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ApJ Referee Report

Reviewer's Comments:
The identification of the gamma-ray sources that can be detected at TeV energies with the current and future slate
of Imaging Atmospheric Cherenkov Telescopes (IACTs) is an important task. The development of efficient methods
to select such TeV targets from the catalog of sources observed by the Fermi LAT instrument is a fundamental task
to maximize the scientific output of both the Fermi and IACTs telescopes and, as a consequence, improve our
understanding of the extremely energetic emission from blazars.

The manuscript "Identifying TeV Source Candidates among Fermi-LAT Unclassified Sources" presents a method
to select potential TeV targets observable by IACTS based on the variability and spectral properties of gamma-ray
sources observed by Fermi LAT instrument. A set of candidate high-synchrotron-peaked (HSPs), the spectral class
of blazars most likely to emit at TeV energies, is selected based on their reported variability by applying an already
tested machine learning technique. Among these candidates, sources which can be detected by IACTs under
reasonable assumption on the duration of the exposures are further identified by comparing their spectral energy
distributions extrapolated to TeV energies with the sensitivity curves for current and future IACTs.
The extrapolation of the SEDs of such sources are based on fitted spectral models of the candidate HSPs sources
from Fermi LAT data collected until April 2017, at the TeV energy

The paper represents a valuable contribution to the literature in this field and deserves publication once the questions and
comments below will be addressed. The manuscript is well written and clear. I would like to see the revised manuscript.

Comments

- The authors should explain why they decided to perform the extrapolation of the SED for high-confidence HSPs sources
only, i.e. with L_HSP>=0.89. Based on the estimated purity of their classification based on the validation of their machine
learning method reported at the end of Section 2, a lower L_HSPs>0.8 threshold will still produce a 75% efficiency that
would probably yield a quite large number of HSPs detectable by IACTs under the same assumptions used for the
high-confidences ones. Is this study a proof-of-concept that will be extended to the other (slightly less likely) HSPs
candidates selected with the Chiaro et al. 2016 method in a future work? Or there are more fundamental reasons
why the other sources in Table 1 and 2 were not investigated that I am missing?

- Section 2: the description of the method used to select HSPs sources from 3FGL is terse. The manuscript, as it stands,
is not self-consistent and does not provide the minimal set of information that are needed to assess the viability of
the machine learning method used to select HSPs based on their gamma-ray flaring activity. I suggest that the authors
add to this section a summary of the basics about the method described in Chiaro+2016.

- As the authors mention in the introduction, other methods have been proposed to select candidate TeV blazars that
do not use gamma-ray information, at least directly. It would be interesting to know if their list of high and low-confidence
HSPs candidates can be spatially associated to candidates HSPs from the catalogs produced by Chang et al. 2017
(2WHSP) and D'Abrusco et al. 2019 (2019ApJS..242....4D).

- In order to model the EBL attenuation, a redshift for the gamma-ray source needs to be provideds. The authors should
specify how they sampled the 0 to 0.5 interval used to obtain the two extreme behaviors, or did they just use the z=0
and z=0.5 values to determine the boundaries of the blue shaded areas between the two fitted SEDs?

- Figure 1: since the focus of the papers is on sources located in scarcely populated bins for large L_HSP, I would
suggest to use a logarithmic y scale.

- Line 136: "did we find" -> "we found"