5 research outputs found

    Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning

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    Modern astronomical experiments are designed to achieve multiple scientific goals, from studies of galaxy evolution to cosmic acceleration. These goals require data of many different classes of night-sky objects, each of which has a particular set of observational needs. These observational needs are typically in strong competition with one another. This poses a challenging multi-objective optimization problem that remains unsolved. The effectiveness of Reinforcement Learning (RL) as a valuable paradigm for training autonomous systems has been well-demonstrated, and it may provide the basis for self-driving telescopes capable of optimizing the scheduling for astronomy campaigns. Simulated datasets containing examples of interactions between a telescope and a discrete set of sky locations on the celestial sphere can be used to train an RL model to sequentially gather data from these several locations to maximize a cumulative reward as a measure of the quality of the data gathered. We use simulated data to test and compare multiple implementations of a Deep Q-Network (DQN) for the task of optimizing the schedule of observations from the Stone Edge Observatory (SEO). We combine multiple improvements on the DQN and adjustments to the dataset, showing that DQNs can achieve an average reward of 87%+-6% of the maximum achievable reward in each state on the test set. This is the first comparison of offline RL algorithms for a particular astronomical challenge and the first open-source framework for performing such a comparison and assessment task.Comment: Accepted in Machine Learning and the Physical Sciences Workshop at NeurIPS 2023; 6 pages, 5 figure

    A new step forward in realistic cluster lens mass modelling: Analysis of Hubble Frontier Field Cluster Abell S1063 from joint lensing, X-ray and galaxy kinematics data

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    International audienceWe present a new method to simultaneously/self-consistently model the mass distribution of galaxy clusters that combines constraints from strong lensing features, X-ray emission and galaxy kinematics measurements. We are able to successfully decompose clusters into their collisionless and collisional mass components thanks to the X-ray surface brightness, as well as using the dynamics of cluster members to obtain more accurate masses with the fundamental plane of elliptical galaxies. Knowledge from all observables is included through a consistent Bayesian approach in the likelihood or in physically motivated priors. We apply this method to the galaxy cluster Abell S1063 and produce a mass model that we publicly release with this paper. The resulting mass distribution presents a different ellipticities for the intra-cluster gas and the other large-scale mass components; and deviation from elliptical symmetry in the main halo. We assess the ability of our method to recover the masses of the different elements of the cluster using a mock cluster based on a simplified version of our Abell S1063 model. Thanks to the wealth of information provided by the mass model and the X-ray emission, we also found evidence for an on-going merger event with gas sloshing from a smaller infalling structure into the main cluster. In agreement with previous findings, the total mass, gas profile and gas mass fraction are consistent with small deviations from the hydrostatic equilibrium. This new mass model for Abell S1063 is publicly available as is the software used to construct it through the \textsc{Lenstool} package

    A new step forward in realistic cluster lens mass modelling: Analysis of Hubble Frontier Field Cluster Abell S1063 from joint lensing, X-ray and galaxy kinematics data

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    International audienceWe present a new method to simultaneously/self-consistently model the mass distribution of galaxy clusters that combines constraints from strong lensing features, X-ray emission and galaxy kinematics measurements. We are able to successfully decompose clusters into their collisionless and collisional mass components thanks to the X-ray surface brightness, as well as using the dynamics of cluster members to obtain more accurate masses with the fundamental plane of elliptical galaxies. Knowledge from all observables is included through a consistent Bayesian approach in the likelihood or in physically motivated priors. We apply this method to the galaxy cluster Abell S1063 and produce a mass model that we publicly release with this paper. The resulting mass distribution presents a different ellipticities for the intra-cluster gas and the other large-scale mass components; and deviation from elliptical symmetry in the main halo. We assess the ability of our method to recover the masses of the different elements of the cluster using a mock cluster based on a simplified version of our Abell S1063 model. Thanks to the wealth of information provided by the mass model and the X-ray emission, we also found evidence for an on-going merger event with gas sloshing from a smaller infalling structure into the main cluster. In agreement with previous findings, the total mass, gas profile and gas mass fraction are consistent with small deviations from the hydrostatic equilibrium. This new mass model for Abell S1063 is publicly available as is the software used to construct it through the \textsc{Lenstool} package

    The BUFFALO HST Survey

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    The Beyond Ultra-deep Frontier Fields and Legacy Observations (BUFFALO) is a 101 orbit + 101 parallel Cycle 25 Hubble Space Telescope (HST) Treasury program taking data from 2018 to 2020. BUFFALO will expand existing coverage of the Hubble Frontier Fields (HFF) in Wide Field Camera 3/IR F105W, F125W, and F160W and Advanced Camera for Surveys/WFC F606W and F814W around each of the six HFF clusters and flanking fields. This additional area has not been observed by HST but is already covered by deep multiwavelength data sets, including Spitzer and Chandra. As with the original HFF program, BUFFALO is designed to take advantage of gravitational lensing from massive clusters to simultaneously find high-redshift galaxies that would otherwise lie below HST detection limits and model foreground clusters to study the properties of dark matter and galaxy assembly. The expanded area will provide the first opportunity to study both cosmic variance at high redshift and galaxy assembly in the outskirts of the large HFF clusters. Five additional orbits are reserved for transient followup. BUFFALO data including mosaics, value-added catalogs, and cluster mass distribution models will be released via MAST on a regular basis as the observations and analysis are completed for the six individual clusters
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