Pulsars, transients and relativistic astrophysics

Abstract:

Many gravitational wave (GW) sources are expected to have non-negligible eccentricity in the millihertz band. These highly eccentric compact object binaries may commonly serve as a progenitor stage of GW mergers, particularly in dynamical channels where environmental perturbations bring a binary with large initial orbital separation into a close pericenter passage, leading to efficient GW emission and a final merger. This work examines the stochastic GW background from highly eccentric (e≳0.9), stellar-mass sources in the mHz band. Our findings suggest that these binaries can contribute a substantial GW power spectrum, potentially exceeding the LISA instrumental noise at ∼3-7 mHz. This stochastic background is likely to be dominated by eccentric sources within the Milky Way, thus introducing anisotropy and time dependence in LISA's detection. However, given efficient search strategies to identify GW transients from highly eccentric binaries, the unresolvable noise level can be substantially lower, approaching ∼2 orders of magnitude below the LISA noise curve. Therefore, we highlight the importance of characterizing stellar-mass GW sources with extreme eccentricity, especially their transient GW signals in the millihertz band.

Abstract:

We present a convolutional neural network (CNN) for use in the real–bogus classification of transient detections made by the Asteroid Terrestrial-impact Last Alert System (ATLAS) and subsequent efforts to improve performance since initial development. In transient detection surveys, the number of alerts made outstrips the capacity for human scanning, necessitating the use of machine learning aids to reduce the number of false positives presented to annotators. We take a sample of recently annotated data from each of the three operating ATLAS telescope with 340 000 real (known transients) and 1030 000 bogus detections per model. We retrained the CNN architecture with these data specific to each ATLAS unit, achieving a median false positive rate (FPR) of 0.72 per cent for a 1.00 per cent missed detection rate. Further investigations indicate that if we reduce the input image size it results in increased FPR. Finally architecture adjustments and comparisons to contemporary CNNs indicate that our retrained classifier is providing an optimal FPR. We conclude that the periodic retraining and readjustment of classification models on survey data can yield significant improvements as data drift arising from changes in the optical and detector performance can lead to new features in the model and subsequent deteriorations in performance.