Radio Supernovae in the Great Survey Era

Radio properties of supernova outbursts remain poorly understood despite longstanding campaigns following events discovered at other wavelengths. After ~ 30 years of observations, only ~ 50 supernovae have been detected at radio wavelengths, none of which are Type Ia. Even the most radio-loud events are ~ 10^4 fainter in the radio than in the optical; to date, such intrinsically dim objects have only been visible in the very local universe. The detection and study of radio supernovae (RSNe) will be fundamentally altered and dramatically improved as the next generation of radio telescopes comes online, including EVLA, ASKAP, and MeerKAT, and culminating in the Square Kilometer Array (SKA); the latter should be > 50 times more sensitive than present facilities. SKA can repeatedly scan large (> 1 deg^2) areas of the sky, and thus will discover RSNe and other transient sources in a new, automatic, untargeted, and unbiased way. We estimate SKA will be able to detect core-collapse RSNe out to redshift z ~ 5, with an all-redshift rate ~ 620 events yr^-1 deg^-2, assuming a survey sensitivity of 50 nJy and radio lightcurves like those of SN 1993J. Hence SKA should provide a complete core-collapse RSN sample that is sufficient for statistical studies of radio properties of core-collapse supernovae. EVLA should find ~ 160 events yr^-1 deg^-2 out to redshift z ~ 3, and other SKA precursors should have similar detection rates. We also provided recommendations of the survey strategy to maximize the RSN detections of SKA. This new radio core-collapse supernovae sample will complement the detections from the optical searches, such as the LSST, and together provide crucial information on massive star evolution, supernova physics, and the circumstellar medium, out to high redshift. Additionally, SKA may yield the first radio Type Ia detection via follow-up of nearby events discovered at other wavelengths.Comment: 21 pages, 5 figures, accepted for publication in Ap

The Diffuse Gamma-ray Background from Type Ia Supernovae

The origin of the diffuse extragalactic gamma-ray background (EGB) has been intensively studied but remains unsettled. Current popular source candidates include unresolved star-forming galaxies, starburst galaxies, and blazars. In this paper we calculate the EGB contribution from the interactions of cosmic rays accelerated by Type Ia supernovae (SNe), extending earlier work which only included core-collapse SNe. We consider Type Ia events in star-forming galaxies, but also in quiescent galaxies that lack star formation. For star-forming galaxies, consistently including Type Ia events makes little change to the star-forming EGB prediction, so long as both SN types have the same cosmic-ray acceleration efficiencies in star-forming galaxies. Thus, our updated EGB estimate continues to show that star-forming galaxies can represent a substantial portion of the signal measured by Fermi. For quiescent galaxies, conversely, we find a wide range of possibilities for the EGB contribution. The dominant uncertainty we investigated comes from the mass in hot gas, which provides targets for cosmic rays; total gas masses are as yet poorly known, particularly at larger radii. Additionally, the EGB estimation is very sensitive to the cosmic-ray acceleration efficiency and confinement, especially in quiescent galaxies. In the most optimistic allowed scenarios, quiescent galaxies can be an important source of the EGB. In this case, star-forming galaxies and quiescent galaxies together will dominate the EGB and leave little room for other contributions. If other sources, such as blazars, are found to have important contributions to the EGB, then either the gas mass or cosmic-ray content of quiescent galaxies must be significantly lower than in their star-forming counterparts. In any case, improved Fermi EGB measurements will provide important constraints on hot gas and cosmic rays in quiescent galaxies.Comment: 29 pages, 4 figures, Accepted for publication in Ap

Occurrence cubes : a new paradigm for aggregating species occurrence data

In this paper we describe a method of aggregating species occurrence data into what we coined “occurrence cubes”. The aggregated data can be perceived as a cube with three dimensions - taxonomic, temporal and geographic - and takes into account the spatial uncertainty of each occurrence. The aggregation level of each of the three dimensions can be adapted to the scope. Built on Open Science principles, the method is easily automated and reproducible, and can be used for species trend indicators, maps and distribution models. We are using the method to aggregate species occurrence data for Europe per taxon, year and 1km2 European reference grid, to feed indicators and risk mapping/modelling for the Tracking Invasive Alien Species (TrIAS) project

Probing the Gamma-Ray Burst Rate with Trigger Simulations of the Swift Burst Alert Telescope

The long gamma-ray burst (GRB) rate is essential for revealing the connection between GRBs, supernovae and stellar evolution. Additionally, the GRB rate at high redshift provides a strong probe of star formation history in the early universe. While hundreds of GRBs are observed by Swift, it remains difficult to determine the intrinsic GRB rate due to the complex trigger algorithm of Swift. Current studies usually approximate the Swift trigger algorithm by a single detection threshold. However, unlike the previously flown GRB instruments, Swift has over 500 trigger criteria based on photon count rate and additional image threshold for localization. To investigate possible systematic biases and explore the intrinsic GRB properties, we developed a program that is capable of simulating all the rate trigger criteria and mimicking the image trigger threshold. We use this program to search for the intrinsic GRB rate. Our simulations show that adopting the complex trigger algorithm of Swift increases the detection rate of dim bursts. As a result, we find that either the GRB rate is much higher than previously expected at large redshift, or the luminosity evolution is non-negligible. We will discuss the best results of the GRB rate in our search, and their impact on the star-formation history.Comment: 6 pages, 3 figures, 7th Huntsville Gamma-Ray Burst Symposium, GRB 2013: paper 35 in eConf Proceedings C130414

Probing the Cosmic Gamma-Ray Burst Rate with Trigger Simulations of the Swift Burst Alert Telescope

The gamma-ray burst (GRB) rate is essential for revealing the connection between GRBs, supernovae and stellar evolution. Additionally, the GRB rate at high redshift provides a strong probe of star formation history in the early universe. While hundreds of GRBs are observed by Swift, it remains difficult to determine the intrinsic GRB rate due to the complex trigger algorithm of Swift. Current studies of the GRB rate usually approximate the Swift trigger algorithm by a single detection threshold. However, unlike the previously flown GRB instruments, Swift has over 500 trigger criteria based on photon count rate and additional image threshold for localization. To investigate possible systematic biases and explore the intrinsic GRB properties, we develop a program that is capable of simulating all the rate trigger criteria and mimicking the image threshold. Our simulations show that adopting the complex trigger algorithm of Swift increases the detection rate of dim bursts. As a result, our simulations suggest bursts need to be dimmer than previously expected to avoid over-producing the number of detections and to match with Swift observations. Moreover, our results indicate that these dim bursts are more likely to be high redshift events than low-luminosity GRBs. This would imply an even higher cosmic GRB rate at large redshifts than previous expectations based on star-formation rate measurements, unless other factors, such as the luminosity evolution, are taken into account. The GRB rate from our best result gives a total number of 4571^{+829}_{-1584} GRBs per year that are beamed toward us in the whole universe. SPECIAL NOTE (2015.05.16): This new version incorporates an erratum. All the GRB rate normalizations ($R_{\rm GRB}(z=0)$) should be a factor of 2 smaller than previously reported. Please refer to the Appendix for more details. We sincerely apologize for the mistake.Comment: 52 pages, 17 figures, published in ApJ 783, 24L (2014). An erratum is included. A typo in Eq. 8 is fixed in this versio

Evidence for a strong 19.5 Hz flux oscillation in Swift BAT and Fermi GBM gamma-ray data from GRB 211211A

The gamma-ray burst (GRB) GRB~211211A is believed to have occurred due to the merger of two neutron stars or a neutron star and a black hole, despite its duration of more than a minute. Subsequent analysis has revealed numerous interesting properties including the possible presence of a $\sim 22$~Hz quasiperiodic oscillation (QPO) during precursor emission. Here we perform timing analysis of Fermi and Swift gamma-ray data on GRB~211211A and, although we do not find a strong QPO during the precursor, we do find an extremely significant 19.5~Hz flux oscillation, which has higher fractional amplitude at higher energies, in a $\sim 0.2$~second segment beginning $\sim 1.6$~seconds after the start of the burst. After presenting our analysis we discuss possible mechanisms for the oscillation.Comment: 16 pages, 7 figures, 2 table

Modeling the Swift BAT Trigger Algorithm with Machine Learning

To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. (2014) is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of approximately greater than 97% (approximately less than 3% error), which is a significant improvement on a cut in GRB flux which has an accuracy of 89:6% (10:4% error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of eta(sub 0) approximately 0.48(+0.41/-0.23) Gpc(exp -3) yr(exp -1) with power-law indices of eta(sub 1) approximately 1.7(+0.6/-0.5) and eta(sub 2) approximately -5.9(+5.7/-0.1) for GRBs above and below a break point of z(sub 1) approximately 6.8(+2.8/-3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting. The code used in this is analysis is publicly available online