HL-LHC Ian Lim September 15, 2017 About Me Stanford University, - - PowerPoint PPT Presentation

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Jul 29, 2023 459 likes •648 views

Vertex Reconstruction at the HL-LHC Ian Lim September 15, 2017 About Me Stanford University, B.S. in Physics with Honors (2017) Interests in cats, baking, and high-energy physics SULI intern, autumn 2017 (August 21-December 8)

SLIDE 1

Vertex Reconstruction at the HL-LHC

Ian Lim September 15, 2017

SLIDE 2

About Me

Stanford University, B.S. in Physics with Honors (2017)
Interests in cats, baking, and high-energy physics
SULI intern, autumn 2017 (August 21-December 8)
Working with Maurice, Ben, and Simone on vertex

reconstruction for the HL-LHC upgrade

SLIDE 3

How well do existing vertexing algorithms perform at large mu?

And how can they be improved?

SLIDE 4

Plan of Attack

1. Intro to vertexing
2. The problem, in more detail
3. My studies so far
4. Future work

SLIDE 5

What is Vertexing?

Primary vertices are locations of proton-proton collisions in the detector
Two main parts– position reconstruction and track association
How well can we determine where a collision happened in space?
Given the tracks left in our detector by collision products, how well can we

associate them to the correct vertex?

SLIDE 6

Definitions: Events, Hard Scatter, and Pile-Up

Today, protons are collided in bunches with a period of 25 ns.
Each bunch crossing (event) results in about 20-40 actual collisions (μ).
One special “hard scatter” vertex and many others (pile-up)
We can also categorize events by how well we reconstruct vertices.

Image credit to Ariel Schwartzman, SLAC. Hard scatter tracks in red and pile-up tracks in blue.

SLIDE 7

Based on https://arxiv.org/pdf/1611.10235.pdf

SLIDE 8

Central problems of vertexing

At the HL-LHC, we expect μ~200– a tenfold increase!
With increased vertex density, performing a clean reconstruction

becomes significantly harder.

Hard scatter is obscured by 10x more pile-up
More tracks to assign
Greater likelihood of merging
But the detector will be upgraded as well (e.g. pseudorapidity |η|<4,
vs. |η|<2.5 now)

SLIDE 9

Current approach (my work)

First, benchmark existing algorithms on simulated μ=200 data
Vertex reconstruction efficiency
Event classification and distribution
Next, determine new/alternate metrics for measuring performance
Bias introduced by misidentified tracks
Finally, implement and test new vertexing procedures.

SLIDE 10

Truth and reco vertices/event

SLIDE 11

Reconstruction efficiency + distribution

SLIDE 12

Event classification vs. μ

SLIDE 13

Vertex density and event classification

SLIDE 14

Hard scatter z-resolution

SLIDE 15

Spread of hard scatter contributions

SLIDE 16

Future work

Continuing to develop interesting metrics for vertexing
Working on improved vertexing methods
Use of high η tracks from the forward region?
Track clustering methods (cf. Meloni 2017, arXiv:1705.00022v1)
Vertex candidate substructure (cf. Schwartzman)

SLIDE 17