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Register a new userSNANA: A Public Software Package for Supernova Analysis
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We describe a general analysis package for supernova (SN) light curves, called SNANA, that contains a simulation, light curve fitter, and cosmology fitter. The software is designed with the primary goal of using SNe Ia as distance indicators for the determination of cosmological parameters, but it can also be used to study efficiencies for analyses of SN rates, estimate contamination from non-Ia SNe, and optimize future surveys. Several SN models are available within the same software architecture, allowing technical features such as K-corrections to be consistently used among multiple models, and thus making it easier to make detailed comparisons between models. New and improved light-curve models can be easily added. The software works with arbitrary surveys and telescopes and has already been used by several collaborations, leading to more robust and easy-to-use code. This software is not intended as a final product release, but rather it is designed to undergo continual improvements from the community as more is learned about SNe. Below we give an overview of the SNANA capabilities, as well as some of its limitations. Interested users can find software downloads and more detailed information from the manuals at http://www.sdss.org/supernova/SNANA.html .
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Gravitational waves in the sensitivity band of ground-based detectors can be emitted by a number of astrophysical sources, including not only binary coalescences, but also individual spinning neutron stars. The most promising signals from such sources, although not yet detected, are long-lasting, quasi-monochromatic Continuous Waves (CWs). The PyFstat package provides tools to perform a range of CW data-analysis tasks. It revolves around the F-statistic, a matched-filter detection statistic for CW signals that has been one of the standard methods for LIGO-Virgo CW searches for two decades. PyFstat is built on top of established routines in LALSuite but through its more modern Python interface it enables a flexible approach to designing new search strategies. Hence, it serves a dual function of (i) making LALSuite CW functionality more easily accessible through a Python interface, thus facilitating the new user experience and, for developers, the exploratory implementation of novel methods; and (ii) providing a set of production-ready search classes for use cases not yet covered by LALSuite itself, most notably for MCMC-based followup of promising candidates from wide-parameter-space searches.
Over the past few decades, the measurement precision of some pulsar-timing experiments has advanced from ~10 us to ~10 ns, revealing many subtle phenomena. Such high precision demands both careful data handling and sophisticated timing models to avoid systematic error. To achieve these goals, we present PINT (PINT Is Not Tempo3), a high-precision Python pulsar timing data analysis package, which is hosted on GitHub and available on Python Package Index (PyPI) as pint-pulsar. PINT is well-tested, validated, object-oriented, and modular, enabling interactive data analysis and providing an extensible and flexible development platform for timing applications. It utilizes well-debugged public Python packages (e.g., the NumPy and Astropy libraries) and modern software development schemes (e.g., version control and efficient development with git and GitHub) and a continually expanding test suite for improved reliability, accuracy, and reproducibility. PINT is developed and implemented without referring to, copying, or transcribing the code from other traditional pulsar timing software packages (e.g., TEMPO and TEMPO2) and therefore provides a robust tool for cross-checking timing analyses and simulating pulse arrival times. In this paper, we describe the design, usage, and validation of PINT, and we compare timing results between it and TEMPO and TEMPO2.
Modern radio pulsar surveys produce a large volume of prospective candidates, the majority of which are polluted by human-created radio frequency interference or other forms of noise. Typically, large numbers of candidates need to be visually inspected in order to determine if they are real pulsars. This process can be labor intensive. In this paper, we introduce an algorithm called PEACE (Pulsar Evaluation Algorithm for Candidate Extraction) which improves the efficiency of identifying pulsar signals. The algorithm ranks the candidates based on a score function. Unlike popular machine-learning based algorithms, no prior training data sets are required. This algorithm has been applied to data from several large-scale radio pulsar surveys. Using the human-based ranking results generated by students in the Arecibo Remote Command enter programme, the statistical performance of PEACE was evaluated. It was found that PEACE ranked 68% of the student-identified pulsars within the top 0.17% of sorted candidates, 95% within the top 0.34%, and 100% within the top 3.7%. This clearly demonstrates that PEACE significantly increases the pulsar identification rate by a factor of about 50 to 1000. To date, PEACE has been directly responsible for the discovery of 47 new pulsars, 5 of which are millisecond pulsars that may be useful for pulsar timing based gravitational-wave detection projects.
We introduce the STAR-MELT Python package that we developed to facilitate the analysis of time-resolved emission line spectroscopy of young stellar objects. STAR-MELT automatically extracts, identifies and fits emission lines. We summarise our analysis methods that utilises the time domain of high-resolution stellar spectra to investigate variability in the line profiles and corresponding emitting regions. This allows us to probe the innermost disc and accretion structures of YSOs. Local temperatures and densities can be determined using Boltzmann statistics, the Saha equation, and the Sobolev large velocity gradient approximation. STAR-MELT allows for new results to be obtained from archival data, as well as facilitating timely analysis of new data as it is obtained. We present the results of applying STAR-MELT to three YSOs, using spectra from UVES, XSHOOTER, FEROS, HARPS, and ESPaDOnS. We demonstrate what can be achieved for data with disparate time sampling, for stars with different inclinations and variability types. For EX Lupi, we confirm the presence of a localised and stable stellar-surface hot spot associated with the footprint of the accretion column. For GQ Lupi A, we find that the maximum infall rate from an accretion column is correlated with lines produced in the lowest temperatures. For CVSO109 we investigate the rapid temporal variability of a redshifted emission wing, indicative of rotating and infalling material in the inner disc. Our results show that STAR-MELT is a useful tool for such analysis, as well as other applications for emission lines.
Mori dream spaces form a large example class of algebraic varieties, comprising the well known toric varieties. We provide a first software package for the explicit treatment of Mori dream spaces and demonstrate its use by presenting basic sample computations. The software package is accompanied by a Cox ring database which delivers defining data for Cox rings and Mori dream spaces in a suitable format. As an application of the package, we determine the common Cox ring for the symplectic resolutions of a certain quotient singularity investigated by Bellamy/Schedler and Donten-Bury/Wisniewski.
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