#include #include #include #include "header.h" /* This is the bias factor for halos. Mass (in h^{-1} M_sol) is the input. * This is not the scale-dependent bias factor, which can be calculated later. * * The parameterization of b(M) is taken from Sheth Mo & Tormen (2001 MNRAS 232 1) * but the actual parameters have been replaced by the values of Appendix A in * Tinker, Weinberg, Zheng, & Zehavi. 2005 Apj (submitted) */ double bias(double mass) { static int prev_cosmo=-1; static long IDUM=-3333; double rm,sig,k,neff,b,logk,khi,klo,phi,plo,nu,psp,x,fac,fac1,fac2; static int flag=0,na=4; static double pnorm,*xa,*ya,*za; int i; /* Original SMT parameters */ double a=0.707,bb=0.5,c=0.6; /* Tinker et al parameters */ a=0.707; bb=0.35; c=0.8; /* Fitting to Mike Warren's simulations. */ bb=0.28; /* Use the globel parameters. */ a=BIAS_A; bb=BIAS_B; c=BIAS_C; if(!flag) { xa=dvector(1,na); ya=dvector(1,na); za=dvector(1,na); flag=1; xa[1] = log(1/2.51); xa[2] = log(1/1.49); xa[3] = log(1/0.905); xa[4] = log(1/0.501); } if(prev_cosmo != RESET_COSMOLOGY) { prev_cosmo=RESET_COSMOLOGY; for(i=1;i<=4;++i) ya[i] = gasdev(&IDUM); spline(xa,ya,na,1.0E+30,1.0E+30,za); } /* First normalize the power spectrum */ pnorm=SIGMA_8/sigmac(8.0); rm=pow(3.0*mass/(4.0*PI*OMEGA_M*RHO_CRIT),1.0/3.0); sig=pnorm*sigmac(rm); nu=DELTA_CRIT/sig; b=1+1.0/(sqrt(a)*DELTA_CRIT)*(sqrt(a)*a*nu*nu + sqrt(a)*bb*pow(a*nu*nu,1-c) - (pow(a*nu*nu,c)/(pow(a*nu*nu,c)+bb*(1-c)*(1-c/2.)))); splint(xa,ya,za,na,log(1/sig),&fac1); fac2 = 0.0158*pow(sig,-2.7) + 0.00251*pow(sig,2.2); fac = 1 + fac1*fac2; if(fac<0.01)fac=0.01; b*=fac; return(b); /* This is the Seljak & Warren (2004) bias. * There's an alpha_s in the correction term, which here I set to zero. (RUNNING.) * See App. A of Tinker et al for a comparison of this bias with above bias. */ x=mass/MSTAR; b=(0.53+0.39*pow(x,0.45)+0.13/(40*x+1) + 5.0e-4*pow(x,1.5)); b=b+log10(x)*(0.4*(OMEGA_M-0.3+SPECTRAL_INDX-1)+0.3*(SIGMA_8-0.9+HUBBLE-0.7)+0.8*0.0); return(b); /* This is the old Mo & White (1996) formula */ return(1+DELTA_CRIT/sig/sig-1/DELTA_CRIT); } /* Just like the halo mass function, we'll set this up such that * you just need to interpolate. * * Now this has been changed to calculate the spatial-scale dependence * of the bias factor. If you don't want the scale dep. b, then just * input r<0. * * If the global flag LINEAR_PSP==0, uses the scale dependence calculated for * for halo bias relative to the non-linear matter \xi_m(r): * * f^2(r) = (1.0+xi*1.17)^1.49/(1.0+xi*0.69)^2.09 --> b(r) = b0*f(r) * * For LINEAR_PSP==1, use scale dependence determined for the linear P(k): * * f(r) = 1 + exp[-(r/A)^0.7] --> where A is a parameter that we're gonna have to * determine in more detail, but now A \approx 1 */ double bias_interp(double m, double r) { static int flag=0,prev_cosmo=0; static double *x,*y,*y2; int i,n=100; double dm,max=16.3,min=9,a,b,m1,m2,dm1,xi; if(!flag || RESET_COSMOLOGY!=prev_cosmo) { if(!ThisTask && OUTPUT) fprintf(stdout,"RESET: resetting bias for %f %f\n",OMEGA_M,SIGMA_8); if(!flag) { x=dvector(1,n); y=dvector(1,n); y2=dvector(1,n); } flag=1; dm=(double)(max-min)/n; for(i=1;i<=n;++i) { x[i]=pow(10.0,min+i*dm); y[i]=log(bias(x[i])); if(DELTA_HALO!=200) { m1 = halo_mass_conversion2(x[i]*1.001,halo_c200(x[i]*1.001),DELTA_HALO,200.0); m2 = halo_mass_conversion2(x[i]*0.999,halo_c200(x[i]*0.999),DELTA_HALO,200.0); dm1 = (x[i]*1.001 - x[i]*0.999)/(m1-m2); y[i] = y[i] + log(dm1); x[i]=log(halo_mass_conversion2(x[i],halo_c200(x[i]),DELTA_HALO,200.0)); } else x[i]=log(x[i]); } spline(x,y,n,2.0E+30,2.0E+30,y2); prev_cosmo=RESET_COSMOLOGY; } m=log(m); splint(x,y,y2,n,m,&a); if(r<0 || r>10)return(exp(a)); if(LINEAR_PSP) { b=exp(-pow(r,0.7)) + 1; } else { xi=xi_interp(r); b=pow(1.0+xi*1.17,1.49*0.5)*pow(1.0+xi*0.69,-2.09*0.5); } a=exp(a)*b; return(a); } /* This is the integrand which qromo or qtrap would call * to calculate the large-scale galaxy bias. * The integral is a number-weighted average of the halo * bias function, integrated over the halo mass function. */ double func_galaxy_bias(double m) { return(dndM_interp(m)*N_avg(m)*bias_interp(m,-1.)); }