Machine Detector Interface Meeting. February 6, 2004

Transcription

1 Preliminary DCH Extrapolations from January 24 Background Scans Machine Detector Interface Meeting February 6, 24 Presented by Tom Glanzman for the DCH Bkgnd Group (thanks especially to Markus Cristinziani)

2 Contents Data Sources and Selection Fitting Process Fit Results Preliminary Extrapolations Concerns and Future Work Tom Glanzman, SLAC Page 1 6 February 24

3 Data Sources and Selection AmbientDB (DCH current) [1459 measurements] VMON (MaxLinkSize, Occupancy) Not quite yet. XTC (detailed Occupancy) Not yet. Data tagged for rejection Bad runs [1785] Bad time intervals (first 2 min of certain runs) [218] DCH Yellow Alerts (low voltage) [151] DCH current outliers [76] Remaining clean events = (84%) Tom Glanzman, SLAC Page 2 6 February 24

4 Table of Data I Tom Glanzman, SLAC Page 3 6 February 24

5 Tom Glanzman, SLAC Page 4 6 February Run #accepted #tagged L Table of Data I

6 Tom Glanzman, SLAC Page 5 6 February Run #accepted #tagged L Table of Data II

7 ,, spread at scan points due to small, random errors and Tom Glanzman, SLAC Page 6 6 February 24 like 3-step but constant term [A] allowed to float 2-step (average) average over individual scan points (runs), ( fit A to pedestal runs use A, then fit B,C,D,E to single-beam runs use A-E, then fit F to lumi runs ) 3-step (average) Multistep fitting sequence where [ ] =, [ ] = A and [ ] = Functional Format (no LER*HER term as no 2-beam, non-collision data taken) Optimistic scenario with regard to machine operation use only clean data Assumptions Fitting Process I

8 Tom Glanzman, SLAC Page 7 6 February 24 good points Outlier runs removed - highest HER current Lumi and single-beam runs (instabilities suggested by Witold) Consider the four Lumi runs both separately and combined (as cross-check) All analysis performed with root (inclu. Minuit) 1-step - simultaneous fit to all parameters (used only as cross-check) as above, but use individual current/lumi measurements (not averages) construct, minimize with Minuit rms errors calculated from individual runs used on individual measurements preferred method 2-step (individual measurements) Multistep fitting sequence (cont.) Fitting Process II

9 Tom Glanzman, SLAC Page 8 6 February LER current (ma) 5 1 HER current (ma) DCH current (ua) Entries 96 Mean x 1171 Mean y RMS x 47.2 RMS y DCH current (ua) 2 3 Entries 629 Mean x Mean y 122 RMS x RMS y Single beam LER Single beam HER Fit Results Ia

10 Tom Glanzman, SLAC Page 9 6 February Luminosity (1e33) DCH current (ua) Entries 7519 Mean x 3664 Mean y RMS x 175 RMS y All good Lumi points Fit of Luminosity term after single current contributions have been subtracted. Fit Results Ib

11 Fit Results II Table 1: Preferred Fit Results A B C D E F Pedestal runs FIX. FIX. FIX. FIX. FIX Single beam LER FIX. FIX. FIX Single beam HER FIX. FIX FIX Lumi HER 2/3, LER scan FIX 4.68 FIX FIX 47.1 FIX Lumi HER max, LER scan FIX 4.68 FIX FIX 47.1 FIX Lumi LER 2/3, HER scan FIX 4.68 FIX FIX 47.1 FIX Lumi LER max, HER scan FIX 4.68 FIX FIX 47.1 FIX Lumi trickle, HER scan FIX 4.68 FIX FIX 47.1 FIX All good Lumi points FIX 4.68 FIX FIX 47.1 FIX Tom Glanzman, SLAC Page 1 6 February 24

12 Tom Glanzman, SLAC Page 11 6 February Fit Fit Measured 3 Entries 96 Mean x Mean y RMS x RMS y Measured 3 Entries 629 Mean x 12.1 Mean y 122 RMS x RMS y Single beam LER Single beam HER Linear and Quadratic Single Beam Fits Fit Results IIIa

13 Tom Glanzman, SLAC Page 12 6 February Fit Fit Measured 6 Entries 2522 Mean x 31.5 Mean y 31.6 RMS x RMS y Measured 1 Entries 2258 Mean x 47.4 Mean y RMS x 213 RMS y Lumi LER 2/3, HER scan Lumi LER max, HER scan Fit Fit Measured 6 Entries 1734 Mean x 49.9 Mean y RMS x RMS y Measured 8 Entries 1822 Mean x Mean y RMS x 117 RMS y Lumi HER 2/3, LER scan Lumi HER max, LER scan Four sets of Luminosity Runs Fit Results IIIb

14 Tom Glanzman, SLAC Page 13 6 February Fit Fit Measured 1 Entries 177 Mean x Mean y RMS x RMS y Measured 9 Entries 7519 Mean x 41.5 Mean y RMS x RMS y Lumi trickle, HER scan All good Lumi points Fit Results IIIc

15 Tom Glanzman, SLAC Page 14 6 February 24 Year Predicted I DCH (µa) % 63% 59% Lumi HER LER 74% 7% Preliminary 24 Extrapolation Extrapolations

16 = and Tom Glanzman, SLAC Page 15 6 February 24 Consistent Jan 24 (more details on Feb 22, see Markus talk at Dec 23 collaboration mtg, where [, [ ] = A and [ ] = Feb 22 Comparison of January 24 with February 22

17 Tom Glanzman, SLAC Page 16 6 February 24 Year Year Predicted I DCH (µa) % 68% 63% 59% 69% 66% 62% 59% Lumi HER LER Predicted I DCH (µa) Lumi HER LER 72% 74% January 24 (preliminary) February 22 Comparison Jan4 with Feb2

18 Medium Term Issues (not in priority order) Continue study of comparing raw data sets with cleaned up data sets. Results presented today are for optimal machine/detector performance. We must somehow sensibly account for known and significant transient fluctuations. Complete study of which fit parameters are fixed during fitting process. Try to understand the source of, for example, additional constant term in non-pedestal runs. Complete characterization of trickle vs. non-trickle scans Consider introducing additional term(s) into fit to test stability of resultant parameters, e.g. terms that are non-physical or inappropriate according to Witold. Look more carefully into the differences between the four luminosity runs and their fitted parameters. Also, can we (should we) combine all four of these runs into a global fit? If not, why not? January 24 scans should be checked against recent Run 4 data (as appropriate) for consistency. Use XTC data to cross-check occupancy, to look for geographical distribution of background and other studies. Tom Glanzman, SLAC Page 17 6 February 24

19 Long-term issues Can we develop a dictionary of known background producing or background changing accelerator events and assign for each a set of characteristics, such as frequency, duration, dependence on machine parameters (current, lumi, etc.), and their approx effect on background,...? If so, might this be used to better characterize transient background sources now and in the future? Such events would include, for example, MCC Operator actions and (semi-)automatic machine adjustments. Are there (significant) systematic errors in the scan data that would degrade their predictive ability? Tom Glanzman, SLAC Page 18 6 February 24

20 Conclusions First look at Jan 24 background scan data completed Results are reasonable and consistent with Feb 22 scan New extrapolations to 27 are basically unchanged in the optimistic scenario Still more work to do to cross-check understanding Must come up with a way to present fluctuations/outliers/etc. Aiming for an update in 2-weeks at collaboration mtg. Tom Glanzman, SLAC Page 19 6 February 24