scientists were asked whether a strong or weak bar was visible in the host galaxy. After considering multiple corrections for observational biases, we find that the bar fraction decreases with redshift in our volume-limited sample (n = 398); from 25−4+6 % at 0.5 < z < 1.0 to 3−1+6 % at 3.0 < z < 4.0. However, we argue it is appropriate to interpret these fractions as lower limits. Disentangling real changes in the bar fraction from detection biases remains challenging. Nevertheless, we find a significant number of bars up to z = 2.5. This implies that disks are dynamically cool or baryon dominated, enabling them to host bars. This also suggests that bar-driven secular evolution likely plays an important role at higher redshifts. When we distinguish between strong and weak bars, we find that the weak bar fraction decreases with increasing redshift. In contrast, the strong bar fraction is constant between 0.5 < z < 2.5. This implies that the strong bars found in this work are robust long-lived structures, unless the rate of bar destruction is similar to the rate of bar formation. Finally, our results are consistent with disk instabilities being the dominant mode of bar formation at lower redshifts, while bar formation through interactions and mergers is more common at higher redshifts.

Abstract:

Automated planetary transit detection has become vital to identify and prioritize candidates for expert analysis and verification given the scale of modern telescopic surveys. Current methods for short-period exoplanet detection work effectively due to periodicity in the transit signals, but a robust approach for detecting single-transit events is lacking. However, volunteer-labeled transits collected by the Planet Hunters TESS (PHT) project now provide an unprecedented opportunity to investigate a data-driven approach to long-period exoplanet detection. In this work, we train a 1D convolutional neural network to classify planetary transits using PHT volunteer scores as training data. We find that this model recovers planet candidates (TESS objects of interest; TOIs) at a precision and recall rate exceeding those of volunteers, with a 20% improvement in the area under the precision-recall curve and 10% more TOIs identified in the top 500 predictions on average per sector. Importantly, the model also recovers almost all planet candidates found by volunteers but missed by current automated methods (PHT community TOIs). Finally we retrospectively utilise the model to simulate live deployment in PHT to reprioritize candidates for analysis. We also find that multiple promising planet candidates, originally missed by PHT, would have been found using our approach, showing promise for upcoming real-world deployment.