Rejecting cosmic rays (CRs) is essential for scientific interpretation of CCD-captured data, but detecting CRs in single-exposure images has remained challenging. Conventional CR-detection algorithms require tuning multiple parameters experimentally, making it hard to automate across different instruments or observation requests. Recent work using deep learning to train CR-detection models has demonstrated promising results. However, instrument-specific models suffer from performance loss on images from ground-based facilities not included in the training data. In this work, we present Cosmic-CoNN, a deep-learning framework designed to produce generic CR-detection models. We build a large, diverse ground-based CR dataset leveraging thousands of images from the Las Cumbres Observatory global telescope network to produce a generic CR-detection model which achieves a 99.91% true-positive detection rate and maintains over 96.40% true-positive rates on unseen data from Gemini GMOS-N/S, with a false-positive rate of 0.01%. Apart from the open-source framework and dataset, we also build a suite of tools including console commands, a web-based application, and Python APIs to make automatic, robust CR detection widely accessible by the community of astronomers.

BackgroundThe purpose of this study is to evaluate the effectiveness of gamma knife radiosurgery (GKRS) in the treatment of pineal region tumors (PRTs).MethodsWe retrospectively reviewed 147 cases of PRTs primarily treated with GKRS at our hospital between 1999 and 2009. Mean follow-up time was 67 months (range 60.5–100.1). The local tumor control rates (LTCRs) and overall survival rates were calculated to evaluate the results of the GKRS treatment.ResultsAt 2 months after GKRS, tumor volume was significantly reduced in 91 cases (61.9 %). At 6 months, average tumor volume was 4.2 cm3 as compared to 8.47 cm3 before GKRS. By 1 year after GKRS, the tumor completely disappeared in 57 patients. Fourteen patients underwent second treatment, and one patient had third treatment. The overall survival rates were 72.1 % at 3 years and 66.7 % at 5 years for all patients and 62.4 % at 3 years and 54.5 % at 5 years for germ cell tumors (GCTs). The LTCRs were 94.30 % at 3 years and 90.80 % at 5 years for all patients and 88.00 % at 3 years and 77.27 % at 5 years for GCTs.ConclusionsGKRS is an effective and safe modality that can be widely used to PRTs as the primary therapy.

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Rejecting cosmic rays (CRs) is essential for the scientific interpretation of CCD-captured data, but detecting CRs in single-exposure images has remained challenging. Conventional CR detectors require experimental parameter tuning for different instruments, and recent deep-learning methods only produce instrument-specific models that suffer from performance loss on telescopes not included in the training data. We present Cosmic-CoNN, a generic CR detector deployed for 24 telescopes at the Las Cumbres Observatory, which has been made possible by the three contributions in this work: (1) We build a large and diverse ground-based CR data set leveraging thousands of images from a global telescope network. (2) We propose a novel loss function and a neural network optimized for telescope imaging data to train generic CR-detection models. At 95% recall, our model achieves a precision of 93.70% on Las Cumbres imaging data and maintains a consistent performance on new ground-based instruments never used for training. Specifically, the Cosmic-CoNN model trained on the Las Cumbres CR data set maintains high precisions of 92.03% and 96.69% on Gemini GMOS-N/S 1 × 1 and 2 × 2 binning images, respectively. (3) We build a suite of tools including an interactive CR mask visualization and editing interface, console commands, and Python APIs to make automatic, robust CR detection widely accessible by the community of astronomers. Our data set, open-source code base, and trained models are available at https://github.com/cy-xu/cosmic-conn.

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