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James Guillochon
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We announce the public release of the application program interface (API) for the Open Astronomy Catalogs (OACs), the OACAPI. The OACs serve near-complete collections of supernova, tidal disruption, kilonova, and fast stars data (including photometry, spectra, radio, and X-ray observations) via a user-friendly web interface that displays the data interactively and offers full data downloads. The OACAPI, by contrast, enables users to specifically download particular pieces of the OAC dataset via a flexible programmatic syntax, either via URL GET requests, or via a module within the astroquery Python package.
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Imaging Atmospheric Cherenkov Telescopes (IACTs) are very-large telescopes designed to detect the nanosecond-timescale flashes produced within extended air showers. Because IACTs are sensitive to the Cherenkov light (UV/blue) and use photodetectors with extremely fast time responses, they are also able to perform simultaneous optical observations. The large reflecting areas of these telescopes (larger than 100 m$^2$) makes them well-suited to studying fast optical transient phenomena with timescales ranging from seconds to milliseconds to nanoseconds, and the unique optical design provides a wide field of view monitoring capability with a modest point spread function. VERITAS, with its recently upgraded PMT current monitoring instrumentation, was able to provide the first detection of asteroid occultations with an IACT, resulting in the highest angular resolution measurements for stellar diameters ever taken in the visible band range. Here we explore the feasibility of using this technique to significantly expand the number of stars with directly measured stellar radii, usable for population studies to test stellar evolution modelling or transiting exoplanet radius measurements. A single observatory with a high-speed visible-band photometer with a sensitivity reaching the 13$^{th}$ magnitude could increase the number of directly measured K stars diameters by 50%.
In classical analyses of $gamma$-ray data from IACTs, such as H.E.S.S., aperture photometry, or photon counting, is applied in a (typically circular) region of interest (RoI) encompassing the source. A key element in the analysis is to estimate the amount of background in the RoI due to residual cosmic ray-induced air showers in the data. Various standard background estimation techniques have been developed in the last decades, most of them rely on a measurement of the background from source-free regions within the observed field of view. However, in particular in the Galactic plane, source analysis and background estimation are hampered by the large number of, sometimes overlapping, $gamma$-ray sources and large-scale diffuse $gamma$-ray emission. For complicated fields of view, a three-dimensional (3D) likelihood analysis shows the potential to be superior to classical analysis. In this analysis technique, a spectromorphological model, consisting of one or multiple source components and a background component, is fitted to the data, resulting in a complete spectral and spatial description of the field of view. For the application to IACT data, the major challenge of such an approach is the construction of a robust background model. In this work, we apply the 3D likelihood analysis to various test data recently made public by H.E.S.S., using the open analysis frameworks ctools and Gammapy. First, we show that, when using these tools in a classical analysis approach and comparing to the proprietary H.E.S.S. analysis framework, virtually identical high-level analysis results are obtained. We then describe the construction of a generic background model from data of H.E.S.S. observations, and demonstrate that a 3D likelihood analysis using this background model yields high-level analysis results that are highly compatible with those obtained from the classical analyses. (abridged)
Progress in astronomy comes from interpreting the signals encoded in the light received from distant objects: the distribution of light over the sky (images), over photon wavelength (spectrum), over polarization angle, and over time (usually called light curves by astronomers). In the time domain we see transient events such as supernovae, gamma-ray bursts, and other powerful explosions; we see periodic phenomena such as the orbits of planets around nearby stars, radio pulsars, and pulsations of stars in nearby galaxies; and persistent aperiodic variations (`noise) from powerful systems like accreting black holes. I review just a few of the recent and future challenges in the burgeoning area of Time Domain Astrophysics, with particular attention to persistently variable sources, the recovery of reliable noise power spectra from sparsely sampled time series, higher-order properties of accreting black holes, and time delays and correlations in multivariate time series.
We present ARC2 (Astrophysically Robust Correction 2), an open-source Python-based systematics-correction pipeline to correct for the Kepler prime mission long cadence light curves. The ARC2 pipeline identifies and corrects any isolated discontinuities in the light curves, then removes trends common to many light curves. These trends are modelled using the publicly available co-trending basis vectors, within an (approximate) Bayesian framework with `shrinkage priors to minimise the risk of over-fitting and the injection of any additional noise into the corrected light curves, while keeping any astrophysical signals intact. We show that the ARC2 pipelines performance matches that of the standard Kepler PDC-MAP data products using standard noise metrics, and demonstrate its ability to preserve astrophysical signals using injection tests with simulated stellar rotation and planetary transit signals. Although it is not identical, the ARC2 pipeline can thus be used as an open source alternative to PDC-MAP, whenever the ability to model the impact of the systematics removal process on other kinds of signal is important.
On a time scale of years to decades, gravitational wave (GW) astronomy will become a reality. Low frequency (nanoHz) GWs are detectable through long-term timing observations of the most stable pulsars. Radio observatories worldwide are currently carrying out observing programmes to detect GWs, with data sets being shared through the International Pulsar Timing Array project. One of the most likely sources of low frequency GWs are supermassive black hole binaries (SMBHBs), detectable as a background due to a large number of binaries, or as continuous or burst emission from individual sources. No GW signal has yet been detected, but stringent constraints are already being placed on galaxy evolution models. The SKA will bring this research to fruition. In this chapter, we describe how timing observations using SKA1 will contribute to detecting GWs, or can confirm a detection if a first signal already has been identified when SKA1 commences observations. We describe how SKA observations will identify the source(s) of a GW signal, search for anisotropies in the background, improve models of galaxy evolution, test theories of gravity, and characterise the early inspiral phase of a SMBHB system. We describe the impact of the large number of millisecond pulsars to be discovered by the SKA; and the observing cadence, observation durations, and instrumentation required to reach the necessary sensitivity. We describe the noise processes that will influence the achievable precision with the SKA. We assume a long-term timing programme using the SKA1-MID array and consider the implications of modifications to the current design. We describe the possible benefits from observations using SKA1-LOW. Finally, we describe GW detection prospects with SKA1 and SKA2, and end with a description of the expectations of GW astronomy.
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