STAR HardProbes PWG

[Nihar Sahoo <nihar AT rcf.rhic.bnl.gov>]

Hello Youqi,

My comment and suggestion on your nice abstract can be found below.


Title: "UNFOLDING JET SUBSTRUCTURE OBSERVABLES WITH  MACHINE LEARNING 
METHOD IN STAR √s = 200 GEV pp COLLISIONS"
-> "UNFOLDING JET SUBSTRUCTURE OBSERVABLES WITH  MACHINE LEARNING METHOD 
AT √s = 200 GEV IN pp COLLISIONS IN STAR"

Line#6_ "  …from a common initial parton …" -> "…from an initial parton…"
Line#8-9: "jet substructure measurements can provide insights into 
perturbative evolution of the parton, and  the ensuing non-perturbative 
processes." -> "jet substructure measurements in vacuum can provide 
insights into the parton evolution and  the ensuing non-perturbative 
processes like the fragmentation and hadronization."
Line#18:                MultiFold  -> provide reference of PRL paper.
Line#23: "We will compare the “MultiFolded” spectra of the observables 
with the previously published STAR data "  I think  better not to 
promise too much at this moment. Otherwise You have good abstract; so my 
suggestion would be just to focus on MultiFold Method and procedures 
only on data. You can add all these comparison later stage if you will 
have.

Besides, you could mention a couple of sentences in the first part of 
your abstract on "ML part of unfolding" like why it is useful and 
important to adopt, etc? For example before line#12 or 17.


Regards,
Nihar

On 2022-06-09 02:28, webmaster--- via Star-hp-l wrote:
> Dear Star-hp-l AT lists.bnl.gov members,
>
> Youqi Song (youqi.song AT yale.edu) has submitted a material for a review,
> please have a look:
> https://drupal.star.bnl.gov/STAR/node/59908
>
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