Results 1351-1400 of 3560 (3466 ASCL, 94 submitted)

[ascl:2211.014]
PDFchem: Average abundance of species from Av-PDFs

PDFchem models the cold ISM at moderate and large scales using functions connecting the quantities of the local and the observed visual extinctions and the local number density with probability density functions. For any given observed visual extinction sampled with thousands of clouds, the algorithm instantly computes the average abundances of the most important species and performs radiative transfer calculations to estimate the average emission of the most commonly observed lines.

[ascl:2309.011]
PCOSTPD: Periodogram Comparison for Optimizing Small Transiting Planet Detection

The Periodogram Comparison for Optimizing Small Transiting Planet Detection R code compares two periodogram algorithms for detecting transiting exoplanets: the Box-fitting Least Squares (BLS) and the Transit Comb Filter (TCF). It calculates the False Alarm Probability (FAP) based on extreme value theory and signal-to-noise ratio (SNR) metrics to quantify periodogram peak significance. The comparison approach is aimed at optimizing the detection of small transiting planets in future transiting exoplanet surveys. The code can be extended for comparing any set of periodograms.

[ascl:1809.002]
PCCDPACK: Polarimetry with CCD

PCCDPACK analyzes polarimetry data. The set of routines is written in CL-IRAF (including compiled Fortran codes) and analyzes dozens of point objects simultaneously on the same CCD image. A subpackage, specpol, is included to analyze spectropolarimetry data.

[ascl:1705.004]
PCAT: Probabilistic Cataloger

PCAT (Probabilistic Cataloger) samples from the posterior distribution of a metamodel, i.e., union of models with different dimensionality, to compare the models. This is achieved via transdimensional proposals such as births, deaths, splits and merges in addition to the within-model proposals. This method avoids noisy estimates of the Bayesian evidence that may not reliably distinguish models when sampling from the posterior probability distribution of each model.

The code has been applied in two different subfields of astronomy: high energy photometry, where transdimensional elements are gamma-ray point sources; and strong lensing, where light-deflecting dark matter subhalos take the role of transdimensional elements.

[ascl:1207.012]
PCA: Principal Component Analysis for spectra modeling

The mid-infrared spectra of ultraluminous infrared galaxies (ULIRGs) contain a variety of spectral features that can be used as diagnostics to characterize the spectra. However, such diagnostics are biased by our prior prejudices on the origin of the features. Moreover, by using only part of the spectrum they do not utilize the full information content of the spectra. Blind statistical techniques such as principal component analysis (PCA) consider the whole spectrum, find correlated features and separate them out into distinct components.

This code, written in IDL, classifies principal components of IRS spectra to define a new classification scheme using 5D Gaussian mixtures modelling. The five PCs and average spectra for the four classifications to classify objects are made available with the code.

[ascl:1403.007]
PC: Unified EOS for neutron stars

The equation of state (EOS) of dense matter is a crucial input for the neutron-star structure calculations. This Fortran code can obtain a "unified EOS" in the many-body calculations based on a single effective nuclear Hamiltonian, and is valid in all regions of the neutron star interior. For unified EOSs, the transitions between the outer crust and the inner crust and between the inner crust and the core are obtained as a result of many-body calculations.

[ascl:1708.007]
PBMC: Pre-conditioned Backward Monte Carlo code for radiative transport in planetary atmospheres

PBMC (Pre-Conditioned Backward Monte Carlo) solves the vector Radiative Transport Equation (vRTE) and can be applied to planetary atmospheres irradiated from above. The code builds the solution by simulating the photon trajectories from the detector towards the radiation source, *i.e.* in the reverse order of the actual photon displacements. In accounting for the polarization in the sampling of photon propagation directions and pre-conditioning the scattering matrix with information from the scattering matrices of prior (in the BMC integration order) photon collisions, PBMC avoids the unstable and biased solutions of classical BMC algorithms for conservative, optically-thick, strongly-polarizing media such as Rayleigh atmospheres.

[ascl:1102.002]
PBL: Particle-Based Lensing for Gravitational Lensing Mass Reconstructions of Galaxy Clusters

Particle-Based Lensing (PBL) does gravitational lensing mass reconstructions of galaxy clusters. Traditionally, most methods have employed either a finite inversion or gridding to turn observational lensed galaxy ellipticities into an estimate of the surface mass density of a galaxy cluster. We approach the problem from a different perspective, motivated by the success of multi-scale analysis in smoothed particle hydrodynamics. In PBL, we treat each of the lensed galaxies as a particle and then reconstruct the potential by smoothing over a local kernel with variable smoothing scale. In this way, we can tune a reconstruction to produce constant signal-noise throughout, and maximally exploit regions of high information density.

PBL is designed to include all lensing observables, including multiple image positions and fluxes from strong lensing, as well as weak lensing signals including shear and flexion. In this paper, however, we describe a shear-only reconstruction, and apply the method to several test cases, including simulated lensing clusters, as well as the well-studied "Bullet Cluster" (1E0657-56). In the former cases, we show that PBL is better able to identify cusps and substructures than are grid-based reconstructions, and in the latter case, we show that PBL is able to identify substructure in the Bullet Cluster without even exploiting strong lensing measurements.

[ascl:1809.003]
PASTA: Python Astronomical Stacking Tool Array

PASTA performs median stacking of astronomical sources. Written in Python, it can filter sources, provide stack statistics, generate Karma annotations, format source lists, and read information from stacked Flexible Image Transport System (FITS) images. PASTA was originally written to examine polarization stack properties and includes a Monte Carlo modeler for obtaining true polarized intensity from the observed polarization of a stack. PASTA is also useful as a generic stacking tool, even if polarization properties are not being examined.

[ascl:1010.073]
partiview: Immersive 4D Interactive Visualization of Large-Scale Simulations

In dense clusters a bewildering variety of interactions between stars can be observed, ranging from simple encounters to collisions and other mass-transfer encounters. With faster and special-purpose computers like GRAPE, the amount of data per simulation is now exceeding 1TB. Visualization of such data has now become a complex 4D data-mining problem, combining space and time, and finding interesting events in these large datasets. We have recently starting using the virtual reality simulator, installed in the Hayden Planetarium in the American Museum for Natural History, to tackle some of these problem. partiview is a program that enables you to visualize and animate particle data. partiview runs on relatively simple desktops and laptops, but is mostly compatible with its big brother VirDir.

[ascl:2207.029]
ParticleGridMapper: Particle data interpolator

ParticleGridMapper.jl interpolates particle data onto either a Cartesian (uniform) grid or an adaptive mesh refinement (AMR) grid where each cell contains no more than one particle. The AMR grid can be trimmed with a user-defined maximum level of refinement. Three different interpolation schemes are supported: nearest grid point (NGP), smoothed-particle hydrodynamics (SPH), and Meshless finite mass (MFM). It is multi-threading parallel.

[ascl:1010.005]
Particle module of Piernik MHD code

Piernik is a multi-fluid grid magnetohydrodynamic (MHD) code based on the Relaxing Total Variation Diminishing (RTVD) conservative scheme. The original code has been extended by addition of dust described within the particle approximation. The dust is now described as a system of interacting particles. The particles can interact with gas, which is described as a fluid. The comparison between the test problem results and the results coming from fluid simulations made with Piernik code shows the most important differences between fluid and particle approximations used to describe dynamical evolution of dust under astrophysical conditions.

[ascl:2306.026]
Parthenon: Portable block-structured adaptive mesh refinement framework

Grete, Philipp; Dolence, Joshua C.; Miller, Jonah M.; Brown, Joshua; Ryan, Ben; Gaspar, Andrew; Glines, Forrest; Swaminarayan, Sriram; Lippuner, Jonas; Solomon, Clell J.; Shipman, Galen; Junghans, Christoph; Holladay, Daniel; Stone, James M.; Roberts, Luke F.; Prather, Ben

The Parthenon framework, derived from Athena++ (ascl:1912.005), handles massively-parallel, device-accelerated adaptive mesh refinement. It provides a device first/device resident approach, transparent packing of data across blocks (to reduce/hide kernel launch latency), and direct device-to-device communication via asynchronous, one-sided MPI communication to enable high performance. Parthenon uses an intermediate abstraction layer to hide complexity of device kernel launches, offers support for particles and abstract variable control via metadata tags, and has a flexible plug-in package system.

[ascl:2110.008]
ParSNIP: Parametrization of SuperNova Intrinsic Properties

ParSNIP learns generative models of transient light curves from a large dataset of transient light curves. It is designed to work with light curves in sncosmo format using the lcdata package to handle large datasets. This code can be used for classification of transients, cosmological distance estimation, and identifying novel transients.

[ascl:1208.020]
ParselTongue: AIPS Python Interface

ParselTongue is a Python interface to classic AIPS, Obit and possibly other task-based data reduction packages. It serves as the software infrastructure for some of the ALBUS implementation. It allows you to run AIPS tasks, and access AIPS headers and extension tables from Python. There is also support for running Obit tasks and accessing data in FITS files. Full access to the visibilities in AIPS UV data is also available.

[ascl:1502.005]
PARSEC: PARametrized Simulation Engine for Cosmic rays

PARSEC (PARametrized Simulation Engine for Cosmic rays) is a simulation engine for fast generation of ultra-high energy cosmic ray data based on parameterizations of common assumptions of UHECR origin and propagation. Implemented are deflections in unstructured turbulent extragalactic fields, energy losses for protons due to photo-pion production and electron-pair production, as well as effects from the expansion of the universe. Additionally, a simple model to estimate propagation effects from iron nuclei is included. Deflections in the Galactic magnetic field are included using a matrix approach with precalculated lenses generated from backtracked cosmic rays. The PARSEC program is based on object oriented programming paradigms enabling users to extend the implemented models and is steerable with a graphical user interface.

[ascl:2007.014]
PARS: Paint the Atmospheres of Rotating Stars

PARS (Paint the Atmospheres of Rotating Stars) quickly computes magnitudes and spectra of rotating stellar models. It uses the star's mass, equatorial radius, rotational speed, luminosity, and inclination as input; the models incorporate Roche mass distribution (where all mass is at the center of the star), solid body rotation, and collinearity of effective gravity and energy flux.

[ascl:1601.010]
PARAVT: Parallel Voronoi Tessellation

PARAVT offers massive parallel computation of Voronoi tessellations (VT hereafter) in large data sets. The code is focused for astrophysical purposes where VT densities and neighbors are widely used. There are several serial Voronoi tessellation codes, however no open source and parallel implementations are available to handle the large number of particles/galaxies in current N-body simulations and sky surveys. Parallelization is implemented under MPI and VT using Qhull library. Domain decomposition take into account consistent boundary computation between tasks, and support periodic conditions. In addition, the code compute neighbors lists, Voronoi density and Voronoi cell volumes for each particle, and can compute density on a regular grid.

[ascl:1103.014]
ParaView: Data Analysis and Visualization Application

ParaView is an open-source, multi-platform data analysis and visualization application. ParaView users can quickly build visualizations to analyze their data using qualitative and quantitative techniques. The data exploration can be done interactively in 3D or programmatically using ParaView's batch processing capabilities.

ParaView was developed to analyze extremely large datasets using distributed memory computing resources. It can be run on supercomputers to analyze datasets of terascale as well as on laptops for smaller data.

[ascl:2008.016]
ParaMonte: Parallel Monte Carlo library

ParaMonte contains serial and parallel Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions. It is used for posterior distributions of Bayesian models in data science, Machine Learning, and scientific inference and unifies the automation of Monte Carlo simulations. ParaMonte is user friendly and accessible from multiple programming environments, including C, C++, Fortran, MATLAB, and Python, and offers high performance at runtime and scalability across many parallel processors.

[ascl:2009.008]
Paramo: PArticle and RAdiation MOnitor

Paramo (PArticle and RAdiation MOnitor) numerically solves the Fokker-Planck kinetic equation, which is used to model the dynamics of a particle distribution function, using a robust implicit method, for the proper modeling of the acceleration processes, and accounts for accurate cooling coefficient (*e.g.*, radiative cooling with Klein-Nishina corrections). The numerical solution at every time step is used to calculate radiations processes, namely synchrotron and IC, with sophisticated numerical techniques, obtaining the multi-wavelength spectral evolution of the system.

[ascl:1010.039]
Parameter Estimation from Time-Series Data with Correlated Errors: A Wavelet-Based Method and its Application to Transit Light Curves

We consider the problem of fitting a parametric model to time-series data that are afflicted by correlated noise. The noise is represented by a sum of two stationary Gaussian processes: one that is uncorrelated in time, and another that has a power spectral density varying as $1/f^gamma$. We present an accurate and fast [O(N)] algorithm for parameter estimation based on computing the likelihood in a wavelet basis. The method is illustrated and tested using simulated time-series photometry of exoplanetary transits, with particular attention to estimating the midtransit time. We compare our method to two other methods that have been used in the literature, the time-averaging method and the residual-permutation method. For noise processes that obey our assumptions, the algorithm presented here gives more accurate results for midtransit times and truer estimates of their uncertainties.

[ascl:1106.009]
PARAMESH V4.1: Parallel Adaptive Mesh Refinement

PARAMESH is a package of Fortran 90 subroutines designed to provide an application developer with an easy route to extend an existing serial code which uses a logically cartesian structured mesh into a parallel code with adaptive mesh refinement (AMR). Alternatively, in its simplest use, and with minimal effort, it can operate as a domain decomposition tool for users who want to parallelize their serial codes, but who do not wish to use adaptivity.

The package builds a hierarchy of sub-grids to cover the computational domain, with spatial resolution varying to satisfy the demands of the application. These sub-grid blocks form the nodes of a tree data-structure (quad-tree in 2D or oct-tree in 3D). Each grid block has a logically cartesian mesh. The package supports 1, 2 and 3D models. PARAMESH is released under the NASA-wide Open-Source software license.

[ascl:1103.008]
Parallel HOP: A Scalable Halo Finder for Massive Cosmological Data Sets

Modern N-body cosmological simulations contain billions ($10^9$) of dark matter particles. These simulations require hundreds to thousands of gigabytes of memory, and employ hundreds to tens of thousands of processing cores on many compute nodes. In order to study the distribution of dark matter in a cosmological simulation, the dark matter halos must be identified using a halo finder, which establishes the halo membership of every particle in the simulation. The resources required for halo finding are similar to the requirements for the simulation itself. In particular, simulations have become too extensive to use commonly-employed halo finders, such that the computational requirements to identify halos must now be spread across multiple nodes and cores. Here we present a scalable-parallel halo finding method called Parallel HOP for large-scale cosmological simulation data. Based on the halo finder HOP, it utilizes MPI and domain decomposition to distribute the halo finding workload across multiple compute nodes, enabling analysis of much larger datasets than is possible with the strictly serial or previous parallel implementations of HOP. We provide a reference implementation of this method as a part of the toolkit yt, an analysis toolkit for Adaptive Mesh Refinement (AMR) data that includes complementary analysis modules. Additionally, we discuss a suite of benchmarks that demonstrate that this method scales well up to several hundred tasks and datasets in excess of $2000^3$ particles. The Parallel HOP method and our implementation can be readily applied to any kind of N-body simulation data and is therefore widely applicable. Parallel HOP is part of yt.

[ascl:2105.020]
PAP: PHANGS-ALMA pipeline

Leroy, Adam K.; Hughes, Annie; Liu, Daizhong; Pety, Jerome; Rosolowsky, Erik; Saito, Toshiki; Schinnerer, Eva; Schruba, Andreas; Usero, Antonio; Faesi, Christopher M.; Herrera, Cinthya N.; Chevance, Melanie; Hygate, Alexander P. S.; Kepley, Amanda A.; Koch, Eric W.; Querejeta, Miguel; Sliwa, Kazimierz; Will, David; Wilson, Christine D.; Anand, Gagandeep S. Barnes, Ashley; Belfiore, Francesco; Beslic, Ivana; Bigiel, Frank; Blanc, Guillermo A.; Bolatto, Alberto D.; Boquien, Mederic; Cao, Yixian; Chandar, Rupali; Chastenet, Jeremy; Chiang, I-Da; Congiu, Enrico; Dale, Daniel A.; Deger, Sinan; den Brok, Jakob S.; Eibensteiner, Cosima; Emsellem, Eric; Garcıa-Rodrıguez, Axel; Glover, Simon C. O.; Grasha, Kathryn; Groves, Brent; Henshaw, Jonathan D.; Jimenez Donaire, Maria J.; Kim, Jenny J.; Klessen, Ralf S.; Kreckel, Kathryn; Kruijssen, J. M. Diederik; Larson, Kirsten L.; Lee, Janice C.; Mayker, Ness; McElroy, Rebecca; Meidt, Sharon E.; Mok, Angus; Pan, Hsi-An; Puschnig, Johannes; Razza, Alessandro; Sanchez-Blazquez, Patricia; Sandstrom, Karin M.; Santoro, Francesco; Sardone, Amy; Scheuermann, Fabian; Sun, Jiayi; Thilker, David A.; Turner, Jordan A.; Ubeda, Leonardo; Utomo, Dyas; Watkins, Elizabeth J.; Williams, Thomas G.

The PHANGS-ALMA pipeline process data from radio interferometer observations. It uses CASA (ascl:1107.013), AstroPy (ascl:1304.002), and other affiliated packages to process data from calibrated visibilities to science-ready spectral cubes and maps. The PHANGS-ALMA pipeline offers a flexible alternative to the scriptForImaging script distributed by ALMA. The pipeline runs in two separate software environments: CASA 5.6 or 5.7 (staging, imaging and post-processing) and Python 3.6 or later (derived products) with modern versions of several packages.

[ascl:2404.010]
Panphasia: Create cosmological and resimulation initial conditions

Panphasia computes a very large realization of a Gaussian white noise field. The field has a hierarchical structure based on an octree geometry with 50 octree levels fully populated. The code sets up Gaussian initial conditions for cosmological simulations and resimulations of structure formation. Panphasia provides an easy way to publish the linear phases used to set up cosmological simulation initial conditions; publishing phases enriches the literature and makes it easier to reproduce and extend published simulation work.

[ascl:1511.009]
Pangloss: Reconstructing lensing mass

Pangloss reconstructs all the mass within a light cone through the Universe. Understanding complex mass distributions like this is important for accurate time delay lens cosmography, and also for accurate lens magnification estimation. It aspires to use all available data in an attempt to make the best of all mass maps.

[ascl:2303.009]
Pandora: Fast exomoon transit detection algorithm

Pandora searches for exomoons by employing an analytical photodynamical model that includes stellar limb darkening, full and partial planet-moon eclipses, and barycentric motion of planet and moon. The code can be used with nested samplers such as UltraNest (ascl:1611.001) or dynesty (ascl:1809.013). Pandora is fast, calculating 10,000 models and log-likelihood evaluation per second (give or take an order of magnitude, depending on parameters and data); this means that a retrieval with 250 Mio. evaluations until convergence takes about 5 hours on a single core. For searches in large amounts of data, it is most efficient to assign one core per light curve.

[ascl:1906.016]
PandExo: Instrument simulations for exoplanet observation planning

Batalha, Natasha E.; Mandell, Avi; Pontoppidan, Klaus; Stevenson, Kevin B.; Lewis, Nikole K.; Kalirai, Jason; Earl, Nick; Greene, Thomas; Albert, Loïc; Nielsen, Louise D.

PandExo generates instrument simulations of JWST’s NIRSpec, NIRCam, NIRISS and NIRCam and HST WFC3 for planning exoplanet observations. It uses throughput calculations from STScI’s Exposure Time Calculator, Pandeia, and offers both an online tool and a python package.

[ascl:2212.008]
panco2: Pressure profile measurements of galaxy clusters

Kéruzoré, F.; Mayet, F.; Artis, E.; Macías-Pérez, J.F.; Muñoz-Echeverría, M.; Perotto, L.; Ruppin, F.

panco2 extracts measurements of the pressure profile of the hot gas inside galaxy clusters from millimeter-wave observations. The extraction is performed using forward modeling the millimeter-wave signal of clusters and MCMC sampling of a posterior distribution for the parameters given the input data. Many characteristic features of millimeter-wave observations can be taken into account, such as filtering (both through PSF smearing and transfer functions), point source contamination, and correlated noise.

[ascl:1805.021]
PampelMuse: Crowded-field 3D spectroscopy

PampelMuse analyzes integral-field spectroscopic observations of crowded stellar fields and provides several subroutines to perform the individual steps of the data analysis. All analysis steps assume that the IFS data has been properly reduced and that all the instrumental artifacts have been removed. PampelMuse is designed to correctly handle IFS data regardless of which instrument was used to observe the data. In addition to the actual data, the software also requires an estimate of the variances for the analysis; optionally, it can use a bad pixel mask. The analysis relies on the presence of a reference catalogue, containing coordinates and magnitudes of the stars in and around the observed field.

[ascl:1406.002]
PAMELA: Optimal extraction code for long-slit CCD spectroscopy

PAMELA is an implementation of the optimal extraction algorithm for long-slit CCD spectroscopy and is well suited for time-series spectroscopy. It properly implements the optimal extraction algorithm for curved spectra, including on-the-fly cosmic ray rejection as well as proper calculation and propagation of the errors. The software is distributed as part of the Starlink software collection (ascl:1110.012).

[ascl:2210.029]
paltas: Simulation-based inference on strong gravitational lensing systems

Wagner-Carena, Sebastian; Aalbers, Jelle; Birrer, Simon; Nadler, Ethan O.; Darragh-Ford, Elise; Marshall, Philip J.; Wechsler, Risa H.

paltas conducts simulation-based inference on strong gravitational lensing images. It builds on lenstronomy (ascl:1804.012) to create large datasets of strong lensing images with realistic low-mass halos, Hubble Space Telescope (HST) observational effects, and galaxy light from HST's COSMOS field. paltas also includes the capability to easily train neural posterior estimators of the parameters of the lensing system and to run hierarchical inference on test populations.

[ascl:2405.021]
PALpy: Python positional astronomy library interface

PALpy provides a Python interface to PAL, the positional Astronomy Library (ascl:1606.002), which is written in C. All arguments modified by the C API are returned and none are modified. The one routine that is different is palObs, which returns a simple dict that can be searched using standard Python. The keys to the dict are the short names and the values are another dict with keys name, long, lat and height.

[ascl:2202.005]
palettable: Color palettes for Python

Palettable is a library of color palettes for Python. The code is written in pure Python with no dependencies; it can be used to supply color maps for matplotlib plots, customize matplotlib plots, and to supply colors for a web application.

[ascl:1606.002]
PAL: Positional Astronomy Library

The PAL library is a partial re-implementation of Pat Wallace's popular SLALIB library written in C using a Gnu GPL license and layered on top of the IAU's SOFA library (or the BSD-licensed ERFA) where appropriate. PAL attempts to stick to the SLA C API where possible.

[ascl:1210.009]
PAHFIT: Properties of PAH Emission

PAHFIT is an IDL tool for decomposing Spitzer IRS spectra of PAH emission sources, with a special emphasis on the careful recovery of ambiguous silicate absorption, and weak, blended dust emission features. PAHFIT is primarily designed for use with full 5-35 micron Spitzer low-resolution IRS spectra. PAHFIT is a flexible tool for fitting spectra, and you can add or disable features, compute combined flux bands, change fitting limits, etc., without changing the code.

PAHFIT uses a simple, physically-motivated model, consisting of starlight, thermal dust continuum in a small number of fixed temperature bins, resolved dust features and feature blends, prominent emission lines (which themselves can be blended with dust features), as well as simple fully-mixed or screen dust extinction, dominated by the silicate absorption bands at 9.7 and 18 microns. Most model components are held fixed or are tightly constrained. PAHFIT uses Drude profiles to recover the full strength of dust emission features and blends, including the significant power in the wings of the broad emission profiles. This means the resulting feature strengths are larger (by factors of 2-4) than are recovered by methods which estimate the underlying continuum using line segments or spline curves fit through fiducial wavelength anchors.

[ascl:2211.004]
PAHDecomp: Decomposing the mid-IR spectra of extremely obscured galaxies

PAHDecomp models mid-infrared spectra of galaxies; it is based on the popular PAHFIT code (ascl:1210.009). In contrast to PAHFIT, this model decomposes the continuum into a star-forming component and an obscured nuclear component based on Bayesian priors on the shape of the star-forming component (using templates + prior on extinction), making this tool ideally suited for modeling the spectra of heavily obscured galaxies. PAHDecomp successfully recovers properties of Compact Obscured Nuclei (CONs) where the inferred nuclear optical depth strongly correlates with the surface brightness of HCN-vib emission in the millimeter. This is currently set up to run on the short low modules of Spitzer IRS data (5.2 - 14.2 microns) but will be ideal for JWST/MIRI MRS data in the future.

[ascl:2404.024]
pAGN: AGN disk model equations solver

Written in Python, pAGN solves AGN disk model equations. The code is highly customizable and, with the correct inputs, provides a fully evolved AGN disk model through parametric 1D curves for key disk parameters such as temperature and density. pAGN can be used to study migration torques in AGN disks, simulations of compact object formation inside gas disks, and comparisons with new, more complex models of AGN disks.

[ascl:1708.014]
PACSman: IDL Suite for Herschel/PACS spectrometer data

PACSman provides an alternative for several reduction and analysis steps performed in HIPE (ascl:1111.001) on PACS spectroscopic data; it is written in IDL. Among the operations possible with it are transient correction, line fitting, map projection, and map analysis, and unchopped scan, chop/nod, and the decommissioned wavelength switching observation modes are supported.

[ascl:2212.013]
PACMAN: Planetary Atmosphere, Crust, and MANtle geochemical evolution

PACMAN (Planetary Atmosphere, Crust, and MANtle geochemical evolution) runs a coupled redox-geochemical-climate evolution model. It runs Monte Carlo calculations over nominal parameter ranges, including number of iterations and number of cores for parallelization, which can be altered to reproduce different scenarios and sensitivity tests. Model outputs and corresponding input parameters are saved in separate files which are used to plot results; the the user can choose which outputs to plot, including all successful outputs, nominal Earth outputs, waterworld false positives, desertworld false positives, and high CO2:H2O false positives. Among other functions, PACMAN contains functions for interpolating the pre-computed Outgoing Longwave Radiation (OLR) grid, the atmosphere-ocean partitioning grid, and the stratospheric water vapor grid, calculating bond albedo and outgassing fluxes.

[ascl:2407.020]
Package-X: Calculate Feynman loop integrals

Package‑X instantly solves one loop Feynman integrals in full generality. Written in Mathematica and extensively tested and adopted, the package computes dimensionally regulated one-loop integrals with up to four distinct propagators of arbitrarily high rank, calculates traces of Dirac matrices in d dimensions for closed fermion loops, or carries out Dirac algebra for open fermion lines. Package‑X also generates analytic results for any kinematic configuration (*e.g.*, at zero external momentum or physical threshold) for real masses and external invariants, provides analytic expressions for UV-divergent, IR-divergent and finite parts either separately or all together, and computes discontinuities across cuts of one-loop integrals, among other tasks.

[ascl:1110.011]
Pacerman: Polarisation Angle CorrEcting Rotation Measure ANalysis

Pacerman, written in IDL, is a new method to calculate Faraday rotation measure maps from multi-frequency polarisation angle data. In order to solve the so called n-pi-ambiguity problem which arises from the observationally ambiguity of the polarisation angle which is only determined up to additions of n times pi, where n is an integer, we suggest using a global scheme. Instead of solving the n-pi-ambiguity for each data point independently, our algorithm, which we chose to call Pacerman solves the n-pi-ambiguity for a high signal-to-noise region "democratically" and uses this information to assist computations in adjacent low signal-to-noise areas.

[ascl:1105.002]
PACCE: Perl Algorithm to Compute Continuum and Equivalent Widths

PACCE (Perl Algorithm to Compute continuum and Equivalent Widths) computes continuum and equivalent widths. PACCE is able to determine mean continuum and continuum at line center values, which are helpful in stellar population studies, and is also able to compute the uncertainties in the equivalent widths using photon statistics.

[ascl:1205.002]
p3d: General data-reduction tool for fiber-fed integral-field spectrographs

p3d is semi-automatic data-reduction tool designed to be used with fiber-fed integral-field spectrographs. p3d is a highly general and freely available tool based on IDL but can be used with full functionality without an IDL license. It is easily extended to include improved algorithms, new visualization tools, and support for additional instruments. It uses a novel algorithm for automatic finding and tracing of spectra on the detector, and includes two methods of optimal spectrum extraction in addition to standard aperture extraction. p3d also provides tools to combine several images, perform wavelength calibration and flat field data.

[ascl:1402.030]
P2SAD: Particle Phase Space Average Density

P2SAD computes the Particle Phase Space Average Density (P2SAD) in galactic haloes. The model for the calculation is based on the stable clustering hypothesis in phase space, the spherical collapse model, and tidal disruption of substructures. The multiscale prediction for P2SAD computed by this IDL code can be used to estimate signals sensitive to the small scale structure of dark matter distributions (e.g. dark matter annihilation). The code computes P2SAD averaged over the whole virialized region of a Milky-Way-size halo at redshift zero.

[ascl:1806.011]
P2DFFT: Parallelized technique for measuring galactic spiral arm pitch angles

P2DFFT is a parallelized version of 2DFFT (ascl:1608.015). It isolates and measures the spiral arm pitch angle of galaxies. The code allows direct input of FITS images, offers the option to output inverse Fourier transform FITS images, and generates idealized logarithmic spiral test images of a specified size that have 1 to 6 arms with pitch angles of -75 degrees to 75 degrees. Further, it can output Fourier amplitude versus inner radius and pitch angle versus inner radius for each Fourier component (m = 0 to m = 6), and calculates the Fourier amplitude weighted mean pitch angle across m = 1 to m = 6 versus inner radius.

[ascl:2111.011]
p-winds: Python implementation of Parker wind models for planetary atmospheres

Dos Santos, Leonardo A.; Vidotto, Aline A.; Vissapragada, Shreyas; Alam, Munazza K.; Allart, Romain; Bourrier, Vincent; Kirk, James; Seidel, Julia V.; Ehrenreich, David

p-winds produces simplified, 1-D models of the upper atmosphere of a planet and performs radiative transfer to calculate observable spectral signatures. The scalable implementation of 1D models allows for atmospheric retrievals to calculate atmospheric escape rates and temperatures. In addition, the modular implementation allows for a smooth plugging-in of more complex descriptions to forward model their corresponding spectral signatures (*e.g.*, self-consistent or 3D models).

[ascl:2009.003]
oxkat: Semi-automated imaging of MeerKAT observations

oxkat semi-automatically performs calibration and imaging of data from the MeerKAT radio telescope. Taking as input raw visibilities in Measurement Set format, the entire processing workflow is covered, from flagging and reference calibration, to imaging and self-calibration, and (optionally) direction-dependent calibration. The oxkat scripts use Python, and draw on numerous existing radio astronomy packages, including CASA (ascl:1107.013), WSClean (ascl:1408.023), and CubiCal (ascl:1805.031), among others, that are containerized using Singularity. Submission scripts for slurm and PBS job schedulers are automatically generated where necessary, catering for HPC facilities that are commonly used for processing MeerKAT data.

[ascl:1611.011]
OXAF: Ionizing spectra of Seyfert galaxies for photoionization modeling

Thomas, Adam D.; Groves, Brent A.; Sutherland, Ralph S.; Dopita, Michael A.; Jin, Chichuan; Kewley, Lisa J.

OXAF provides a simplified model of Seyfert Active Galactic Nucleus (AGN) continuum emission designed for photoionization modeling. It removes degeneracies in the effects of AGN parameters on model spectral shapes and reproduces the diversity of spectral shapes that arise in physically-based models. OXAF accepts three parameters which directly describe the shape of the output ionizing spectrum: the energy of the peak of the accretion disk emission *E _{peak}*, the photon power-law index of the non-thermal X-ray emission Γ, and the proportion of the total flux which is emitted in the non-thermal component

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