# Presentation: 207021

## MINERvA Detector “Optimization”

- Kevin McFarlandUniversity of Rochester26 February 2005MINERvA Collaboration meeting

## Optimization?

- We were beaten up at our Temple review for not being able to show sufficient evidence of physics vs. $ optimization
- my opinion: probably more than we deserved
- but it is still a valid point
- we want such information to understand what happens if we get on 80% of our proposed funds, for example, or to see if we can build more slowly than we propose and do physics during construction

## In which direction?

- Dave C. pointed out that “optimization” also means we should think of add-ons
- if we have project funds left, what could we do at the end to improve the experiment?  DS HCAL magnetization, for example
- But the sense in which the committee wanted us to study was clearly de-scoping, not up-scoping...

## Tools

- We need to do some more Monte Carlo simulation studies with different geometries
- We need better tools for evaluating the impact of changes on costs
- Here is what was presented to the Temple review

## What Does it Take to Reduce Costs in Channels and Modules?

- Number of channels...
- roughly, I guestimate the marginal channel cost to be $110
- largest items: ~$20 for electronics, ~$35 for PMT and testing, $30 for PMT box, ~$15 for fiber
- so saving $1M takes approximately 9K channels (of 31K)
- Note... there are granularity issues that make this tough to realize
- More gains by reducing number of modules
- win on channels, module and plane production
- there are 49 modules in current design, 30 fully active, 9 nuclear targets, 10 downstream calorimeters, each with roughly 2% of the channels
- I guestimate marginal cost of a module to be 0.11M$, about $0.7M of which is in the channel count; so that means 9 modules away to save $1M
- Please take this with very large grains of salt; it is based on quick fudges to a costing model we had when optimizing

## Constraints on Extrusions

- MINOS experience: 1x4cm extrusions are near limit of surface are to volume ratio for successful extrusion
- so the largest granularity we might consider is 5x2.5cm triangles, just on technical grounds?  we certainly never considered anything beyond this
- for comparison, 3cm^2 MINERvA cross-section is same as SciBar at K2K
- Then, we looked at our key design parameters
- z resolution: width and thickness both contribute
- efficiency for quasi-elastics: primarily thickness of extrusions
- in the end, we took a hit on the latter in exchange for preserving the former
- Final fiducial volume: want roughly a cube
- based on acceptance studies, particles go at wide angles
- 3 ton fiducial volume target.

## Optimization of Tracking in Active Target

- technique pioneered by D0upgrade pre-shower detector

- Excellent tracking resolution w/ triangular extrusion
- ~3 mm in transverse direction from light sharing
- More effective than rectangles (resolution/segmentation)
- Key resolution parameters:
- transverse segmentation and light yield
- longitudinal segmentation for z vertex determination

**Notes:**
Intro: optimizing detector: granularity and the other is OD size
light sharing gets us good resolution
optimization: pick width well matched with multiple-scattering limitations, light yield limitations (photostatistics)
segmentation in z set by need for efficiency with short tracks 

## CCQE Vertex Efficiency vs. Absorber

- CCQE

- Here what is being studied is the resolution (and efficiency) of reconstructing CCQE vertex
- Green is 1.0cm z samplingBlue is 1.5cm z samplingRead is 2.0cm z sampling(we opted for 1.7cm)
- Obviously, this one hurts...
- Big effect is inability to pick up short proton tracks
- For nuclear targets, require resolution < 1cm

## Size of the Outer Detector

- Outer Detector (OD) steel is a cost-driver (and mass-driver) for the experiment
- optimized for: containment of hadronic energy in DIS eventsand tracking of sideways going muons (B-field)
- “step” in detector is result of shaving upstream end thickness

**Notes:**
outer detector: need to contain most particles in events, or else lose high multiplicity events (DIS) with energy leakage
but OD width is a cost driver
two compromises: step granularity (5cm->10cm); and step in z since most US part is not populated (backwards particles soft)

## Size of OD (cont’d)

- How chosen?
- Hadronic energy resolution of approximately 20-30%/sqrt(E) for DIS
- This is the average leakage
- RMS fluctuations are five times larger than average leakage due to tail (could not dig up this plot; sorry)
- So goal was to keep leakage contribution to this to <10%
- Rationale was that this resolution effect would be hard to understand
- Ergo... 50 cm
- later thinned upstream part to 30cm

- Energy Containment in Nuclear Target Region vs. Outer Detector Thickness

- thickest part of OD

## What’s Next?

- I compiled a list of ID granularity and OD size optimization studies to pursue
- input from Steve B. and Dave C.
- these are “de-scoping” studies

## OD optimization

- Start with an OD like ours but with 30cm extra material everywhere.
- For a hypothetical OD with 10cm, 20cm, 30cm extra and 10cm, 20cm less, plot:
- muons escaping for QE Q2>3 GeV^2 in ID and in nuclear targets.  Do this only for "trackable" muons (6 or 9 planes of scint.)
- fraction of DIS energy contained (high x and low x?, ID and nuclear targets separated?)
- RMS fractional DIS energy leaked (high x and low x?  ID and nuclear targets separated?)
- I think we more or less have first one in hand.  We've done things similar to the others; it's not hard.

## z segmentation of ID

- Dave C. showed this well at the Aug '03 JLab meeting, but we could repeat the study?
- He looked at efficiency for QE (low Q^2) reconstruction vs. z spacing.
- Could use 1.7cm, 2.2 cm, 2.7cm, 1.2cm, 0.7cm...
- Want to show both efficiency and z vertex resolution.

## Strip width optimization

- This one is much more tricky.
- I think something like 2.8, 3.3, 3.8, 4.3, 4.8 is a good range of cells to try.  So...
- sigma z_vertex for two track QE events
- efficiency for two track QE events (are tracks separated with such big cells)
- for coherent CC events, Hugh's t variable which plays a big role in his cuts
- for DIS events, how close to the vertex can the track be tracked?  I'm not sure we have a way to translate that into an effective z vertex resolution?  maybe look at events with multiple tracks?
- I'd like to figure out how it affects photon ID (since I think track width is likely to be a key parameter) but we don't really have the tools

## We also should catalog

- Effects of different numbers of modules (or module width) on fiducial events for different flag ship analyses
- QEs, single pi0 resonant, coherent CC/NC, DIS