Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2504.00982

UVIT Data Release version 7: Regenerated high-level UVIT data products

Prajwel Joseph·S. N. Tandon·S. K. Ghosh·C. S. Stalin
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Ultra-Violet Imaging Telescope (UVIT) on board AstroSat is an active telescope capable of high-resolution far-ultraviolet imaging (<1.5'') and low resolution ($λ/δλ$ $\approx$ 100) slitless spectroscopy with a field of view as large as ~0.5 degrees. Now almost a decade old, UVIT continues to be operational and generates valuable data for the scientific community. UVIT is also capable of near-ultraviolet imaging (<1.5''); however, the near-ultraviolet channel stopped working in August 2018 after providing data for nearly three years. This article gives an overview of the latest version (7.0.1) of the UVIT pipeline and UVIT Data Release version 7. The high-level products generated using pipeline versions having a major version number of seven will be called "UVIT Data Release version 7". The latest pipeline version overcomes the two limitations of the previous version (6.3), namely (a) the inability to combine all episode-wise images and (b) the failure of the astrometry module in a large fraction of the observations. The procedures adopted to overcome these two limitations, as well as a comparison of the performance of this new version over the previous one, are presented in this paper. The UVIT Data Release version 7 products are available at the Indian Space Science Data Center of the Indian Space Research Organisation for archival and dissemination from 01 June 2024. The new pipeline version is open source and made available on GitHub.

Explore related subjects

astro-ph.IMastro-ph.GAastro-ph.HEastro-ph.SR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Prajwel Joseph, S. N. Tandon, S. K. Ghosh, C. S. Stalin. 2025-04-01. UVIT Data Release version 7: Regenerated high-level UVIT data products. https://arxiv.org/abs/2504.00982

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The Simons Observatory: Development of a Pipeline to Detect Rapid Transients in Time-Ordered Data

We introduce a method for detecting astrophysical transients evolving on timescales of milliseconds to minutes using cosmic microwave background (CMB) survey telescopes. While previous transient searches in CMB data operate in map space, our pipeline directly processes the raw time-ordered data, enabling sensitivity to fast, dynamic signals. We integrate our detection approach into the Simons Observatory time-domain pipeline and assess the performance by injecting symmetric, stellar flare-like light curves into simulated observations. For events flaring with a timescale of 0.5 s, the pipeline detects $\gtrsim90$ % of events at flux densities of 800, 1150, 1650, and 4250\,mJy when measured in the 93, 145, 225, and 280 GHz bands respectively. At a fixed peak flux density, the pipeline more readily detects longer flares. The limiting flux density for 90 % completeness is four times lower for a $\ge5$ s flare than for a 0.5 s flare, while the flux density limits for $\gtrsim50$ % detection efficiency are comparable to the rms noise of the time-ordered data. We are able to determine the position of detected events in each observing band, with a positional uncertainty at the detection threshold comparable to the telescope resolution at that band. These results demonstrate the readiness of this pipeline for incorporation into upcoming Simons Observatory data analyses.

astro-ph.IM↗
arXiv2026

Fitting Moving Objects in Up-The-Ramp Data with Applications to the Roman Space Telescope and JWST

A moving object breaks the fundamental property of constant per-pixel count rates in an astronomical image read out up-the-ramp. In this paper, we show how to fit a moving object's path across a detector as that detector is read out nondestructively. We write the full likelihood function for every pixel subject to a constant count rate plus a time-dependent count rate due to a moving source. Assuming the moving source to be point-like and assuming the effective point-spread function to be known, we are left with four parameters that enter the likelihood nonlinearly: two for position and two for velocity. All remaining parameters can be optimized using closed-form expressions. Our approach extracts maximal information on a moving source's position and speed and enables the source to be accurately removed from the image. We investigate the dependence of flux, position, and velocity precision on the target's speed and the readout pattern. We also find a small, positive bias on the recovered flux due to the need to fit for an uncertain position and speed. Our approach can be used for space-based images with minor Solar system bodies in the foreground, e.g.~from Roman and JWST, or for ground-based observations with satellites in the foreground. We demonstrate the promise of our method with a fit to an asteroid track observed serendipitously by the NIRISS instrument on JWST, comparing it to the performance of the JWST pipeline. Python code implementing our approach is available at https://github.com/t-brandt/moving_source. The total computational cost to fit the track of a moving object is $\sim$1 second on a 2023 Macbook Pro.

astro-ph.IM↗
arXiv2026

Options for Compression of radio interferometry data: lossy compression of visibilities and lossless compression of uv-visibility grids for the MHONGOOSE survey

Next generation radio astronomy telescopes are challenging existing data reduction paradigms. With ever more antennas, larger bandwidths, and sometimes multiple primary beams, they often generate more observed data products than can readily be stored long-term. Thus, data storage becomes a major cost driver and processing constraint. In this paper, we test two methods of addressing this problem: grid-stacking, a two-stage lossless compression solution; and the lossy compression of the raw visibilities before traditional processing. To demonstrate these solutions we utilised a deep imaging pipeline based on software for the ASKAP telescope, ASKAPSoft, but applied to a strong source (NGC1566) from the deep MeerKAT HI spectral line project, MHONGOOSE. The grid-stacking solution reproduces the spectrum from traditional processing to within better than 0.7%, and also allows for the reconstruction of other weighting scales without significant computing costs. In comparison, image-stacking also reproduces the spectrum from the traditional processing, to within better than 3% but with worse image residuals in the cube. The lossy compression, even at a near ten-fold reduction in file size, reproduces the spectra almost perfectly (to better than ~0.01% in all cases). Thus both compression methods are promising solutions, and we discuss considerations for their application.

astro-ph.IM↗