More Like this

1. Stingray : A Modern Python Library for Spectral Timing

   https://doi.org/10.3847/1538-4357/ab258d  

   Huppenkothen, Daniela; Bachetti, Matteo; Stevens, Abigail L.; Migliari, Simone; Balm, Paul; Hammad, Omar; Khan, Usman Mahmood; Mishra, Himanshu; Rashid, Haroon; Sharma, Swapnil; et al (August 2019, The Astrophysical Journal)
2. Mahakala : A Python -based Modular Ray-tracing and Radiative Transfer Algorithm for Curved Spacetimes

   https://doi.org/10.3847/1538-4357/adc104  

   Sharma, Aniket; Medeiros, Lia; Wong, George N; Chan, Chi-kwan; Halevi, Goni; Mullen, Patrick D; Stone, James M (May 2025, The Astrophysical Journal)

   Abstract We introduceMahakala, aPython-based, modular, radiative ray-tracing code for curved spacetimes. We employ Google’sJAXframework for accelerated automatic differentiation, which can efficiently compute Christoffel symbols directly from the metric, allowing the user to easily and quickly simulate photon trajectories through non-Kerr spacetimes.JAXalso enablesMahakalato run in parallel on both CPUs and GPUs.Mahakalanatively uses the Cartesian Kerr–Schild coordinate system, which avoids numerical issues caused by the pole in spherical coordinate systems. We demonstrateMahakala’s capabilities by simulating 1.3 mm wavelength images (the wavelength of Event Horizon Telescope observations) of general relativistic magnetohydrodynamic simulations of low-accretion rate supermassive black holes. The modular nature ofMahakalaallows us to quantitatively explore how different regions of the flow influence different image features. We show that most of the emission seen in 1.3 mm images originates close to the black hole and peaks near the photon orbit. We also quantify the relative contribution of the disk, forward jet, and counterjet to 1.3 mm images. 

   more » « less
3. PyQMC : An all-Python real-space quantum Monte Carlo module in PySCF

   https://doi.org/10.1063/5.0139024  

   Wheeler, William A.; Pathak, Shivesh; Kleiner, Kevin G.; Yuan, Shunyue; Rodrigues, João N. B.; Lorsung, Cooper; Krongchon, Kittithat; Chang, Yueqing; Zhou, Yiqing; Busemeyer, Brian; et al (March 2023, The Journal of Chemical Physics)

   We describe a new open-source Python-based package for high accuracy correlated electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC implements modern versions of QMC algorithms in an accessible format, enabling algorithmic development and easy implementation of complex workflows. Tight integration with the PySCF environment allows for a simple comparison between QMC calculations and other many-body wave function techniques, as well as access to high accuracy trial wave functions. 

   more » « less
4. GRay2 : A General Purpose Geodesic Integrator for Kerr Spacetimes

   https://doi.org/10.3847/1538-4357/aadfe5  

   Chan, Chi-kwan; Medeiros, Lia; Özel, Feryal; Psaltis, Dimitrios (November 2018, The Astrophysical Journal)
5. STICAP : Spatio-temporal Interactive Attention for Citywide Crowd Activity Prediction

   https://doi.org/10.1145/3603375  

   Huang, Huiqun; He, Suining; Yang, Xi; Tabatabaie, Mahan (March 2024, ACM Transactions on Spatial Algorithms and Systems)

   Accurate citywide crowd activity prediction (CAP) can enable proactive crowd mobility management and timely responses to urban events, which has become increasingly important for a myriad of smart city planning and management purposes. However, complex correlations across the crowd activities, spatial and temporal urban environment features and theirinteractivedependencies, and relevant external factors (e.g., weather conditions) make it highly challenging to predict crowd activities accurately in terms of different venue categories (for instance, venues related to dining, services, and residence) and varying degrees (e.g., daytime and nighttime). To address the above concerns, we proposeSTICAP, a citywide spatio-temporal interactive crowd activity prediction approach. In particular,STICAPtakes in the location-based social network check-in data (e.g., from Foursquare/Gowalla) as the model inputs and forecasts the crowd activity within each time step for each venue category. Furthermore, we have integrated multiple levels of temporal discretization to interactively capture the relations with historical data. Then, three parallelResidual Spatial Attention Networks(RSAN) in theSpatial Attention Componentexploit the hourly, daily, and weekly spatial features of crowd activities, which are further fused and processed by theTemporal Attention Componentforinteractive CAP. Along with other external factors such as weather conditions and holidays,STICAPadaptively and accurately forecasts the final crowd activities per venue category, enabling potential activity recommendation and other smart city applications. Extensive experimental studies based on three different real-world crowd activity datasets have demonstrated that our proposedSTICAPoutperforms the baseline and state-of-the-art algorithms in CAP accuracy, with an average error reduction of 35.02%. 

   more » « less