Cautionary Analysis of Spectral Radiance from Io’s Active Volcanoes Derived from Galileo Near-Infrared Mapping Spectrometer Data

Between 1996 and 2001, the Galileo Near-Infrared Mapping Spectrometer (NIMS) obtained 190 observations of the volcanic Jovian moon Io. Rathbun et al. (2018) [Astron. J., 156, 207] published a list of 287 measurements of 3.5 μm spectral radiance from some of Io’s active volcanoes, derived from a subset of the NIMS data. However, the spectral radiances reported by Rathbun et al. are lower, in some cases by multiple orders of magnitude, than other analyses of the same observations and spectral radiances derived from contemporaneous ground-based data. In many cases, the Rathbun et al. hot-spot radiances are underreported by a factor of π, likely due to a mistake in unit conversion. For a small number of powerful hot spots, additional discrepancies appear to be the result of poor fits to data limited in wavelength range by NIMS detector saturation and a methodology that discards short-wavelength NIMS data that otherwise would have provided more robust temperature model fits.

A Data-Challenge Case Study of Analyte Detection and Identification with Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry (GC×GC-MS)

This case study describes data analysis of a chromatogram distributed for the 2019 GC×GC Data Challenge for the Tenth Multidimensional Chromatography Workshop (Liege, Belgium). The chromatogram resulted from chemical analysis of a terpene-standards sample by comprehensive two-dimensional chromatography with mass spectrometry (GC×GC-MS). First, several aspects of the data quality are assessed, including detector saturation and oscillation, and operations to prepare the data for analyte detection and identification are described, including phase roll for modulation-cycle alignment and baseline correction to account for the non-zero detector baseline. Then, the case study presents operations for analyte detection with filtering, a new method to flag false detections, interactive review to confirm detected peaks, and ion-peaks detection to reveal peaks that are obscured by noise or coelution. Finally, the case study describes analyte identification including mass-spectral library search with a new method for optimizing spectra extraction, retention-index calibration from preliminary identifications, and expression-based identification checks. Processing of the first 40 min of data detected 144 analytes, 21 of which have at least one percent response, plus an additional 20 trace and/or coeluted analytes.

Data processing pipeline for serial femtosecond crystallography at SACLA

A data processing pipeline for serial femtosecond crystallography at SACLA was developed, based onCheetah[Bartyet al.(2014).J. Appl. Cryst.47, 1118–1131] andCrystFEL[Whiteet al.(2016).J. Appl. Cryst.49, 680–689]. The original programs were adapted for data acquisition through the SACLA API, thread and inter-node parallelization, and efficient image handling. The pipeline consists of two stages: The first, online stage can analyse all images in real time, with a latency of less than a few seconds, to provide feedback on hit rate and detector saturation. The second, offline stage converts hit images into HDF5 files and runsCrystFELfor indexing and integration. The size of the filtered compressed output is comparable to that of a synchrotron data set. The pipeline enables real-time feedback and rapid structure solution during beamtime.