LyaCoLoRe version used for DESI DR2 FS analysis.

py/DLA.py

@@ -23,6 +23,13 @@
     print('WARN: pyigm not found, DLA distributions will not be perfect!')
     use_pyigm = False
 
+# This is an empirical 2LPT threshold for objects with b=2 coming from CoLoRe-2LPT snapshot version.
+def lpt_threshold(z):
+    a=5.33080934
+    b=-2.44382773
+    c= -0.26016998
+    return a * (z+1)**b + c
+
 def get_threshold(b2f):
     """ Compute the field threshold for a given bias
     Arguments:
@@ -219,35 +226,38 @@ def get_DLA_table(object,dla_bias=2.0,dla_bias_z=2.25,extrapolate_z_down=None,NH
         else:
             raise ValueError('DLA bias evol not recognised.')
         deltas = object.GAUSSIAN_DELTA_rows
+        nu_arr = get_threshold(b2f)
+        flagged_cells = flag_DLA(z_qso,z_cell,deltas,nu_arr,sigma_g)
+
+        #Mean of the poisson is the mean number of DLAs with redshift scaled up by
+        #the proportion of expected flagged cells.
+        p_nu_z = 1.0-norm.cdf(nu_arr)
+        mean_N_per_cell = z_width * dndz(z_cell,NHI_min=NHI_min,NHI_max=NHI_max)
+        w = p_nu_z>0
+        mu = np.zeros(p_nu_z.shape)
+        mu[w] = mean_N_per_cell[w]/p_nu_z[w]        
 
     elif object.DENSITY_DELTA_rows is not None:
-        """
-        ## Also need the pdf pf the skewers.
-        ## For now, just raise an error.
-        if evol == "b_const":
-            b2f = dla_bias
-        elif evol == "bD_const":
-            b2f = dla_bias*y(dla_bias_z)*np.ones(z.shape)/D_cell
-        else:
-            raise ValueError('DLA bias evol not recognised.')
+        #This only supports constant bias = 2.
+        #We use the threshold derived from CoLoRe-2LPT snapshots for b_HCD = 2, check above
         deltas = object.DENSITY_DELTA_rows
-        """
-        raise ValueError('Adding DLAs without Gaussian skewers is not implemented as yet.')
+        nu_arr = lpt_threshold(z_cell) #Get the 2LPT threshold
+        flagged_cells = flag_DLA(z_qso,z_cell,deltas,nu_arr,1) #We flag cells assuming (by construction) sigma_G=1 for 2LPT case
+
+        #Get number of expected flagged cells for each z_cell (here we first assume that all z cells are flagged!)
+        mean_N_per_cell = z_width * dndz(z_cell,NHI_min=NHI_min,NHI_max=NHI_max)
+
+        #Now we correct for the fact that not all z cells are flagged. We rescale the previous mean by the expected number of flagged cells per z_cell.
+        #We get this probability computing it directly from a full-sky LyaCoLoRe-2LPT realization and just read it (see Ruiz-Herrera+2026).
+        p_emp_t = np.genfromtxt(os.environ['LYACOLORE_PATH']+'/input_files/dla_files/expected_2lpt_flagged_cells_vs_z_cell.txt')
+        p_emp = p_emp_t[:,1]
+        w = p_emp>0
+        mu = np.zeros(p_emp.shape)
+        mu[w] = mean_N_per_cell[w]/p_emp[w]
 
     else:
         raise ValueError('Cannot find Gaussian or density delta skewers when flagging cells for DLAs')
 
-    nu_arr = get_threshold(b2f)
-    flagged_cells = flag_DLA(z_qso,z_cell,deltas,nu_arr,sigma_g)
-
-    #Mean of the poisson is the mean number of DLAs with redshift scaled up by
-    #the proportion of expected flagged cells.
-    mean_N_per_cell = z_width * dndz(z_cell,NHI_min=NHI_min,NHI_max=NHI_max)
-    p_nu_z = 1.0-norm.cdf(nu_arr)
-    w = p_nu_z>0
-    mu = np.zeros(p_nu_z.shape)
-    mu[w] = mean_N_per_cell[w]/p_nu_z[w]
-
     #Draw number of DLAs per cell from a Poisson distribution, place them only
     #in flagged cells.
     pois = np.random.poisson(mu,size=(len(z_qso),len(mu)))

run_CoLoRe/CoLoRe-2LPT/README_RUN.txt

@@ -0,0 +1,5 @@
+CoLoRe-2LPT boxes were run with the same configuration as CoLoRe-QL mocks using the 2LPT flags of CoLoRe + no smoothing + the input files in this same folder.
+
+LyaCoLoRe-2LPT was run with the same configuration as in London but with 2lpt flag on + the DESI DR2 tuning file (see LyaCoLoRe input folder).
+
+Both set of runs were performed full sky.

run_CoLoRe/CoLoRe-2LPT/input_files/Bz_clustering.txt

@@ -0,0 +1,77 @@
+0.000000000000000000e+00 5.230000000000000204e-01
+4.999999999999999584e-02 5.620253503242086390e-01
+9.999999999999999167e-02 6.019437360778474577e-01
+1.499999999999999944e-01 6.427335991075497912e-01
+1.999999999999999833e-01 6.843748270797710420e-01
+2.499999999999999722e-01 7.268485996383640124e-01
+2.999999999999999889e-01 7.701372564980800695e-01
+3.499999999999999778e-01 8.142241836137981137e-01
+3.999999999999999667e-01 8.590937143638941453e-01
+4.499999999999999556e-01 9.047310432966375604e-01
+4.999999999999999445e-01 9.511221504601894416e-01
+5.499999999999999334e-01 9.982537347048534571e-01
+5.999999999999999778e-01 1.046113154636084452e+00
+6.499999999999999112e-01 1.094688376126965457e+00
+6.999999999999999556e-01 1.143967925483159620e+00
+7.499999999999998890e-01 1.193940847501944846e+00
+7.999999999999999334e-01 1.244596667787633004e+00
+8.499999999999999778e-01 1.295925358784301373e+00
+8.999999999999999112e-01 1.347917309067868441e+00
+9.499999999999999556e-01 1.400563295506624062e+00
+9.999999999999998890e-01 1.453854457955084722e+00
+1.049999999999999822e+00 1.507782276192663806e+00
+1.099999999999999867e+00 1.562338548857779452e+00
+1.149999999999999911e+00 1.617515374161041075e+00
+1.199999999999999956e+00 1.673305132189134570e+00
+1.250000000000000000e+00 1.729700468634828736e+00
+1.299999999999999822e+00 1.786694279808847652e+00
+1.349999999999999867e+00 1.844279698806787771e+00
+1.399999999999999911e+00 1.902450082719245206e+00
+1.449999999999999956e+00 1.961199000786274738e+00
+1.499999999999999778e+00 2.020520223408500904e+00
+1.549999999999999822e+00 2.080407711936949511e+00
+1.599999999999999867e+00 2.140855609172144902e+00
+1.649999999999999911e+00 2.201858230510456593e+00
+1.699999999999999956e+00 2.263410055682141042e+00
+1.749999999999999778e+00 2.325505721031281059e+00
+1.799999999999999822e+00 2.388140012292810255e+00
+1.849999999999999867e+00 2.451307857826277026e+00
+1.899999999999999911e+00 2.515004322269925652e+00
+1.949999999999999734e+00 2.579224600582149307e+00
+1.999999999999999778e+00 2.643964012440484179e+00
+2.049999999999999822e+00 2.709217996971056142e+00
+2.099999999999999645e+00 2.774982107783869001e+00
+2.149999999999999911e+00 2.841252008291516695e+00
+2.199999999999999734e+00 2.908023467290875352e+00
+2.250000000000000000e+00 2.975292354789122129e+00
+2.299999999999999822e+00 3.043054638056980732e+00
+2.349999999999999645e+00 3.111306377893582109e+00
+2.399999999999999911e+00 3.180043725088569140e+00
+2.449999999999999734e+00 3.249262917068280743e+00
+2.500000000000000000e+00 3.318960274713909087e+00
+2.549999999999999822e+00 3.389132199340449958e+00
+2.599999999999999645e+00 3.459775169826182939e+00
+2.649999999999999911e+00 3.530885739883174779e+00
+2.699999999999999734e+00 3.602460535460036084e+00
+2.749999999999999556e+00 3.674496252268825369e+00
+2.799999999999999822e+00 3.746989653428570044e+00
+2.849999999999999645e+00 3.819937567218449015e+00
+2.899999999999999911e+00 3.893336884934168296e+00
+2.949999999999999734e+00 3.967184558841521103e+00
+2.999999999999999556e+00 4.041477600221553779e+00
+3.049999999999999822e+00 4.116213077502131945e+00
+3.099999999999999645e+00 4.191388114471068072e+00
+3.149999999999999911e+00 4.266999888566308741e+00
+3.199999999999999734e+00 4.343045629238929450e+00
+3.249999999999999556e+00 4.419522616385072489e+00
+3.299999999999999822e+00 4.496428178843051349e+00
+3.349999999999999645e+00 4.573759692952281242e+00
+3.399999999999999911e+00 4.651514581170737017e+00
+3.449999999999999734e+00 4.729690310747955095e+00
+3.499999999999999556e+00 4.808284392450754119e+00
+3.549999999999999822e+00 4.887294379338975148e+00
+3.599999999999999645e+00 4.966717865588800684e+00
+3.649999999999999911e+00 5.046552485361268658e+00
+3.699999999999999734e+00 5.126795911713784015e+00
+3.749999999999999556e+00 5.207445855552572667e+00
+3.799999999999999822e+00 5.288500064624081176e+00