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Christoph Franck

Publications and source records attributed to Christoph Franck.

2 recordsLinked to original sources

A SPHEREx Pipeline and Spectral Library for Ultracool Dwarfs

We present a Python spectrophotometry extraction tool tailored for fast-moving point sources detected in the SPHEREx mission, and use it to construct a set of 0.75-5.0 $\mu$m low-resolution ($\lambda/\Delta\lambda \sim 50$) spectrophotometry data products based on the SPHEREx Quick Release 2 (QR2) for a set of 6003 L0-Y1 ultracool dwarfs: 2050 known ultracool dwarfs, 3008 known photometric ultracool dwarf candidates, and 947 newly identified ultracool dwarfs. This work more than doubles the number of ultracool dwarfs with spectroscopy, from 3449 to 7402. We provide SPHEREx templates for each spectral subtype and a set of tools to assign automated spectral types. The QR2 data release generates spectrophotometry with an average signal-to-noise per spectral channel above $\sim$10 for most objects with WISE W2 magnitudes of 14.0 mag and brighter. The compiled data set is made available publicly at https://mocadb.ca, where new spectral compilations from future data releases will also be made available as they are published. These new data provide a significant increase in the number of substellar objects for which the 2.4-5.0 $\mu$m window is now accessible, making it possible to probe important molecular chemistry of key CNOS-bearing species for the coolest brown dwarfs. We flag 2668 ultracool dwarfs as candidate young brown dwarfs, 250 as candidate subdwarfs, and 865 as possibly otherwise peculiar for future investigation. The SPIFF library presented here opens the doors to efficient confirmation of candidate substellar objects and follow-up studies of population-level atmospheric properties of cold brown dwarfs.

astro-ph.SR

Detecting Urban PM$_{2.5}$ Hotspots with Mobile Sensing and Gaussian Process Regression

Low-cost mobile sensors can be used to collect PM$_{2.5}$ concentration data throughout an entire city. However, identifying air pollution hotspots from the data is challenging due to the uneven spatial sampling, temporal variations in the background air quality, and the dynamism of urban air pollution sources. This study proposes a method to identify urban PM$_{2.5}$ hotspots that addresses these challenges, involving four steps: (1) equip citizen scientists with mobile PM$_{2.5}$ sensors while they travel; (2) normalise the raw data to remove the influence of background ambient pollution levels; (3) fit a Gaussian process regression model to the normalised data and (4) calculate a grid of spatially explicit 'hotspot scores' using the probabilistic framework of Gaussian processes, which conveniently summarise the relative pollution levels throughout the city. We apply our method to create the first ever map of PM$_{2.5}$ pollution in Kigali, Rwanda, at a 200m resolution. Our results suggest that the level of ambient PM$_{2.5}$ pollution in Kigali is dangerously high, and we identify the hotspots in Kigali where pollution consistently exceeds the city-wide average. We also evaluate our method using simulated mobile sensing data for Beijing, China, where we find that the hotspot scores are probabilistically well calibrated and accurately reflect the 'ground truth' spatial profile of PM$_{2.5}$ pollution. Thanks to the use of open-source software, our method can be re-applied in cities throughout the world with a handful of low-cost sensors. The method can help fill the gap in urban air quality information and empower public health officials.

cs.LG