Software Technology
Technological offers in software technology
Information about the technology offers currently available at ICE-CSIC. For further information, please contact the person indicated in each offer.
AMPERE | Description: Ampere is a Bayesian fitting environment for astronomers: a tool for modelling several kinds of astronomical data at once — SEDs, spectra, and more — even when the model cannot explain everything in the data. Details of the code are published in: https://arxiv.org/abs/2510.26333 Area of application: Astrophysics – Dust, extinction Repository: https://github.com/ICSM/ampere License: GPL-3.0 Contact: |
MATINS | Description: MATINS is a 3D numerical framework designed to model the magneto-thermal evolution within the crust of isolated neutron stars. It solves the induction equation in the crust, accounting for Ohmic dissipation and the Hall drift, to describe the evolution of the magnetic field. The induction equation is coupled to the 3D cooling model, which considers the local temperature evolution in the crust and treats the core as a single thermal cell, with an envelope model serving as a boundary condition. The code also computes the Tolman–Oppenheimer–Volkoff (TOV) structure, allowing the use of different tabulated cold-matter equations of state and stellar masses, applied consistently across the crust and core. In both regions, MATINS evaluates temperature-dependent microphysical properties that are crucial for accurately coupling thermal and magnetic evolution. Numerically, it employs a finite-volume scheme on a cubed-sphere grid, representing the stellar surface with six smoothly connected patches and avoiding the coordinate singularities present in standard spherical systems. The code can be used to model various observables of isolated neutron stars, including X-ray thermal emission, surface magnetic fields, and rotational properties. MATINS has been funded by the European Research Council via the ERC Consolidator grant 'MAGNESIA' (No. 817661; PI: N. Rea). Details of the code are published in: - Dehman, C., Viganò, D., Pons, J. A., & Rea, N. (2023), MNRAS, 518, 1222, "3D code for MAgneto-Thermal evolution in Isolated Neutron Stars, MATINS: the magnetic field formalism", DOI: 10.1093/mnras/stac2761 - Ascenzi, S., Viganò, D., Dehman, C., Pons, J. A., Rea, N., & Perna, R. (2024), MNRAS, 533, 201, "3D code for MAgneto-Thermal evolution in Isolated Neutron Stars, MATINS: thermal evolution and light curves", DOI: 10.1093/mnras/stae1749 Area of application: Astrophysics – Neutron Stars Repository: https://ice-csic-astroexotic.github.io/code/matins/ License: GPL-3.0 Contact: |
ML-Poppyns | Description: ML-Poppyns is a new population synthesis framework for Galactic neutron stars that employs deep learning and simulation based inference (SBI) to investigate the birth, evolution and emission properties of the neutron star population in our Galaxy. It starts from some initial conditions on the birth positions in the Galaxy, kick velocities, birth spin-periods and magnetic field distributions. It then performs dynamical and magneto-rotational evolution to generate an evolved population of neutron star. Finally, it models the radio emission and detection with radio surveys. To perform parameter inference it employs the power of neural networks, in particular a neural density estimator is trained on simulated data to learn a mapping between model parameters and simulated observables and reconstruct an approximation of the posterior distribution of the model parameters. Once trained the neural density estimator can be applied to the true observed neutron star population to infer its physical properties. ML-Poppyns has been funded by the European Research Council via the ERC Consolidator grant 'MAGNESIA' (No. 817661; PI: N. Rea). Details of the code are published in: - Ronchi, M., Graber, V., Garcia-Garcia, A., Rea, N., & Pons, J. A. (2021), ApJ, 916, 100, "Analyzing the Galactic Pulsar Distribution with Machine Learning", DOI: 10.3847/1538-4357/ac05bd . - Graber, V., Ronchi, M., Pardo-Araujo, C., & Rea, N. (2024), ApJ, 968, 16, "Isolated Pulsar Population Synthesis with Simulation-based Inference", DOI: 10.3847/1538-4357/ad3e78 - Pardo-Araujo, C., Ronchi, M., Graber, V., & Rea, N. (2025), A&A, 696, A114, "Radio pulsar population synthesis with consistent flux measurements using simulation-based inference", DOI: 10.1051/0004-6361/202453314. Area of application: Astrophysics – Neutron Stars Repository: https://ice-csic-astroexotic.github.io/code/ml_poppyns/ License: GPL-3.0 Contact: |
GLIDErS (GaLaxy Image DEcontamination Scripts) | A Python package for cleaning astronomical images while preserving the main source of interest, such as a galaxy, and reducing contamination from foreground stars, neighbouring objects, and overexposure artifacts. License: MIT license Contact: |