Review comments: Update documentation

cpp/settings.h

@@ -3,6 +3,7 @@
 #ifndef RADLER_DECONVOLUTION_SETTINGS_H_
 #define RADLER_DECONVOLUTION_SETTINGS_H_
 
+#include <optional>
 #include <set>
 #include <string>
 #include <vector>
@@ -86,11 +87,11 @@ struct Settings {
    * @brief Sigma value for automatically setting the cleaning threshold.
    *
    * If set, Radler will calculate the standard deviation of the residual image
-   * before the start of every major deconvolution iteration, and clean up to
-   * this sigma value times the found noise standard deviation. The standard
-   * deviation is calculated using the medium absolute deviation, which is a
-   * robust estimator that is not very sensitive to source structure still
-   * present in the image.
+   * before the start of every major deconvolution iteration, and continue
+   * deconvolving until the peak flux density is below this sigma value times
+   * the noise standard deviation. The standard deviation is calculated using
+   * the medium absolute deviation, which is a robust estimator that is not very
+   * sensitive to source structure still present in the image.
    *
    * If unset, automatic thresholding is not used.
    */
@@ -102,17 +103,12 @@ struct Settings {
    * If set, Radler performs these steps:
    * # Radler starts cleaning towards a threshold of the given sigma value.
    * # Once the sigma level is reached, Radler generates a mask using the
-   * positions and scale of each component (when using multi-scale cleaning).
+   * positions and (when using multi-scale cleaning) scale of each component.
    * # Cleaning then continues until the final threshold value, as set using the
-   * @ref threshold or @ref auto_threshold_sigma values. During this final step,
-   * the generated mask constrains the cleaning.
+   * @ref threshold or @ref auto_threshold_sigma values. During this final
+   * deconvolution stage, the generated mask constrains the cleaning.
    *
    * If unset, automatic masking is not used.
-   *
-   * Using auto masking has two advantages:
-   * - Radler generates and applies mask images in a single run.
-   * - Radler maintains scale-dependent masks, which improves multi-scale
-   * cleaning.
    */
   std::optional<double> auto_mask_sigma = std::nullopt;