astroplan: An Open Source Observation Planning Package in Python

We present astroplan - an open source, open development, Astropy affiliated package for ground-based observation planning and scheduling in Python. astroplan is designed to provide efficient access to common observational quantities such as celestial rise, set, and meridian transit times and simple transformations from sky coordinates to altitude-azimuth coordinates without requiring a detailed understanding of astropy's implementation of coordinate systems. astroplan provides convenience functions to generate common observational plots such as airmass and parallactic angle as a function of time, along with basic sky (finder) charts. Users can determine whether or not a target is observable given a variety of observing constraints, such as airmass limits, time ranges, Moon illumination/separation ranges, and more. A selection of observation schedulers are included which divide observing time among a list of targets, given observing constraints on those targets. Contributions to the source code from the community are welcome

Gammapy: A Python package for gamma-ray astronomy

In this article, we present Gammapy, an open-source Python package for the analysis of astronomical $\gamma$-ray data, and illustrate the functionalities of its first long-term-support release, version 1.0. Built on the modern Python scientific ecosystem, Gammapy provides a uniform platform for reducing and modeling data from different $\gamma$-ray instruments for many analysis scenarios. Gammapy complies with several well-established data conventions in high-energy astrophysics, providing serialized data products that are interoperable with other software packages. Starting from event lists and instrument response functions, Gammapy provides functionalities to reduce these data by binning them in energy and sky coordinates. Several techniques for background estimation are implemented in the package to handle the residual hadronic background affecting $\gamma$-ray instruments. After the data are binned, the flux and morphology of one or more $\gamma$-ray sources can be estimated using Poisson maximum likelihood fitting and assuming a variety of spectral, temporal, and spatial models. Estimation of flux points, likelihood profiles, and light curves is also supported. After describing the structure of the package, we show, using publicly available $\gamma$-ray data, the capabilities of Gammapy in multiple traditional and novel $\gamma$-ray analysis scenarios, such as spectral and spectro-morphological modeling and estimations of a spectral energy distribution and a light curve. Its flexibility and power are displayed in a final multi-instrument example, where datasets from different instruments, at different stages of data reduction, are simultaneously fitted with an astrophysical flux model.Comment: 26 pages, 16 figure

Early Ultraviolet Observations of Type IIn Supernovae Constrain the Asphericity of Their Circumstellar Material

We present a survey of the early evolution of 12 Type IIn supernovae (SNe IIn) at ultraviolet and visible light wavelengths. We use this survey to constrain the geometry of the circumstellar material (CSM) surrounding SN IIn explosions, which may shed light on their progenitor diversity. In order to distinguish between aspherical and spherical CSM, we estimate the blackbody radius temporal evolution of the SNe IIn of our sample, following the method introduced by Soumagnac et al. We find that higher-luminosity objects tend to show evidence for aspherical CSM. Depending on whether this correlation is due to physical reasons or to some selection bias, we derive a lower limit between 35% and 66% for the fraction of SNe IIn showing evidence for aspherical CSM. This result suggests that asphericity of the CSM surrounding SNe IIn is common—consistent with data from resolved images of stars undergoing considerable mass loss. It should be taken into account for more realistic modeling of these events

Rotation periods for very low mass stars in Praesepe

We investigate the rotation periods of fully convective very low mass (VLM, M <0.3 M⊙) stars, with the aim to derive empirical constraints for the spin-down due to magnetically driven stellar winds. Our analysis is based on a new sample of rotation periods in the main-sequence cluster Praesepe (age 600 Myr). From photometric light curves obtained with the Isaac Newton Telescope, we measure rotation periods for 49 objects, among them 26 in the VLM domain. This enlarges the period sample in this mass and age regime by a factor of 6. Almost all VLM objects in our sample are fast rotators with periods 0.6 M⊙ in this cluster which have periods of 7-14 d. Thus, we confirm that the period-mass distribution in Praesepe exhibits a radical break at M˜ 0.3-0.6 M⊙. Our data indicate a positive period-mass trend in the VLM regime, similar to younger clusters. In addition, the scatter of the periods increases with mass. For the M > 0.3 M⊙ objects in our sample, the period distribution is probably affected by binarity. By comparing the Praesepe periods with literature samples in the cluster NGC 2516 (age ˜ 150 Myr) we constrain the spin-down in the VLM regime. An exponential rotational braking law P∝ exp (t/τ) with a mass-dependent τ is required to reproduce the data. The spin-down time-scale τ increases steeply towards lower masses; we derive τ˜ 0.5 Gyr for 0.3 M⊙ and >1 Gyr for 0.1 M⊙. These constraints are consistent with the current paradigm of the spin-down due to wind braking. We discuss possible physical origins of this behaviour and prospects for future work

pyspeckit: A spectroscopic analysis and plotting package

pyspeckit is a toolkit and library for spectroscopic analysis in Python. We describe the pyspeckit package and highlight some of its capabilities, such as interactively fitting a model to data, akin to the historically widely-used splot function in IRAF. pyspeckit employs the Levenberg-Marquardt optimization method via the mpfit and lmfit implementations, and important assumptions regarding error estimation are described here. Wrappers to use pymc and emcee as optimizers are provided. A parallelized wrapper to fit lines in spectral cubes is included. As part of the astropy affiliated package ecosystem, pyspeckit is open source and open development and welcomes input and collaboration from the community.Comment: Version 1.0 of pyspeckit is released alongside this paper; https://pyspeckit.readthedocs.io/en/latest

spacetelescope/gwcs: GWCS v 0.20.0

<p><strong>Changes in GWCS 0.20.0</strong></p> <ul> <li>Replace pkg_resources with importlib.metadata. [#478]</li> <li>Serialize and deserialize pixel_shape with asdf. [#480]</li> </ul&gt

desihub/speclite: Several new filter sets

<p>Added several new filters, including GALEX, CFHT, ODIN, Gaia. See the <a href="https://speclite.readthedocs.io/en/stable/changes.html">Change Log</a> for details.</p&gt

spacetelescope/spherical_geometry: Multiple bug fixes and compatibility improvements

<ul> <li>Improved compatibility with <code>numpy</code> 2.0</li> <li>Fixed crash in multi-union for nearly identical input polygons [#233]</li> <li>Fixed crash in convex_hull when input includes repeated points [#254]</li> <li>Fixed a bug in quad-precision functions normalize and intersection functions [#256]</li> </ul&gt