Presentation FERMI _HBL presentation.pdf
HBL_answers_agncoordinators.docx
Internal reviewer comments on the April, 2019, ApJ version
Changes made April, 2019, due to suggestions from Vaidehi:
Updated wording as suggested
Dropped TSvar, because that variability analysis was not used in the paper.
Combined and consolidated tables.
Response to the ApJ Review:
We extend our thanks to the referee for a careful reading and helpful suggestions. Responses to the recommendations are given below. Changes in the text appear in bold font.
- 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?
Authors: This new method of identifying HSP blazars was untested. We know that the 4FGL catalog will have many more sources to investigate if this method is useful; therefore we concentrated our spectral analysis on the Very High Confidence sample. We added text to that effect at the beginning of section 4, line 120.
- 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.
Authors: We have reorganized the description of the machine learning method and added information about the basic idea of the method (line 80) and the way the neural network works (line 88).
- 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).
Authors: We added a paragraph comparing our results to the 2WHSP catalog at the end of section 3, line 112. We did not try to compare our results with the D'Abrusco catalog, because their work attempts to identify the general BL Lac population, and not specifically HSPs.
- 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?
Authors: We did the calculation for z=0 and z=0.5, without attempting to do a sampling. These two values encompass most of the known HSP redshifts, providing limits. We added this information to the text at line 165.
- 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.
Authors: We changed to logarithmic scaling for Figure 1.
- Line 136: "did we find" -> "we found"
We prefer to keep the original wording. We feel it reads more smoothly, and it is grammatically correct.
G.Chiaro, M.Meyer, M. Di Mauro, D.Salvetti, G. La Mura, D.J. Thompson
Blazars and in particular the subclass of high synchrotron peaked objects are the main targets for the present generation of Imaging Atmospheric Cherenkov Telescopes (IACTs)and will remain of great importance for very high-energy gamma-ray science in light of the future Cherenkov Telescope Array (CTA).
...
The aim of this study is to search for unclassified blazars that are likely detectable with Cherenkov telescopes within reasonable observation times , using Artificial Neural Network (ANN) algorithm that can realistically observed with IACTs or CTA in 50 or 5 hours.
The 3FGL catalogue contains two classes of source with uncertain classification that offer opportunities to identify HBLs and subsequently TeV candidates according with the TeV catalog census : (i) the 573 BCU and (ii) 1010 UCSs. Recently in https://arxiv.org/abs/1602.00385 the authors applied a number of machine-learning techniques to classify 3FGL UCSs as pulsars or AGN. The authors found 334 pulsars, 559 sources of AGN type (UCS$_agn$) and 117 remained uncertain.In https://arxiv.org/abs/1705.09832 the 559 UCS_agn have been further analyzed with the ANN algorithm and 271 UCS_bll, 185 UCS_fsrq and 103 UCS_bcu have been identifyied . authors found 334 pulsars, 559 sources of AGN type and 117 remained uncertain.
The resulting 573 BCU and 103 UCS_bcu 559 AGN type sources represent the first targets for this search and we apply an optimized version of the ANN described in https://arxiv.org/abs/1709.05727 in order to compute the likelihood distribution of HBL and non-HBL sources
...
We also compare the extrapolated fluxes of the candidates against the sensitivity of present IACTs and the future CTA. We use the Fermi-LAT spectral shape of the sources obtained in the range between 0.1 and 300 GeV using the best-fit model parameters from the LAT 4-year 3FGL Catalogue and particularly we referred to the following relation derived from the spectral model that fits the data.
The study is in progress and paper wil come soon.
ref.contact Graziano Chiaro chiaro@lambrate.iasf graziano.chiaro@inaf.it
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Data
DATA
Blazar 3FGL blazar subclasses HBL and non-HBL distribution against gamma-ray flux
Likelihood distribution of HBL and non-HBL sources in 3FGL blazars by our applied ANN algorithm.
...
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"