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I am developing Python software for reducing residual gas analysis mass spectrometry data, specifically focusing on outlier detection via helium-3 spike intensities. We use helium-3 as a reference spike to measure helium-4, however, erroneous spike intensities due to e.g. hydrogen deuteride interference can invalidate the analysis. Before I can identify such outliers, however, I need to correct for the effects of beam suppression.

Beam Suppression

Beam suppression is where a large concentration of one gas, say $^4$He at 4 amu, suppresses the mass spec's ability to measure the full intensity of another gas, like $^3$He at 3 amu. Beam suppression is roughly linear (note the log scale on the $x$-axis):

beam suppression 1

beam suppression 2

beam suppression 3

Without correcting for beam suppression, the scatter in raw 3 amu intensities is high:

high scatter over sequence

In the above figure, purple triangles and some green circles show significant beam suppression with data scatter reaching up to 5 error ranges. Samples with even higher helium-4 concentrations experience further suppression, pushing this scatter even higher. This limits my outlier detection capacity to extreme values only.

Challenge

The optimal outlier detection method should first correct for beam suppression, then identify outliers within a smaller range.

To accomplish this, I need to programmatically identify the 4 amu intensity threshold above which beam suppression occurs.

This is further complicated by the following:

  1. $^3$He spike depletes with each analysis following $y = ae^{bx}$ where $x$ is analysis number and $y$ is intensity. This decay is so small that it is practically linear over a single sequence; in some cases the decay is lost in the scatter completely. Still, this trend makes traditional statistical methods like the mean and standard deviation inappropriate, as the data do not have a normal distribution.
  2. The proportion of analyses requiring correction may vary, with some sequences having more analyses needing correction than not.

How can I programmatically identify the 4 amu intensity threshold where beam suppression begins, and distinguish which analyses need correction?

Here are some example data:

analysis number: [3072, 3073, 3074, 3075, 3076, 3077, 3078, 3079, 3080, 3081, 3082, 3083, 3084, 3085, 3086, 3087, 3088, 3089, 3090, 3091, 3092, 3093, 3094, 3095, 3096, 3097, 3098, 3099, 3100, 3101, 3102, 3103, 3104, 3105, 3106, 3107, 3108, 3109, 3110, 3111, 3112, 3113, 3114, 3115, 3116, 3117, 3118, 3119, 3120, 3121, 3122, 3123, 3124, 3125, 3126, 3127, 3128, 3129, 3130, 3131, 3132, 3133, 3134, 3135, 3136, 3137, 3138, 3139]
3 amu: [7.902247180966696e-10, 7.894322788473899e-10, 7.75192352846054e-10, 7.73938454990039e-10, 7.751150261600025e-10, 7.902661264530252e-10, 7.88887076907539e-10, 7.88710449681167e-10, 7.892751658033371e-10, 7.889499885195002e-10, 7.882350356665988e-10, 7.904308872238497e-10, 7.898302812219745e-10, 7.925021509317708e-10, 7.843025375921496e-10, 7.952036488404707e-10, 7.971472344684745e-10, 7.844056568346896e-10, 7.979437149185154e-10, 7.980422393636198e-10, 7.990720170108935e-10, 7.970410742958998e-10, 7.97018793957161e-10, 7.956195848829496e-10, 8.004119381549109e-10, 7.972656498101729e-10, 7.966864772687667e-10, 7.790114913438407e-10, 7.969479656000517e-10, 7.996934862896009e-10, 7.841333194332546e-10, 7.97034686449044e-10, 7.977992943579927e-10, 7.96762771694868e-10, 7.966966797283409e-10, 7.957027686461644e-10, 7.970077213244511e-10, 7.987547855356413e-10, 7.990928143485798e-10, 7.872294314913151e-10, 7.991282952126249e-10, 7.996996154320075e-10, 7.840188084382355e-10, 7.974592802090021e-10, 7.979831441559331e-10, 7.94313708313506e-10, 7.974202496222153e-10, 7.945769899770333e-10, 7.989189319332251e-10, 7.96391846920186e-10, 7.982638977817534e-10, 7.82989201718891e-10, 7.974257092665038e-10, 7.96637400681832e-10, 7.847531512165508e-10, 7.954547701859193e-10, 7.951978488429227e-10, 7.951657032060133e-10, 7.963988837735334e-10, 7.9666641848498e-10, 7.974257077249156e-10, 7.951288772463126e-10, 7.953641614139227e-10, 7.875991133163015e-10, 7.95915408669814e-10, 7.938317201604924e-10, 7.832400164007236e-10, 7.935808337220685e-10]
4 amu: [3.4531613676106733e-13, 3.3366166766220147e-13, 3.6988393114508855e-13, 5.961258499688195e-10, 5.970954234076243e-10, 3.3135943383048437e-13, 3.174663651862393e-11, 3.429288558679257e-13, 1.498399412104261e-11, 3.5220279314430447e-13, 2.5262369198935363e-11, 3.653210774058152e-13, 1.5874799493237576e-11, 3.7591848853645757e-13, 5.688803077892193e-10, 4.034124020003251e-13, 4.0035327970012827e-13, 6.042332602054686e-10, 1.4183267421644794e-11, 3.831671585218368e-13, 3.6760320465208133e-13, 2.3341252415085237e-12, 3.844970580704981e-13, 1.0571451687178263e-11, 3.8382660954355845e-13, 8.200382190821025e-12, 3.913148573404574e-13, 1.560203809184596e-09, 4.0489182829201056e-13, 3.795900104505717e-13, 6.042755762441724e-10, 1.3988539372116802e-12, 3.846049343112061e-13, 1.3319585168540297e-11, 3.9771840308213876e-13, 4.805999006071973e-12, 3.867074959999706e-13, 3.313577766561125e-12, 3.744314402797234e-13, 6.988886300943138e-10, 4.042693494216982e-13, 3.8217513835498493e-13, 6.043691789681499e-10, 5.944734086176057e-12, 3.7644022305327873e-13, 1.0931464175829169e-11, 3.821912123145932e-13, 5.821603916411063e-12, 3.853558180169586e-13, 6.074935756081214e-12, 3.761774399661647e-13, 1.0734844854098086e-09, 5.4860372258075055e-12, 3.7660101885287676e-13, 6.050900851245702e-10, 8.540715457007659e-12, 3.6749344852280326e-13, 8.384015787133257e-12, 3.7729690113938666e-13, 9.574894653551193e-12, 3.7085506394298535e-13, 3.2667374998333825e-12, 3.7503327170460714e-13, 5.528634421707689e-10, 3.802721615638516e-13, 3.6080139240454957e-13, 6.042719499645437e-10, 3.656337191419462e-13]
analysis number: [3276, 3277, 3278, 3279, 3280, 3281, 3282, 3283, 3284, 3285, 3286, 3287, 3288, 3289, 3290, 3291, 3292, 3293, 3294, 3295, 3296, 3297, 3298, 3299, 3300, 3301, 3302, 3303, 3304, 3305, 3306, 3307, 3308, 3309, 3310, 3311, 3312, 3313, 3314, 3315, 3316, 3317, 3318, 3319, 3320, 3321, 3322, 3323, 3324, 3325, 3326, 3327, 3328, 3329, 3330, 3331, 3332, 3333, 3334, 3335, 3336, 3337, 3338, 3339, 3340, 3341, 3342, 3343, 3344, 3345, 3346, 3347, 3348, 3349, 3350, 3351, 3352, 3353, 3354, 3355, 3356]
3 amu: [2.993232809745656e-10, 2.758432308019711e-10, 2.7500800747963717e-10, 2.7309891076894257e-10, 2.4891889666588953e-10, 2.4017748982710046e-10, 2.4498267634135793e-10, 2.6695249088891075e-10, 2.697818888003866e-10, 2.7146816958418764e-10, 2.7134379274303456e-10, 2.715824008867223e-10, 2.7414563103702527e-10, 2.7582827916439875e-10, 2.747061461560547e-10, 2.7469316491596424e-10, 2.733921376055414e-10, 2.7485445834777047e-10, 2.7271860166605506e-10, 2.7440322167566896e-10, 2.7086236930879e-10, 2.7311954349770675e-10, 2.7200216558810403e-10, 2.743303626686615e-10, 2.651454063525448e-10, 2.7471636992180705e-10, 2.731570097334424e-10, 2.7277664283728946e-10, 2.685016315502734e-10, 2.7261991926158883e-10, 2.724281199854046e-10, 2.738517010536505e-10, 2.7140154617544906e-10, 2.7299974365340646e-10, 2.6096206203779177e-10, 2.7242167114922815e-10, 2.7108413210229964e-10, 2.717861550914157e-10, 2.7202408601173795e-10, 2.707864708041623e-10, 2.4940511841347725e-10, 2.461652614907221e-10, 2.4246508641506813e-10, 2.6521951097088415e-10, 2.672916209288258e-10, 2.6858653313129376e-10, 2.6979554808516567e-10, 2.676215807945852e-10, 2.701442584075416e-10, 2.6875091534724346e-10, 2.702144207500158e-10, 2.6932728761634265e-10, 2.6858993860787203e-10, 2.687085903398567e-10, 2.690223304534166e-10, 2.682607740755479e-10, 2.6735134462693204e-10, 2.686139942817035e-10, 2.684564201291062e-10, 2.691410875207599e-10, 2.629699754477367e-10, 2.6770128502575184e-10, 2.6755871596406563e-10, 2.676093792402415e-10, 2.668953077617681e-10, 2.6896299136178434e-10, 2.664378248537012e-10, 2.673890239282435e-10, 2.662775878835712e-10, 2.6944785152766334e-10, 2.6335368711754856e-10, 2.725970405733179e-10, 2.642439630403252e-10, 2.6778472172982503e-10, 2.696591097866928e-10, 2.713658459608838e-10, 2.4942324490500977e-10, 2.4534836885412227e-10, 2.450282527014461e-10, 2.634997426429196e-10, 2.6707617826498506e-10]
4 amu: [1.1778226488399289e-13, 1.1585969396306585e-13, 1.194103819842898e-13, 1.110306745185171e-13, 3.343506759203781e-10, 3.2415976000194466e-10, 3.282822362672445e-10, 1.1368210514297983e-13, 1.0784323455737277e-13, 1.2252633319139738e-13, 1.0337789671801992e-13, 1.1948118773957652e-13, 1.1530731069669136e-13, 1.087507763002924e-13, 1.0065382119863639e-13, 8.988573899103181e-14, 8.616948381805773e-14, 8.630813426522908e-14, 2.065211529647099e-11, 8.097864890969572e-14, 4.935261064431398e-11, 9.443768042736721e-14, 3.5429267630749507e-11, 9.370858756318766e-14, 1.4708677657728752e-10, 1.064393497146623e-13, 1.691462545564634e-11, 1.086172160780605e-13, 7.11236626150374e-11, 1.2198298032064658e-13, 2.572786927143115e-11, 1.1398954224042433e-13, 5.207922529362331e-11, 1.0626233693275609e-13, 1.5137372340521278e-10, 8.928064881900744e-14, 2.765965410143622e-11, 1.1904457541093595e-13, 1.2458065896252615e-13, 1.267490112546463e-13, 3.3468996648376433e-10, 3.297490557974959e-10, 3.2649394068634107e-10, 1.3033422865392657e-13, 1.3132583312101482e-13, 1.4267438105644652e-13, 1.1546736588819987e-13, 1.292593882627721e-13, 1.0988411045108127e-13, 1.1854053405701426e-13, 1.1156879563126661e-13, 1.1748508333343027e-13, 1.2530082584774972e-13, 1.330727013555199e-13, 1.9563760972579177e-11, 1.3928606813206969e-13, 4.590337788670679e-11, 1.201366376692417e-13, 1.1878442557653435e-11, 1.0688522317697435e-13, 1.0668920783160711e-10, 1.2092274513094857e-13, 4.0233200255329735e-11, 1.2085573595859198e-13, 4.429122526454737e-11, 1.284394024941915e-13, 7.461929082605818e-11, 1.0309329230748312e-13, 5.04149131491202e-11, 1.8194920452638716e-13, 5.668594243578721e-11, 1.2206938883881162e-13, 1.4093116652971717e-10, 1.206067135396686e-13, 1.0978170708248181e-13, 9.419702653975424e-14, 3.3434307587675236e-10, 3.290596125523293e-10, 3.288131176473908e-10, 1.282689820885239e-13, 1.225893324032757e-13]
analysis number: [6859, 6860, 6861, 6862, 6863, 6864, 6865, 6866, 6867, 6868, 6869, 6870, 6871, 6872, 6873, 6874, 6875, 6876, 6877, 6878, 6879, 6880, 6881, 6882, 6883, 6884, 6885, 6886, 6887, 6888, 6889, 6890, 6891, 6892, 6893, 6894, 6895, 6896, 6897, 6898, 6899, 6900, 6901, 6902, 6903, 6904, 6905, 6906, 6907, 6908, 6909, 6910, 6911, 6912, 6913, 6914, 6915, 6916, 6917, 6918, 6919, 6920, 6921, 6922, 6923, 6924, 6925, 6926, 6927, 6928, 6929, 6930, 6931, 6932, 6933, 6934, 6935, 6936, 6937, 6938, 6939, 6940, 6941, 6942, 6943, 6944, 6945, 6946, 6947, 6948, 6949, 6950, 6951, 6952, 6953, 6954, 6955, 6956, 6957, 6958, 6959, 6960, 6961, 6962, 6963, 6964, 6965, 6966, 6967, 6968, 6969, 6970, 6971, 6972, 6973, 6974, 6975, 6976, 6977, 6978, 6979, 6980, 6981, 6982, 6983, 6984, 6985, 6986, 6987, 6988, 6989, 6990, 6991, 6992, 6993, 6994, 6995, 6996, 6997, 6998, 6999, 7000, 7001, 7002, 7003, 7004, 7005, 7006, 7007, 7008, 7009, 7010, 7011, 7012, 7013, 7014, 7015, 7016, 7017, 7018, 7019, 7020, 7021, 7022, 7023, 7024, 7025, 7026, 7027, 7028, 7029, 7030, 7031, 7032, 7033, 7034, 7035, 7036, 7037, 7038, 7039, 7040, 7041, 7042, 7043, 7044, 7045, 7046, 7047, 7048, 7049, 7050]
3 amu: [3.6255764022151506e-10, 3.508510161840758e-10, 3.217814266815697e-10, 3.179376549891513e-10, 3.194778623624382e-10, 3.401444058291269e-10, 3.4223388678594747e-10, 3.440489401063371e-10, 3.441057437695379e-10, 3.425749620155861e-10, 3.435744386811953e-10, 3.4489191568630124e-10, 3.434716905499254e-10, 3.425260524167747e-10, 3.43461984597375e-10, 3.439188052414678e-10, 3.435774248953598e-10, 3.4383101118885907e-10, 3.449123916761429e-10, 3.4290760314430305e-10, 3.4280669764925373e-10, 3.4397652806580837e-10, 3.4461231511951843e-10, 3.437634944102224e-10, 3.4172525670125826e-10, 3.477274668516251e-10, 3.4958634673293644e-10, 3.490095226658467e-10, 3.4883622619984005e-10, 3.4827992864831115e-10, 3.484871000283174e-10, 3.4539380102373657e-10, 3.4949889360766894e-10, 3.461791015909947e-10, 3.473576066805718e-10, 3.4687587165429547e-10, 3.502428914546513e-10, 3.4661039108758977e-10, 3.476744036012117e-10, 3.4673375574695467e-10, 3.484904917692616e-10, 3.430785920617636e-10, 3.4719520295415734e-10, 3.460715206577883e-10, 3.485013280301459e-10, 3.425313275190909e-10, 3.450513766954601e-10, 3.44540303364892e-10, 3.459360571733889e-10, 3.4086939780074047e-10, 3.4551435852961704e-10, 3.4551965165372766e-10, 3.447387429296374e-10, 3.3539611321753234e-10, 3.473991794645757e-10, 3.4727868411658677e-10, 3.471920319378224e-10, 3.452501067141513e-10, 3.4313765813352933e-10, 3.452251201245261e-10, 3.4493446463194697e-10, 3.4717811582825653e-10, 3.4328004844544023e-10, 3.4196982494004094e-10, 3.4412315935490977e-10, 3.4466862719102705e-10, 3.47596219236958e-10, 3.4625275413097686e-10, 3.47132845098501e-10, 3.4547803433554077e-10, 3.4238740885210175e-10, 3.456655555868335e-10, 3.4816307833737724e-10, 3.4774824288539e-10, 3.473801573260447e-10, 3.433386138236303e-10, 3.4226474466968337e-10, 3.4224013535827093e-10, 3.429253350139061e-10, 3.429791155256504e-10, 3.389800568146646e-10, 3.387964758853137e-10, 3.43279299723862e-10, 3.417311237130251e-10, 3.4107453219714175e-10, 3.1616841428928924e-10, 3.1645565619827947e-10, 3.1606963934053716e-10, 3.3703103359216717e-10, 3.3857185026857926e-10, 3.403011739148351e-10, 3.3965642261787963e-10, 3.4224761009864215e-10, 3.403131541693085e-10, 3.407340254648663e-10, 3.416729431072837e-10, 3.4176817148726347e-10, 3.4000314727141564e-10, 3.4114803792419063e-10, 3.402940524124258e-10, 3.402685547806598e-10, 3.4253518896261156e-10, 3.390903832777625e-10, 3.4197493826295586e-10, 3.4278716482642597e-10, 3.3878884973446745e-10, 3.416973242779153e-10, 3.4199735705397183e-10, 3.4528719278713487e-10, 3.455919437079698e-10, 3.44611027806822e-10, 3.459704819427114e-10, 3.4518756948277017e-10, 3.450548418122571e-10, 3.455295753730432e-10, 3.4260222173453664e-10, 3.4419521838354026e-10, 3.453971532179556e-10, 3.453820797948062e-10, 3.459635300719046e-10, 3.4410455731363674e-10, 3.460494013265236e-10, 3.4648258817096355e-10, 3.486318647996109e-10, 3.408929854394461e-10, 3.422273781909444e-10, 3.437119121872867e-10, 3.4624646576825474e-10, 3.261418415733458e-10, 3.439730461327148e-10, 3.4718673165724445e-10, 3.423200582333211e-10, 3.430154826767904e-10, 3.4324072674119334e-10, 3.458457865506733e-10, 3.452066918463927e-10, 3.429010947143837e-10, 3.461007265192867e-10, 3.4424749735859486e-10, 3.448006688012594e-10, 3.458284856407212e-10, 3.4420381107170645e-10, 3.424458220889927e-10, 3.4198377980417427e-10, 3.4329421592589523e-10, 3.430758917157396e-10, 3.422898688710079e-10, 3.449468653388248e-10, 3.434908034990462e-10, 3.444671373451212e-10, 3.4387857291249254e-10, 3.4582875178117944e-10, 3.4418676540293166e-10, 3.432756865571895e-10, 3.4572690964747247e-10, 3.434853534507701e-10, 3.454413458278558e-10, 3.4710999354880977e-10, 3.4265161735350964e-10, 3.396879937980639e-10, 3.366602886636423e-10, 3.391096555459298e-10, 3.401192119060491e-10, 3.3720408846535306e-10, 3.3693816651527765e-10, 3.3590043834572743e-10, 3.3627056281641927e-10, 3.3958849119392304e-10, 3.1615597804003225e-10, 3.1171008739236775e-10, 3.137278712787108e-10, 3.1262213841779304e-10, 3.1150763658181847e-10, 3.1198696830524167e-10, 3.094293018984173e-10, 3.3313476728492545e-10, 3.3634960209093403e-10, 3.1138057893085315e-10, 3.325633407288151e-10, 3.1173323313760973e-10, 3.3312770858031297e-10, 3.1192969441473545e-10, 3.336159228867155e-10, 3.1351583525732356e-10, 3.348838746937062e-10, 3.108374904052273e-10, 3.340030639317837e-10, 3.1104279821773107e-10, 3.3260851296317647e-10, 3.110176857863856e-10, 3.3455048003228917e-10, 3.354825534291982e-10]
4 amu: [3.3101361978669214e-14, 4.146407133070773e-14, 3.8464600151370457e-10, 3.7963540805461484e-10, 3.8222815769902544e-10, 7.316644418396674e-14, 5.18292075607939e-14, 4.4366476617626473e-14, 4.4766944262047194e-14, 5.60781643502124e-14, 3.882287433469317e-14, 3.369855206340818e-14, 4.041942883722857e-14, 5.0717494307246745e-14, 4.917725215040309e-14, 3.4327442463349075e-14, 4.7628251245258705e-14, 3.704984089120418e-14, 3.637035082699088e-14, 4.331504500036978e-14, 3.584480311668591e-14, 4.0188425740746565e-14, 4.521133060708128e-14, 3.967489159883654e-14, 4.7297310164724074e-14, 4.302990037294614e-14, 3.3454929600493866e-14, 5.822706353885703e-14, 4.073999885580924e-14, 5.214789428758753e-14, 5.041742932561961e-14, 4.042398006519017e-14, 4.148910745335829e-14, 5.747826405197776e-14, 4.2689319703184144e-14, 3.2631935556374465e-14, 4.7572963933387944e-14, 2.634205411137258e-11, 2.5257412136927374e-14, 4.3735104441346613e-14, 4.889800583281691e-14, 2.2352831080115307e-11, 4.0536792322871984e-14, 5.0783532591792995e-14, 5.3982005182701933e-14, 6.66156159607468e-11, 5.752026924290862e-14, 4.8363496375356645e-14, 4.4263841568911176e-14, 7.680922498153327e-11, 4.577275293657866e-14, 3.6343975307978394e-14, 4.830266547786961e-14, 1.3217493362975924e-10, 5.691012936221051e-14, 4.624805551981382e-14, 5.191044431642304e-14, 7.35598046588805e-14, 4.032273723346593e-14, 5.475821830681722e-14, 3.572362676067935e-14, 4.53792267029052e-14, 4.811150381617244e-14, 4.18870247568556e-14, 5.269425371163631e-14, 4.749954821050258e-14, 3.659440562889052e-14, 4.0618464298095687e-14, 2.457723337204267e-14, 5.17645245024611e-14, 3.028353821051941e-14, 3.8090818295311995e-14, 4.0785927578973433e-14, 2.8305244090869045e-14, 4.241041689114752e-14, 4.11345055897064e-14, 4.2660570038734026e-14, 4.043589749262442e-14, 3.248775113507995e-14, 3.579286664449709e-14, 4.837611831081787e-14, 3.1654015572088e-14, 3.578682399679274e-14, 2.8659530832379557e-14, 3.7500902793910864e-14, 3.7910551589975395e-10, 3.8001259116141904e-10, 3.7917414959777875e-10, 6.919239229840155e-14, 4.638295045447225e-14, 4.2281261745635005e-14, 4.689993397406153e-14, 4.145679893439644e-14, 5.301270362402919e-14, 5.54318765004603e-14, 2.889297680780083e-14, 2.2165609621229645e-14, 6.552388545157717e-16, 4.1485735572339217e-14, 4.180138910440657e-14, 4.4891456417402014e-14, 4.436565493115589e-14, 6.45665060306906e-14, 4.3465977067901715e-14, 4.6683688055107376e-14, 3.8500651765732604e-14, 3.11936705001375e-14, 3.0385240224120554e-14, 4.320332364320882e-14, 3.2078816198818235e-14, 4.158664276801585e-14, 3.7535506365951966e-14, 4.618522896670219e-14, 3.690459532838156e-14, 5.048828597118978e-14, 2.925271758588507e-14, 2.5413549811533986e-14, 4.9393102062324443e-14, 4.657551007595595e-14, 3.94134839225784e-14, 1.7427666865630518e-11, 2.1353374163224927e-14, 5.610865680587635e-14, 3.264729990705068e-14, 6.069825759162198e-11, 4.67248561610634e-14, 4.15335284559848e-14, 5.1439924765759663e-14, 2.4147614489828565e-10, 4.8014476530530357e-14, 6.285631018121028e-14, 4.1762026240590943e-14, 8.077915690862838e-12, 4.276969119633478e-14, 1.6556310800242732e-14, 4.8000055585905586e-14, 2.8866036952655548e-11, 4.0328744198179854e-14, 4.980894462203276e-14, 4.106879860099725e-14, 5.1096309070337053e-14, 3.020088699257153e-14, 3.2657773289848625e-14, 5.2618974165599034e-14, 4.502629377009243e-14, 2.893613996870109e-14, 3.677504465995457e-14, 3.71930615926864e-14, 2.7404005154562947e-14, 3.9521121796552014e-14, 4.0545310857880814e-14, 2.8390214328469127e-14, 4.124354881093567e-14, 4.9514233184564065e-14, 4.720026077121709e-14, 4.107593530984168e-14, 4.560105491515524e-14, 3.9012756458184725e-14, 4.7409714785179807e-14, 3.727409729275244e-14, 4.78755482287444e-14, 4.5461439431259405e-14, 2.3543509362318603e-14, 3.5079202249607776e-14, 3.9289578578539414e-14, 4.1378692858654526e-14, 2.666006418858889e-14, 5.1138270939919936e-14, 3.825896960099374e-10, 3.786718269713149e-10, 3.808311043196966e-10, 3.7943461684820726e-10, 3.778783725761114e-10, 3.7916225638860694e-10, 3.7465904378822767e-10, 7.361306857492091e-14, 6.550083181745619e-14, 3.7718206905798687e-10, 6.188546465519851e-14, 3.764141728416682e-10, 5.919675518997156e-14, 3.785038369646022e-10, 6.641770717734108e-14, 3.806240038986748e-10, 5.728379344637129e-14, 3.769801139051104e-10, 5.869522147391884e-14, 3.7689230408369244e-10, 5.658865207851127e-14, 3.779555026303843e-10, 5.522906103262253e-14, 5.363501621434117e-14]
$\endgroup$
4
  • $\begingroup$ Please do not create multiple posts asking essentially the same question. Instead use the "edit" button to include new information based on the comments. The input you got from Christian Hennig is not visible to anyone viewing only this question. $\endgroup$ Commented Aug 10 at 18:10
  • $\begingroup$ While the contexts are similar, both are asking two fundamentally different questions: one is asking about clustering, and the other is asking about extreme outliers. Still, given that the previous question is no longer relevant, I've deleted it just to be sure. I hope this is satisfactory. $\endgroup$ Commented Aug 10 at 18:12
  • $\begingroup$ While it's not a direct answer to the question so I won't "answer" this, a satisfying rough estimate of where to draw this line appears to be about 1000x above the 4 amu blank level. Correcting data for the exponential curve above this threshold yields a set of data for which ~3$\varepsilon$ is a reasonable outlier range. $\endgroup$ Commented Aug 11 at 3:47
  • $\begingroup$ What does the literature say? Also IMHO it would be helpful to link to a more thorough description of the problem. $\endgroup$
    – cbeleites
    Commented Aug 11 at 8:40

0

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