9 research outputs found
The Main Belt Comets and ice in the Solar System
We review the evidence for buried ice in the asteroid belt; specifically the questions around the so-called Main Belt Comets (MBCs). We summarise the evidence for water throughout the Solar System, and describe the various methods for detecting it, including remote sensing from ultraviolet to radio wavelengths. We review progress in the first decade of study of MBCs, including observations, modelling of ice survival, and discussion on their origins. We then look at which methods will likely be most effective for further progress, including the key challenge of direct detection of (escaping) water in these bodies
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The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package*
Abstract
The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates on the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project.</jats:p
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The zwicky transient facility: System overview, performance, and first results
The Zwicky Transient Facility (ZTF) is a new optical time-domain survey that uses the Palomar 48 inch Schmidt telescope. A custom-built wide-field camera provides a 47 deg 2 field of view and 8 s readout time, yielding more than an order of magnitude improvement in survey speed relative to its predecessor survey, the Palomar Transient Factory. We describe the design and implementation of the camera and observing system. The ZTF data system at the Infrared Processing and Analysis Center provides near-real-time reduction to identify moving and varying objects. We outline the analysis pipelines, data products, and associated archive. Finally, we present on-sky performance analysis and first scientific results from commissioning and the early survey. ZTF’s public alert stream will serve as a useful precursor for that of the Large Synoptic Survey Telescope
Harvest: an open-source tool for the validation and improvement of peptide identification metrics and fragmentation exploration
Background// Protein identification using mass spectrometry is an important tool in many areas of the life sciences, and in proteomics research in particular. Increasing the number of proteins correctly identified is dependent on the ability to include new knowledge about the mass spectrometry fragmentation process, into computational algorithms designed to separate true matches of peptides to unidentified mass spectra from spurious matches. This discrimination is achieved by computing a function of the various features of the potential match between the observed and theoretical spectra to give a numerical approximation of their similarity. It is these underlying “metrics” that determine the ability of a protein identification package to maximise correct identifications while limiting false discovery rates. There is currently no software available specifically for the simple implementation and analysis of arbitrary novel metrics for peptide matching and for the exploration of fragmentation patterns for a given dataset. Results// We present Harvest: an open source software tool for analysing fragmentation patterns and assessing the power of a new piece of information about the MS/MS fragmentation process to more clearly differentiate between correct and random peptide assignments. We demonstrate this functionality using data metrics derived from the properties of individual datasets in a peptide identification context. Using Harvest, we demonstrate how the development of such metrics may improve correct peptide assignment confidence in the context of a high-throughput proteomics experiment and characterise properties of peptide fragmentation. Conclusions// Harvest provides a simple framework in C++ for analysing and prototyping metrics for peptide matching, the core of the protein identification problem. It is not a protein identification package and answers a different research question to packages such as Sequest, Mascot, X!Tandem, and other protein identification packages. It does not aim to maximise the number of assigned peptides from a set of unknown spectra, but instead provides a method by which researchers can explore fragmentation properties and assess the power of novel metrics for peptide matching in the context of a given experiment. Metrics developed using Harvest may then become candidates for later integration into protein identification packages