# Presentation: 207017

## Response Regarding Detector Optimization

- Kevin McFarlandUniversity of RochesterMINERvA Director’s Review10-11 January 2005

## Basic Detector Geometry

- 

- Active segmented scint. detector 5.87 tons
- ~1 ton of US nuclear target planes (C, Fe, Pb)

- DS Cals, Nucl. Targets just add absorber to scintillator planes
- Magnetized OD (HCAL) frames

- Response regarding Detector Optimization

**Notes:**
Cartoon of the detector: walk through from inside
emphasize: active and upstream targets, magnetized OD

## Vital Statistics of MINERvA

| Number of Channels | 30992 |
| --- | --- |
| Channels in ID+CALS | 25088 |
| Channels in OD | 5904 |
| Volume of Scintillator (m3) | 22.5 |
| WLS Fiber (km) | 90.7 |
| Clear Fiber (km) | 41.6 |
| Number of M-64 PMTs | 503 |
| Mass of ID (metric tons) | 10.8 |
| Mass of OD in ID region (metric tons) | 98.0 |
| Mass of CALS, Nuclear Targets (metric tons) | 27.2 |
| Mass of OD in CAL region (metric tons) | 62.9 |
| Total MINERvA Mass (metric tons) | 199 |
| Plastic Region Mass (metric tons) | 5.87 |
| Data Rate (bits/spill) | 7.9E+6 |

- Response regarding Detector Optimization

**Notes:**
channels: dominated by ID and nuclear targets (~20% from calorimeters, ~20% from OD)
mass dominated by OD

## Cost Summaries

- Response regarding Detector Optimization

## 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

- Response regarding Detector Optimization

## 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.

- Response regarding Detector Optimization

## 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

- Response regarding Detector Optimization

**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

- Response regarding Detector Optimization

## 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

- Response regarding Detector Optimization

**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

- Response regarding Detector Optimization

## Example Events

- Quasi-elastic n–p
- proton and muon tracks are clearly resolved
- observed energy deposit is shown as size of hit; can clearly see larger proton dE/dx
- precise determination of vertex and measurement of Q2 from tracking

- 

- p

- 

- Response regarding Detector Optimization

## Example Events (cont’d)

- 0 Production
- two photons clearly resolved (tracked).  can find vertex.
- some photons shower in ID,some in side ECAL (Pb absorber) region
- photon energy resolution is ~6%/sqrt(E) (average)

- 

- 

- 

- Response regarding Detector Optimization

**Notes:**
note ability to track photons... resolve vertex and look for activity (find it here)