Probabilistic Classification of Infrared-selected targets for SPHEREx mission: In search of YSOs

We apply machine learning algorithms to classify Infrared (IR)-selected targets for NASA's upcoming SPHEREx mission. In particular, we are interested in classifying Young Stellar Objects (YSOs), which are essential for understanding the star formation process. Our approach differs from previous work, which has relied heavily on broadband color criteria to classify IR-bright objects, and are typically implemented in color-color and color-magnitude diagrams. However, these methods do not state the confidence associated with the classification and the results from these methods are quite ambiguous due to the overlap of different source types in these diagrams. Here, we utilize photometric colors and magnitudes from seven near and mid-infrared bands simultaneously and employ machine and deep learning algorithms to carry out probabilistic classification of YSOs, Asymptotic Giant Branch (AGB) stars, Active Galactic Nuclei (AGN) and main-sequence (MS) stars. Our approach also sub-classifies YSOs into Class I, II, III and flat spectrum YSOs, and AGB stars into carbon-rich and oxygen-rich AGB stars. We apply our methods to infrared-selected targets compiled in preparation for SPHEREx which are likely to include YSOs and other classes of objects. Our classification indicates that out of $8,308,384$ sources, $1,966,340$ have class prediction with probability exceeding $90\%$, amongst which $\sim 1.7\%$ are YSOs, $\sim 58.2\%$ are AGB stars, $\sim 40\%$ are (reddened) MS stars, and $\sim 0.1\%$ are AGN whose red broadband colors mimic YSOs. We validate our classification using the spatial distributions of predicted YSOs towards the Cygnus-X star-forming complex, as well as AGB stars across the Galactic plane.Comment: 17 pages, 12 figures, Accepted for publication in MNRA

The Ninth Visual Object Tracking VOT2021 Challenge Results

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The SPHEREx Target List of Ice Sources (SPLICES)

One of the primary objectives of the SPHEREx mission is to understand the origin of molecules such as H _2 O, CO _2 , and other volatile compounds at the early stages of planetary system formation. Because the vast majority of these compounds—typically exceeding 95%—exist in the solid phase rather than the gaseous phase in the systems of concern here, the observing strategy planned to characterize them is slightly unusual. Specifically, SPHEREx will target highly obscured sources throughout the Milky Way, and observe the species of concern in absorption against background illumination. SPHEREx spectrophotometry will yield ice column density measurements for millions of obscured Milky Way sources of all ages and types. By correlating those column densities with source ages, the SPHEREx mission will shed light on whether those molecules were formed in situ along with their nascent stellar systems, or whether instead they formed elsewhere and were introduced into those systems after their formation. To that end, this work describes version 7.1 of the SPHEREx target List of ICE Sources (SPLICES) for the community. It contains 8.6 × 10 ^6 objects brighter than W 2 ∼ 12 Vega mag over much of the sky, principally within a broad strip running the length of the Milky Way midplane, but also within high-latitude molecular clouds and even the Magellanic Clouds

The Sixth Visual Object Tracking VOT2018 Challenge Results

The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).Funding agencies: Slovenian research agencySlovenian Research Agency - Slovenia [P2-0214, P2-0094, J2-8175]; Czech Science FoundationGrant Agency of the Czech Republic [GACR P103/12/G084]; WASP; VR (EMC2); SSF (SymbiCloud); SNIC; AIT Strategic Research Programme 2017 Visua</p