The Full Event Interpretation -- An exclusive tagging algorithm for the Belle II experiment

The Full Event Interpretation is presented: a new exclusive tagging algorithm used by the high-energy physics experiment Belle II. The experimental setup of Belle II allows the precise measurement of otherwise inaccessible $B$ meson decay-modes. The Full Event Interpretation algorithm enables many of these measurements. The algorithm relies on machine learning to automatically identify plausible $B$ meson decay chains based on the data recorded by the detector. Compared to similar algorithms employed by previous experiments, the Full Event Interpretation provides a greater efficiency, yielding a larger effective sample size usable in the measurement.Comment: 11 pages, 7 figures, 1 tabl

Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10–30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced. The algorithms were in good agreement for thick cloud detection, which were opaque and had lower uncertainties in their identification, in contrast to thin/semi-transparent clouds detection. Not only did CMIX allow identification of strengths and weaknesses of existing algorithms and potential areas of improvements, but also the problems associated with the existing reference datasets. The paper concludes with recommendations on generating new reference datasets, metrics, and an analysis framework to be further exploited and additional input datasets to be considered by future CMIX activities

Študija razpadov BtoKKellnuellB to KKellnu_ell pri Belle

We present the branching fraction measurement of the charmless semileptonic decay $B^+ to K^+K^-ell^+nu_ell$. The measurement has been performed on a data sample corresponding to 710 $rm{fb^{-1}}$ of integrated luminosity, collected with the Belle detector at the KEKB asymmetric-energy $e^+$$e^-$ collider in Tsukuba, Japan. We present the results obtained with the B2BII data format converter. This is the first measurement of the decay, where we obtain the branching fraction of $mathcal{B}(B^+ to K^+K^-ell^+nu) = (3.04 pm 0.51 pm ^{+0.67} _{-0.66}) times 10^{-5}$. With the signal significance of 4.6$sigma$, this measurement counts as the first evidence for the decay.V disertaciji predstavljamo meritev razvejitvenega razmerja ne-čarobnega semileptonskega razpada $B^+ to K^+K^-ell^+nu_ell$. Meritev je bila opravljena na vzorcu podatkov, ki ustreza integrirani luminoznosti 710 $rm{fb^{-1}}$, zbranim z detektorjem Belle na asimetričnem trkalniku delcev $e^+$$e^-$ KEKB v mestu Tsukuba, na Japonskem. V delu predstavimo rezultate, ki so bili pridobljeni s konverzijo B2BII. To je prva meritev tega razpada, kjer izmerimo razvejitveno razmerje $mathcal{B}(B^+ to K^+K^-ell^+nu) = (3.04 pm 0.51 pm ^{+0.67} _{-0.66}) times 10^{-5}$. S signifikanco 4.6$sigma$ ta meritev šteje kot prvi dokaz za ta razpad

CMIX: Cloud Mask Intercomparison eXercise

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masks have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. Here, we summarize results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10-30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), were evaluated within the CMIX. Those algorithms varied in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs were evaluated against existing reference cloud mask datasets. Those datasets varied in sampling methods, geographical distribution, sample unit (points, polygons, or full image labels), and generation approach (experts annotations, machine learning, or sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in cloud definitions used when producing the reference datasets. Average overall accuracy (across algorithms) varied 80.0±5.3% to 89.4±2.4% for Sentinel-2, and 79.8±7.1% to 97.6±0.8% for Landsat 8, depending on the reference dataset. An overall accuracy of 90% yields half the errors than an overall accuracy of 80%. The study identified algorithms that provided a balance between commission and omission errors, as well as algorithms, which are cloud conservative (high user’s accuracy) and non-cloud (clear) conservative (high producer’s accuracy). With repetitive observations like those of Sentinel-2, it seems reasonable to favor non-cloud conservative approaches, with maybe the exception of very cloudy regions where every cloud free observation is critical. When thin/semi-transparent clouds were not considered in the reference datasets algorithms’ performance generally improved: overall accuracy values increased by +1.5% to 7.4%. It should be noted though that these clouds are commonly occurring and are often present in optical imagery. Within CMIX, we also developed recommendations for further activities, which include provision of a quantitative definition for clouds (targeting moderate spatial resolution imagery by Landsat 8 and Sentinel-2), generation of new reference datasets, and expansion of the analysis framework (for example, multi-temporal analysis and application-driven validation). Such intercomparison studies will hopefully help the community to improve the algorithms and move towards standardization of cloud masking. Given the importance of cloud masking in optical satellite imagery we encourage CEOS to continue the CMIX activities