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We report the discovery of two mini-Neptunes in near 2:1 resonance orbits (P = 7.610303 d for HIP 113103 b and P  = 14.245651 d for HIP 113103 c) around the adolescent K-star HIP 113103 (TIC 121490076). The planet system was first identified from the TESS mission, and was confirmed via additional photometric and spectroscopic observations, including a ∼17.5 h observation for the transits of both planets using ESA CHEOPS. We place ≤4.5 min and ≤2.5 min limits on the absence of transit timing variations over the 3 yr photometric baseline, allowing further constraints on the orbital eccentricities of the system beyond that available from the photometric transit duration alone. With a planetary radius of Rp  =  $1.829_{-0.067}^{+0.096}$ R⊕, HIP 113103 b resides within the radius gap, and this might provide invaluable information on the formation disparities between super-Earths and mini-Neptunes. Given the larger radius Rp  = $2.40_{-0.08}^{+0.10}$ R⊕ for HIP 113103 c, and close proximity of both planets to HIP 113103, it is likely that HIP 113103 b might have lost (or is still losing) its primordial atmosphere. We therefore present simulated atmospheric transmission spectra of both planets using JWST, HST, and Twinkle. It demonstrates a potential metallicity difference (due to differences in their evolution) would be a challenge to detect if the atmospheres are in chemical equilibrium. As one of the brightest multi sub-Neptune planet systems suitable for atmosphere follow up, HIP 113103 b and HIP 113103 c could provide insight on planetary evolution for the sub-Neptune K-star population.

 

We analyze 5108 AFGKM stars with at least five high-precision radial velocity points, as well as Gaia and Hipparcos astrometric data, utilizing a novel pipeline developed in previous work. We find 914 radial velocity signals with periods longer than 1000 days. Around these signals, 167 cold giants and 68 other types of companions are identified, through combined analyses of radial velocity, astrometry, and imaging data. Without correcting for detection bias, we estimate the minimum occurrence rate of the wide-orbit brown dwarfs to be 1.3%, and find a significant brown-dwarf valley around 40M_Jup. We also find a power-law distribution in the host binary fraction beyond 3 au, similar to that found for single stars, indicating no preference of multiplicity for brown dwarfs. Our work also reveals nine substellar systems (GJ 234 B, GJ 494 B, HD 13724 b, HD 182488 b, HD 39060 b and c, HD 4113 C, HD 42581 d, HD 7449 B, and HD 984 b) that have previously been directly imaged, and many others that are observable at existing facilities. Depending on their ages, we estimate that an additional 10–57 substellar objects within our sample can be detected with current imaging facilities, extending the imaged cold (or old) giants by an order of magnitude.

 

We introduce the idea of Citizen Scientist Amplification applying the method to data gathered from the top 10 contributing citizen scientists on the Supernova Hunters project. We take a novel approach to avail of the complementary strengths of deep learning and citizen science achieving results that are competitive with experts. 

In 2017, the Muon Hunter project on the Zooniverse.org citizen science platform successfully gathered more than two million classification labels for nearly 140,000 camera images from VER- ITAS. The aim was to select and parameterize muon events for use in training convolutional neural networks. The success of this project proved that crowdsourcing labels for IACT image analy- sis is a viable avenue for further development of advanced machine-learning algorithms. These algorithms could potentially lend themselves to improving class separation between gamma-ray and hadronic event types. Nonetheless, it took two months to gather these labels from volun- teers, which could be a bottleneck for future applications of this method. Here we present Muon Hunters 2.0: the follow-on project that demonstrates the development of unsupervised clustering techniques to gather muon labels more efficiently from volunteer classifiers. 

Storm direction modulates a hydrograph's magnitude and duration, thus having a potentially large effect on local flood risk. However, how changes in the preferential storm direction affect the probability distribution of peak flows remains unknown. We address this question with a novel Monte Carlo approach where stochastically transposed storms drive hydrologic simulations over medium and mesoscale watersheds in the Midwestern United States. Systematic rotations of these watersheds are used to emulate changes in the preferential storm direction. We found that the peak flow distribution impacts are scale‐dependent, with larger changes observed in the mesoscale watershed than in the medium‐scale watershed. We attribute this to the high diversity of storm patterns and the storms' scale relative to watershed size. This study highlights the potential of the proposed stochastic framework to address fundamental questions about hydrologic extremes when our ability to observe these events in nature is hindered by technical constraints and short time records.

 

A description is presented of the algorithms used to reconstruct energy deposited in the CMS hadron calorimeter during Run 2 (2015–2018) of the LHC. During Run 2, the characteristic bunch-crossing spacing for proton-proton collisions was 25 ns, which resulted in overlapping signals from adjacent crossings. The energy corresponding to a particular bunch crossing of interest is estimated using the known pulse shapes of energy depositions in the calorimeter, which are measured as functions of both energy and time. A variety of algorithms were developed to mitigate the effects of adjacent bunch crossings on local energy reconstruction in the hadron calorimeter in Run 2, and their performance is compared.