sPHENIX MAPS tracker discussion

["Huang, Jin" <jhuang AT bnl.gov>]

Hi Ming

 

Thanks for your support,

 

> What is the Jet-TG goal?

 

I think a sample of high momentum (~40 GeV) jet would be produced [1]. Looking forward to the updated discussion in the coming GM.

 

Cheers

 

Jin

 

 

 

[1] https://indico.bnl.gov/event/9997/

 

 

______________________________

 

Jin HUANG

 

Physicist, Ph.D.

Brookhaven National Laboratory

Physics Department, Bldg 510 C

Upton, NY 11973-5000

 

Office: 631-344-5898

Cell:   757-604-9946

______________________________

 

From: Ming Liu <ming AT bnl.gov>
Sent: Tuesday, November 10, 2020 6:08 PM
To: Huang, Jin <jhuang AT bnl.gov>; sphenix-hf-jets-l <sphenix-hf-jets-l AT lists.bnl.gov>
Cc: Camelia Mironov <camelia.mironov AT cern.ch>; sphenix-mvtx-l <sphenix-maps-l AT lists.bnl.gov>
Subject: Re: [Sphenix-hf-jets-l] DMC data sample and task discussion

 

Hi Jin, Hideki and all,

 

The proposal sounds very good to me.

For the b-jet sample on the wish list, ~10M events with pT >~15GeV would be nice and this could be part of the Jet-TG sample also.

What is the Jet-TG goal?

 

Cheers,

Ming

-- 

Ming Xiong Liu

P-25, MS H846                     TEL: 505-667-7125 

Physics Division                            631-344-7821(BNL)

LANL                                               630-840-5708(FNAL)

Los Alamos, NM 87545      FAX:  505-665-7020

 

 

From: sPHENIX-HF-jets-l <sphenix-hf-jets-l-bounces AT lists.bnl.gov> on behalf of Jin Huang <jhuang AT bnl.gov>
Date: Tuesday, November 10, 2020 at 12:06 PM
To: sPHENIX-HF-jets-l <sphenix-hf-jets-l AT lists.bnl.gov>
Cc: camelia mironov <camelia.mironov AT cern.ch>
Subject: [Sphenix-hf-jets-l] DMC data sample and task discussion

 

Hi Everyone

 

Following our last TG meeting [ https://indico.bnl.gov/event/9980/ ], we are writing to discuss the data sample and task for the coming MDC.

 

As Camelia pointed out in our meeting, our first MDC will focus on day-1 measurements and are aimed to complete in 3 months under limited manpower. In addition, we would prefer the MDC tasks to be along the same direction with the on-going development.

 

Meanwhile, thanks to Cameron, we have the KFParticle integrated into the analysis chain for HF resonances (as below and more follow up at https://chat.sdcc.bnl.gov/sphenix/channels/kfparticle ). We encourage more collaborates to test it on your interested resonances states, and we do require further simulation data sample.

 

Therefore, we would like to propose in this MDC to generate a data set of concentrated HF signal events, and analyze them with the KFParticle package. Although this does not represent the background for HF analysis, it would give us opportunity to run the whole the reco+analysis chain, and to explore a broad spectrum of HF signals. And this setup is also likely what we would be run as online analysis during the data taking as part of the data QA too, which would be good to exercise now:  

 

• MDC data samples:

• Pythia8 pp -> c c_bar channel w/o final state cuts

• 0.2/pb or 50M events
  -> 1M D0->PiK prior to acceptance efficiency + all other charm states
• 1.8 MB/event
  -> 90 TB disk for DST
• 0.03 hr/event
  -> 62.5k CPU * day

Pythia8 pp -> b b_bar channel w/o final state cuts:

• 30/pb or 50M events
  -> 100k B-> D0->PiK prior to acceptance efficiency + all other bottom final states
  -> 4k exclusive B+ -> pi K pi prior to acceptance efficiency
• 1.8 MB/event
  -> 90 TB disk for DST
• 0.03 hr/event
  -> 62.5k CPU * day

If resource allows: 1M embedding sample of enhanced charm embedding (10x Pythia8 c cbar events) to Hijing 0-10% central AuAu collisions

• Just to exercise same analysis in a signal enhanced HI environment

Data format:

• Standard DST including reco tracks and calorimeter towers, and high level truth association maps

Analysis:

• separate KFParticle analysis runs for D0, D_s, D*, L_c etc.
• extract spectrum (signal concentrated), acceptance, efficiency

 

We would also like to propose a wish list data set, which are not committed for completing in three months, but would be useful sample for us in the coming year:

• b-jet sample for update b-jet tagging algorithms: 10M events in pp and embedded
• MB hijing AuAu sample for fake jet study: 10 M events

 

We would like to encourage your comments on this plan either in this email chain or at https://chat.sdcc.bnl.gov/sphenix/channels/kfparticle . We also welcome analyzes volunteering for looking into a channel of your interest in Jan 2021, when the simulation data sample is projected to be delivered to us.

 

Cheers,

 

Jin & Hideki

 

 

 

______________________________

 

Jin HUANG

 

Physicist, Ph.D.

Brookhaven National Laboratory

Physics Department, Bldg 510 C

Upton, NY 11973-5000

 

Office: 631-344-5898

Cell:   757-604-9946

______________________________

 

From: sPHENIX-HF-jets-l <sphenix-hf-jets-l-bounces AT lists.bnl.gov> On Behalf Of Dean, Cameron
Sent: Tuesday, November 3, 2020 12:45 PM
To: sphenix-hf-jets-l <sphenix-hf-jets-l AT lists.bnl.gov>
Cc: Camelia Mironov <camelia.mironov AT cern.ch>
Subject: [Sphenix-hf-jets-l] Signal separation: Vertex chi2/nDoF vs vertex volume

 

Hi all,

I followed up on Camelia’s suggestion of comparing the vertex chi^2/nDoF with the volume of the decay vertex to see what variable gives the best discrimination between signal and background.

I’ve attached a few plots of these variables and the mass for some simulated D0 decays. It looks like the vertex chi2/nDof gives slightly better discrimination than the volume but it is possible to use the volume to reduce the background as well. I’ll keep the vertex chi^2/nDoF requirement in the selection and still write the vertex volume out to the nTuple as an offline cut for analysts (the vertex chi^2/nDoF can be constructed offline by taking the ratio of the two branches in the nTuple).

Cheers,
Cameron

For a little more information on what the “vertex volume” is:

 

We can use the covariance matrix of the decay vertex to map out an ellipsoid in covariance space. The diagonals of the covariance matrix are the squared uncertainties on the vertex which creates an ellipsoid while the off diagonals, the covariances, effectively rotate this in its space. The volume of an ellipsoid is (4*pi/3)*abc where a, b and c are the 3 radii of the ellipsoid. If you have a diagonalized matrix then this is the same as the determinant of the matrix so Vol. = (4*pi/3)*sqrt(det|Diag|), the square root is because the uncertainties are squared in covariance space. The determinant of a matrix is the same as its diagonalized matrix so the volume of the decay vertex is just Vol. = (4*pi/3)*sqrt(det|Covariance|).

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