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Hot dust in the proximity of AGNs strongly emits in the Near Infraredproducing a red excess that, in type 2 sources, can be modeled to measure itstemperature. In the era of high spatial-resolution multi-wavelength data,mapping the hot dust around Supermassive Black Holes is important for theefforts to achieve a complete picture of the dust role and distribution aroundthese compact objects.  In this work we propose a methodology to detect the hot dust emission in theproximity of Type 2 AGNs and measure its temperature using K-band spectra($\lambda_c$ = 2.2\,$\mu$m).  To achieve this, we have developed NIRDust, a Python package for modelingK-band spectra, estimate the dust temperature and characterize the involveduncertainties. We tested synthetic and real spectra in order to check theperformance and suitability of the physical model over different types of data.  Our tests on synthetic spectra demonstrated that the obtained results areinfluenced by the signal-to-noise ratio (S/N) of the input spectra. However, weaccurately characterized the uncertainties, which remained below $\sim$150 Kfor an average S/N per pixel exceeding 20. Applying NIRDust to NGC 5128(Centaurus A), observed with the Gemini South Telescope, we estimated a dusttemperature of 662 and 667 K from Flamingos-2 spectra and 697 and 607 K fromGNIRS spectra using two different approaches.

NIRDust: Probing Hot Dust Emission Around Type 2 AGN Using K-band Spectra