STAR HardProbes PWG

[Barbara Trzeciak <barbara.trzeciak AT gmail.com>]

Hi Younqi,

the abstract looks good to me and I sign off.

I just have a question for my understanding, but we can also discuss it at one of the next HP PWG meetings.

You say that a one-dimensional iterative Bayesian method is usually used to unfold jet substructure observables and that it might be useful to unfold in higher dimensions.

This is true, but the Bayesian method can be also used in two dimensions. Does MultiFold help already beyond one dimension, or is the performance in two dimensions expected to be similar to "standard" methods and we should see a real gain above ? 

Thanks,

Barbara

On Wed, Jun 22, 2022 at 5:54 AM Yi Yang via Star-hp-l <star-hp-l AT lists.bnl.gov> wrote:

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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