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star-hp-l - Re: [Star-hp-l] STAR presentation by Youqi Song for DNP 2022 submitted for review

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Subject: STAR HardProbes PWG

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  • From: Yi Yang <yiyang0429 AT gmail.com>
  • To: Nihar Sahoo <nihar AT rcf.rhic.bnl.gov>, STAR HardProbes PWG <star-hp-l AT lists.bnl.gov>
  • Subject: Re: [Star-hp-l] STAR presentation by Youqi Song for DNP 2022 submitted for review
  • Date: Wed, 22 Jun 2022 11:53:15 +0800

Hi Youqi,

Thanks a lot for the updated abstract and the presentation last week.
I only have two minor comments/suggestions for your consideration. 
  - L7: in ultra-relativistic collisions --> in particle collisions (?)   
  - Reference: should list it in the normal way as paper:  
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman, and J. Thaler Phys. Rev. Lett. 124, 182001, 2020

Cheers,
Yi


On Tue, Jun 21, 2022 at 4:37 PM Nihar Sahoo via Star-hp-l <star-hp-l AT lists.bnl.gov> wrote:
Hello Youqi,

Your abstract looks in good shape.
I sign-off.

Regards
Nihar

On 2022-06-21 02:01, Youqi Song wrote:
> Hi Nihar and Sooraj,
>
> Thanks for the comments! I just updated my abstract on the drupal
> page. Regarding the statement on L12, I mean unfolding in higher
> dimensions can give us correlations between multiple observables. We
> don't get this correlation information from one dimensional unfolding.
> Hopefully the way I phrase it now in the abstract makes it clearer.
>
> Best,
> Youqi
>
> On Mon, Jun 20, 2022 at 12:53 PM Sooraj Radhakrishnan
> <skradhakrishnan AT lbl.gov> wrote:
>
>> Hi Youqi,
>> Thanks for preparing the nice abstract.
>>
>> L12: Potentially its more useful ...: Could you rephrase/expand this
>> part? Why is it more useful? Are our existing measurements carrying
>> some bias? Does it help reduce systematic uncertainties? Or is it
>> the observables studied making multidimensional unfolding more
>> useful?
>>
>> thanks
>> Sooraj
>>
>> On Sat, Jun 18, 2022 at 8:02 AM Nihar Sahoo via Star-hp-l
>> <star-hp-l AT lists.bnl.gov> wrote:
>>
>>> Hello Youqi,
>>>
>>> Please find my additional comments below.
>>>
>>> Line#10: "non-perturbative processes like fragmentation and
>>> hadronization." -> "non-perturbative processes, like fragmentation
>>> and
>>> hadronization."
>>> Line#18-19: "…and neutral energy towers from the Barrel
>>> Electromagnetic
>>> Calorimeter." -> "…and neutral particles from the Barrel
>>> Electromagnetic
>>> Calorimeter."
>>> Line20-21: "We will present preliminary studies of jet
>>> substructure
>>> observables from this dataset, unfolded with MultiFold1, a machine
>>>
>>> learning method that simultaneously considers…" -> "We will
>>> present
>>> preliminary studies of jet substructure observables unfolded with
>>> MultiFold1, a machine learning method, that simultaneously
>>> considers…"
>>> ("From this dataset" not required; "a machine learning method" is
>>> a
>>> parenthetical element here, not "unfolded with MultiFold")
>>> Line23: "…jet observables." -> "…jet observables in the STAR
>>> experiment."
>>>
>>> Regards
>>> Nihar
>>>
>>> On 2022-06-18 00:35, Youqi Song wrote:
>>>> Hi Nihar,
>>>>
>>>> Thanks for the comments! I have updated the abstract on the
>>> drupal
>>>> page. I removed the sentense of jet substructure observables
>>> being
>>>> useful for heavy-ions since I noticed that there's a character
>>> limit
>>>> of 1300 and I am slightly over that. I am also not too sure how
>>> to
>>>> include the reference of MultiFold since there doesn't seem to
>>> be a
>>>> place for footnotes on the submission page wordbox.
>>>>
>>>> Best,
>>>> Youqi
>>>>
>>>> On Fri, Jun 17, 2022 at 1:07 PM Nihar Sahoo via Star-hp-l
>>>> <star-hp-l AT lists.bnl.gov> wrote:
>>>>
>>>>> 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
>>>>>>
>>>>>> ---
>>>>>> If you have any problems with the review process, please
>>> contact
>>>>>> webmaster AT www.star.bnl.gov
>>>>>> _______________________________________________
>>>>>> Star-hp-l mailing list
>>>>>> Star-hp-l AT lists.bnl.gov
>>>>>> https://lists.bnl.gov/mailman/listinfo/star-hp-l
>>>>> _______________________________________________
>>>>> Star-hp-l mailing list
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>>
>> --
>>
>> Sooraj Radhakrishnan
>>
>> Research Scientist,
>> Department of Physics
>>
>> Kent State University
>> Kent, OH 44243
>>
>> Physicist Postdoctoral AffiliateNuclear Science Division
>> Lawrence Berkeley National Lab
>> MS70R0319, One Cyclotron Road
>> Berkeley, CA 94720
>> Ph: 510-495-2473 [1]
>>
>> Email: skradhakrishnan AT lbl.gov
>
>
> Links:
> ------
> [1] tel:%28510%29%20495-2473
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