Zooniverse

Abstract:

Abstract In this study, we examine over 14,000 radio galaxies finely selected from Radio Galaxy Zoo (RGZ) project and provide classifications for approximately 5,900 FRIs and 8,100 FRIIs. We present an analysis of these predicted radio galaxy morphologies for the RGZ catalogue, classified using a pre-trained radio galaxy foundation model that has been fine-tuned to predict Fanaroff-Riley (FR) morphology. As seen in previous studies, our results show overlap between morphologically classified FRI and FRII luminosity-size distributions and we find that the model’s confidence in its predictions is lowest in this overlap region, suggesting that source morphologies are more ambiguous. We identify the presence of low-luminosity FRII sources, the proportion of which, with respect to the total number of FRIIs, is consistent with previous studies. However, a comparison of the low-luminosity FRII sources found in this work with those identified by previous studies reveals differences that may indicate their selection is influenced by the choice of classification methodology. We investigate the impacts of both pre-training and fine-tuning data selection on model performance for the downstream classification task, and show that while different pre-training data choices affect model confidence they do not appear to cause systematic generalisation biases for the range of physical and observational characteristics considered in this work; however, we note that the same is not necessarily true for fine-tuning. As automated approaches to astronomical source identification and classification become increasingly prevalent, we highlight training data choices that can affect the model outputs and propagate into downstream analyses.

Abstract:

A key science goal of large sky surveys such as those conducted by the Vera C. Rubin Observatory and precursors to the Square Kilometre Array is the identification of variable and transient objects. One approach is analyzing time series of the changing brightness of sources, namely, light curves. However, finding adequate statistical representations of light curves is challenging because of the sparsity of observations, irregular sampling, and nuisance factors inherent in astronomical data collection. The wide diversity of objects that a large-scale survey will observe also means that making parametric assumptions about the shape of light curves is problematic. We present a Gaussian process (GP) regression approach for characterizing light-curve variability that addresses these challenges. Our approach makes no assumptions about the shape of a light curve and, therefore, is general enough to detect a range of variable and transient source types. In particular, we propose using the joint distribution of GP amplitude hyperparameters to distinguish variable and transient candidates from nominally stable ones and apply this approach to 6394 radio light curves from the ThunderKAT survey. We compare our results with two variability metrics commonly used in radio astronomy, namely ην and Vν, and show that our approach has better discriminatory power and interpretability. Finally, we conduct a rudimentary search for transient sources in the ThunderKAT data set to demonstrate how our approach might be used as an initial screening tool. Computational notebooks in Python and R are available to help deploy this framework to other surveys.

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.