PAs: Helen Caines (Yale), Raghav Kunnawalkam Elayavalli (Vanderbilt), Youqi Song (Yale)
GPC members: Sevil Salur (Chair), Saehanseul Oh, Evan Finch (English QA), Monika Robotková (Code QA), Yi Yang (PWG Rep), Youqi Song (PA Rep)

Target journal: PRL

Title: Measurement of CollinearDrop jet mass and its correlation with substructure observables in pp collisions

Abstract: Jets are collimated sprays of final-state particles produced from initial high-momentum-transfer partonic scatterings in particle collisions. Substructure variables aim to reveal details of the parton fragmentation and hadronization processes that create a jet. By removing collinear radiation while maintaining most of the soft radiation components, one can construct CollinearDrop jet observables, which have enhanced sensitivity to the soft phase space within jets. We present the first CollinearDrop jet measurement, corrected for detector effects with a machine learning method, MultiFold, and its correlation with SoftDrop groomed jet observables. We observe that the amount of grooming affects the angular and momentum scales of the first hard splitting of the jet and is related to the formation time of such splitting. These measurements indicate that the non-perturbative effects are strongly correlated with the perturbative fragmentation process.

Conclusions: In this Letter, we have presented the first CollinearDrop groomed observable measurement, the CollinearDrop groomed mass, and its correlations with groomed jet substructure observables, in $pp$ collisions at $\sqrt{s}=200$ GeV with the STAR experiment. A machine learning driven method to correct for detector effects, MultiFold, has been applied for the first time to hadronic collision data, which allows for access of multi-dimensional correlations on a jet-by-jet basis. We demonstrate how MultiFold allows us to present measurements in multiple dimensions and shows promising potential for future multi-differential measurements as the community enters high-statistics, precision QCD era.

Event generator predictions and theoretical calculation were shown to qualitatively describe the data for the CollinearDrop groomed mass, which probes the soft radiation within jets. From the investigation of the correlation between the CollinearDrop groomed mass $a$ and the SoftDrop groomed observables $z_{\mathrm{g}}$ and $R_{\mathrm{g}}$, we observe that on average, a large nonperturbative radiation biases the perturbative splitting to happen late. We also observed a strong correlation between the CollinearDrop groomed mass and $R_{\mathrm{g}}$. In particular, a large $R_{\mathrm{g}}$ biases toward a higher probability that the jet has no radiation prior to the perturbative splitting, and a small $R_{\mathrm{g}}$ biases towards a higher probability that the jet has some radiation prior to the splitting. These measurements demonstrate the interplay between the nonperturbative processes and the perturbative jet fragmentation.

Analysis note: version_1, link to STAR notes 

Paper: version_1, version_2, version_3, version_4, version_5

Public presentations:
WWND 24
CFNS npQCD workshop
QM 23 (poster)
BOOST 23
DIS 23, proceedings
Hot Quarks 22
DNP 22

Analysis related presentations in STAR:
06/16/22
07/21/22
08/18/22
09/14/22
09/28/22: preliminary request
01/05/23
02/09/23: paper proposal
02/28/23: paper reproposal
03/09/23: preliminary request
03/17/23: PWGC review
01/17/24
03/20/24: GPC formation request

GPC meeting presentations:
05/10/24, follow-up on 05/21/24
07/12/24: closure tests

Embedding related presentations:
10/17/23 
12/05/23
12/05/23: QA of 2021 embedding (by Andrew Tamis and I)
 
Request for new embedding: link to Drupal page, link to PDF