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 Jul 16

[submitted] Supervised star, galaxy and QSO classification with sharpened dimensionality reduction

Aims. We explore the use of broadband colors to classify stars, galaxies and QSOs. Specifically, we apply sharpened dimensionality reduction (SDR)-aided classification to this problem, with the aim of enhancing cluster separation in the projections of high-dimensional data clusters to allow for better classification performance and more informative projections.
Methods. The main objective of this work is to apply SDR to large sets of broadband colors derived from the CPz catalog first introduced by Fotopoulou & Paltani (2018) to obtain projections with clusters of star, galaxy and QSO data that exhibit a high degree of separation. The SDR method achieves this by combining density-based clustering with conventional dimensionality-reduction techniques. To make SDR scalable and have out-of-sample ability, we use a deep neural network trained to reproduce the SDR projections. Subsequently classification is done by applying a k-nearest neighbors (k-NN) classifier to the sharpened projections.
Results. Based on a qualitative and quantitative analysis of the embeddings produced by SDR, we find that SDR consistently produces accurate projections with a high degree of cluster separation. A number of projection performance metrics are used to evaluate this separation, including the trustworthiness, continuity, Shepard goodness, and distribution consistency metrics. Using the k-NN classifier and consolidating the results of various data sets we obtain precisions of 99.7%, 98.9%, and 98.5% for classifying stars, galaxies, and QSOs, respectively. Furthermore, we achieve completenesses of 97.8%, 99.3%, and 86.8%, respectively. In addition to classification we explore the structure of the embeddings produced by SDR by cross-matching with data from Gaia DR3, Galaxy Zoo 1 and a catalog of specific star formation rates, stellar masses and dust luminosities. We discover that the embeddings reveal astrophysical information, which allows one to understand the structure of the high-dimensional broadband color data in greater detail.
Conclusions. We find that SDR-aided star, galaxy, and QSO classification performs comparably to another unsupervised learning method using hierarchical density-based spatial clustering of applications with noise (HDBSCAN) but offers advantages in terms of scalability and interpretability. Furthermore, it outperforms traditional color selection methods in terms of QSO classification performance. Overall, we demonstrate the potential of SDR-aided classification to provide an accurate and physically insightful classification of astronomical objects based on their broadband colors.

2024 Jul 14

[ascl:2407.015] AstroCLIP: Multimodal contrastive pretraining for astronomical data

AstroCLIP performs contrastive pre-training between two different kinds of astronomical data modalities (multi-band imaging and optical spectra) to yield a meaningful embedding space which captures physical information about galaxies and is shared between both modalities. The embeddings can be used as the basis for competitive zero- and few-shot learning on a variety of downstream tasks, including similarity search, redshift estimation, galaxy property prediction, and morphology classification.

[ascl:2407.014] PFFT: Parallel fast Fourier transforms

PFFT computes massively parallel, fast Fourier transformations on distributed memory architectures. PFFT can be understood as a generalization of FFTW-MPI (ascl:1201.015) to multidimensional data decomposition; in fact, using PFFT is very similar to FFTW. The library is written in C and MPI; a Fortran interface is also available.

[ascl:2407.013] cola_halo: Parallel cosmological N-body simulator

cola_halo generates hundreds of realizations on the fly. This parallel cosmological N-body simulation code generates random Gaussian initial condition using 2LPTIC (ascl:1201.005), time evolves N-body particles with colacode (ascl:1602.021), and finds dark-matter halos with the Friends-of-Friends code (ascl:2407.012).

[ascl:2407.012] Fof: Friends-of-friends code to find groups

Fof uses the friends-of-friends method to find groups. A particle belongs to a friends-of-friends group if it is within some linking length of any other particle in the group. After all such groups are found, those with less than a specified minimum number of group members are rejected. The program takes input files in the TIPSY (ascl:1111.015) binary format and produces a single ASCII output file called fof.grp. This output file is in the TIPSY array format and contains the group number to which each particle belongs. A group number of zero means that the particle does not belong to a group. The fof.grp file can be read in by TIPSY and used to color each particle by group number to visualize the groups. Simulations with periodic boundary conditions can also be handled by fof by specifying the period in each dimension on the command line.

[ascl:2407.011] bigfile: A reproducible massively parallel IO library for hierarchical data

bigfile stores data from cosmology simulations from HPC systems and beyond. It provides a hierarchical structure of data columns via File, Dataset and Column. A Column stores a two dimensional table. Numerical typed columns are supported; attributes can be attached to a Column and both numerical attributes and string attributes are supported. Type casting is performed on-the-fly if read/write operations request a different data type than the file has stored.

2024 Jul 13

[ascl:2407.010] UFalcon: Ultra Fast Lightcone

UFalcon rapidly post-processes N-body code output into signal maps for many different cosmological probes. The package is able to produce maps of weak-lensing convergence, linear-bias galaxy over-density, cosmic microwave background (CMB) lensing convergence and the integrated Sachs-Wolfe temperature perturbation given a set of N-body lightcones. It offers high flexibility for lightcone construction, such as user-specific survey-redshift ranges, redshift distributions and single-source redshifts. UFalcon also computes the galaxy intrinsic alignment signal, which can be treated as an additive component to the cosmological signal.

[ascl:2407.009] ATM: Asteroid Thermal Modeling

ATM (Asteroid Thermal Modeling) models asteroid flux measurements to estimate an asteroid's size, surface temperature distribution, and emissivity, and creates model spectral energy distributions for the different thermal models. After downloading lookup tables for relevant models, it can also fit observations of asteroids.

[ascl:2407.008] RealSim: Statistical observational realism for synthetic images from galaxy simulations

RealSim generates survey-realistic synthetic images of galaxies from hydrodynamical simulations of galaxy formation and evolution. The main functionality of this code inserts "idealized" simulated galaxies into Sloan Digital Sky Survey (SDSS) images in such a way that the statistics of sky brightness, resolution, and crowding are matched between simulated galaxies and observed galaxies in the SDSS. The suite accepts idealized synthetic images in calibrated AB surface brightnesses and rebins them to the desired redshift and CCD angular scale; RealSim can add Poisson noise, if desired, by adopting generic values of photometric calibrations in survey fields. Images produced by the suite can be inserted into real image fields to incorporate real skies, PSF degradation, and contamination by neighboring sources in the field of view. The RealSim methodology can be applied to any existing galaxy imaging survey.

[ascl:2407.007] GRDzhadzha: Evolve matter on curved spacetimes

GRDzhadzha evolves matter on curved spacetimes with an analytic time and space dependence. Written in C++14, it uses hybrid MPI/OpenMP parallelism to achieve good performance. The code is based on publicly available 3+1D numerical relativity code GRChombo (ascl:2306.039) and inherits all of the capabilities of the main GRChombo code, which uses the Chombo library for adaptive mesh refinement.