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tokens: based on color to focus for example on red galaxies. We explored making cuts based on color like focusing on red galaxies; however, these cuts reduced the galaxy samples too much and did not allow one to obtain sufficient statistics. Hopefully with other incoming larger surveys we can explore those kinds of selections and their effects on the self-calibration. We find here a negative IG intrinsic alignment signal using the self-calibration method in the third and fourth bins. This is in agreement with the negative IA amplitude of found by KIDS450 team in Hildebrandt et al. ( 2017 ) from using the marginalization method and the linear tidal alignment model (Catelan et al. 2001 ; Hirata Seljak 2004 ; Bridle King 2007 ). In brief, this model is physically motivated by the concept that large-scale correlations between intrinsic ellipticities of galaxies are related linearly to perturbations in the primordial gravitational tidal field where these galaxies formed, leading to galaxy linear alignment with large-scale structure. The IG type of IA comes from the fact that a structure of matter aligns radially its close galaxies and shears tangentially background galaxies creating a correlation between these two as shown in Figure 1 (left panel). Our results are also consistent with the further findings of the KiDS+Gamma results of Johnston et al. ( 2019 ) where a negative amplitude also was found using the same model. We do not use any IA model in the self-calibration, but for comparisons we plotted the theory curves from this linear tidal alignment model with the IA amplitude as found in the in KIDS450 paper (Hildebrandt et al. 2017 ). Some overall consistency is good to find, but it is not surprising that some discrepancies would be present—IA modeling is an active area of work and self-calibration offers the opportunity to test such models once the signal is extracted. Although we are limited here in pursuing such a task, we expect in future work to pursue such an endeavor using more and better quality data from ongoing and planned surveys to constrain models using this approach. This will also allow one to compare to models like the Halo model introduced in Piras et al. ( 2018 ), or the higher-order nonlinear model of Blazek et al. ( 2019 ). 4. Conclusion A first detection of intrinsic shape–gravitational shear (IG) and the intrinsic shape–galaxy density (Ig) in a photometric redshift survey using the self-calibration method is reported. The IG cross-correlation between the third and fourth bins of the KiDS-450 data set is measured with a significance of 3.51 σ . The Ig correlation is measured in the third bin with a 3.65 σ significance. The negative IA signal we find here from the self-calibration method in the third and fourth bins, using the BPZ-determined best redshift estimate of Hildebrandt et al. ( 2017 ) is in agreement with the negative sign IA amplitude found there using the marginalization approach and the BPZ method and also is