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Register a new userRecommended Target Fields for Commissioning the Vera C. Rubin Observatory
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The commissioning team for the Vera C. Rubin observatory is planning a set of engineering and science verification observations with the Legacy Survey of Space and Time (LSST) commissioning camera and then the Rubin Observatory LSST Camera. The time frame for these observations is not yet fixed, and the commissioning team will have flexibility in selecting fields to observe. In this document, the Dark Energy Science Collaboration (DESC) Commissioning Working Group presents a prioritized list of target fields appropriate for testing various aspects of DESC-relevant science performance, grouped by season for visibility from Rubin Observatory at Cerro Pachon. Our recommended fields include Deep-Drilling fields (DDFs) to full LSST depth for photo-$z$ and shape calibration purposes, HST imaging fields to full depth for deblending studies, and an $sim$200 square degree area to 1-year depth in several filters for higher-level validation of wide-area science cases for DESC. We also anticipate that commissioning observations will be needed for template building for transient science over a broad RA range. We include detailed descriptions of our recommended fields along with associated references. We are optimistic that this document will continue to be useful during LSST operations, as it provides a comprehensive list of overlapping data-sets and the references describing them.
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Astronomy has entered the multi-messenger data era and Machine Learning has found widespread use in a large variety of applications. The exploitation of synoptic (multi-band and multi-epoch) surveys, like LSST (Legacy Survey of Space and Time), requires an extensive use of automatic methods for data processing and interpretation. With data volumes in the petabyte domain, the discrimination of time-critical information has already exceeded the capabilities of human operators and crowds of scientists have extreme difficulty to manage such amounts of data in multi-dimensional domains. This work is focused on an analysis of critical aspects related to the approach, based on Machine Learning, to variable sky sources classification, with special care to the various types of Supernovae, one of the most important subjects of Time Domain Astronomy, due to their crucial role in Cosmology. The work is based on a test campaign performed on simulated data. The classification was carried out by comparing the performances among several Machine Learning algorithms on statistical parameters extracted from the light curves. The results make in evidence some critical aspects related to the data quality and their parameter space characterization, propaedeutic to the preparation of processing machinery for the real data exploitation in the incoming decade.
Strong lensing by galaxy clusters can be used to significantly expand the survey reach, thus allowing observation of magnified high-redshift supernovae that otherwise would remain undetected. Strong lensing can also provide multiple images of the galaxies that lie behind the clusters. Detection of strongly lensed Type Ia supernovae (SNe Ia) is especially useful because of their standardizable brightness, as they can be used to improve either cluster lensing models or independent measurements of cosmological parameters. The cosmological parameter, the Hubble constant, is of particular interest given the discrepancy regarding its value from measurements with different approaches. Here, we explore the feasibility of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) of detecting strongly lensed SNe in the field of five galaxy clusters (Abell 1689 and Hubble Frontier Fields clusters) that have well-studied lensing models. Considering the 88 systems composed of 268 individual multiple images in the five cluster fields, we find that the LSST will be sensitive to SNe~Ia (SNe~IIP) exploding in 41 (23) galaxy images. The range of redshift of these galaxies is between $1.01 < z < 3.05$. During its 10 years of operation, LSST is expected to detect $0.2pm0.1$ SN~Ia and $0.9pm0.3$ core collapse SNe. However, as LSST will observe many more massive galaxy clusters, it is likely that the expectations are higher. We stress the importance of having an additional observing program for photometric and spectroscopic follow-up of the strongly lensed SNe detected by LSST.
Survey telescopes such as the Vera C. Rubin Observatory will increase the number of observed supernovae (SNe) by an order of magnitude, discovering millions of events; however, it is impossible to spectroscopically confirm the class for all the SNe discovered. Thus, photometric classification is crucial but its accuracy depends on the not-yet-finalized observing strategy of Rubin Observatorys Legacy Survey of Space and Time (LSST). We quantitatively analyze the impact of the LSST observing strategy on SNe classification using the simulated multi-band light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC). First, we augment the simulated training set to be representative of the photometric redshift distribution per supernovae class, the cadence of observations, and the flux uncertainty distribution of the test set. Then we build a classifier using the photometric transient classification library snmachine, based on wavelet features obtained from Gaussian process fits, yielding similar performance to the winning PLAsTiCC entry. We study the classification performance for SNe with different properties within a single simulated observing strategy. We find that season length is an important factor, with light curves of 150 days yielding the highest classification performance. Cadence is also crucial for SNe classification; events with median inter-night gap of <3.5 days yield higher performance. Interestingly, we find that large gaps (>10 days) in light curve observations does not impact classification performance as long as sufficient observations are available on either side, due to the effectiveness of the Gaussian process interpolation. This analysis is the first exploration of the impact of observing strategy on photometric supernova classification with LSST.
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Vera C. Rubin Observatory LSST Solar System Science Collaboration
, R.n Lynne Jones
, Michelle T. Bannister
2020
Vera C. Rubin Observatory will be a key facility for small body science in planetary astronomy over the next decade. It will carry out the Legacy Survey of Space and Time (LSST), observing the sky repeatedly in u, g, r, i, z, and y over the course of ten years using a 6.5 m effective diameter telescope with a 9.6 square degree field of view, reaching approximately r = 24.5 mag (5-{sigma} depth) per visit. The resulting dataset will provide extraordinary opportunities for both discovery and characterization of large numbers (10--100 times more than currently known) of small solar system bodies, furthering studies of planetary formation and evolution. This white paper summarizes some of the expected science from the ten years of LSST, and emphasizes that the planetary astronomy community should remain invested in the path of Rubin Observatory once the LSST is complete.
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will observe several Deep Drilling Fields (DDFs) to a greater depth and with a more rapid cadence than the main survey. In this paper, we describe the ``DeepDrill survey, which used the Spitzer Space Telescope Infrared Array Camera (IRAC) to observe three of the four currently defined DDFs in two bands, centered on 3.6 $mu$m and 4.5 $mu$m. These observations expand the area which was covered by an earlier set of observations in these three fields by the Spitzer Extragalactic Representative Volume Survey (SERVS). The combined DeepDrill and SERVS data cover the footprints of the LSST DDFs in the Extended Chandra Deep Field-South field (ECDFS), the ELAIS-S1 field (ES1), and the XMM Large-Scale Structure Survey field (XMM-LSS). The observations reach an approximate $5sigma$ point-source depth of 2 $mu$Jy (corresponding to an AB magnitude of 23.1; sufficient to detect a 10$^{11} M_{odot}$ galaxy out to $zapprox 5$) in each of the two bands over a total area of $approx 29,$deg$^2$. The dual-band catalogues contain a total of 2.35 million sources. In this paper we describe the observations and data products from the survey, and an overview of the properties of galaxies in the survey. We compare the source counts to predictions from the SHARK semi-analytic model of galaxy formation. We also identify a population of sources with extremely red ([3.6]$-$[4.5] $>1.2$) colours which we show mostly consists of highly-obscured active galactic nuclei.
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