Engineering topics
Publications and source records attributed to Neilsen, Eric.
Poster discussing the use of reinforcement learning for ground telescope survey design.
Optimizing observational astronomy campaigns is becoming a complex and expensive task for next-generation telescopes, where manual planning of observations may tend to reach suboptimal results in terms of optimization.Reinforcement Learning (RL) has been well-demonstrated as a valuable approach for training autonomous systems, and it may provide the basis for self-driving telescopes capable of scanning the sky and optimizing the scheduling for astronomy campaigns.We have developed a framework for the optimization of telescope scheduling using RL techniques, based on a dataset containing data on a discrete set of sky locations that the telescope should visit, and a reward metric. We compared several RL algorithms applied to an offline simulation dataset based at the Stone Edge Observatory, considering a discrete set of sky locations to visit and using “t-effective” as a reward metric, a measure of the quality of the data.Deep Q-Networks (DQNs), belonging to the class of value-based methods, have shown remarkable success in the optimization of astronomical observations in our dataset. In the full environment, the average reward value in each state was found to be 92%±5% of the maximum possible reward, while on the test set it resulted in 87%±9% of the maximum possible reward.
On 2019 August 14 at 21:10:39 UTC, the LIGO/Virgo Collaboration (LVC) detected a possible neutron star–black hole merger (NSBH), the first ever identified. An extensive search for an optical counterpart of this event, designated GW190814, was undertaken using the Dark Energy Camera on the 4 m Victor M. Blanco Telescope at the Cerro Tololo Inter-American Observatory. Target of Opportunity interrupts were issued on eight separate nights to observe 11 candidates using the 4.1 m Southern Astrophysical Research (SOAR) telescope’s Goodman High Throughput Spectrograph in order to assess whether any of these transients was likely to be an optical counterpart of the possible NSBH merger. Here, we describe the process of observing with SOAR, the analysis of our spectra, our spectroscopic typing methodology, and our resultant conclusion that none of the candidates corresponded to the gravitational wave merger event but were all instead other transients. Finally, we describe the lessons learned from this effort. Application of these lessons will be critical for a successful community spectroscopic follow-up program for LVC observing run 4 (O4) and beyond.
The Dark Energy Survey is a “stage III” dark energy experiment, performing an optical imaging survey to measure cos-mological equation of state parameters using four independent methods. The scope and complexity of the survey, originallyscheduled for 525 nights of observing spread over five years and combining a 5000 degree wide survey with a 10 field weeklytime-domain survey, introduced complex strategic and tactical scheduling problems that needed to be addressed. We beginwith an overview of the process used to develop DES strategy and tactics, from the inception of the project, to task forces thatstudied and developed strategy changes over the course of the survey, to the nightly pre-observing meeting in which imme-diate tactical issues were addressed. We then summarize the strategic choices made for each sub-survey, including metrics,scheduling considerations, choice of time domain fields and their sequences of exposures, and wide survey footprint andpointing layout choices. We go on to describe the detailed process that determined which specific exposures were taken atwhich specific times. We give a chronology of the strategic and tactical peculiarities of each year of observing, including theproposal and execution of a sixth year. We give an overview ofobstac, the implementation of the DES scheduler used to sim-ulate and evaluate strategic and tactical options, and automate exposure scheduling; and describe developments inobstacfor use after DES. Appendices describe further details of data quality evaluation,τ, andteff; airmass calculation; and model-ing of the seeing and sky brightness. The significant corpus of DES data indicates that the simple scaling relations for seeingas a function of wavelength and airmass derived from the Kolmogorov turbulence model work adequately for exposureplanning purposes: deviations from these relations are modest in comparison with short time-scale seeing variations
The Dark Energy Spectroscopic Instrument (DESI) is a new instrument currently under construction for the Mayall 4-m telescope at Kitt Peak National Observatory. It will consist of a wide-field optical corrector with a 3.2 degree diameter field of view, a focal plane with 5,000 robotically controlled fiber positioners and 10 fiber-fed broad-band spectrographs. The DESI Instrument Control System (ICS) coordinates fiber positioner operations, interfaces to the Mayall telescope control system, monitors operating conditions, reads out the 30 spectrograph CCDs and provides observer support and data quality monitoring. In this paper, we summarize the ICS design, review the current status of the project and present results from a multi-stage test plan that was developed to ensure the system is fully operational by the time the instrument arrives at the observatory in 2019.
Simsee is a program for generating artificial seeing data for simulations of astronomical surveys. It includes a jupyter notebook for deriving model parameters from Dual Image Motion Monitor (DIMM) data, and a program that reads these model parameters and creates a data file suitable for use by survey strategy simulation code (which is outside the scope of this package). It is specifically designed to provide input data for LSST’s opsim4 survey simulator, but may be adapted for use with different simulators as well. The code is verified to work on a Linux workstation with an Intel Xeon E5-6980 CPU and 256GB or memory, but should work fine on systems with far more limited resources.