Searching for dark matter substructure: a deeper wide-area community survey for Roman

We recommend a deeper extension to the High-Latitute Wide Area Survey planned to be conducted by the Nancy Grace Roman Space Telescope (\emph{Roman}). While this deeper-tier survey extension can support a range of astrophysical investigations, it is particularly well suited to characterize the dark matter substructure in galactic halos and reveal the microphysics of dark matter through gravitational lensing. We quantify the expected yield of \emph{Roman} for finding galaxy-galaxy-type gravitational lenses and motivate observational choices to optimize the \emph{Roman} core community surveys for studying dark matter substructure. In the proposed survey, we expect to find, on average, one strong lens with a characterizable substructure per \emph{Roman} tile (0.28 squared degrees), yielding approximately 500 such high-quality lenses. With such a deeper legacy survey, \emph{Roman} will outperform any current and planned telescope within the next decade in its potential to characterize the concentration and abundance of dark matter subhalos in the mass range 10$^7$-10$^{11}$\,M$_{\odot}$.Comment: 5 pages, 3 figures, Roman Core Community Survey (CCS) White Pape

Multiband Probabilistic Cataloging: A Joint Fitting Approach to Point Source Detection and Deblending

Probabilistic cataloging (PCAT) outperforms traditional cataloging methods on single-band optical data in crowded fields. We extend our work to multiple bands, achieving greater sensitivity (~0.4 mag) and greater speed (500×) compared to previous single-band results. We demonstrate the effectiveness of multiband PCAT on mock data, in terms of both recovering accurate posteriors in the catalog space and directly deblending sources. When applied to Sloan Digital Sky Survey (SDSS) observations of M2, taking Hubble Space Telescope data as truth, our joint fit on r- and i-band data goes ~0.4 mag deeper than single-band probabilistic cataloging and has a false discovery rate less than 20% for F606W ≤ 20. Compared to DAOPHOT, the two-band SDSS catalog fit goes nearly 1.5 mag deeper using the same data and maintains a lower false discovery rate down to F606W ~ 20.5. Given recent improvements in computational speed, multiband PCAT shows promise in application to large-scale surveys and is a plausible framework for joint analysis of multi-instrument observational data. https://github.com/RichardFeder/multiband_pcat

PCAT-DE: Reconstructing point-like and diffuse signals in astronomical images using spatial and spectral information

Observational data from astronomical imaging surveys contain information about a variety of source populations and environments, and its complexity will increase substantially as telescopes become more sensitive. Even for existing observations, measuring the correlations between point-like and diffuse emission can be crucial to correctly inferring the properties of any individual component. For this task information is typically lost, either because of conservative data cuts, aggressive filtering or incomplete treatment of contaminated data. We present the code PCAT-DE, an extension of probabilistic cataloging designed to simultaneously model point-like and diffuse signals. This work incorporates both explicit spatial templates and a set of non-parametric Fourier component templates into a forward model of astronomical images, reducing the number of processing steps applied to the observed data. Using synthetic Herschel-SPIRE multiband observations, we demonstrate that point source and diffuse emission can be reliably separated and measured. We present two applications of this model. For the first, we perform point source detection/photometry in the presence of galactic cirrus and demonstrate that cosmic infrared background (CIB) galaxy counts can be recovered in cases of significant contamination. In the second we show that the spatially extended thermal Sunyaev-Zel'dovich (tSZ) effect signal can be reliably measured even when it is subdominant to the point-like emission from individual galaxies.Comment: 23 pages, 13 figures, Accepted for publication in The Astronomical Journa

Hubble Space Telescope transmission spectroscopy for the temperate sub-Neptune TOI-270d: a possible hydrogen-rich atmosphere containing water vapour

TOI-270d is a temperate sub-Neptune discovered by the Transiting Exoplanet Survey Satellite (TESS) around a bright (J=9.1mag) M3V host star. With an approximate radius of 2RE and equilibrium temperature of 350K, TOI-270d is one of the most promising small exoplanets for atmospheric characterisation using transit spectroscopy. Here we present a primary transit observation of TOI-270d made with the Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3) spectrograph across the 1.126-1.644 micron wavelength range, and a 95% credible upper limit of $8.2 \times 10^{-14}$ erg s$^{-1}$ cm$^{-2}$ A$^{-1}$ arcsec$^{-2}$ for the stellar Ly-alpha emission obtained using the Space Telescope Imaging Spectrograph (STIS). The transmission spectrum derived from the TESS and WFC3 data provides evidence for molecular absorption by a hydrogen-rich atmosphere at 4-sigma significance relative to a featureless spectrum. The strongest evidence for any individual absorber is obtained for H2O, which is favoured at 3-sigma significance. When retrieving on the WFC3 data alone and allowing for the possibility of a heterogeneous stellar brightness profile, the detection significance of H2O is reduced to 2.8-sigma. Further observations are therefore required to robustly determine the atmospheric composition of TOI-270d and assess the impact of stellar heterogeneity. If confirmed, our findings would make TOI-270d one of the smallest and coolest exoplanets to date with detected atmospheric spectral features.Comment: Accepted for publication in AAS journals on November 22, 2022 (received July 5, 2022; revised October 30, 2022

Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image Observations

The TESS mission produces a large amount of time series data, only a small fraction of which contain detectable exoplanetary transit signals. Deep learning techniques such as neural networks have proved effective at differentiating promising astrophysical eclipsing candidates from other phenomena such as stellar variability and systematic instrumental effects in an efficient, unbiased and sustainable manner. This paper presents a high quality dataset containing light curves from the Primary Mission and 1st Extended Mission full frame images and periodic signals detected via Box Least Squares (Kov\'acs et al. 2002; Hartman 2012). The dataset was curated using a thorough manual review process then used to train a neural network called Astronet-Triage-v2. On our test set, for transiting/eclipsing events we achieve a 99.6% recall (true positives over all data with positive labels) at a precision of 75.7% (true positives over all predicted positives). Since 90% of our training data is from the Primary Mission, we also test our ability to generalize on held-out 1st Extended Mission data. Here, we find an area under the precision-recall curve of 0.965, a 4% improvement over Astronet-Triage (Yu et al. 2019). On the TESS Object of Interest (TOI) Catalog through April 2022, a shortlist of planets and planet candidates, Astronet-Triage-v2 is able to recover 3577 out of 4140 TOIs, while Astronet-Triage only recovers 3349 targets at an equal level of precision. In other words, upgrading to Astronet-Triage-v2 helps save at least 200 planet candidates from being lost. The new model is currently used for planet candidate triage in the Quick-Look Pipeline (Huang et al. 2020a,b; Kunimoto et al. 2021).Comment: accepted for publication in AJ. code can be found at: https://github.com/mdanatg/Astronet-Triage and data can be found at: https://zenodo.org/record/741157