ASCL.net

Astrophysics Source Code Library

Making codes discoverable since 1999

Searching for codes credited to 'Ng, C.'

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[ascl:2005.015] AMPEL: Alert Management, Photometry, and Evaluation of Light curves
Nordin, J.; Brinnel, V.; van Santen, J.; Bulla, M.; Feindt, U.; Franckowiak, A.; Fremling, C.; Gal-Yam, A.; Giomi, M.; Kowalski, M.; Mahabal, A.; Miranda, N.; Rauch, L.; Reusch, S.; Rigault, M.; Schulze, S.; Sollerman, J.; Stein, R.; Yaron, O.; van Velzen, S.; Ward, C.

AMPEL provides an analysis framework for high-throughput surveys and is suited for streamed data. The package combines the functionality of an alert broker with a generic framework capable of hosting user-contributed code; it encourages provenance and keeps track of the varying information states that a transient displays. The latter concept includes information gathered over time and data policies such as access or calibration levels.

[ascl:2210.009] NEMESIS: Non-linear optimal estimator for multivariate spectral analysis
Irwin, P. G. J.; Teanby, N. A.; de Kok, R.; Fletcher, L. N.; Howett, C. J. A.; Tsang, C. C. C.; Wilson, C. F.; Calcutt, S. B.; Nixon, C. A.; Parrish, P. D.

NEMESIS (Non-linear optimal Estimator for MultivariatE spectral analySIS) is the general purpose correlated-k/LBL retrieval code developed from the RADTRAN project (ascl:2210.008). Originally based on the correlated-k approximation, NEMESIS also works in line-by-line (LBL) mode. It has been designed to be generally applicable to any planet and with any observing mode and so is suitable for both solar-system studies and also exoplanetary studies.

[ascl:2307.038] WarpX: Time-based electromagnetic and electrostatic Particle-In-Cell code
Vay, J.L.; Myers, A.; Almgren, A.; Amorim, L. D.; Bell, J.; Fedeli, L.; Ge, L.; Gott, K.; Grote, D. P.; Hogan, M.; Huebl, A.; Jambunathan, R.; Lehe, R.; Ng, C.; Park, J.; Rowan, M.; Shapoval, O.; Thévenet, M.; Vincenti, H.; Yang, E.; Zaïm, N.; Zhang, W.; Zhao, Y.; Zoni, E.

WarpX is an advanced electromagnetic & electrostatic Particle-In-Cell code. It supports many features including Perfectly-Matched Layers (PML), mesh refinement, and the boosted-frame technique. A highly-parallel and highly-optimized code, WarpX can run on GPUs and multi-core CPUs, includes load balancing capabilities, and scales to the largest supercomputers.

[ascl:2310.008] clfd: Clean folded data
Morello, V.; Barr, E. D.; Cooper, S.; Bailes, M.; Bates, S.; Bhat, N. D. R.; Burgay, M.; Burke-Spolaor, S.; Cameron, A. D.; Champion, D. J.; Eatough, R. P.; Flynn, C. M. L.; Jameson, A.; Johnston, S.; Keith, M. J.; Keane, E. F.; Kramer, M.; Levin, L.; Ng, C.; Petroff, E.; Possenti, A.; Stappers, B. W.; van Straten, W.; Tiburzi, C.

clfd (clean folded data) implements GPU-accelerated smart interference removal algorithms to be used on folded pulsar search and pulsar timing data. The code converts each source profile to a small set of representative features, flagging outliers in the resulting feature space. clfd further visualizes the outlier flagging process, as well as the resulting two-dimensional time-frequency mask that is applied to the clean archive. The code provides access to cleaning algorithms that were initially developed for the High Time Resolution Universe (HTRU) survey which found several pulsars.

[ascl:2401.020] escatter: Electron scattering in Python
Brennan, S. J.; Schulze, S.; Lunnan, R.; Sollerman, J.; Yan, L.; Fransson, C.; Irani, I.; Melinder, J.; Chen, T. W.; De, K.; Fremling, C.; Kim, Y. L.; Perley, D.; Pessi, P. J.; Drake, A. J.; Graham, M. J.; Laher, R. R.; Masci, F. J.; Purdum, J.; Rodriguez, H.

escatter.py performs Monte Carlo simulations of electron scattering events. The code was developed to better understand the emission lines from the interacting supernova SN 2021adxl, specifically the blue excess seen in the Hα 6563A emission line. escatter follows a photon that was formed in a thin interface between the supernova ejecta and surrounding material as it travels radially outwards through the dense material, scattering electrons outwards until it reaches an optically thin region, and plots a histogram of the emergent photons.

[ascl:2405.001] SPEDAS: Space Physics Environment Data Analysis System
Angelopoulos, V.; Cruce, P.; Drozdov, A.; Grimes, E. W.; Hatzigeorgiu, N.; King, D. A.; Larson, D.; Lewis, J. W.; McTiernan, J. M.; Roberts, D. A.; Russell, C. L.; Hori, T.; Kasahara, Y.; Kumamoto, A.; Matsuoka, A.; Miyashita, Y.; Miyoshi, Y.; Shinohara, I.; Teramoto, M.; Faden, J. B.; Halford, A. J.; McCarthy, M.; Millan, R. M.; Sample, J. G.; Smith, D. M.; Woodger, L. A.; Masson, A.; Narock, A. A.; Asamura, K.; Chang, T. F.; Chiang, C. Y.; Kazama, Y.; Keika, K.; Matsuda, S.; Segawa, T.; Seki, K.; Shoji, M.; Tam, S. W. Y.; Umemura, N.; Wang, B. J.; Wang, S. Y.; Redmon, R.; Rodriguez, J. V.; Singer, H. J.; Vandegriff, J.; Abe, S.; Nose, M.; Shinbori, A.; Tanaka, Y. M.; UeNo, S.; Andersson, L.; Dunn, P.; Fowler, C.; Halekas, J. S.; Hara, T.; Harada, Y.; Lee, C. O.; Lillis, R.; Mitchell, D. L.; Argall, M. R.; Bromund, K.; Burch, J. L.; Cohen, I. J.; Galloy, M.; Giles, B.; Jaynes, A. N.; Le Contel, O.; Oka, M.; Phan, T. D.; Walsh, B. M.; Westlake, J.; Wilder, F. D.; Bale, S. D.; Livi, R.; Pulupa, M.; Whittlesey, P.; DeWolfe, A.; Harter, B.; Lucas, E.; Auster, U.; Bonnell, J. W.; Cully, C. M.; Donovan, E.; Ergun, R. E.; Frey, H. U.; Jackel, B.; Keiling, A.; Korth, H.; McFadden, J. P.; Nishimura, Y.; Plaschke, F.; Robert, P.; Turner, D. L.; Weygand, J. M.; Candey, R. M.; Johnson, R. C.; Kovalick, T.; Liu, M. H.; McGuire, R. E.; Breneman, A.; Kersten, K.; Schroeder, P.

The SPEDAS (Space Physics Environment Data Analysis Software) framework supports multi-mission data ingestion, analysis and visualization for the Space Physics community. It standardizes the retrieval of data from distributed repositories, the scientific processing with a powerful set of legacy routines, the quick visualization with full output control and the graph creation for use in papers and presentations. SPEDAS includes a GUI for ease of use by novice users, works on multiple platforms, and though based on IDL, can be used with or without an IDL license. The framework supports plugin modules for multiple projects such as THEMIS, MMS, and WIND, and provides interfaces for software modules developed by the individual teams of those missions. A Python implementation of the framework, PySPEDAS (ascl:2405.005), is also available.