Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2111.12814

A Joint Ranking Statistic for Multi-messenger Astronomical Searches with Gravitational Waves

Brandon Piotrzkowski·Amanda Baylor·Ignacio Magaña Hernandez
View original source ↗Read source document ↗Export metadata ↓

Abstract

Joint ranking statistics are used to distinguish real from random coincidences, ideally considering whether shared parameters are consistent with each other as well as whether the individual candidates are distinguishable from noise. We expand on previous works to include additional shared parameters, we use galaxy catalogues aspriors for sky localization and distance, and avoid some approximations previously used. We develop methods to calculate this statistic both in low-latency using HEALPix sky maps, as well as with posterior samples. We show that these changes lead to a factor of one to two orders of magnitude improvement for GW170817-GRB 170817A depending on the method used, placing this significant event further into the foreground. We also examined the more tenuous joint candidate GBM-GW150914, which was largely penalized by these methods. Finally, we performed a simplistic simulation that argues these changes could better help distinguish between real and random coincidences in searches, although more realistic simulations are needed to confirm this.

Explore related subjects

astro-ph.IMgr-qc

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Brandon Piotrzkowski, Amanda Baylor, Ignacio Magaña Hernandez. 2021-11-24. A Joint Ranking Statistic for Multi-messenger Astronomical Searches with Gravitational Waves. https://doi.org/10.1088/1361-6382%2Fac5c00

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↗