VERITAS Observations of the Coma Cluster of Galaxies

Clusters of galaxies are one of the few prominent classes of objects predicted to emit gamma rays not yet detected by satellites like EGRET or ground-based Imaging Atmospheric Cherenkov Telescopes (IACTs). The detection of Very High Energy (VHE, E > 100 GeV) gamma rays from galaxy clusters would provide insight into the morphology of non-thermal particles and fields in clusters. VERITAS, an array of four 12-meter diameter IACTs, is ideally situated to observe the massive Coma cluster, one of the best cluster candidates in the Northern Hemisphere. This contribution details the results of VERITAS observations of the Coma cluster of galaxies during the 2007-2008 observing season.Comment: Submitted to Proceedings of "4th Heidelberg International Symposium on High Energy Gamma-Ray Astronomy 2008

Weather, Not Climate, Defines Distributions of Vagile Bird Species

Background\ud \ud Accurate predictions of species distributions are essential for climate change impact assessments. However the standard practice of using long-term climate averages to train species distribution models might mute important temporal patterns of species distribution. The benefit of using temporally explicit weather and distribution data has not been assessed. 1We hypothesized that short-term weather associated with the time a species was recorded should be superior to long-term climate measures for predicting distributions of mobile species.\ud \ud Methodology\ud \ud We tested our hypothesis by generating distribution models for 157 bird species found in Australian tropical savannas (ATS) using modelling algorithm Maxent. The variable weather of the ATS supports a bird assemblage with variable movement patterns and a high incidence of nomadism. We developed “weather” models by relating climatic variables (mean temperature, rainfall, rainfall seasonality and temperature seasonality) from the three month, six month and one year period preceding each bird record over a 58 year period (1950–2008). These weather models were compared against models built using long-term (30 year) averages of the same climatic variables.\ud \ud Conclusions\ud \ud Weather models consistently achieved higher model scores than climate models, particularly for wide-ranging, nomadic and desert species. Climate models predicted larger range areas for species, whereas weather models quantified fluctuations in habitat suitability across months, seasons and years. Models based on long-term climate averages over-estimate availability of suitable habitat and species' climatic tolerances, masking species potential vulnerability to climate change. Our results demonstrate that dynamic approaches to distribution modelling, such as incorporating organism-appropriate temporal scales, improves understanding of species distributions

BurstCube: A CubeSat for Gravitational Wave Counterparts

BurstCube will detect long GRBs, attributed to the collapse of massive stars, short GRBs (sGRBs), resulting from binary neutron star mergers, as well as other gamma-ray transients in the energy range 10-1000 keV. sGRBs are of particular interest because they are predicted to be the counterparts of gravitational wave (GW) sources soon to be detectable by LIGO/Virgo. BurstCube contains 4 CsI scintillators coupled with arrays of compact low-power Silicon photomultipliers (SiPMs) on a 6U Dellingr bus, a flagship modular platform that is easily modifiable for a variety of 6U CubeSat architectures. BurstCube will complement existing facilities such as Swift and Fermi in the short term, and provide a means for GRB detection, localization, and characterization in the interim time before the next generation future gamma-ray mission flies, as well as space-qualify SiPMs and test technologies for future use on larger gamma-ray missions. The ultimate configuration of BurstCube is to have a set of $\sim10$ BurstCubes to provide all-sky coverage to GRBs for substantially lower cost than a full-scale mission.Comment: In the 35th International Cosmic Ray Conference, Busan, Kore

Machine-learned climate model corrections from a global storm-resolving model

Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution (${\gtrsim}50$ km) than is optimal for accurately resolving important physical processes. Such processes are approximated in GCMs via subgrid parameterizations, which contribute significantly to the uncertainty in GCM predictions. One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned state-dependent corrections at each simulation timestep, such that the climate model evolves more like a high-resolution global storm-resolving model (GSRM). We train neural networks to learn the state-dependent temperature, humidity, and radiative flux corrections needed to nudge a 200 km coarse-grid climate model to the evolution of a 3~km fine-grid GSRM. When these corrective ML models are coupled to a year-long coarse-grid climate simulation, the time-mean spatial pattern errors are reduced by 6-25% for land surface temperature and 9-25% for land surface precipitation with respect to a no-ML baseline simulation. The ML-corrected simulations develop other biases in climate and circulation that differ from, but have comparable amplitude to, the baseline simulation

Emulating Fast Processes in Climate Models

Cloud microphysical parameterizations in atmospheric models describe the formation and evolution of clouds and precipitation, a central weather and climate process. Cloud-associated latent heating is a primary driver of large and small-scale circulations throughout the global atmosphere, and clouds have important interactions with atmospheric radiation. Clouds are ubiquitous, diverse, and can change rapidly. In this work, we build the first emulator of an entire cloud microphysical parameterization, including fast phase changes. The emulator performs well in offline and online (i.e. when coupled to the rest of the atmospheric model) tests, but shows some developing biases in Antarctica. Sensitivity tests demonstrate that these successes require careful modeling of the mixed discrete-continuous output as well as the input-output structure of the underlying code and physical process.Comment: Accepted at the Machine Learning and the Physical Sciences Workshop at the 36th conference on Neural Information Processing Systems (NeurIPS) December 3, 202

AstroPix: novel monolithic active pixel silicon sensors for future gamma-ray telescopes

Space-based gamma-ray telescopes such as the Fermi Large Area Telescope have used single sided silicon strip detectors to track secondary charged particles produced by primary gamma-rays with high resolution. At the lower energies targeted by keV-MeV telescopes, two dimensional position information within a single detector is required for event reconstruction - especially in the Compton regime. This work describes the development of monolithic CMOS active pixel silicon sensors - AstroPix - as a novel technology for use in future gamma-ray telescopes. Based upon sensors (ATLASPix) designed for use in the ATLAS detector at the Large Hadron Collider, AstroPix has the potential to maintain high performance while reducing noise with low power consumption. This is achieved with the dual detection and readout capabilities in each CMOS pixel. The status of AstroPix development and testing, as well as outlook for future testing and application, will be presented