Disc detection rates are presently low around M-dwarfs: the Herschel DEBRIS survey detected just 2 debris discs from 89 M-dwarfs. However, detection rates are inherently tied to the sensitivity of the instrument used and the observation wavelength. The question remains whether or not true incidence rates for M-dwarf discs are similar to earlier type stars and it is the low luminosity of the host stars that limits the temperature and luminosity of the discs, thus requiring highly sensitive observations made at far-infrared/sub-millimetre wavelengths to detect. The alternative is that discs are indeed less common around these late type stars, perhaps due to effects that more significantly affect discs around low mass hosts such as stripping from stellar encounters or photoevaporation of the primordial disc. It is also possible that efficient planet formation around low mass stars could use up all the disc material, this ties in to exoplanet observations finding many terrestrial planets in low mass systems, a la Trappist-1. Also, M-dwarf ages span up to 10 billion years, and debris discs are typically found to be brightest when youngest, as their planetesimal belts have been depleted little by collisional evolution, so perhaps simply many M star discs have decayed past our observational sensitivities. Hence the need for the unique capabilities of ALMA. Just a handful of M-dwarf debris discs have been observed by ALMA but they will play an important role in our understanding of M-dwarf discs and the M dwarf planet formation process.

When processing ALMA data, we have a lot of control over how we want to tweak the necessary modelling process when Fourier transforming from the measured 'visibility' data to a sky image. One control we have is how the visibilities are weighted. We can choose to more strongly weight short or long baseline points, which in the produced image will accentuate large scale or small scale structure respectively. Depending on the scales of the structures in your image, certain baselines will be more sensitive to those structures, and we want to give these baselines more weight, but we must also consider the Signal-to-Noise Ratio (SNR) of the image. If we are using so-called 'Briggs' weighting, we can vary our 'Robust' parameter R from between -2 and 2. An R of -2 is called 'Uniform' weighting which is where all baselines are equal, this works well for high SNR data and returns a high resolution, but it does not work well for low SNR data as you can see below. An R close to 0 compromises between resolution and sensitivity. An R of +2 is called 'Natural' weighting which returns a larger point spread function (PSF) but has higher sensitivity. We can see how our PSF, also called 'the beam' and represented by the white oval, changes in the bottom left of the gif below. For this low SNR data, we prefer a Natural like weighting.

We can also introduce a 'Taper' which further down-weights long baselines, reducing sensitivity to small scales, and is equivalent to smoothing out the PSF with a Gaussian. We can see how the PSF gets larger as we increase the Taper. The disc begins to become more apparent, but quickly all detail in the image is washed out.