arXiv subjects

Samantha Adams

Publications and source records attributed to Samantha Adams.

3 recordsLinked to original sources

arXiv2026

ACES VIII: A Survey of Compact, High-Velocity Features Observed in CS(2-1)

The extreme kinematics of the Milky Way's Central Molecular Zone (CMZ) are influenced by processes such as dynamical shearing, cloud collisions, and stellar feedback. These events are visible in molecular data as vertically spiked features in position-velocity (PV) diagrams referred to as high velocity dispersion compact clouds (HVCCs). Using ALMA CMZ Exploration Survey (ACES) CS (2-1) molecular data, we identify a total of 235 HVCC candidates, 163 of which are visually identified, and an additional 72 identified via automated dendrogram methods. For each HVCC we catalog and report the physical and kinematic properties, explore line ratios of the cold dense gas tracer \HNCO with C-shock tracers, classify the morphology of their PV diagrams, and view their position-position-velocity distribution. The sample includes structures which are compact (d<5 pc) and have large velocity extents (20 km/s $<\Delta \mathrm{V} <$ 140 km/s), with most structures showing thin, `spiked' PV morphologies. We highlight areas of high ratios between HNCO and C-shock tracers along the edge of known orbital streams, implying a buildup of bar lane gas accreting onto the CMZ. We also find a collection of HVCCs overlapping with the 50 km/s cloud and known circumnuclear disk features. This catalog will be used for future investigation of nuclear inflow and determining dominant mechanisms disrupting average CMZ gas flows.

astro-ph.GA↗

arXiv2026

Properties of Polarized Radio Sources in the Wide Chandra Deep Field South from 2 to 4GHz

We present a study of the linear polarization properties of radio sources within the 10 deg$^2$ Wide Chandra Deep Field South (W-CDFS) in S-band (2-4 GHz). Our W-CDFS image has an angular resolution of 15 arcsec and a 1$σ$ RMS in Stokes $I$ of $\approx$50 $μ$Jy/beam. We detect 1920 distinct source components in Stokes $I$ and 175 in linear polarization. We examine the polarized source counts, Faraday Rotation measures, and fractional polarization of the sources in the survey. We show that sources with a total intensity above $\approx$10mJy have a mean fractional polarization value of $\approx$3% from modeling the polarized counts. We also calculate an estimate for the limit on the fractional polarization level of sources with a total intensity below 1mJy (mostly star-forming galaxies) of $\stackrel{<}{_{\sim}}$3% using stacking. The mean Faraday Rotation we measure is consistent with that due to the Milky Way. We also show that fractional polarization is correlated with in-band spectral index, consistent with a lower mean fractional polarization for the flat-spectrum population. In addition to characterizing the S-band polarization properties of sources in the W-CDFS, this study will be used to validate the shallower, but higher angular resolution S-band polarimetric information that the VLA Sky Survey will provide for the whole sky above Declination -40 degrees over the next few years.

astro-ph.GA↗

arXiv2020

A review of radar-based nowcasting of precipitation and applicable machine learning techniques

A 'nowcast' is a type of weather forecast which makes predictions in the very short term, typically less than two hours - a period in which traditional numerical weather prediction can be limited. This type of weather prediction has important applications for commercial aviation; public and outdoor events; and the construction industry, power utilities, and ground transportation services that conduct much of their work outdoors. Importantly, one of the key needs for nowcasting systems is in the provision of accurate warnings of adverse weather events, such as heavy rain and flooding, for the protection of life and property in such situations. Typical nowcasting approaches are based on simple extrapolation models applied to observations, primarily rainfall radar. In this paper we review existing techniques to radar-based nowcasting from environmental sciences, as well as the statistical approaches that are applicable from the field of machine learning. Nowcasting continues to be an important component of operational systems and we believe new advances are possible with new partnerships between the environmental science and machine learning communities.

physics.ao-ph↗