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Changed log to log10 and fixed the equations for Radio Calibrate #57

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Changed log to log10 and fixed the equations for Radio Calibrate #57

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5c36969
Changed log to log10
AnshMuthiyan Apr 8, 2025
3b0e5e9
Fixed radio calibrate equations
AnshMuthiyan Apr 18, 2025
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64 changes: 43 additions & 21 deletions src/app/tools/radioCalibrate/radioCalibrate.util.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -75,7 +75,7 @@ export class RadioCalibrateData {

// Compute the output and modify the fields of the output object
private compute(): RadioCalibrateOutput {
if (this.input.startFreq >= this.input.endFreq || this.input.date.getUTCFullYear() < 2000 || this.input.startFreq < 0 || this.input.endFreq < 0) {
if (this.input.startFreq >= this.input.endFreq || this.input.date.getUTCFullYear() < 2000 || this.input.startFreq < 0 || this.input.endFreq < 0) {
return {
flux: null,
fluxError: null,
Expand Down Expand Up @@ -105,21 +105,42 @@ export class RadioCalibrateData {
let t = this.input.date.getUTCFullYear() + this.input.date.getUTCMonth() / 12 + this.input.date.getUTCDate()/30;

if (this.input.source == RadioCalibrateSources.CAS_A) {
t_ref = 2006.9
if (1970 > this.input.date.getUTCFullYear()) {
logF_0 = 3.3731
varianceLogF_0 = 0.0037 ** 2
mnu_0 = -0.003884
variencemnu_0 = 0.000092 ** 2
t_ref = 1963.9
}
else if (1992 > this.input.date.getUTCFullYear() && this.input.date.getUTCFullYear() > 1970) {
logF_0 = 3.3173
varianceLogF_0 = 0.0047 ** 2
mnu_0 = -0.00227
variencemnu_0 = 0.00024 ** 2
t_ref = 1983.8
}
else{
logF_0 = 3.2530
varianceLogF_0 = 0.0051 ** 2
mnu_0 = -0.0035
variencemnu_0 = 0.00023 ** 2
t_ref = 2006.9
}

t_0 = 2005.64
logF_0 = 3.2530

a_1 = -0.732
nu_ref = 1477
a_2 = -0.0094
a_3 = 0.0053
mnu_0 = -0.00350

mdeltlog = 0.00124
nu_0 = 1315
varianceLogF_0 = 0.0051 ** 2

variancea_1 = 0.011 ** 2
variencea_2 = 0.0058 ** 2
variencea_3 = 0.0058 ** 2
variencemnu_0 = 0.00022 ** 2

variencemdeltlog = 0.00018 ** 2
} else if (this.input.source == RadioCalibrateSources.CYG_A) {
t_ref = 0
Expand All @@ -131,7 +152,7 @@ export class RadioCalibrateData {
a_3 = 0.0170
mnu_0 = 0
mdeltlog = 0
nu_0 = 1 //should be null, just need to avoid dividing by zero -- Math.log(nu / nu_0) will never be zero because nu is never negative or zero
nu_0 = 1 //should be null, just need to avoid dividing by zero -- Math.log10(nu / nu_0) will never be zero because nu is never negative or zero
varianceLogF_0 = 0.0046 ** 2
variancea_1 = 0.011 ** 2
variencea_2 = 0.0075 ** 2
Expand All @@ -148,7 +169,7 @@ export class RadioCalibrateData {
a_3 = -0.0275
mnu_0 = -0.00044
mdeltlog = 0
nu_0 = 1 //should be null, just need to avoid dividing by zero -- Math.log(nu / nu_0) will never be zero because nu is never negative or zero
nu_0 = 1 //should be null, just need to avoid dividing by zero -- Math.log10(nu / nu_0) will never be zero because nu is never negative or zero
varianceLogF_0 = 0.0044 ** 2
variancea_1 = 0.014 ** 2
variencea_2 = 0.0081 ** 2
Expand All @@ -165,7 +186,7 @@ export class RadioCalibrateData {
a_3 = -0.073
mnu_0 = 0
mdeltlog = 0
nu_0 = 1 //should be null, just need to avoid dividing by zero -- Math.log(nu / nu_0) will never be zero because nu is never negative or zero
nu_0 = 1 //should be null, just need to avoid dividing by zero -- Math.log10(nu / nu_0) will never be zero because nu is never negative or zero
varianceLogF_0 = 0.0045 ** 2
variancea_1 = 0.017 ** 2
variencea_2 = 0.0031 ** 2
Expand All @@ -176,26 +197,27 @@ export class RadioCalibrateData {

// use the trapezoidal rule to aproximate eq 14 -- stepsize 0.001
let deltax = (this.input.endFreq - this.input.startFreq) / 100000

for (let nu = this.input.startFreq; nu < this.input.endFreq + deltax; nu = nu + deltax) {
if (nu == this.input.startFreq || nu == this.input.endFreq) {
let equation14 = logF_0 + a_1 * Math.log(nu / nu_ref) + a_2 * (Math.log(nu / nu_ref)) ** 2 + a_3 * (Math.log(nu / nu_ref)) ** 3
+ (mnu_0 * (t - t_ref) + mdeltlog * (t - t_0) * Math.log(nu / nu_0))
let equation14 = logF_0 + a_1 * Math.log10(nu / nu_ref) + a_2 * (Math.log10(nu / nu_ref)) ** 2 + a_3 * (Math.log10(nu / nu_ref)) ** 3
+ (mnu_0 * (t - t_ref) + mdeltlog * (t - t_0) * Math.log10(nu / nu_0))
// Keeping the temporal component in the equation, since they will drop anyway for all unecessary sources
let equation15 = Math.sqrt(varianceLogF_0 + variancea_1 * (Math.log(nu / nu_ref)) ** 2 + variencea_2 * (Math.log(nu / nu_ref)) ** 4
+ variencea_3 * (Math.log(nu / nu_ref)) ** 6 + variencemnu_0 * (t - t_ref) ** 2 + variencemdeltlog * (Math.log(nu / nu_0)) ** 2 * (t - t_0) ** 2)
let effectiveFreq = nu * 10 ** (logF_0 + a_1 * Math.log(nu / nu_ref) + a_2 * (Math.log(nu / nu_ref)) ** 2 + a_3 * (Math.log(nu / nu_ref)) ** 3
+ (mnu_0 * (t - t_ref) + mdeltlog * (t - t_0) * Math.log(nu / nu_0)))
let equation15 = Math.sqrt(varianceLogF_0 + variancea_1 * (Math.log10(nu / nu_ref)) ** 2 + variencea_2 * (Math.log10(nu / nu_ref)) ** 4
+ variencea_3 * (Math.log10(nu / nu_ref)) ** 6 + variencemnu_0 * (t - t_ref) ** 2 + variencemdeltlog * (Math.log10(nu / nu_0)) ** 2 * (t - t_0) ** 2)
let effectiveFreq = nu * 10 ** (logF_0 + a_1 * Math.log10(nu / nu_ref) + a_2 * (Math.log10(nu / nu_ref)) ** 2 + a_3 * (Math.log10(nu / nu_ref)) ** 3
+ (mnu_0 * (t - t_ref) + mdeltlog * (t - t_0) * Math.log10(nu / nu_0)))
fluxSum += 10 ** (equation14)
sigmaFluxSum += 10 ** (equation14 + equation15)
effectiveFreqSum += effectiveFreq
}
if (this.input.startFreq < nu && nu < this.input.endFreq) {
let equation14 = logF_0 + a_1 * Math.log(nu / nu_ref) + a_2 * (Math.log(nu / nu_ref)) ** 2 + a_3 * (Math.log(nu / nu_ref)) ** 3
+ (mnu_0 * (t - t_ref) + mdeltlog * (t - t_0) * Math.log(nu / nu_0))
let equation15 = Math.sqrt(varianceLogF_0 + variancea_1 * (Math.log(nu / nu_ref)) ** 2 + variencea_2 * (Math.log(nu / nu_ref)) ** 4
+ variencea_3 * (Math.log(nu / nu_ref)) ** 6 + variencemnu_0 * (t - t_ref) ** 2 + variencemdeltlog * (Math.log(nu / nu_0)) ** 2 * (t - t_0) ** 2)
let effectiveFreq = nu * 10 ** (logF_0 + a_1 * Math.log(nu / nu_ref) + a_2 * (Math.log(nu / nu_ref)) ** 2 + a_3 * (Math.log(nu / nu_ref)) ** 3
+ (mnu_0 * (t - t_ref) + mdeltlog * (t - t_0) * Math.log(nu / nu_0)))
let equation14 = logF_0 + (a_1 * Math.log10(nu / nu_ref)) + a_2 * (Math.log10(nu / nu_ref)) ** 2 + a_3 * (Math.log10(nu / nu_ref)) ** 3
+ (mnu_0 * (t - t_ref) + mdeltlog * (t - t_0) * Math.log10(nu / nu_0))
let equation15 = Math.sqrt(varianceLogF_0 + variancea_1 * (Math.log10(nu / nu_ref)) ** 2 + variencea_2 * (Math.log10(nu / nu_ref)) ** 4
+ variencea_3 * (Math.log10(nu / nu_ref)) ** 6 + variencemnu_0 * (t - t_ref) ** 2 + variencemdeltlog * (Math.log10(nu / nu_0)) ** 2 * (t - t_0) ** 2)
let effectiveFreq = nu * 10 ** (logF_0 + a_1 * Math.log10(nu / nu_ref) + a_2 * (Math.log10(nu / nu_ref)) ** 2 + a_3 * (Math.log10(nu / nu_ref)) ** 3
+ (mnu_0 * (t - t_ref) + mdeltlog * (t - t_0) * Math.log10(nu / nu_0)))
fluxSum += 10 ** (equation14) * 2
// adding equation 14 to 15 gives us the value of the flux one sigma above the average -- our method of getting uncertainty
sigmaFluxSum += 10 ** (equation14 + equation15) * 2
Expand Down