diff --git a/app/host/SBI/run_sbi_blast.py b/app/host/SBI/run_sbi_blast.py
index 17381330..c9523b05 100644
--- a/app/host/SBI/run_sbi_blast.py
+++ b/app/host/SBI/run_sbi_blast.py
@@ -40,10 +40,10 @@
 }
 
 sbi_params = {
-    "anpe_fname_global": f"{settings.SBIPP_ROOT}/SBI_model_global.pt",  # trained sbi model
-    "train_fname_global": f"{settings.SBIPP_PHOT_ROOT}/sbi_phot_global.h5",  # training set
-    "anpe_fname_local": f"{settings.SBIPP_ROOT}/SBI_model_local.pt",  # trained sbi model
-    "train_fname_local": f"{settings.SBIPP_PHOT_ROOT}/sbi_phot_local.h5",  # training set
+    "anpe_fname_standard": f"{settings.SBIPP_ROOT}/SBI_model_standard.pt",  # trained sbi model
+    "train_fname_standard": f"{settings.SBIPP_PHOT_ROOT}/sbi_phot_standard.h5",  # training set
+    "anpe_fname_lowz": f"{settings.SBIPP_ROOT}/SBI_model_lowz.pt",  # trained sbi model
+    "train_fname_lowz": f"{settings.SBIPP_PHOT_ROOT}/sbi_phot_lowz.h5",  # training set
     "nhidden": 500,  # architecture of the trained density estimator
     "nblocks": 15,  # architecture of the trained density estimator
 }
@@ -76,7 +76,7 @@
 
 # training set
 def run_training_set():
-    for _fit_type in ["global", "local"]:
+    for _fit_type in ["standard", "lowz"]:
         data = h5py.File(sbi_params[f"train_fname_{_fit_type}"], "r")
         x_train = np.array(data["theta"])  # physical parameters
         y_train = np.array(data["phot"])  # fluxes & uncertainties
@@ -109,72 +109,72 @@ def run_training_set():
             )
         )
         anpe._x_shape = Ut.x_shape_from_simulation(y_tensor)
-        if _fit_type == "global":
-            hatp_x_y_global = anpe.build_posterior(
+        if _fit_type == "standard":
+            hatp_x_y_standard = anpe.build_posterior(
                 p_x_y_estimator, sample_with="rejection"
             )
-            y_train_global = y_train[:]
-            x_train_global = x_train[:]
-        elif _fit_type == "local":
-            hatp_x_y_local = anpe.build_posterior(
+            y_train_standard = y_train[:]
+            x_train_standard = x_train[:]
+        elif _fit_type == "lowz":
+            hatp_x_y_lowz = anpe.build_posterior(
                 p_x_y_estimator, sample_with="rejection"
             )
-            y_train_local = y_train[:]
-            x_train_local = x_train[:]
+            y_train_lowz = y_train[:]
+            x_train_lowz = x_train[:]
 
     print("""Storing training sets as data files...""")
     with open(
-        os.path.join(settings.SBI_TRAINING_ROOT, "hatp_x_y_global.pkl"), "wb"
+        os.path.join(settings.SBI_TRAINING_ROOT, "hatp_x_y_standard.pkl"), "wb"
     ) as handle:
-        pickle.dump(hatp_x_y_global, handle)
+        pickle.dump(hatp_x_y_standard, handle)
     with open(
-        os.path.join(settings.SBI_TRAINING_ROOT, "y_train_global.pkl"), "wb"
+        os.path.join(settings.SBI_TRAINING_ROOT, "y_train_standard.pkl"), "wb"
     ) as handle:
-        pickle.dump(y_train_global, handle)
+        pickle.dump(y_train_standard, handle)
     with open(
-        os.path.join(settings.SBI_TRAINING_ROOT, "x_train_global.pkl"), "wb"
+        os.path.join(settings.SBI_TRAINING_ROOT, "x_train_standard.pkl"), "wb"
     ) as handle:
-        pickle.dump(x_train_global, handle)
+        pickle.dump(x_train_standard, handle)
     with open(
-        os.path.join(settings.SBI_TRAINING_ROOT, "hatp_x_y_local.pkl"), "wb"
+        os.path.join(settings.SBI_TRAINING_ROOT, "hatp_x_y_lowz.pkl"), "wb"
     ) as handle:
-        pickle.dump(hatp_x_y_local, handle)
+        pickle.dump(hatp_x_y_lowz, handle)
     with open(
-        os.path.join(settings.SBI_TRAINING_ROOT, "y_train_local.pkl"), "wb"
+        os.path.join(settings.SBI_TRAINING_ROOT, "y_train_lowz.pkl"), "wb"
     ) as handle:
-        pickle.dump(y_train_local, handle)
+        pickle.dump(y_train_lowz, handle)
     with open(
-        os.path.join(settings.SBI_TRAINING_ROOT, "x_train_local.pkl"), "wb"
+        os.path.join(settings.SBI_TRAINING_ROOT, "x_train_lowz.pkl"), "wb"
     ) as handle:
-        pickle.dump(x_train_local, handle)
+        pickle.dump(x_train_lowz, handle)
 
 
 try:
     print("""Loading training sets from data files...""")
     with open(
-        os.path.join(settings.SBI_TRAINING_ROOT, "hatp_x_y_global.pkl"), "rb"
+        os.path.join(settings.SBI_TRAINING_ROOT, "hatp_x_y_standard.pkl"), "rb"
     ) as handle:
-        hatp_x_y_global = pickle.load(handle)
+        hatp_x_y_standard = pickle.load(handle)
     with open(
-        os.path.join(settings.SBI_TRAINING_ROOT, "y_train_global.pkl"), "rb"
+        os.path.join(settings.SBI_TRAINING_ROOT, "y_train_standard.pkl"), "rb"
     ) as handle:
-        y_train_global = pickle.load(handle)
+        y_train_standard = pickle.load(handle)
     with open(
-        os.path.join(settings.SBI_TRAINING_ROOT, "x_train_global.pkl"), "rb"
+        os.path.join(settings.SBI_TRAINING_ROOT, "x_train_standard.pkl"), "rb"
     ) as handle:
-        x_train_global = pickle.load(handle)
+        x_train_standard = pickle.load(handle)
     with open(
-        os.path.join(settings.SBI_TRAINING_ROOT, "hatp_x_y_local.pkl"), "rb"
+        os.path.join(settings.SBI_TRAINING_ROOT, "hatp_x_y_lowz.pkl"), "rb"
     ) as handle:
-        hatp_x_y_local = pickle.load(handle)
+        hatp_x_y_lowz = pickle.load(handle)
     with open(
-        os.path.join(settings.SBI_TRAINING_ROOT, "y_train_local.pkl"), "rb"
+        os.path.join(settings.SBI_TRAINING_ROOT, "y_train_lowz.pkl"), "rb"
     ) as handle:
-        y_train_local = pickle.load(handle)
+        y_train_lowz = pickle.load(handle)
     with open(
-        os.path.join(settings.SBI_TRAINING_ROOT, "x_train_local.pkl"), "rb"
+        os.path.join(settings.SBI_TRAINING_ROOT, "x_train_lowz.pkl"), "rb"
     ) as handle:
-        x_train_local = pickle.load(handle)
+        x_train_lowz = pickle.load(handle)
     print("""Training sets loaded.""")
 except Exception as err:
     print(f"""Error loading training sets: {err}. Regenerating...""")
@@ -189,14 +189,14 @@ def maggies_to_asinh(x):
     return -a * math.asinh((x / 2.0) * np.exp(mu / a)) + mu
 
 
-def fit_sbi_pp(observations, n_filt_cuts=True, fit_type="global"):
+def fit_sbi_pp(observations, n_filt_cuts=True, fit_type="standard"):
     np.random.seed(100)  # make results reproducible
 
     # toy noise model
     meds_sigs, stds_sigs = [], []
 
     for f in all_filters:
-        toy_noise_x, toy_noise_y = np.loadtxt(
+        toy_noise_x, toy_noise_y, toy_noise_std = np.loadtxt(
             f"host/SBI/snrfiles/{f.name}_magvsnr.txt", dtype=float, unpack=True
         )
         meds_sigs += [
@@ -211,7 +211,7 @@ def fit_sbi_pp(observations, n_filt_cuts=True, fit_type="global"):
         stds_sigs += [
             interp1d(
                 toy_noise_x,
-                1.0857 * 1 / toy_noise_y,
+                (1.0857 * 1 / toy_noise_y)*(toy_noise_std/toy_noise_y),
                 kind="slinear",
                 fill_value="extrapolate",  # (0.01,1.0),
                 bounds_error=False,
@@ -268,14 +268,14 @@ def fit_sbi_pp(observations, n_filt_cuts=True, fit_type="global"):
         return {}, 1
 
     # prepare to pass the reconstructed model to sbi_pp
-    if fit_type == "global":
-        sbi_params["y_train"] = y_train_global
-        sbi_params["theta_train"] = x_train_global
-        sbi_params["hatp_x_y"] = hatp_x_y_global
-    elif fit_type == "local":
-        sbi_params["y_train"] = y_train_local
-        sbi_params["hatp_x_y"] = hatp_x_y_local
-        sbi_params["theta_train"] = x_train_local
+    if fit_type == "standard":
+        sbi_params["y_train"] = y_train_standard
+        sbi_params["theta_train"] = x_train_standard
+        sbi_params["hatp_x_y"] = hatp_x_y_standard
+    elif fit_type == "lowz":
+        sbi_params["y_train"] = y_train_lowz
+        sbi_params["hatp_x_y"] = hatp_x_y_lowz
+        sbi_params["theta_train"] = x_train_lowz
 
     # Run SBI++
     chain, obs, flags = sbi_pp.sbi_pp(
diff --git a/app/host/SBI/sbi_pp.py b/app/host/SBI/sbi_pp.py
index 4a9b5174..c07a3603 100644
--- a/app/host/SBI/sbi_pp.py
+++ b/app/host/SBI/sbi_pp.py
@@ -118,12 +118,13 @@ def chidof(mags, obsphot, obsphot_unc, individual=False):
         return chi / np.sum(np.isfinite(obsphot))
 
 
-def gauss_approx_missingband(obs, run_params, sbi_params, max_neighbors=200):
+def gauss_approx_missingband(obs, run_params, sbi_params, max_neighbors=10):
     """nearest neighbor approximation of missing bands;
     see sec. 4.1.2 for details
     """
     use_res = False
-    y_train = sbi_params["y_train"]
+    redshift_idx = np.where(abs(obs["redshift"]-sbi_params["y_train"][:,-1]) < 0.05)[0]
+    y_train = sbi_params["y_train"][redshift_idx]
 
     y_obs = np.copy(obs["mags_sbi"])
     sig_obs = np.copy(obs["mags_unc_sbi"])
@@ -190,9 +191,10 @@ def sbi_missingband(obs, run_params, sbi_params, seconditer=False):
         print("sbi missing bands")
     ave_theta = []
 
-    max_neighbors = 200
+    max_neighbors = 10
+    redshift_idx = np.where(abs(obs["redshift"]-sbi_params["y_train"][:,-1]) < 0.05)[0]
     hatp_x_y = sbi_params["hatp_x_y"]
-    y_train = sbi_params["y_train"]
+    y_train = sbi_params["y_train"][redshift_idx]
     y_obs = np.copy(obs["mags_sbi"])
     sig_obs = np.copy(obs["mags_unc_sbi"])
     invalid_mask = np.copy(obs["missing_mask"])
@@ -288,20 +290,21 @@ def sbi_missingband(obs, run_params, sbi_params, seconditer=False):
                 x[22:][not_valid_idx[j]] = y_train[idx_chi2_selected][
                     np.random.choice(range(len(guess_ndata.T[j])))
                 ][22:][not_valid_idx[j]]
-                # x[not_valid_idx_unc[j]] = toy_noise(
-                #    flux=x[not_valid_idx[j]],
-                #    meds_sigs=sbi_params["toynoise_meds_sigs"][idx],
-                #    stds_sigs=sbi_params["toynoise_stds_sigs"][idx],
-                #    verbose=run_params["verbose"],
-                # )[1]
+                # D. Jones - 7/11/26 -- re-trying toy noise
+                #x[not_valid_idx_unc[j]] = toy_noise(
+                #   flux=x[not_valid_idx[j]],
+                #   meds_sigs=sbi_params["toynoise_meds_sigs"][idx],
+                #   stds_sigs=sbi_params["toynoise_stds_sigs"][idx],
+                #   verbose=run_params["verbose"],
+                #)[1]
 
             # the noise model in the training isn't quite right
             # Pan-STARRS in particular seems a little off
             # we'll have to re-train at some point, but for now just pull
             # uncertainties from the training sample
-            for idx, fname in zip(valid_idx, obs["filternames"][valid_idx]):
-                chc = np.random.choice(range(len(y_train[idx_chi2_selected])))
-                x[22:-1][idx] = y_train[idx_chi2_selected][chc][22:-1][idx]
+            #for idx, fname in zip(valid_idx, obs["filternames"][valid_idx]):
+            #    chc = np.random.choice(range(len(y_train[idx_chi2_selected])))
+            #    x[22:-1][idx] = y_train[idx_chi2_selected][chc][22:-1][idx]
 
             all_x.append(x)
 
@@ -371,7 +374,8 @@ def lim_of_noisy_guass(obs, run_params, sbi_params):
 
     use_res = 1
 
-    y_train = sbi_params["y_train"]
+    redshift_idx = np.where(abs(obs["redshift"]-sbi_params["y_train"][:,-1]) < 0.05)[0]
+    y_train = sbi_params["y_train"][redshift_idx]
 
     y_obs = np.copy(obs["mags_sbi"])
     sig_obs = np.copy(obs["mags_unc_sbi"])
@@ -420,7 +424,7 @@ def lim_of_noisy_guass(obs, run_params, sbi_params):
     return lims, use_res
 
 
-def sbi_mcnoise(obs, run_params, sbi_params, max_neighbors=200):
+def sbi_mcnoise(obs, run_params, sbi_params, max_neighbors=10):
     """used when observations have out-of-distribution uncertainties;
     see sec. 4.1.1 for details
     """
@@ -431,8 +435,9 @@ def sbi_mcnoise(obs, run_params, sbi_params, max_neighbors=200):
 
     ave_theta = []
 
+    redshift_idx = np.where(abs(obs["redshift"]-sbi_params["y_train"][:,-1]) < 0.05)[0]
     hatp_x_y = sbi_params["hatp_x_y"]
-    y_train = sbi_params["y_train"]
+    y_train = sbi_params["y_train"][redshift_idx]
 
     y_obs = np.copy(obs["mags_sbi"])
     sig_obs = np.copy(obs["mags_unc_sbi"])
@@ -498,11 +503,11 @@ def sbi_mcnoise(obs, run_params, sbi_params, max_neighbors=200):
                 )[1]
                 # signal.alarm(run_params["tmax_per_obj"])
 
-                for idx, fname in enumerate(obs["filternames"]):
-                    chc = np.random.choice(range(len(y_train[idx_chi2_selected])))
-                    samp_y_guess[22:-1][idx] = y_train[idx_chi2_selected][chc][22:-1][
-                        idx
-                    ]
+                #for idx, fname in enumerate(obs["filternames"]):
+                #    chc = np.random.choice(range(len(y_train[idx_chi2_selected])))
+                #    samp_y_guess[22:-1][idx] = y_train[idx_chi2_selected][chc][22:-1][
+                #        idx
+                #    ]
 
                 do_continue = False
                 for tmax, npost in zip(
@@ -564,8 +569,9 @@ def sbi_missing_and_noisy(obs, run_params, sbi_params):
 
     ave_theta = []
 
+    redshift_idx = np.where(abs(obs["redshift"]-sbi_params["y_train"][:,-1]) < 0.05)[0]
     hatp_x_y = sbi_params["hatp_x_y"]
-    y_train = sbi_params["y_train"]
+    y_train = sbi_params["y_train"][redshift_idx]
 
     y_obs = np.copy(obs["mags_sbi"])
     sig_obs = np.copy(obs["mags_unc_sbi"])
@@ -642,10 +648,10 @@ def sbi_missing_and_noisy(obs, run_params, sbi_params):
             # Pan-STARRS in particular seems a little off
             # we'll have to re-train at some point, but for now just pull
             # uncertainties from the training sample
-            for idx, fname in zip(valid_idx, obs["filternames"][valid_idx]):
+            #for idx, fname in zip(valid_idx, obs["filternames"][valid_idx]):
                 # if 'PanSTARRS' in fname or '2MASS' in fname or 'SDSS' in fname or 'DES' in fname:
-                chc = np.random.choice(range(len(y_train[idx_chi2_selected])))
-                samp_y_guess[22:][idx] = y_train[idx_chi2_selected][chc][22:][idx]
+            #    chc = np.random.choice(range(len(y_train[idx_chi2_selected])))
+            #    samp_y_guess[22:][idx] = y_train[idx_chi2_selected][chc][22:][idx]
 
             # if we can't get one posterior sample in one second, we should move along
             # to the next MC sample
@@ -696,7 +702,7 @@ def sbi_missing_and_noisy(obs, run_params, sbi_params):
     return ave_theta, use_res, timeout_flag, cnt
 
 
-def sbi_baseline(obs, run_params, sbi_params, max_neighbors=200):
+def sbi_baseline(obs, run_params, sbi_params, max_neighbors=10):
     signal.signal(signal.SIGALRM, timeout_handler)
 
     if run_params["verbose"]:
@@ -714,8 +720,9 @@ def sbi_baseline(obs, run_params, sbi_params, max_neighbors=200):
         "nsamp_noisy": -99,  # number of MC samples drawn
     }
 
+    redshift_idx = np.where(abs(obs["redshift"]-sbi_params["y_train"][:,-1]) < 0.05)[0]
     hatp_x_y = sbi_params["hatp_x_y"]
-    y_train = sbi_params["y_train"]
+    y_train = sbi_params["y_train"][redshift_idx]
 
     y_obs = np.copy(obs["mags"])
     sig_obs = np.copy(obs["mags_unc"])
@@ -754,9 +761,9 @@ def sbi_baseline(obs, run_params, sbi_params, max_neighbors=200):
 
     x = np.concatenate([y_obs, sig_obs, [obs["redshift"]]])
 
-    for idx, fname in enumerate(obs["filternames"]):
-        chc = np.random.choice(range(len(y_train[idx_chi2_selected])))
-        x[22:-1][idx] = y_train[idx_chi2_selected][chc][22:-1][idx]
+    #for idx, fname in enumerate(obs["filternames"]):
+    #    chc = np.random.choice(range(len(y_train[idx_chi2_selected])))
+    #    x[22:-1][idx] = y_train[idx_chi2_selected][chc][22:-1][idx]
 
     try:
         ave_theta = hatp_x_y.sample(
@@ -777,7 +784,7 @@ def sbi_baseline(obs, run_params, sbi_params, max_neighbors=200):
     return ave_theta, obs, flags
 
 
-def sbi_pp(obs, run_params, sbi_params, max_neighbors=200):
+def sbi_pp(obs, run_params, sbi_params, max_neighbors=10):
     """wrapper for sbi++; this should be the only function needed to be called outside this scipt under normal circumstances
 
     obs: a dictionary at least containing
@@ -801,11 +808,13 @@ def sbi_pp(obs, run_params, sbi_params, max_neighbors=200):
         "nsamp_noisy": -99,  # number of MC samples drawn
     }
 
+    redshift_idx = np.where(abs(obs["redshift"]-sbi_params["y_train"][:,-1]) < 0.05)[0]
     hatp_x_y = sbi_params["hatp_x_y"]
-    y_train = sbi_params["y_train"]
+    y_train = sbi_params["y_train"][redshift_idx]
 
     y_obs = np.copy(obs["mags"])
     sig_obs = np.copy(obs["mags_unc"])
+
     # copy the observed data to be used by sbi
     # missing data, if any, will be filled in later
     obs["mags_sbi"] = y_obs
@@ -825,9 +834,10 @@ def sbi_pp(obs, run_params, sbi_params, max_neighbors=200):
             stds_sigs=sbi_params["toynoise_stds_sigs"][j],
             verbose=run_params["verbose"],
         )
-        noisy_mask[j] = (sig_obs[j] - _toynoise[1]) / _toynoise[2] >= run_params[
+        noisy_mask[j] = abs((sig_obs[j] - _toynoise[1]) / _toynoise[2]) >= run_params[
             "noisy_sig"
         ]
+
         # if noisy_mask[j]:
         #    import pdb; pdb.set_trace()
     noisy_mask &= np.isfinite(y_obs)  # idx of noisy bands
@@ -868,9 +878,9 @@ def sbi_pp(obs, run_params, sbi_params, max_neighbors=200):
 
         x = np.concatenate([y_obs, sig_obs, [obs["redshift"]]])
 
-        for idx, fname in enumerate(obs["filternames"]):
-            chc = np.random.choice(range(len(y_train[idx_chi2_selected])))
-            x[22:-1][idx] = y_train[idx_chi2_selected][chc][22:-1][idx]
+        #for idx, fname in enumerate(obs["filternames"]):
+        #    chc = np.random.choice(range(len(y_train[idx_chi2_selected])))
+        #    x[22:-1][idx] = y_train[idx_chi2_selected][chc][22:-1][idx]
 
         try:
             ave_theta = hatp_x_y.sample(
diff --git a/app/host/SBI/snrfiles/2MASS_H_magvsnr.txt b/app/host/SBI/snrfiles/2MASS_H_magvsnr.txt
index 0ca38da7..36dad951 100644
--- a/app/host/SBI/snrfiles/2MASS_H_magvsnr.txt
+++ b/app/host/SBI/snrfiles/2MASS_H_magvsnr.txt
@@ -1,38 +1,118 @@
-10.251 102.154
-10.501 88.819
-10.751 81.025
-11.001 66.689
-11.251 60.840
-11.501 57.260
-11.751 38.349
-12.001 36.082
-12.251 36.400
-12.501 31.461
-12.751 25.081
-13.001 24.932
-13.251 23.476
-13.501 18.236
-13.751 17.191
-14.001 17.036
-14.251 13.051
-14.501 11.815
-14.751 11.906
-15.001 8.395
-15.251 8.130
-15.501 7.529
-15.751 6.889
-16.001 5.267
-16.251 4.883
-16.501 3.658
-16.751 3.944
-17.001 3.034
-17.251 2.283
-17.501 2.528
-17.751 1.964
-18.001 1.278
-18.251 1.744
-18.501 1.456
-18.751 0.732
-19.001 0.891
-19.251 1.053
-19.501 0.818
+10.673 84.234 14.376
+10.923 81.819 12.295
+11.173 74.140 16.535
+11.423 66.978 14.108
+11.673 62.777 14.573
+11.923 56.863 14.955
+12.173 52.438 16.545
+12.423 44.738 15.687
+12.673 41.178 16.286
+12.923 36.609 15.279
+13.173 30.575 15.266
+13.423 28.089 14.633
+13.673 23.978 12.931
+13.923 21.537 11.788
+14.173 18.981 10.329
+14.423 16.332 9.281
+14.673 14.038 7.414
+14.923 11.830 6.642
+15.173 10.142 5.054
+15.423 8.516 4.056
+15.673 7.305 3.365
+15.923 6.109 2.778
+16.173 5.119 2.151
+16.423 4.315 1.757
+16.673 3.592 1.338
+16.923 3.082 1.086
+17.173 2.576 0.865
+17.423 2.156 0.688
+17.673 1.774 0.533
+17.923 1.501 0.441
+18.173 1.231 0.356
+18.423 1.013 0.284
+18.673 0.823 0.226
+18.923 0.653 0.183
+19.173 0.532 0.147
+19.423 0.425 0.118
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diff --git a/app/host/SBI/snrfiles/2MASS_J_magvsnr.txt b/app/host/SBI/snrfiles/2MASS_J_magvsnr.txt
index f02bc061..64a66fca 100644
--- a/app/host/SBI/snrfiles/2MASS_J_magvsnr.txt
+++ b/app/host/SBI/snrfiles/2MASS_J_magvsnr.txt
@@ -1,40 +1,117 @@
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diff --git a/app/host/SBI/snrfiles/2MASS_K_magvsnr.txt b/app/host/SBI/snrfiles/2MASS_K_magvsnr.txt
index dfeedb70..30fe1375 100644
--- a/app/host/SBI/snrfiles/2MASS_K_magvsnr.txt
+++ b/app/host/SBI/snrfiles/2MASS_K_magvsnr.txt
@@ -1,41 +1,116 @@
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diff --git a/app/host/SBI/snrfiles/DES_g_magvsnr.txt b/app/host/SBI/snrfiles/DES_g_magvsnr.txt
index ac213267..5d19a436 100644
--- a/app/host/SBI/snrfiles/DES_g_magvsnr.txt
+++ b/app/host/SBI/snrfiles/DES_g_magvsnr.txt
@@ -1,33 +1,107 @@
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diff --git a/app/host/SBI/snrfiles/DES_r_magvsnr.txt b/app/host/SBI/snrfiles/DES_r_magvsnr.txt
index 3a64ffc7..fe8141ad 100644
--- a/app/host/SBI/snrfiles/DES_r_magvsnr.txt
+++ b/app/host/SBI/snrfiles/DES_r_magvsnr.txt
@@ -1,35 +1,108 @@
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diff --git a/app/host/SBI/snrfiles/DES_z_magvsnr.txt b/app/host/SBI/snrfiles/DES_z_magvsnr.txt
index 46badb51..039d0310 100644
--- a/app/host/SBI/snrfiles/DES_z_magvsnr.txt
+++ b/app/host/SBI/snrfiles/DES_z_magvsnr.txt
@@ -1,34 +1,111 @@
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diff --git a/app/host/SBI/snrfiles/GALEX_FUV_magvsnr.txt b/app/host/SBI/snrfiles/GALEX_FUV_magvsnr.txt
index 467ebe2b..44b88395 100644
--- a/app/host/SBI/snrfiles/GALEX_FUV_magvsnr.txt
+++ b/app/host/SBI/snrfiles/GALEX_FUV_magvsnr.txt
@@ -1,35 +1,99 @@
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diff --git a/app/host/SBI/snrfiles/GALEX_NUV_magvsnr.txt b/app/host/SBI/snrfiles/GALEX_NUV_magvsnr.txt
index 518a1950..d267bd9b 100644
--- a/app/host/SBI/snrfiles/GALEX_NUV_magvsnr.txt
+++ b/app/host/SBI/snrfiles/GALEX_NUV_magvsnr.txt
@@ -1,41 +1,101 @@
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diff --git a/app/host/SBI/snrfiles/PanSTARRS_g_magvsnr.txt b/app/host/SBI/snrfiles/PanSTARRS_g_magvsnr.txt
index 05d6aadd..a876d5bb 100644
--- a/app/host/SBI/snrfiles/PanSTARRS_g_magvsnr.txt
+++ b/app/host/SBI/snrfiles/PanSTARRS_g_magvsnr.txt
@@ -1,42 +1,110 @@
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diff --git a/app/host/SBI/snrfiles/PanSTARRS_i_magvsnr.txt b/app/host/SBI/snrfiles/PanSTARRS_i_magvsnr.txt
index c56d88a2..dc3393d0 100644
--- a/app/host/SBI/snrfiles/PanSTARRS_i_magvsnr.txt
+++ b/app/host/SBI/snrfiles/PanSTARRS_i_magvsnr.txt
@@ -1,38 +1,49 @@
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diff --git a/app/host/SBI/snrfiles/PanSTARRS_r_magvsnr.txt b/app/host/SBI/snrfiles/PanSTARRS_r_magvsnr.txt
index 02d0d546..609da940 100644
--- a/app/host/SBI/snrfiles/PanSTARRS_r_magvsnr.txt
+++ b/app/host/SBI/snrfiles/PanSTARRS_r_magvsnr.txt
@@ -1,40 +1,49 @@
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diff --git a/app/host/SBI/snrfiles/PanSTARRS_y_magvsnr.txt b/app/host/SBI/snrfiles/PanSTARRS_y_magvsnr.txt
index 953ff904..eaeff36a 100644
--- a/app/host/SBI/snrfiles/PanSTARRS_y_magvsnr.txt
+++ b/app/host/SBI/snrfiles/PanSTARRS_y_magvsnr.txt
@@ -1,44 +1,115 @@
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diff --git a/app/host/SBI/snrfiles/PanSTARRS_z_magvsnr.txt b/app/host/SBI/snrfiles/PanSTARRS_z_magvsnr.txt
index 7adeb0de..73842cbc 100644
--- a/app/host/SBI/snrfiles/PanSTARRS_z_magvsnr.txt
+++ b/app/host/SBI/snrfiles/PanSTARRS_z_magvsnr.txt
@@ -1,37 +1,114 @@
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diff --git a/app/host/SBI/snrfiles/SDSS_g_magvsnr.txt b/app/host/SBI/snrfiles/SDSS_g_magvsnr.txt
index 752a9027..cbf4a674 100644
--- a/app/host/SBI/snrfiles/SDSS_g_magvsnr.txt
+++ b/app/host/SBI/snrfiles/SDSS_g_magvsnr.txt
@@ -1,34 +1,108 @@
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diff --git a/app/host/SBI/snrfiles/SDSS_i_magvsnr.txt b/app/host/SBI/snrfiles/SDSS_i_magvsnr.txt
index 2808d971..2f40be04 100644
--- a/app/host/SBI/snrfiles/SDSS_i_magvsnr.txt
+++ b/app/host/SBI/snrfiles/SDSS_i_magvsnr.txt
@@ -1,34 +1,112 @@
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diff --git a/app/host/SBI/snrfiles/SDSS_r_magvsnr.txt b/app/host/SBI/snrfiles/SDSS_r_magvsnr.txt
index 05f2ec34..9cb41bbd 100644
--- a/app/host/SBI/snrfiles/SDSS_r_magvsnr.txt
+++ b/app/host/SBI/snrfiles/SDSS_r_magvsnr.txt
@@ -1,35 +1,110 @@
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diff --git a/app/host/SBI/snrfiles/SDSS_u_magvsnr.txt b/app/host/SBI/snrfiles/SDSS_u_magvsnr.txt
index 1211fafc..9629ba5d 100644
--- a/app/host/SBI/snrfiles/SDSS_u_magvsnr.txt
+++ b/app/host/SBI/snrfiles/SDSS_u_magvsnr.txt
@@ -1,32 +1,101 @@
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diff --git a/app/host/SBI/snrfiles/SDSS_z_magvsnr.txt b/app/host/SBI/snrfiles/SDSS_z_magvsnr.txt
index 14b09577..33615043 100644
--- a/app/host/SBI/snrfiles/SDSS_z_magvsnr.txt
+++ b/app/host/SBI/snrfiles/SDSS_z_magvsnr.txt
@@ -1,35 +1,112 @@
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diff --git a/app/host/SBI/snrfiles/WISE_W1_magvsnr.txt b/app/host/SBI/snrfiles/WISE_W1_magvsnr.txt
index a3c44685..b9d95eba 100644
--- a/app/host/SBI/snrfiles/WISE_W1_magvsnr.txt
+++ b/app/host/SBI/snrfiles/WISE_W1_magvsnr.txt
@@ -1,46 +1,114 @@
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diff --git a/app/host/SBI/snrfiles/WISE_W2_magvsnr.txt b/app/host/SBI/snrfiles/WISE_W2_magvsnr.txt
index f5004e81..9f79261b 100644
--- a/app/host/SBI/snrfiles/WISE_W2_magvsnr.txt
+++ b/app/host/SBI/snrfiles/WISE_W2_magvsnr.txt
@@ -1,46 +1,113 @@
-9.589 569.064
-9.839 566.647
-10.089 564.231
-10.339 476.468
-10.589 437.646
-10.839 379.485
-11.089 304.809
-11.339 302.738
-11.589 256.046
-11.839 252.251
-12.089 116.095
-12.339 204.962
-12.589 170.444
-12.839 181.098
-13.089 105.873
-13.339 93.013
-13.589 134.440
-13.839 99.905
-14.089 63.594
-14.339 72.594
-14.589 69.651
-14.839 35.510
-15.089 41.823
-15.339 53.339
-15.589 36.689
-15.839 29.656
-16.089 25.770
-16.339 26.362
-16.589 18.262
-16.839 19.280
-17.089 15.059
-17.339 18.528
-17.589 18.100
-17.839 14.429
-18.089 10.376
-18.339 3.785
-18.589 5.034
-18.839 6.282
-19.089 5.212
-19.339 4.833
-19.589 4.455
-19.839 3.528
-20.089 2.600
-20.339 2.936
-20.589 3.272
-20.839 2.582
+11.973 72.888 28.639
+12.223 79.647 25.971
+12.473 69.999 26.424
+12.723 79.848 27.823
+12.973 80.552 25.614
+13.223 79.813 21.478
+13.473 76.819 21.615
+13.723 74.658 19.827
+13.973 70.083 20.341
+14.223 66.989 16.906
+14.473 62.458 17.485
+14.723 58.719 14.407
+14.973 54.309 14.280
+15.223 49.482 12.666
+15.473 44.843 11.652
+15.723 40.856 10.983
+15.973 36.549 10.178
+16.223 32.293 8.699
+16.473 28.522 8.279
+16.723 25.203 7.331
+16.973 22.044 6.526
+17.223 19.381 5.204
+17.473 16.605 5.088
+17.723 14.233 4.156
+17.973 12.068 3.663
+18.223 10.394 3.066
+18.473 8.817 2.760
+18.723 7.369 2.567
+18.973 6.262 2.181
+19.223 5.168 1.677
+19.473 4.308 1.288
+19.723 3.584 1.205
+19.973 2.926 0.803
+20.223 2.371 0.669
+20.473 1.936 0.673
+20.723 1.593 0.450
+20.973 1.295 0.420
+21.223 1.054 0.291
+21.473 0.854 0.225
+21.723 0.687 0.188
+21.973 0.539 0.158
+22.223 0.429 0.125
+22.473 0.337 0.102
+22.723 0.285 0.084
+22.973 0.218 0.141
+23.223 0.183 0.048
+23.473 0.133 0.034
+23.723 0.133 0.034
+23.973 0.133 0.034
+24.223 0.133 0.034
+24.473 0.133 0.034
+24.723 0.133 0.034
+24.973 0.133 0.034
+25.223 0.133 0.034
+25.473 0.133 0.034
+25.723 0.133 0.034
+25.973 0.133 0.034
+26.223 0.133 0.034
+26.473 0.133 0.034
+26.723 0.133 0.034
+26.973 0.133 0.034
+27.223 0.133 0.034
+27.473 0.133 0.034
+27.723 0.133 0.034
+27.973 0.133 0.034
+28.223 0.133 0.034
+28.473 0.133 0.034
+28.723 0.133 0.034
+28.973 0.133 0.034
+29.223 0.133 0.034
+29.473 0.133 0.034
+29.723 0.133 0.034
+29.973 0.133 0.034
+30.223 0.133 0.034
+30.473 0.133 0.034
+30.723 0.133 0.034
+30.973 0.133 0.034
+31.223 0.133 0.034
+31.473 0.133 0.034
+31.723 0.133 0.034
+31.973 0.133 0.034
+32.223 0.133 0.034
+32.473 0.133 0.034
+32.723 0.133 0.034
+32.973 0.133 0.034
+33.223 0.133 0.034
+33.473 0.133 0.034
+33.723 0.133 0.034
+33.973 0.133 0.034
+34.223 0.133 0.034
+34.473 0.133 0.034
+34.723 0.133 0.034
+34.973 0.133 0.034
+35.223 0.133 0.034
+35.473 0.133 0.034
+35.723 0.133 0.034
+35.973 0.133 0.034
+36.223 0.133 0.034
+36.473 0.133 0.034
+36.723 0.133 0.034
+36.973 0.133 0.034
+37.223 0.133 0.034
+37.473 0.133 0.034
+37.723 0.133 0.034
+37.973 0.133 0.034
+38.223 0.133 0.034
+38.473 0.133 0.034
+38.723 0.133 0.034
+38.973 0.133 0.034
+39.223 0.133 0.034
+39.473 0.133 0.034
+39.723 0.133 0.034
+39.973 0.133 0.034
diff --git a/app/host/SBI/snrfiles/WISE_W3_magvsnr.txt b/app/host/SBI/snrfiles/WISE_W3_magvsnr.txt
index 7e515c3d..471d0f0d 100644
--- a/app/host/SBI/snrfiles/WISE_W3_magvsnr.txt
+++ b/app/host/SBI/snrfiles/WISE_W3_magvsnr.txt
@@ -1,42 +1,117 @@
-6.708 123.838
-6.958 172.067
-7.208 220.297
-7.458 268.526
-7.708 718.484
-7.958 427.705
-8.208 281.304
-8.458 334.133
-8.708 137.463
-8.958 177.734
-9.208 133.900
-9.458 96.406
-9.708 155.706
-9.958 99.294
-10.208 137.089
-10.458 129.070
-10.708 48.637
-10.958 91.261
-11.208 56.861
-11.458 65.960
-11.708 54.875
-11.958 55.912
-12.208 54.285
-12.458 53.958
-12.708 22.026
-12.958 30.829
-13.208 14.193
-13.458 13.210
-13.708 17.521
-13.958 10.946
-14.208 14.874
-14.458 7.505
-14.708 6.917
-14.958 9.443
-15.208 6.776
-15.458 4.108
-15.708 5.635
-15.958 4.907
-16.208 2.644
-16.458 2.634
-16.708 2.261
-16.958 2.909
+10.955 37.412 33.759
+11.205 43.722 34.391
+11.455 48.181 32.517
+11.705 69.017 30.374
+11.955 51.271 30.287
+12.205 75.419 29.893
+12.455 70.422 26.499
+12.705 68.467 24.084
+12.955 65.449 21.673
+13.205 58.185 20.679
+13.455 52.390 18.558
+13.705 48.917 17.487
+13.955 45.011 16.676
+14.205 39.503 15.475
+14.455 34.441 13.585
+14.705 30.195 12.702
+14.955 25.965 11.858
+15.205 22.323 9.645
+15.455 19.355 8.993
+15.705 16.602 7.741
+15.955 13.712 7.181
+16.205 11.589 5.172
+16.455 9.725 4.225
+16.705 8.049 3.419
+16.955 6.785 3.231
+17.205 5.600 2.306
+17.455 4.719 2.048
+17.705 3.985 1.708
+17.955 3.288 1.245
+18.205 2.725 1.426
+18.455 2.280 0.831
+18.705 1.889 0.694
+18.955 1.572 0.574
+19.205 1.271 0.514
+19.455 1.057 0.411
+19.705 0.853 0.407
+19.955 0.702 0.265
+20.205 0.556 0.252
+20.455 0.459 0.230
+20.705 0.360 0.164
+20.955 0.285 0.115
+21.205 0.230 0.089
+21.455 0.178 0.068
+21.705 0.146 0.061
+21.955 0.112 0.060
+22.205 0.092 0.036
+22.455 0.073 0.040
+22.705 0.055 0.024
+22.955 0.055 0.024
+23.205 0.055 0.024
+23.455 0.055 0.024
+23.705 0.055 0.024
+23.955 0.055 0.024
+24.205 0.055 0.024
+24.455 0.055 0.024
+24.705 0.055 0.024
+24.955 0.055 0.024
+25.205 0.055 0.024
+25.455 0.055 0.024
+25.705 0.055 0.024
+25.955 0.055 0.024
+26.205 0.055 0.024
+26.455 0.055 0.024
+26.705 0.055 0.024
+26.955 0.055 0.024
+27.205 0.055 0.024
+27.455 0.055 0.024
+27.705 0.055 0.024
+27.955 0.055 0.024
+28.205 0.055 0.024
+28.455 0.055 0.024
+28.705 0.055 0.024
+28.955 0.055 0.024
+29.205 0.055 0.024
+29.455 0.055 0.024
+29.705 0.055 0.024
+29.955 0.055 0.024
+30.205 0.055 0.024
+30.455 0.055 0.024
+30.705 0.055 0.024
+30.955 0.055 0.024
+31.205 0.055 0.024
+31.455 0.055 0.024
+31.705 0.055 0.024
+31.955 0.055 0.024
+32.205 0.055 0.024
+32.455 0.055 0.024
+32.705 0.055 0.024
+32.955 0.055 0.024
+33.205 0.055 0.024
+33.455 0.055 0.024
+33.705 0.055 0.024
+33.955 0.055 0.024
+34.205 0.055 0.024
+34.455 0.055 0.024
+34.705 0.055 0.024
+34.955 0.055 0.024
+35.205 0.055 0.024
+35.455 0.055 0.024
+35.705 0.055 0.024
+35.955 0.055 0.024
+36.205 0.055 0.024
+36.455 0.055 0.024
+36.705 0.055 0.024
+36.955 0.055 0.024
+37.205 0.055 0.024
+37.455 0.055 0.024
+37.705 0.055 0.024
+37.955 0.055 0.024
+38.205 0.055 0.024
+38.455 0.055 0.024
+38.705 0.055 0.024
+38.955 0.055 0.024
+39.205 0.055 0.024
+39.455 0.055 0.024
+39.705 0.055 0.024
+39.955 0.055 0.024
diff --git a/app/host/SBI/snrfiles/WISE_W4_magvsnr.txt b/app/host/SBI/snrfiles/WISE_W4_magvsnr.txt
index 8244284d..fcdb4447 100644
--- a/app/host/SBI/snrfiles/WISE_W4_magvsnr.txt
+++ b/app/host/SBI/snrfiles/WISE_W4_magvsnr.txt
@@ -1,44 +1,118 @@
-5.363 150.200
-5.613 164.116
-5.863 178.032
-6.113 111.280
-6.363 116.695
-6.613 85.341
-6.863 90.811
-7.113 99.952
-7.363 37.822
-7.613 44.851
-7.863 25.629
-8.113 26.360
-8.363 23.568
-8.613 25.448
-8.863 26.994
-9.113 22.456
-9.363 28.704
-9.613 22.412
-9.863 13.099
-10.113 14.707
-10.363 15.735
-10.613 12.176
-10.863 8.584
-11.113 10.425
-11.363 7.485
-11.613 7.685
-11.863 6.615
-12.113 5.545
-12.363 5.060
-12.613 4.973
-12.863 4.023
-13.113 3.073
-13.363 2.698
-13.613 2.126
-13.863 4.472
-14.113 1.762
-14.363 1.467
-14.613 1.173
-14.863 1.677
-15.113 1.330
-15.363 1.101
-15.613 1.011
-15.863 0.920
-16.113 0.830
+10.747 17.342 16.659
+10.997 18.044 15.441
+11.247 19.325 11.651
+11.497 17.634 11.598
+11.747 18.520 9.291
+11.997 19.758 9.576
+12.247 17.764 7.376
+12.497 17.071 10.076
+12.747 14.806 6.961
+12.997 13.136 5.558
+13.247 11.193 5.967
+13.497 10.580 4.457
+13.747 9.213 4.811
+13.997 7.961 4.091
+14.247 7.003 2.656
+14.497 6.053 2.530
+14.747 5.213 4.075
+14.997 4.511 1.821
+15.247 3.933 1.486
+15.497 3.330 2.011
+15.747 2.842 1.536
+15.997 2.438 1.017
+16.247 2.065 0.981
+16.497 1.762 0.893
+16.747 1.521 0.783
+16.997 1.278 0.541
+17.247 1.084 0.340
+17.497 0.894 0.353
+17.747 0.743 0.319
+17.997 0.604 0.265
+18.247 0.493 0.347
+18.497 0.399 0.160
+18.747 0.317 0.247
+18.997 0.258 0.104
+19.247 0.196 0.101
+19.497 0.160 0.068
+19.747 0.122 0.076
+19.997 0.096 0.039
+20.247 0.084 0.056
+20.497 0.061 0.025
+20.747 0.050 0.017
+20.997 0.041 0.016
+21.247 0.032 0.011
+21.497 0.026 0.010
+21.747 0.026 0.010
+21.997 0.026 0.010
+22.247 0.026 0.010
+22.497 0.026 0.010
+22.747 0.026 0.010
+22.997 0.026 0.010
+23.247 0.026 0.010
+23.497 0.026 0.010
+23.747 0.026 0.010
+23.997 0.026 0.010
+24.247 0.026 0.010
+24.497 0.026 0.010
+24.747 0.026 0.010
+24.997 0.026 0.010
+25.247 0.026 0.010
+25.497 0.026 0.010
+25.747 0.026 0.010
+25.997 0.026 0.010
+26.247 0.026 0.010
+26.497 0.026 0.010
+26.747 0.026 0.010
+26.997 0.026 0.010
+27.247 0.026 0.010
+27.497 0.026 0.010
+27.747 0.026 0.010
+27.997 0.026 0.010
+28.247 0.026 0.010
+28.497 0.026 0.010
+28.747 0.026 0.010
+28.997 0.026 0.010
+29.247 0.026 0.010
+29.497 0.026 0.010
+29.747 0.026 0.010
+29.997 0.026 0.010
+30.247 0.026 0.010
+30.497 0.026 0.010
+30.747 0.026 0.010
+30.997 0.026 0.010
+31.247 0.026 0.010
+31.497 0.026 0.010
+31.747 0.026 0.010
+31.997 0.026 0.010
+32.247 0.026 0.010
+32.497 0.026 0.010
+32.747 0.026 0.010
+32.997 0.026 0.010
+33.247 0.026 0.010
+33.497 0.026 0.010
+33.747 0.026 0.010
+33.997 0.026 0.010
+34.247 0.026 0.010
+34.497 0.026 0.010
+34.747 0.026 0.010
+34.997 0.026 0.010
+35.247 0.026 0.010
+35.497 0.026 0.010
+35.747 0.026 0.010
+35.997 0.026 0.010
+36.247 0.026 0.010
+36.497 0.026 0.010
+36.747 0.026 0.010
+36.997 0.026 0.010
+37.247 0.026 0.010
+37.497 0.026 0.010
+37.747 0.026 0.010
+37.997 0.026 0.010
+38.247 0.026 0.010
+38.497 0.026 0.010
+38.747 0.026 0.010
+38.997 0.026 0.010
+39.247 0.026 0.010
+39.497 0.026 0.010
+39.747 0.026 0.010
+39.997 0.026 0.010
diff --git a/app/host/plotting_utils.py b/app/host/plotting_utils.py
index 9bdd112b..ecbace52 100644
--- a/app/host/plotting_utils.py
+++ b/app/host/plotting_utils.py
@@ -1,6 +1,7 @@
 import math
 import os
 from math import pi
+from packaging.version import Version
 
 import numpy as np
 import pandas as pd
@@ -252,7 +253,7 @@ def generate_empty_plot(title):
     return generate_plot(fig, image_data=image_data)
 
 
-def plot_sed(transient=None, sed_results_file=None, type="", sed_modeldata_file=None):
+def plot_sed(transient=None, sed_results_file=None, type="", sed_modeldata_file=None, offset_sed_model=False):
     """
     Plot SED from aperture photometry.
     """
@@ -344,7 +345,7 @@ def plot_sed(transient=None, sed_results_file=None, type="", sed_modeldata_file=
         model_data = np.load(sed_modeldata_file, allow_pickle=True)
 
         # best = result["bestfit"]
-        if transient.best_redshift < 0.015:
+        if transient.best_redshift < 0.015 and offset_sed_model:
             a = result["obs"]["redshift"] - 0.015 + 1
             mag_off = (
                 cosmo.distmod(result["obs"]["redshift"]).value
@@ -397,7 +398,7 @@ def plot_sed(transient=None, sed_results_file=None, type="", sed_modeldata_file=
             except Exception:
                 pwave = [f.wave_effective for f in obs["filters"]]
 
-            if transient.best_redshift < 0.015:
+            if transient.best_redshift < 0.015 and offset_sed_model:
                 fig.scatter(
                     pwave,
                     maggies_to_mJy(model_data["phot"]) * 10 ** (0.4 * mag_off),
@@ -585,6 +586,7 @@ def render_sed_plot(transient, scope):
     sed_results_tmp_filepath = None
     sed_modeldata_tmp_filepath = None
     sed_obj = SEDFittingResult.objects.filter(transient=transient, aperture__type__exact=scope)
+    offset_sed_model = False
     if sed_obj.exists():
         canonical_path = sed_obj[0].posterior.name
         sed_results_tmp_filepath, sed_results_object_key = temp_results_paths_from_canonical_path(canonical_path)
@@ -592,12 +594,15 @@ def render_sed_plot(transient, scope):
         sed_modeldata_object_key = sed_results_object_key.replace(".h5", "_modeldata.npz")
         download_file_from_s3(sed_results_tmp_filepath, sed_results_object_key)
         download_file_from_s3(sed_modeldata_tmp_filepath, sed_modeldata_object_key)
+        if sed_obj[0].software_version is None or Version(sed_obj[0].software_version) <= Version('1.13.1'):
+            offset_sed_model = True
     # Generate a SED plot using Bokeh
     plot = plot_sed(
         transient=transient,
         type=scope,
         sed_results_file=sed_results_tmp_filepath,
         sed_modeldata_file=sed_modeldata_tmp_filepath,
+        offset_sed_model=offset_sed_model
     )
     # Purge temporary cached files
     delete_cached_file(sed_results_tmp_filepath)
diff --git a/app/host/prospector.py b/app/host/prospector.py
index c574f404..8d867695 100644
--- a/app/host/prospector.py
+++ b/app/host/prospector.py
@@ -29,6 +29,7 @@
 from prospect.utils.obsutils import fix_obs
 from scipy.special import gamma
 from scipy.special import gammainc
+from scipy.interpolate import interp1d
 
 from .host_utils import get_dust_maps
 from .models import AperturePhotometry
@@ -180,10 +181,30 @@ def build_obs(transient, aperture_type, use_mag_offset=True):
 
         filters.append(trans_curve)
         flux_maggies.append(mJy_to_maggies(flux_mwcorr * 10 ** (-0.4 * mag_offset)))
-        flux_maggies_error.append(
-            mJy_to_maggies(fluxerr_mwcorr * 10 ** (-0.4 * mag_offset))
-        )
-
+        if mag_offset > 0:
+            toy_noise_x, toy_noise_y, toy_noise_std = np.loadtxt(
+                f"host/SBI/snrfiles/{filter.name}_magvsnr.txt", dtype=float, unpack=True
+            )
+            meds_sigs = \
+                interp1d(
+                    toy_noise_x,
+                    1.0857 * 1 / toy_noise_y,
+		    kind="slinear",
+                    fill_value="extrapolate",  # (0.01,1.0),
+                    bounds_error=False,
+                )
+            
+            magerr_off = meds_sigs(-2.5*np.log10(mJy_to_maggies(flux_mwcorr))+mag_offset) - \
+                meds_sigs(-2.5*np.log10(mJy_to_maggies(flux_mwcorr)))
+            magerr = 1.0857*fluxerr_mwcorr/flux_mwcorr + magerr_off
+            flux_maggies_error.append(
+                mJy_to_maggies(0.4*np.log(10)*magerr*flux_mwcorr * 10 ** (-0.4 * mag_offset))
+            )
+        else:
+            flux_maggies_error.append(
+                mJy_to_maggies(fluxerr_mwcorr * 10 ** (-0.4 * mag_offset))
+            )
+            
     obs_data = dict(
         wavelength=None,
         spectrum=None,
@@ -224,7 +245,7 @@ def build_model_nonparam(obs=None, **extras):
         "N": 1,
         "isfree": True,
         "init": 0.5,
-        "prior": priors.FastUniform(a=0, b=0.2),
+        "prior": priors.FastUniform(a=0, b=1.0),
     }
 
     model_params["logmass"] = {
@@ -743,7 +764,7 @@ def build_model(observations):
 
 
 def fit_model(
-    observations, model_components, fitting_kwargs, sbipp=False, fit_type="global"
+    observations, model_components, fitting_kwargs, sbipp=False, fit_type="standard"
 ):
     """Fit the model"""
 
diff --git a/app/host/transient_tasks.py b/app/host/transient_tasks.py
index 72a73c7a..7e26a55f 100644
--- a/app/host/transient_tasks.py
+++ b/app/host/transient_tasks.py
@@ -993,15 +993,19 @@ def _run_process(
         #     logger.warning('All SED output files exist. Skipping SED fitting calculation...')
         #     return "processed"
 
-        if transient.best_redshift is None or transient.best_redshift > 0.2:
+        if transient.best_redshift is None or transient.best_redshift > 1.0:
             # training sample doesn't work here
             return "redshift too high"
-
+        if transient.best_redshift < 0.015:
+            fit_type = 'lowz'
+        else:
+            fit_type = 'standard'
+        
         aperture = Aperture.objects.filter(**query)
         if len(aperture) == 0:
             raise RuntimeError(f"no apertures found for transient {transient.name}")
 
-        observations = build_obs(transient, aperture_type)
+        observations = build_obs(transient, aperture_type, use_mag_offset=False)
         model_components = build_model(observations)
 
         if mode == "test" and not sbipp:
@@ -1039,7 +1043,7 @@ def _run_process(
             model_components,
             fitting_settings,
             sbipp=sbipp,
-            fit_type=aperture_type,
+            fit_type=fit_type,
         )
         if errflag:
             return "not enough filters"
diff --git a/docs/usage/data_files.rst b/docs/usage/data_files.rst
index dc93d38f..01d3b6d1 100644
--- a/docs/usage/data_files.rst
+++ b/docs/usage/data_files.rst
@@ -48,7 +48,7 @@ Description
 
 This file is broadly the same as the parameter chains file above, but each parameter has an array of three elements (or sometimes three columns) corresponding to the 16th, 50th, and 84th percentiles from the posterior samples.  Additionally, these files include confidence intervals for the model spectra and model photometry, in :code:`modspec` and :code:`modphot`, respectively.  The :code:`theta_lbs` key contains the full set of prospector model parameters for reference.
 
-Note: for transients at z < 0.015, an offset is applied in the model spectra and photometry due to practical considerations.  See :ref:`low_z` below for help interpreting these data.
+Note: for previous versions of blast in the case of transients at z < 0.015, an offset is applied in the model spectra and photometry due to practical considerations.  See :ref:`low_z` below for help interpreting these data.
 
 
 .. _model_file:
@@ -183,6 +183,9 @@ Finally, to plot the observed photometry alongside the SED model above, we just
 Low-Redshift Transients
 -----------------------
 
+*Note*: We have updated our models such that this section is no longer
+needed for SED fitting with Blast version x.x.x and later.
+
 In the local volume, small changes in redshift equate to large differences in predicted magnitude,
 making it impractical to generate a sufficiently large training set for the SBI approach in this regime.  Instead,
 we artificially redshift the transient photometry by an additional :math:`\Delta z = 0.015` using the WMAP 2009 cosmology.