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DOE OSTI2021

Data Product: Ka ARM Zenith Radar (KAZR), in CfRadial format: general mode, corrected

The KAZRCFRCOR Value Added Product (VAP) performs several corrections to the ingested KAZR data and also creates a significant detection mask for each radar mode. The VAP computes gaseous attenuation as a function of time and radial distance from the radar antenna, based on ambient relative humidity, and corrects observed reflectivities for that effect. KAZR reflectivities are also corrected in the antenna ‘nearfield’ range to compensate for the near-field antenna pattern and gain. KAZRCFRCOR also dealiases mean Doppler velocities, to correct velocities whose magnitudes exceed the radar’s Nyquist velocity. This dataset contains the 'kazrge' (general) mode output from this VAP.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Data Product: Ka ARM Zenith Radar (KAZR), in CfRadial format: moderate sensitivity mode, corrected

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Broad absorption line quasars in the Dark Energy Spectroscopic Instrument Early Data Release

Broad absorption line (BAL) quasars are characterized by gas clouds that absorb flux at the wavelength of common quasar spectral features, although blueshifted by velocities that can exceed $0.1c$. BAL features are interesting as signatures of significant feedback, yet they can also compromise cosmological studies with quasars by distorting the shape of the most prominent quasar emission lines, impacting redshift accuracy and measurements of the matter density distribution traced by the Lyman $\alpha$ forest. We present a catalogue of BAL quasars discovered in the Dark Energy Spectroscopic Instrument (DESI) survey Early Data Release, which were observed as part of DESI Survey Validation, as well as the first two months of the main survey. We describe our method to automatically identify BAL quasars in DESI data, the quantities we measure for each BAL, and investigate the completeness and purity of this method with mock DESI observations. We mask the wavelengths of the BAL features and re-evaluate each BAL quasar redshift, finding new redshifts which are $243\, {\rm km}\, {\rm s}^{-1}$ smaller on average for the BAL quasar sample. These new, more accurate redshifts are important to obtain the best measurements of quasar clustering, especially at small scales. Finally, we present some spectra of rarer classes of BALs that illustrate the potential of DESI data to identify such populations for further study.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

A Cloud-Tracking Data Set for the CSAPR2 Adaptive Scanning during TRACER

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility (Mather and Voyles 2013) deployed the first ARM Mobile Facility (AMF1; Miller et al. 2016) near LaPorte, Texas to support the Tracking Aerosol Convection Interactions Experiment (TRACER) (Jensen et al. 2025) near Houston, Texas. From October 2021 to September 2022, AMF1 was deployed to 29.67° N, 95.06° W near LaPorte, Texas and the 2nd Generation C-band Scanning ARM Precipitation Radar (CSAPR2) was deployed to a supplementary site at 29.53° N, 95.28° W (Figure 1). During an intensive operational period (IOP) from 1 June to 30 September 2022, the CSAPR2 sampled precipitation echoes in an adaptive scanning mode following the Multisensor Agile Adaptive Scanning (MAAS) framework (Kollias et al. 2020). MAAS helped optimize the CSAPR2 scan strategy to perform frequent plan position indicator (PPI) and range height indicator (RHI) scans (Lamer et al. 2023). Details of the CSAPR2 scanning, data processing, and calibration procedures used by the principal investigator (PI), and the PI data files are described by Oue et al. (2023). Details of the CSAPR2 operational performance, ARM data processing and correction procedures, and data quality masks are described by Feng et al. (2024a).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Prospects for future studies using deep imaging: analysis of individual Galactic cirrus filaments

The presence of Galactic cirrus is an obstacle for studying both faint objects in our Galaxy and low surface brightness extragalactic structures. With the aim of studying individual cirrus filaments in Sloan Digital Sky Survey (SDSS) Stripe 82 data, we develop techniques based on machine learning and neural networks that allow one to isolate filaments from foreground and background sources in the entirety of Stripe 82 with a precision similar to that of the human expert. Our photometric study of individual filaments indicates that only those brighter than 26 mag arcsec −2 in the SDSS r band are likely to be identified in SDSS Stripe 82 data by their distinctive colours in the optical bands. We also show a significant impact of data processing (e.g. flat-fielding, masking of bright stars, and sky subtraction) on colour estimation. Analysing the distribution of filaments’ colours with the help of mock simulations, we conclude that most filaments have colours in the following ranges: 0.55 ≤g − r ≤ 0.73 and 0.01 ≤ r − i ≤ 0.33. Our work provides a useful framework for an analysis of all types of low surface brightness features (cirri, tidal tails, stellar streams, etc.) in existing and future deep optical surveys. For practical purposes, we provide the catalogue of dust filaments.

79 ASTRONOMY AND ASTROPHYSICS↗