Day 1790

Wednesday, Thursday, Friday.

2101.01281

Laboratory measurements of instrumental signatures of the LSST camera focal plane

Snyder, et al

Electro-optical testing and characterization of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Camera focal plane, consisting of 205 charge-coupled devices (CCDs) arranged into 21 stand-alone Raft Tower Modules (RTMs) and 4 Corner Raft Tower Modules (CRTMs), is currently being performed at the SLAC National Accelerator Laboratory. Testing of the camera sensors is performed using a set of custom-built optical projectors, designed to illuminate the full focal plane or specific regions of the focal plane with a series of light illumination patterns: the crosstalk projector, the flat illuminator projector, and the spot grid projector. In addition to measurements of crosstalk, linearity and full well, the ability to project realistically-sized sources, using the spot grid projector, makes possible unique measurements of instrumental signatures such as deferred charge distortions, astrometric shifts due to sensor effects, and the brighter-fatter effect, prior to camera first light. Here we present the optical projector designs and usage, the electro-optical measurements and how these results have been used in testing and improving the LSST Camera instrumental signature removal algorithms.

2101.01542

Starlet L1-norm for weak lensing cosmology

Ajani, et al

We present a new summary statistic for weak lensing observables, higher than second order, suitable for extracting non-Gaussian cosmological information and inferring cosmological parameters. We name this statistic the 'starlet ℓ1-norm' as it is computed via the sum of the absolute values of the starlet (wavelet) decomposition coefficients of a weak lensing map. In comparison to the state-of-the-art higher-order statistics -- weak lensing peak counts and minimum counts, or the combination of the two -- the ℓ1-norm provides a fast multi-scale calculation of the full void and peak distribution, avoiding the problem of defining what a peak is and what a void is: The ℓ1-norm carries the information encoded in all pixels of the map, not just the ones in local maxima and minima. We show its potential by applying it to the weak lensing convergence maps provided by the MassiveNus simulations to get constraints on the sum of neutrino masses, the matter density parameter, and the amplitude of the primordial power spectrum. We find that, in an ideal setting without further systematics, the starlet ℓ1-norm remarkably outperforms commonly used summary statistics, such as the power spectrum or the combination of peak and void counts, in terms of constraining power, representing a promising new unified framework to simultaneously account for the information encoded in peak counts and voids. We find that the starlet ℓ1-norm outperforms the power spectrum by 72% on Mν, 60% on Ωm, and 75% on As for the Euclid-like setting considered; it also improves upon the state-of-the-art combination of peaks and voids for a single smoothing scale by 24%on Mν, 50% on Ωm, and 24% on As.

2101.01723

Completeness of the Gaia-verse IV: the astrometry spread function of Gaia DR2

Everall, et al

Gaia DR2 published positions, parallaxes and proper motions for an unprecedented 1,331,909,727 sources, revolutionising the field of Galactic dynamics. We complement this data with the Astrometry Spread Function (ASF), the expected uncertainty in the measured positions, proper motions and parallax for a non-accelerating point source. The ASF is a Gaussian function for which we construct the 5D astrometric covariance matrix as a function of position on the sky and apparent magnitude using the Gaia DR2 scanning law and demonstrate excellent agreement with the observed data. This can be used to answer the question `What astrometric covariance would Gaia have published if my star was a non-accelerating point source?'. 
The ASF will enable characterisation of binary systems, exoplanet orbits, astrometric microlensing events and extended sources which add an excess astrometric noise to the expected astrometry uncertainty. By using the ASF to estimate the unit weight error (UWE) of Gaia DR2 sources, we demonstrate that the ASF indeed provides a direct probe of the excess source noise. 
We use the ASF to estimate the contribution to the selection function of the Gaia astrometric sample from a cut on astrometric_sigma5d_max showing high completeness for G<20 dropping to <1% in underscanned regions of the sky for G=21. 
We have added an ASF module to the Python package SCANNINGLAW (https://github.com/gaiaverse/scanninglaw) through which users can access the ASF.

2101.01725

Predicting the observability of the population III stars with ELT-HARMONI via the Helium 1640A emission line

Grisdale, et al

Population III (Pop. III) stars, as of yet, have not been detected, however as we move into the era of extremely large telescopes this is likely to change. One likely tracer for Pop. III stars is the HeIIλ1640 emission line, which will be detectable by the HARMONI spectrograph on the European Extremely Large Telescope (ELT) over a broad range of redshifts (2≤z≤14). By post-processing galaxies from the cosmological, AMR-hydrodynamical simulation NewHorizon with theoretical spectral energy distributions (SED) for Pop. III stars and radiative transfer (i.e. the Yggdrasil Models and CLOUDY look-up tables respectively) we are able to compute the flux of HeIIλ1640 for individual galaxies. From mock 10 hour observations of these galaxies we show that HARMONI will be able to detect Pop. III stars in galaxies up to z∼10 provided Pop. III stars have a top heavy Initial Mass Function (IMF). Furthermore, we find that should Pop. III stars instead have an IMF similar to those of the Pop. I stars, the HeIIλ1640 line would only be observable for galaxies with Pop. III stellar masses in excess of 107M⊙yr−1, average stellar age <1Myr at z=4. Finally, we are able to determine the minimal intrinsic flux required for HARMONI to detect Pop. III stars in a galaxy up to z=10.

2101.02623

Automatic identification of outliers in Hubble Space Telescope galaxy images

Shamir

Rare extragalactic objects can carry substantial information about the past, present, and future universe. Given the size of astronomical databases in the information era it can be assumed that very many outlier galaxies are included in existing and future astronomical databases. However, manual search for these objects is impractical due to the required labor, and therefore the ability to detect such objects largely depends on computer algorithms. This paper describes an unsupervised machine learning algorithm for automatic detection of outlier galaxy images, and its application to several Hubble Space Telescope fields. The algorithm does not require training, and therefore is not dependent on the preparation of clean training sets. The application of the algorithm to a large collection of galaxies detected a variety of outlier galaxy images. The algorithm is not perfect in the sense that not all objects detected by the algorithm are indeed considered outliers, but it reduces the dataset by two orders of magnitude to allow practical manual identification. The catalogue contains 147 objects that would be very difficult to identify without using automation.