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The eShel, an off-the-shelf, fiber-fed echelle spectrograph ( R 10,000 ), was installed on the 1 m telescope at the Wise observatory in Israel. We report the installation of the multi-order spectrograph, and describe our pipeline to extract stellar radial velocity from the obtained spectra. We also introduce a new algorithm-UNICOR, to remove radial-velocity systematics that can appear in some of the observed orders. We show that the system performance is close to the photon-noise limit for exposures with more than 107 counts, with a precision that can get better than 200 m s−1 for F-K stars, for which the eShel spectral response is optimal. This makes the eShel at Wise a useful tool for studying spectroscopic binaries brighter than mV = 11. We demonstrate this capability with orbital solutions of two binaries from projects being performed at Wise.

Given a set of items of unknown utility, we need to select one with a utility as high as possible (“the selection problem”). Measurements (possibly noisy) of item values prior to selection are allowed, at a known cost. The goal is to optimize the overall sequential decision process of measurements and selection. Value of information (VOI) is a well-known scheme for selecting measurements, but the intractability of the problem typically leads to using myopic VOI estimates. Other schemes have also been proposed, some with approximation guarantees, based on submodularity criteria. However, it was observed that the VOI is not submodular in general. In this paper we examine theoretical properties of VOI for the selection problem, and identify cases of submodularity and supermodularity. We suggest how to use these properties to compute approximately optimal measurement batch policies, with an example based on a “wine selection problem”.

With the upcoming third Gaia data release (DR3), the first Gaia astrometric orbital solutions for binary sources will become available. Potentially, many rarely seen single-degenerate massive binaries with a black hole (OB+BH) will be revealed. Here, we investigate how many OB+BHs are expected to be detected as binaries in Gaia astrometry by using tailored models for the massive star population. We use a method based on the astrometric data to investigate how many OB+BH binaries will be uncovered by Gaia. We estimate that ∼200 OB+BHs are detectable among the sources in the second Alma Luminous Star massive star catalogue, either in DR3 or in upcoming data releases. Moreover, we show that BH-formation scenarios could be constrained from the distributions of parameters such as the orbital periods and eccentricities.

The early evolution of a supernova (SN) can reveal information about the environment and the progenitor star. When a star explodes in vacuum, the first photons to escape from its surface appear as a brief, hours-long shock-breakout flare 1 , 2 , followed by a cooling phase of emission. However, for stars exploding within a distribution of dense, optically thick circumstellar material (CSM), the first photons escape from the material beyond the stellar edge and the duration of the initial flare can extend to several days, during which the escaping emission indicates photospheric heating 3 . Early serendipitous observations 2 , 4 that lacked ultraviolet (UV) data were unable to determine whether the early emission is heating or cooling and hence the nature of the early explosion event. Here we report UV spectra of the nearby SN 2023ixf in the galaxy Messier 101 (M101). Using the UV data as well as a comprehensive set of further multiwavelength observations, we temporally resolve the emergence of the explosion shock from a thick medium heated by the SN emission. We derive a reliable bolometric light curve that indicates that the shock breaks out from a dense layer with a radius substantially larger than typical supergiants. Using ultraviolet data as well as a comprehensive set of further multiwavelength observations of the supernova 2023ixf, a reliable bolometric light curve is derived that indicates the heating nature of the early emission.

The eShel, an off-the-shelf, fiber-fed echelle spectrograph (R ≈ 10,000), was installed on the 1 m telescope at the Wise observatory in Israel. We report the installation of the multi-order spectrograph, and describe our pipeline to extract stellar radial velocity from the obtained spectra. We also introduce a new algorithm—UNICOR, to remove radial-velocity systematics that can appear in some of the observed orders. We show that the system performance is close to the photon-noise limit for exposures with more than 10⁷ counts, with a precision that can get better than 200 m s−1 for F–K stars, for which the eShel spectral response is optimal. This makes the eShel at Wise a useful tool for studying spectroscopic binaries brighter than mv  = 11. We demonstrate this capability with orbital solutions of two binaries from projects being performed at Wise.

Observing a supernova explosion shortly after it occurs can reveal important information about the physics of stellar explosions and the nature of the progenitor stars of supernovae (SNe). When a star with a well-defined edge explodes in vacuum, the first photons to escape from its surface appear as a brief shock-breakout flare. The duration of this flare can extend to at most a few hours even for nonspherical breakouts from supergiant stars, after which the explosion ejecta should expand and cool. Alternatively, for stars exploding within a distribution of sufficiently dense optically thick circumstellar material, the first photons escape from the material beyond the stellar edge, and the duration of the initial flare can extend to several days, during which the escaping emission indicates photospheric heating. The difficulty in detecting SN explosions promptly after the event has so far limited data regarding supergiant stellar explosions mostly to serendipitous observations that, owing to the lack of ultraviolet (UV) data, were unable to determine whether the early emission is heating or cooling, and hence the nature of the early explosion event. Here, we report observations of SN 2023ixf in the nearby galaxy M101, covering the early days of the event. Using UV spectroscopy from the Hubble Space Telescope (HST) as well as a comprehensive set of additional multiwavelength observations, we trace the photometric and spectroscopic evolution of the event and are able to temporally resolve the emergence and evolution of the SN emission.

Parallelization and External Memory (PEM) techniques have significantly enhanced the capabilities of search algorithms when solving large-scale problems. Previous research on PEM has primarily centered on unidirectional algorithms, with only one publication on bidirectional PEM that focuses on the meet-in-the-middle (MM) algorithm. Building upon this foundation, this paper presents a framework that integrates both uni- and bi-directional best-first search algorithms into this framework. We then develop a PEM variant of the state-of-the-art bidirectional heuristic search (BiHS) algorithm BAE* (PEM-BAE*). As previous work on BiHS did not focus on scaling problem sizes, this work enables us to evaluate bidirectional algorithms on hard problems. Empirical evaluation shows that PEM-BAE* outperforms the PEM variants of A* and the MM algorithm, as well as a parallel variant of IDA*. These findings mark a significant milestone, revealing that bidirectional search algorithms clearly outperform unidirectional search algorithms across several domains, even when equipped with state-of-the-art heuristics.

In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission. Such problems are inherently mixed discrete-continuous: a high-level action sequence and a physically feasible continuous trajectory. The resulting trajectory and action sequence must also satisfy problem constraints such as deadlines, time windows, and velocity or acceleration limits. While hybrid temporal planners attempt to address this challenge, they typically model motion using linear (first-order) dynamics, which cannot guarantee that the resulting plan respects the robot's true physical constraints. Consequently, even when the high-level action sequence is fixed, producing a dynamically feasible trajectory becomes a bi-level optimization problem. We address this problem via reinforcement learning in continuous space. We define a Markov Decision Process that explicitly incorporates analytical second-order constraints and use it to refine first-order plans generated by a hybrid planner. Our results show that this approach can reliably recover physical feasibility and effectively bridge the gap between a planner's initial first-order trajectory and the dynamics required for real execution.

Many sequential decision-making problems can be formulated as shortest-path problems, where the objective is to reach a goal state from a given starting state. Heuristic search is a standard approach for solving such problems, relying on a heuristic function to estimate the cost to the goal from any given state. Recent approaches leverage reinforcement learning to learn heuristics by applying deep approximate value iteration. These methods typically rely on single-step Bellman updates, where the heuristic of a state is updated based on its best neighbor and the corresponding edge cost. This work proposes a generalized approach that enhances both state sampling and heuristic updates by performing limited-horizon searches and updating each state's heuristic based on the shortest path to the search frontier, incorporating both edge costs and the heuristic values of frontier states.