- Timestamp:
- Dec 14, 2016, 5:04:00 PM (10 years ago)
- File:
-
- 1 edited
-
trunk/doc/release.2015/ps1.detrend/detrend.tex (modified) (30 diffs)
Legend:
- Unmodified
- Added
- Removed
-
trunk/doc/release.2015/ps1.detrend/detrend.tex
r39860 r39862 14 14 %\usepackage{subcaption} 15 15 %\usepackage{natbib} 16 %\bibliographystyle{apj}17 %\bibliographystyle{plain}18 16 19 17 % online version may use color, but print version needs b/w … … 123 121 \keywords{Surveys:\PSONE } 124 122 125 %% http://articles.adsabs.harvard.edu/cgi-bin/nph-iarticle_query?2007ASPC..364..153M&data_type=PDF_HIGH&whole_paper=YES&type=PRINTER&filetype=.pdf126 123 \section{Introduction and Survey Description} 127 124 … … 192 189 Pan-STARRS 1 Science Consortium members. 193 190 194 %% \czwdraft{Nigel: you mention calibrating to the reference catalog without telling us195 %% what this is composed of (maybe this is in a different section, but would be196 %% nice to have some idea here).}197 198 %% \czwdraft{Can we get around this point by simply adding a reference to199 %% the paper describing the reference catalog? It's not really part of200 %% the detrending process, and is discussed here mostly to give an201 %% overview of the stages, and later to find sources of ghosts for202 %% masking.}203 204 191 The Pan-STARRS image processing pipeline (IPP) is described elsewhere 205 192 \citep{magnier2017a}, but a short summary follows. The … … 290 277 % Discuss 2-phase/3-phase device differnces 291 278 292 %\section{General Detrend Discussion}293 %\label{sec:detrending}294 295 279 296 280 \section{GPC1 Detrend Details} 297 281 \label{sec:detrending} 298 299 %% \czwdraft{Nigel: I forgot: when we are talking about the various bias corrections it might be300 %% worth pointing out that we expect these to be more of an issue in the g-band301 %% (and maybe r?) where read noise is a significant contributor.302 %% }303 282 304 283 Ensuring a consistent and uniform detector response across the … … 472 451 \subsection{Non-linearity Correction} 473 452 \label{sec:nonlinearity} 474 % check notebook, 2010-07/08475 453 476 454 The pixels of GPC1 are not uniformly linear at all flux levels. In … … 518 496 rejected. 519 497 520 %% exptime n_included/det_id = 372521 %% clearly this isn't the one used, as 3-12 spans three data points, poorly.x522 %% 0.01 2523 %% 0.14 2524 %% 0.27 2525 %% 0.49 2526 %% 0.72 2527 %% 1.06 2528 %% 1.41 2529 %% 2.02 2530 %% 2.63 2531 %% 3.94 2532 %% 5.25 2533 %% 8.74 2534 %% 13.09 2535 %% 17.4 2536 %% 20.86 2537 %% 24.3 2538 %% 27.78 2539 %% 31.24 2540 %% 34.65 2541 %% 38.12 2542 %% 42.41 2543 %% 46.69 2544 %% 51.89 2545 %% 57.04 2546 547 548 %http://svn.pan-starrs.ifa.hawaii.edu/trac/ipp/wiki/DetectorLinearity_AllEdges549 %http://svn.pan-starrs.ifa.hawaii.edu/trac/ipp/wiki/DetectorLinearityArchive550 551 498 \begin{figure} 552 499 \centering … … 557 504 \subsection{Dark/Bias Subtraction} 558 505 \label{sec:dark} 559 % http://svn.pan-starrs.ifa.hawaii.edu/trac/ipp/wiki/Background_Dark_Model560 506 561 507 The dark model we make for GPC1 considers each pixel individually, … … 796 742 reduces the amplitude of these errors produces a flat field model that 797 743 better represents the true detector response. 798 799 %% \czwdraft{EAM: the flat-field construction part needs to make a clearer discussion of800 %% the skyflat vs the photometric correction (photflat) built initially for801 %% the survey vs the flat-field corrections determined in the database as part802 %% of ubercal (for the latter, you should just mention the concept -- it will803 %% also be mentioned in the calibration paper). The statement that the804 %% flat-field response was stable is not true since we did need 5 'seasons'.}805 744 806 745 In addition to this flat field applied to the individual images, the … … 887 826 size of the cell (598 pixels = 150"). 888 827 889 %% \czwdraft{keep this?} This row-by-row offset is visible in similar890 %% camera designs, and has been removed by identifying the noise signal891 %% in the pixel data stream. By taking the FFT of the pixels and a892 %% reference signal, the frequency of this noise can be isolated and893 %% removed, resulting in a much cleaner image. However, GPC1 does not894 %% record the value of the reference signal, instead automatically895 %% subtracting it from the data values. Without this comparison signal,896 %% we have been unable to reproduce this method, as there is no obvious897 %% FFT component visible.898 899 828 \begin{deluxetable}{lcccc} 900 829 \tablecolumns{3} … … 976 905 this correction on an image profile is shown in Figure \ref{fig:dark switching}. 977 906 978 %% \begin{figure}979 %% \centering980 %% \caption{Continuity example, with background issue.}981 %% \label{fig: continuity example}982 %% \end{figure}983 907 984 908 \subsection{Fringe correction} … … 1026 950 \end{minipage} 1027 951 \caption{Example of the \yps{} filter fringe pattern on exposure o5220g0025o OTA53 (\yps{} filter 30s). The left panel shows the OTA mosaic with all detrending except the fringe correction, while the right shows the same including the fringe correction. Both images have been smoothed with a Gaussian with $\sigma = 3$ pixels to highlight the faint and large scale fringe patterns. 1028 %\czwdraft{See if there's a way to have mana produce images larger than the screen size.}1029 952 } 1030 953 \label{fig: fringe example} … … 1205 1128 \end{deluxetable} 1206 1129 1207 %% \begin{figure}1208 %% \centering1209 %% \caption{Figure of crosstalk ghost and bright star source. Plot of cut across ghost to illustrate the flat-top shape.}1210 %% \end{figure}1211 1130 1212 1131 \subsubsubsection{Optical ghosts} 1213 1132 \label{sec:optical_ghosts} 1214 % http://arxiv.org/pdf/1207.2513v1.pdf1215 1133 1216 1134 Due to imperfections in the anti-reflective coating on the optical … … 1294 1212 \label{sec:glints} 1295 1213 1296 % I finally tracked it down:1297 %% > On 8/26/2010 9:24 AM, John Tonry wrote:1298 %% >1299 %% > Gene,1300 %% >1301 %% > This is a bit of a case of the dog that didn't bark, but the shutter mask1302 %% > went in on Tuesday.1303 %% >1304 %% > Can you can tell us whether1305 %% >1306 %% > a) it's helped the shutter glint problem and1307 %% > b) whether there's any discernable vignetting anywhere?1308 %% >1309 %% > - John1310 1311 %% On Thu, Aug 26, 2010 at 4:00 PM, Chris Waters <watersc1@ifa.hawaii.edu>wrote:1312 1313 %% > I'm not entirely sure why I'm not on the ps-ipp mailing list, but1314 %% > Heather forwarded this to me. I compared 240 exposures from1315 %% > 2010-08-22/ThreePi/y.00000 and 2010-08-25/ThreePi/y.00000.1316 %% >1317 %% > a) For the 22nd, I counted 28 star glints visible. For the 25th, I1318 %% > counted maybe 0-2 (I think the first is a conveniently placed satellite,1319 %% > and the other has a companion, so I think it's actually a moon glint).1320 %% >1321 %% > b) I was going to compare flat field images, but we don't have any1322 %% > from after the mask was applied. Blinking between a few pairs of the1323 %% > 240x2 exposures does not show any vignetting that I can detect from1324 %% > the IPP jpeg mosaics.1325 1326 1214 Prior to 2010-08-24, a reflective surface at the edge of the camera 1327 1215 aperture was incompletely screened to light passing through the … … 1339 1227 by 17 arcminutes, and extend outwards an additional degree. 1340 1228 1341 %%1342 %% GLINT_MAX_MAG F32 -21.01343 %% GLINT.REGION MULTI1344 1345 %% GLINT.REGION METADATA1346 %% REGION STR [-38000:-24000,-20000:+20000]1347 %% GLINT.TYPE STR LEFT1348 %% END1349 1350 %% GLINT.REGION METADATA1351 %% REGION STR [+24000:+38000,-20000:+20000]1352 %% GLINT.TYPE STR RIGHT1353 %% END1354 1355 %% GLINT.REGION METADATA1356 %% REGION STR [-20000:+20000,+24000:+38000:]1357 %% GLINT.TYPE STR TOP1358 %% END1359 1360 %% GLINT.REGION METADATA1361 %% REGION STR [-20000:+20000,-38000:-24000]1362 %% GLINT.TYPE STR BOTTOM1363 %% END1364 1365 1229 \begin{figure} 1366 1230 \centering … … 1380 1244 oriented at $\theta = n * \frac{\pi}{2} - \mathrm{ROTANGLE} + 0.798$, 1381 1245 based on the header keyword. 1382 1383 %\subsubsection{Saturated stars}1384 %\label{sec:saturated_stars}1385 1246 1386 1247 The cores of stars that are saturated are masked as well, with a … … 1442 1303 \end{deluxetable} 1443 1304 1444 1445 1446 1447 %% mysql> select filter,AVG(camProcessedExp.maskfrac_ref_static), AVG(camProcessedExp.maskfrac_ref_dynamic), AVG(camProcessedExp.maskfrac_ref_advisory), AVG(camProcessedExp.maskfrac_max_static),AVG(camProcessedExp.maskfrac_max_dynamic),AVG(camProcessedExp.maskfrac_max_advisory) from camRun join camProcessedExp USING(cam_id) JOIN chipRun USING(chip_id) JOIN rawExp USING(exp_id) WHERE camRun.label = 'LAP.PV3.20140730.final' GROUP BY filter;1448 %% +---------+------------------------------------------+-------------------------------------------+--------------------------------------------+------------------------------------------+-------------------------------------------+--------------------------------------------+1449 %% | filter | AVG(camProcessedExp.maskfrac_ref_static) | AVG(camProcessedExp.maskfrac_ref_dynamic) | AVG(camProcessedExp.maskfrac_ref_advisory) | AVG(camProcessedExp.maskfrac_max_static) | AVG(camProcessedExp.maskfrac_max_dynamic) | AVG(camProcessedExp.maskfrac_max_advisory) |1450 %% +---------+------------------------------------------+-------------------------------------------+--------------------------------------------+------------------------------------------+-------------------------------------------+--------------------------------------------+1451 %% static dynamic advisory1452 %% | g.00000 | 0.19642137972007 | 0.00010322263512709 | 0.0268384454697661453 %% | 0.20949461794863 | 9.89200027293e-05 | 0.026431927734548 |1454 %% | r.00000 | 0.19675996201399 | 0.00025214447869606 | 0.0326410546007881455 %% | 0.20989768279138 | 0.00023994155711801 | 0.032178525485201 |1456 %% | i.00000 | 0.19677587604327 | 0.00057470697316504 | 0.0380962519370721457 %% | 0.21003570722292 | 0.00053987093278142 | 0.037471018638997 |1458 %% | z.00000 | 0.1974290315691 | 0.00024758901226967 | 0.030641237489731459 %% | 0.21055007930696 | 0.00023452690039757 | 0.030144453360769 |1460 %% | y.00000 | 0.19828990634315 | 0.00014523787521897 | 0.0219848464179871461 %% | 0.21130344126869 | 0.00013634812877977 | 0.02163070300815 |1462 1463 1464 1305 \subsection{Background subtraction} 1465 1306 \label{sec:background} 1466 1467 %% \czwdraft{Nigel: 2.10 The background section is rather short, given all the fuss DRAVG made1468 %% about it. What is done to eliminate contamination by bright objects - isn't1469 %% there some sort of clipping? We also have a confusing number of ``bins'' in the1470 %% text (``These bins have 10000 .... a binned cumulative distribution is1471 %% generated. These bins are interpolated ... levels. Repeating this across all1472 %% bins ...''). There is no mention of the fact that this will subtract real1473 %% astrophysics backgrounds if they are on a suitably large scale, or of the fact1474 %% that the subtraction is not perfect (don't I remember that the stacks end up1475 %% with a non-zero background which scales with the number of input warps?).1476 %% }1477 1478 %% \czwdraft{Based on the wiki page on 2014-05-21 the stack background issue should be resolved.}1479 1307 1480 1308 Once all other detrending is done, the pixels from each cell are … … 1588 1416 stage images, but this special processing was not used for the large 1589 1417 scale $3\Pi$ PV3 reduction. 1590 1591 %% * Magic1592 %% * Warping1593 %% * warping kernel1594 %% * linear-by-pieces1595 %% * Covariance1596 %% * def of skycells?1597 %% * Stacking1598 %% * pixel combination rules1599 %% * pixel rejections1600 %% * convolution for matching (success and failure)1601 %% * Difference Image analysis1602 1418 1603 1419 \section{GPC1 Detrend Construction} … … 1835 1651 \end{figure} 1836 1652 1837 1838 % Read all images and astrometry1839 % Check which input images overlap with output image. => 8007 when the inputs don't overlap.1840 % Loop over each image.1841 % Append detections from input into output detection list.1842 % Determine transform back from warp pixels to source image.1843 %% 2nd order polynomial in both x and y for this transformation. and save to header1844 % Break warp image into 128x128pixel locally linear areas1845 % Mask non finite pixels as saturated.1846 % Define Lanczos-3 interpolation over the input image.1847 % Iterate over warp pixel space (on each locally linear grid) and map interpolated input pixel positions onto warp.1848 % Warp pixel space is defined as center based, so that's where the intpolation point comes from.1849 % Covariance calculated based on the interpolation kernel at the center of the ll grid.1850 % image = interp_image * jacobian1851 % var = interp_var * jacobian**21852 % mask = interp_mask1853 % jacobian = abs(mapXx * mapYy - mapYx * mapXy)1854 % I don't understand that jacobian.1855 %1856 1857 1858 1653 \section{Stacking} 1859 1654 \label{sec:stacking} … … 1915 1710 in the transparency values as a result of this \citet{magnier2017c}. 1916 1711 1917 %% \czwdraft{Nigel: 5. ``The ouput exposure time is set to the sum of the input exposure times.''1918 %% True, but we should note that as warps can be rejected later on in the1919 %% stacking process this output time is notional in some sense.1920 %% Calibration - for PV3 what photometric calibration has been used at this stage1921 %% for the input warps? Should we make it clear here that pixels are not subject1922 %% to the final (any?) ubercal?1923 %% }1924 1925 % PREPARE1926 % load sources1927 % load psfs1928 % determine target as envelope of input psfs1929 % FWHM clipping at 101930 % measure seeing1931 % - // M_app = m_inst + zp + c1 * airmass + 2.5log(t) - transparency1932 % // EAM : the discussion here was not quite right (or at least sloppy). Here is a replacement explanation:1933 % // For any star, the observed instrumental magnitude on an image and the apparent magnitude are related by:1934 % // M_app = m_inst + zp + c1 * airmass + 2.5log(t) - transparency1935 % // NOTE the sign of 'transparency' this must agree with the definition in pmSourceMatch.c. see, eg, line 457 where1936 % // transparency = m_inst + zp + c1 * airmass + 2.5log(t) - M_app1937 % // we want to adjust the input images to be in a consistent flux system so that the1938 % // final stack can be generated with a specific target zero point. Any adjustment to1939 % // the flux scale of the image must be made in coordination with the resulting1940 % // zeropoint, exposure time, and airmass such that the above relationship yields the1941 % // same apparent magnitude for a given star:1942 % // m_inst_i : instrumental mags on input image (in)1943 % // m_inst_o : instrumental mags on re-normalized image (out)1944 % // m_inst_o + zp_o + c1 * airmass_o + 2.5log(t_o) - trans_o = m_inst_i + zp_i + c1 * airmass_i + 2.5log(t_i) - trans_i1945 % // m_inst_o = m_inst_i + (zp_i - zp_o) + c1 * (airmass_i - airmass_o) + 2.5log(t_i) - 2.5log(t_o) - trans_i + trans_o1946 % // zp_i, airmass_i, t_i, trans_i : reported or measured for input image1947 % // zp_o = zpTarget (from recipe)1948 % // airmass_o = airmassTarget (from recipe)1949 % // t_o = sumExpTime [sum of input exposure times: once images are scale to this time, they can be avereaged]1950 % // trans_o = 0.0 [obviously!]1951 % // we have 2 cases: (a) all reported ZPs are good or (b) some are bad:1952 % // (a) FPA.ZP = zp_i + c1 * airmass_i1953 % // --> zp[i] = zp_i + c1 * airmass_i + 2.5log(exptime_i)1954 % // (b) zp[i] = c1 * airmass_i + 2.5log(exptime_i)1955 % // NOTE: in case (b), the current code is equating the TARGET zp with the NOMINAL zp, which is wrong.1956 % // m_inst_o - m_inst_i = zp[i] - zpTarget - c1 * airmassTarget - 2.5log(sumExpTime) - trans_i1957 1958 1712 With the flux normalization factors and target PSF chosen, the 1959 1713 convolution kernels can be calculated for each image. ISIS kernels … … 1977 1731 the square of it, scaling all inputs to the common zeropoint. 1978 1732 1979 % MATCH1980 % match to target PSF.1981 % use ISIS kernels to do matching/convolution1982 % Input sources used for psf matching.1983 % @ISIS.WIDTHS F32 1.5 3.0 6.0 # Gaussian kernel FWHM values1984 % @ISIS.ORDERS S32 6 4 2 # Polynomial orders for ISIS kernels1985 1986 1733 Once the convolution kernels are defined for each image, they are used 1987 1734 to convolve the image to match the target PSF. Any input image that … … 1994 1741 warping process). 1995 1742 1996 % CONVOLVE1997 % Normalization to match target zeropoint/exptime1998 % Reject images with bad match chi^2 values. MATCH.REJ * rms + median threshold.1999 % Additional variance from the convolution chi^22000 % Calculate image weights based on variance: W_i = 1 / (ROBUST_MEDIAN(variance_image_i) * CovarianceFactor)2001 % CovarianceFactor = covariance->kernel[0][0]2002 2003 1743 Following the convolution, an initial stack is constructed. For a 2004 1744 given pixel coordinate, the values at that coordinate are extracted … … 2032 1772 The output mask value is taken to be zero (no masked bits), unless 2033 1773 there were no valid inputs, in which case the BLANK mask bit is set. 2034 2035 % INITIAL COMBINE2036 % Calculate weighted mean of input images2037 % mu = sum_i(f_i * W_i) / sum_i(W_i)2038 % sigma = 1 / sum_i(1 / W_i)2039 % nu = sum_i(m_i)2040 % // We're not using the input pixel variances to generate a weighted average for the pixel flux (because2041 % // that introduces systematic biases), so the variance of the output pixel value should simply be:2042 % // simga^2 = sum(weight_i^2 * sigma_i^2) / (sum(weight_i))^22043 % // This reduces, when the weights are all identically unity, to:2044 % // variance_combination = sum(variance_i) / N^22045 % // and if the variances are all equal:2046 % // variance_combination = variance_individual / N2047 % // which makes sense --- the standard deviation of the combination is reduced by a factor of sqrt(N).2048 % sumValueWeight = sum_i(values * weights)2049 % sumWeight = sum_i(weights)2050 % sumVarianceWeight == sum( 1 / variances)2051 % sumExp = sum_i(exptimes)2052 % sumExpWeight = sum_i(exptims * weights)2053 % mean = sumValueWeight / sumWeight2054 % var = 1 / sumVarianceWeight2055 % exp = sumExp2056 % expWeight = sumExpWeight2057 2058 % EXCEPT: if N = 1, accept it. if N = 2, take average.2059 2060 % if (!pmStackCombine(outRO, NULL, stack, maskBad, maskSuspect, maskBlank, kernelSize, iter,2061 % combineRej, combineSys, combineDiscard, useVariance, safe, nminpix, false)) {2062 %bool pmStackCombine(2063 % pmReadout *combined, // output stacked readout2064 % pmReadout *expmaps, // output exposure map information2065 % psArray *input, // input exposures2066 % psImageMaskType badMaskBits, // treat these bits as 'bad'2067 % psImageMaskType suspectMaskBits, // treat these bits as 'suspect'2068 % psImageMaskType blankMaskBits, // use this mask value for pixels missing input data (distinguish between Ninput = 0 and Ngood = 0?)2069 % int kernelSize,2070 % float iter, 0.52071 % float rej, 4.02072 % float sys, 0.12073 % float olympic, 0.22074 % bool useVariance,2075 % bool safe,2076 % int nminpix,2077 % bool rejection)2078 %{2079 2080 % combineExtract2081 %% pixels with mask values as suspect are appended to suspect pixel list.2082 % combinePixels2083 %% As described above.2084 1774 2085 1775 Due to the various non-astronomical ghosts that can occur on GPC1, and … … 2155 1845 number of inputs. 2156 1846 2157 % combineTest2158 %% if (Ninput > 6) { use KMM }2159 %% KMM:2160 %% Calculate KMMmu KMMsigma KMMpi KMMPunimodal2161 %% SumWeights = sum(pixelWeights)2162 %% SysVar = KMMSigma**2 OR (sys * pixelData[i])**22163 %% pixelLimts[i] = rej**2 * (pixelVariances[i] + sysVar)2164 % Iterate 0.5 * Ninput times (at least once)2165 %% Ninput = 1 => accept2166 %% Ninput = 2 => if (0.5 * (A - B))**2 > rej**2 * (pixelVariance[A] + pixelVariance[B] + (sys * A)**2 + (sys * B)**2)2167 %% then if (suspect) mark reject else mark inspect2168 %% Else => if (useKMM and Punimodal < 0.05) median = KMMmean2169 %% => else median = combinationWeightedOlympic{}2170 %% => if (pixelData - median)**2 > pixelLimits[i] then find single worst deviant pixel value2171 %% then => if suspect (mark reject) else (mark reject worst deviant pixel value)2172 2173 2174 %% combinationWeightedOlympic =>2175 %% numBad = frac * Ninput + 0.52176 %% low = numBad / 2, high = low + numGood - numBad2177 %% sort(values) =>2178 %% if (i > low && i <= high) { sumValues = sum_i(values * weights); sumWeight = sum_i(weights)2179 %% return (sumValues / sumWeight)2180 2181 % obtain lists of inspect and reject pixels.2182 2183 % normalize:?2184 % float normalise = powf(10.0, -0.4 * norm->data.F32[i]); // Normalisation2185 % psBinaryOp(ro->image, ro->image, ``*'', psScalarAlloc(normalise, PS_TYPE_F32));2186 % psBinaryOp(ro->variance, ro->variance, ``*'', psScalarAlloc(PS_SQR(normalise), PS_TYPE_F32));2187 2188 1847 With the initial list of rejected pixels generated, a rejection mask 2189 1848 is made for the input warp by constructing an empty image that has the … … 2194 1853 more than 10\% of all pixels from an input image are rejected, then 2195 1854 the entire image is rejected as it likely has some systematic issue. 2196 2197 % PIXEL REJECTION2198 % Construct 15-pixel wide ISIS kernel with 5 pixel FWHM 0-order.2199 % Construct image of pixels to inspect and convolve with kernel (normalize out kernel power)2200 % Determine pixels are bad if they're larger than THRESHOLD.MASK = 0.5.2201 % If more than IMAGE.REJ = 0.1 fraction of pixels are rejected, the entire image is rejected.2202 2203 1855 2204 1856 Finally, a second pass at rejecting pixels is conducted, by growing the … … 2215 1867 a map of the number of inputs per pixel. 2216 1868 2217 % FINAL COMBINE2218 % Grow rejected pixels2219 %% set threshold of (POOR.FRACTION = 0.25) * sum(kernel)**22220 %% Choose the largest square box that contains just under that threshold.2221 %% Convolve that box with the rejected pixels to grow them.2222 % Run combination pass again, but without doing rejection, simply applying the rejection lists already calculated.2223 % ::2224 % if (!ppStackCombineFinal(stack, options->convCovars, options, config, false, true, true, true)) {2225 % iter = 02226 % combineRej = NAN2227 % combineSys = NAN2228 % combineDiscard = NAN2229 % if (!pmStackCombine(outRO, expRO, stack, maskBad, maskSuspect, maskBlank, 0, iter, combineRej,2230 % combineSys, combineDiscard, useVariance, safe, nminpix, rejected)) {2231 2232 1869 These convolved stack products are not retained, as the convolution 2233 1870 reduces the resolution of the final image. Instead, we apply the … … 2238 1875 across the image, as the different PSF widths of the input images 2239 1876 print through in the different regions to which they have contributed. 2240 2241 % UNCONVOLVED IMAGE2242 % if (!ppStackCombineFinal(stack, options->origCovars, options, config, false, true, false, true)) {2243 % no grow2244 2245 % only retain unconvolved products.2246 1877 2247 1878 %% Asinh compression … … 2450 2081 There is some evidence that we have not fully identified all of these 2451 2082 crosstalk rules, based on a study of PV3 images. For example, 2452 extremely bright stars %\czwdraft{exp o5677g0123o has this rule, find a 2453 % magnitude} 2454 may be able to create crosstalk ghosts between the second 2083 extremely bright stars may be able to create crosstalk ghosts between the second 2455 2084 cell column of OTA01 and OTA21, with possibly fainter ghosts appearing 2456 2085 on OTA11. Despite the symmetry observed in the main ghost rules, … … 2483 2112 stacks if fewer pixels need to be rejected. 2484 2113 2485 % \czwdraft{one, I believe}2486 2114 The fringe model used currently is based on only a limited number of 2487 2115 days of data. This means that the model calculated may not be fully … … 2491 2119 others, and so improving this by expanding the number of input 2492 2120 exposures may improve a wider range of dates. 2493 % o5818g0349o is a good example of bad fringe correction.2494 2121 2495 2122 Finally, a large number of issues arise due to the row-to-row bias … … 2505 2132 2506 2133 \section{Conclusion} 2507 2508 %\czwdraft{Not happy with this.}2509 2134 2510 2135 The Pan-STARRS1 PV3 processing has reduced an unprecedented volume of
Note:
See TracChangeset
for help on using the changeset viewer.
