The search for evidence of life or its processes on other worlds takes on two major themes: the detection of biosignatures indicating extinct or extant life, or the determination that an environment either has or once had the potential to harbor living organisms. In situ elemental imaging is useful in either case, since features on the mm to μm scale reveal geological processes which may indicate past or present habitability. Further, biomineralization can leave traces in the morphology and element distribution of surfaces.

The Mapping X-ray Fluorescence Spectrometer (MapX) [Walroth et al., 2019, Sarrazin et al., 2016]  is an in-situ instrument designed to identify these features on planetary surfaces. MapX provides element maps with less than or equal to 100 microns resolution over a 2.5 cm X 2.5 cm area, as well as quantitative XRF spectra from ground- or instrument-selected Regions of Interest (ROI).

Instrument Description

MapX is a full field elemental imager capable of analyzing samples in-situ without sample preparation. The instrument consists of: 

  • X-ray sources: Either radioisotope (e.g., 244Cm) or X-ray tube sources can be used. 
  • Focusing optic: The focusing lens is an X-ray micro-pore optic (MPO) which focuses X-rays 1:1 onto the CCD. The MPO has a large depth of field, allowing rough unprepared surfaces to be imaged with minimal resolution loss. 
  • CCD: The CCD is read out fast enough (several frames per second) so that each pixel records either a single photon or background. The number of electron hole pairs generated is directly proportional to the energy of the photon, and after summing a large number of individual frames, an XRF spectrum is generated for each pixel of the CCD. Each individual frame represents a full image; however multiple frames are necessary to produce quantifiable XRF spectra. Longer collection times will allow for improved signal to noise, but in the event a collection is interrupted the partial data will still yield a complete image.
Schematic diagram of the MapX instrument.

Data Analysis

The images collected by the CCD are binned by energy and combined into an x, y, energy data cube, the size of which will make downlinking of
the raw data infeasible. Obtaining energy band maps at the characteristic energies of different elements from this data cube is trivial. However, these maps do not provide precise elemental composition information as different characteristic lines can overlap and background effects cannot be easily subtracted. With long enough collection times, a full fit of the XRF spectrum may be performed for each pixel, but this requires extensive computational resources as well as unrealistic collection times. Using machine learning, regions of similar composition can be identified based on the
rough element maps mentioned above. These regions are identified by finding clusters in N dimensional space where N is the number of relevant elements.

These clusters are then applied to the 2D image to generate a map of ROI. The XRF spectra for these ROI can be summed to generate high signal to noise spectra for ground processing. Thus, the original data cube is reduced to a set of energy band (“element”) maps and a set of ROI (“mineral maps”) of distinct composition along with companion XRF spectra for quantitative analysis.

  1. Optical image of a petrologic thin section of an ultramafic xenolith (field of view, 18 mm) imaged with MapX-2 prototype in 2b, 2d.
  2. False color image showing Fe in red, Ca in green, and Cr in blue.
  3. Correlation between Fe, Ca, and Cr as a 3D scatter plot with clusters of similar composition color annotated. 
  4. Labels applied to the 2D image showing the spatial relationship of the different ROI. 
  5. Summed spectra from the different ROI with assigned mineralogy (cpx: clinopyroxene, slide: glass slide, opx: orthopyroxene, spnl: spinel, int. reg.: interfacial regions).

Image Deconvolution

The MPO employed by MapX consists of an array of 20 μm square pores coated with Ir. It operates by reflecting X-rays in x and y to refocus them 1:1 onto the CCD. Optimal focusing requires a single reflection in x and y. In the event that a photon only reflects off of one wall, or reflects off of one wall twice, it will only be refocused in one axis. This results in a cross shaped PSF. A customized script based on the AIDA deconvolution package [Hom et al., 2007] was developed to use the PSF to recover resolution. 

Top left: SEM image of MPO. Top right: Point spread function collected at SSRL. Bottom left: Raw image collected from MapX-2 of electron microscopy grids, Ni in green and Ti in blue. Bottom right: image after deconvolution.


Erik F. Y. Hom, Franck Marchis, Timothy K. Lee, Sebastian Haase, David A. Agard, and John W. Sedat (2007) AIDA: an adaptive image deconvolution algorithm with application to multi-frame and three-dimensional data. J. Opt. Soc. Am. A 24, 1580-1600.

Sarrazin et al. (2016) The Map-X µ-XRF Imaging Spectrometer. LPSC XLVII #2883. 

Walroth, R.C., Blake, D., Sarrazin, P. et al. (2019) MapX: An in-situ Mapping X-ray Fluorescence Instrument for Detection of Biosignatures and Habitable
Planetary Environments. 50th LPSC, LPI Contrib. No. 2132.