The Line Emission Mapper (LEM) is a Probe mission concept developed in response to NASA’s Astrophysics Probe Explorer (APEX) Announcement of Opportunity. LEM has a single science instrument composed of a large-area, wide-field X-ray optic and a microcalorimeter X-ray imaging spectrometer in the focal plane. LEM is optimized to observe low-surface-brightness diffuse X-ray emission over a 30′ equivalent diameter field of view with 1.3 and 2.5 eV spectral resolution in the 0.2−2.0 keV band. Our primary scientific objective is to map the thermal, kinetic, and elemental properties of the diffuse gas in the extended X-ray halos of galaxies, the outskirts of galaxy clusters, the filamentary structures between these clusters, the Milky Way star-formation regions, the Galactic halo, and supernova remnants in the Milky Way and Local Group. The combination of a wide-field optic with 18′′ angular resolution end-to-end and a microcalorimeter array with 1.3 eV spectral resolution in a 5′ × 5′ inner array (2.5 eV outside of that) offers unprecedented sensitivity to extended low-surface-brightness X-ray emission. This allows us to study feedback processes, gas dynamics, and metal enrichment over seven orders of magnitude in spatial scales, from parsecs to tens of megaparsecs. LEM will spend approximately 11% of its five-year prime science mission performing an All-Sky Survey, the first all-sky X-ray survey at high spectral resolution. The remainder of the five-year science mission will be divided between directed science (30%) and competed General Observer science (70%). LEM and the NewAthena/XIFU are highly complementary, with LEM’s optimization for soft X-rays, large FOV, 1.3 eV spectral resolution, and large grasp balancing the NewAthena/X-IFU’s broadband sensitivity, large effective area, and unprecedented spectral resolving power at 6 keV. In this presentation, we will provide an overview of the mission architecture, the directed science driving the mission design, and the broad scope these capabilities offer to the entire astrophysics community.
Traditional image segmentation methods employed with X-ray imaging detectors aboard X-ray space telescopes consist of two stages: first, a low energy threshold is applied; groups of activated pixels are then classified according to their shapes and identified as valid X-ray events or rejected as being possibly induced by cosmic rays. This method is fast and removes up to 98% of the cosmic ray-induced background. However, these traditional methods fail to address two important problems: first, they struggle to recover the true energies of, and sometimes fail to detect entirely, low-energy photons (photon energies less than 0.5keV); second, they consider only the shape of the active pixel regions, ignoring the longer-range context within the image frames. This limits their sensitivity to a specific type of cosmic ray signal: ”islands” created by secondary particles produced by cosmic rays hitting the body of the telescope (the shapes of which are often indistinguishable from X-ray photon signals). Together, these limitations hinder investigations of faint, diffuse targets, such as the outskirts of galaxies and galaxy clusters, and of ”low energy” targets such as individual stars, galaxies and high redshift systems. Both limitations can, however, be addressed with machine learning (ML) models. This work is part of our effort to develop fast and efficient background reduction methods for future astronomical X-ray missions using ML methods. We highlight several significant improvements in the classification and semantic segmentation of our background filtering pipeline. Our more realistic training and test data now incorporate the effects of readout noise and charge diffusion. In the presence of charge diffusion, our model is able to obtain an 80% relative improvement in lost signal recovery compared to the traditional background reduction techniques. We identify several directions for further development of the model.
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