1. Since Rich wanted some relatively quick info on what detector might be needed to help MuTr pattern recognition, I did a scan on a central HIJING file I had to look at some hit occupancies. As an intro just let me say that if we don't solve the issues at station 1 with our current detector, then I believe we would want: 1) a detector which covers the entire active volume of station 1 (partial coverage would leave us with same ambiguities that are now causing us problems) 2) Redundancy (at least two layers of readout I would think), performance at all radii, and a resolution which is better than inter-track distances which we see in the central collisions for the same reasons as (1). Also note that I know that HIJING underestimates the occupancy that we see in real events. However, when looking at HIJING compared to real data in the past it seemed to me that the increased occupancy came from an overall increase in hit rate over the entire detector rather than primarily at the inner radii, for instance, which would definitely imply higher segmentation needed. So it's possible that a HIJING simulation will tell us the segmentation we want fairly well, but we probably want to add some safety factor. So, I scanned the HIJING file, which just had 100 events, and looked at the inter-track distances of tracks at station 1 for these _central_ events. The plot is at: https://www.phenix.bnl.gov/WWW/p/draft/brooks/forwardPR/ 3% of pairs have inter-track distances of <5 cm and 10% of pairs are <10 cm apart. So, it seems plausible that a detector that has a resolution somewhere between 1 and 5 cm would still work fine, 10 cm is probably too large segmentation. 1 cm should cover everything. I also made a few plots versus occupancy within this centrality bin, versus radii in the chamber, etc. and can post anything like that if you are interested. I'll think about what other quick studies I can do to tell us more about station 1 help and trying to determine if we might want help at station 3 or not. I have another plot which may (?) be more relevant but looks a little less promising. I made a plot of the minimum inter-track distance per event in station 1, assuming that you always want to be able to tag every track that went through the station and got the plot: https://www.phenix.bnl.gov/WWW/p/draft/brooks/forwardPR/min_delr_st1.gif It might imply the smaller 1 cm segmentation really is needed to help the pattern recognition the majority of the time.

2. central – delta distance

3. I have another plot which may (?) be more relevant but looks a little less promising. I made a plot of the minimum inter-track distance per event in station 1, assuming that you always want to be able to tag every track that went through the station and got the plot: https://www.phenix.bnl.gov/WWW/p/draft/brooks/forward PR/min_delr_st1.gifhttps://www.phenix.bnl.gov/WWW/p/draft/brooks/forward PR/min_delr_st1.gif It might imply the smaller 1 cm segmentation really is needed to help the pattern recognition the majority of the time.

4. min inter-track disktance per event - central

5. RKS - conclusion 1 cm will certainly work since for the ~ 1% of times when there is a double hit, we can resolve it using techniques (stereo resolution) that are used now Work needs to be done to determine the exact segmentation. I would like to be safe though and design for 1 cm