ASCL.net

Astrophysics Source Code Library

Making codes discoverable since 1999

Welcome to the ASCL

The Astrophysics Source Code Library (ASCL) is a free online registry and repository for source codes of interest to astronomers and astrophysicists, including solar system astronomers, and lists codes that have been used in research that has appeared in, or been submitted to, peer-reviewed publications. The ASCL is indexed by the SAO/NASA Astrophysics Data System (ADS) and Web of Science and is citable by using the unique ascl ID assigned to each code. The ascl ID can be used to link to the code entry by prefacing the number with ascl.net (i.e., ascl.net/1201.001).


Most Recently Added Codes

2024 Mar 18

[submitted] BTSbot: Automated Identification and Follow-up of Bright Transients with Deep Learning

BTSbot is a multi-modal convolutional neural network designed for real-time identification bright extragalactic transients in Zwicky Transient Facility (ZTF) data. BTSbot provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. BTSbot is able to eliminate the need for daily visual inspection of new transients by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. BTSbot recovers all bright transients in our test split and performs on par with human experts in terms of identification speed (on average, ∼1 hour quicker than scanners).

2024 Mar 14

[submitted] kinematic_scaleheight: Infer the vertical distribution of clouds in the solar neighborhood

kinematic_scaleheight implements several methods to infer the vertical distribution of clouds in the solar neighborhood, including the least squares analysis of Crovisier (1978), an updated least squares analysis using a modern Galactic rotation model, and a Bayesian model sampled via MCMC as described in Wenger et al. (2024).

2024 Mar 07

[submitted] KCWIKit: KCWI Post-Processing and Improvements

KCWIKit extends the official KCWI DRP with a variety of stacking tools and DRP improvements. The software offers masking and median filtering scripts to be used while running the KCWI DRP, and a step-by-step KCWI_DRP implementation for finer control over the reduction process. Once the DRP has finished, KCWIKit can be used to stack the output cubes via the Montage package. Various functions cross-correlate and mosaic the constituent cubes and the final stacked cubes are WCS corrected. Helper functions can then be used to deproject the stacked cube into lower-dimensional representations should the user desire.

[submitted] cbeam: a coupled-mode propagator for slowly-varying waveguides

cbeam is a Python/Julia package which models the propagation of guided light through slowly-varying few-mode waveguides using the coupled-mode theory (CMT). When compared with more general numerical methods for waveguide simulation, such as the finite-differences beam propagation method (FD-BPM), numerical implementations of the CMT can be much more computationally efficient. cbeam also provides a Pythonic class structure to define waveguides, with simple classes for directional couplers and photonic lanterns already provided. Finally, cbeam doubles as a finite-element eigenmode solver.

2024 Mar 06

[submitted] Light Curve Classification with DistClassiPy: a new distance-based classifier

The rise of synoptic sky surveys has ushered in an era of big data in time-domain astronomy, making data science and machine
learning essential tools for studying celestial objects. Tree-based (e.g. Random Forests) and deep learning models represent the
current standard in the field. We explore the use of different distance metrics to aid in the classification of objects. For this,
we developed a new distance metric based classifier called DistClassiPy. The direct use of distance metrics is an approach
that has not been explored in time-domain astronomy, but distance-based methods can aid in increasing the interpretability of the
classification result and decrease the computational costs. In particular, we classify light curves of variable stars by comparing the
distances between objects of different classes. Using 18 distance metrics applied to a catalog of 6,000 variable stars in 10 classes,
we demonstrate classification and dimensionality reduction. We show that this classifier meets state-of-the-art performance but
has lower computational requirements and improved interpretability. We have made DistClassiPy open-source and accessible
at https://pypi.org/project/distclassipy/ with the goal of broadening its applications to other classification scenarios
within and beyond astronomy.

2024 Mar 03

[submitted] Pynkowski

A Python package to compute Minkowski Functionals and other higher order statistics of input fields, as well as their expected values for different kinds of fields.

The statistics currently supported by this package are Minkowski functionals, and maxima and minima distributions. The formats currently supported for input data are the following: scalar HEALPix maps, as the ones used by healpy; polarisation HEALPix maps in the SO(3) formalism; 2D and 3D numpy arrays (coming soon). The theoretical expectation of some statistics is currently supported for the following theoretical fields: Gaussian fields (such as CMB Temperature or the initial density field); Chi squared fields (such as CMB polarization intensity); spin 2 maps in the SO(3) formalism.

We are actively working on the implementation of more statistics, data formats, and theoretical fields. If you want to contribute, we welcome and appreciate pull requests. If you have any comments or suggestions, please feel free to contact us by email (1 and 2) or by opening a discussion thread or issue.

The repository can be found on https://github.com/javicarron/pynkowski.

2024 Feb 28

[ascl:2402.010] 2cosmos: Monte Python modification for two independent instances of CLASS

2cosmos is a modification of Monte Python (ascl:1307.002) and allows the user to write likelihood modules that can request two independent instances of CLASS (ascl:1106.020) and separate dictionaries and structures for all cosmological and nuisance parameters. The intention is to be able to evaluate two independent cosmological calculations and their respective parameters within the same likelihood. This is useful for evaluating a likelihood using correlated datasets (e.g. mutually exclusive subsets of the same dataset for which one wants to take into account all correlations between the subsets).

2024 Feb 27

[ascl:2402.009] SkyLine: Generate mock line-intensity maps

SkyLine generates mock line-intensity maps (both in 3D and 2D) in a lightcone from a halo catalog, accounting for the evolution of clustering and astrophysical properties, and observational effects such as spectral and angular resolutions, line-interlopers, and galactic foregrounds. Using a given astrophysical model for the luminosity of each line, the code paints the signal for each emitter and generates the map, adding coherently all contributions of interest. In addition, SkyLine can generate maps with the distribution of Luminous Red Galaxies and Emitting Line Galaxies.

[ascl:2402.008] star_shadow: Analyze eclipsing binary light curves, find eccentricity, and more

star_shadow automatically analyzes space based light curves of eclipsing binaries and provide a measurement of eccentricity, among other parameters. It measures the timings of eclipses using the time derivatives of the light curves, using a model of orbital harmonics obtained from an initial iterative prewhitening of sinusoids. Since the algorithm extracts the harmonics from the rest of the sinusoidal variability eclipse timings can be measured even in the presence of other (astrophysical) signals, thus determining the orbital eccentricity automatically from the light curve along with information about the other variability present in the light curve. The output includes, but is not limited to, a sinusoid plus linear model of the light curve, the orbital period, the eccentricity, argument of periastron, and inclination.

[ascl:2402.007] ECLIPSR: Automatically find individual eclipses in light curves, determine ephemerides, and more

ECLIPSR fully and automatically analyzes space based light curves to find eclipsing binaries and provide some first order measurements, such as the binary star period and eclipse depths. It provides a recipe to find individual eclipses using the time derivatives of the light curves, including eclipses in light curves of stars where the dominating variability is, for example, pulsations. Since the algorithm detects each eclipse individually, even light curves containing only one eclipse can (in principle) be successfully analyzed and classified. ECLIPSR can find eclipsing binaries among both pulsating and non-pulsating stars in a homogeneous and quick manner and process large amounts of light curves in reasonable amounts of time. The output includes, among other things, the individual eclipse markers, the period and time of first (primary) eclipse, and a score between 0 and 1 indicating the likelihood that the analyzed light curve is that of an eclipsing binary.