diff --git a/libs/architectures/architectures/__init__.py b/libs/architectures/architectures/__init__.py
index 7ff6db14c..350160f02 100644
--- a/libs/architectures/architectures/__init__.py
+++ b/libs/architectures/architectures/__init__.py
@@ -6,4 +6,5 @@
     SupervisedSpectrogramDomainResNet,
     SupervisedTimeDomainResNet,
     SupervisedTimeSpectrogramResNet,
+    SupervisedHeterodyneTimeDomainResNet,
 )
diff --git a/libs/architectures/architectures/supervised.py b/libs/architectures/architectures/supervised.py
index 08fc950e1..a0ae7e539 100644
--- a/libs/architectures/architectures/supervised.py
+++ b/libs/architectures/architectures/supervised.py
@@ -281,3 +281,43 @@ def forward(self, X, X_spec):
         time_domain_output = self.time_domain_resnet(X)
         spec_domain_output = self.spectrogram_resnet(X_spec)
         return time_domain_output, spec_domain_output
+
+
+class SupervisedHeterodyneTimeDomainResNet(SupervisedArchitecture):
+    """
+    Time Domain ResNet that processes a Heterodyned timeseries.
+
+    Args:
+        num_chirp_masses (int):
+            Number of chirp masses used to define the input channel
+            dimension (in_channels = num_ifos x num_chirp_masses).
+    """
+
+    def __init__(
+        self,
+        num_ifos: int,
+        num_chirp_masses: int,
+        layers: list[int],
+        kernel_size: int = 3,
+        zero_init_residual: bool = False,
+        groups: int = 1,
+        width_per_group: int = 64,
+        stride_type: Optional[list[Literal["stride", "dilation"]]] = None,
+        norm_layer: Optional[NormLayer] = None,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.time_domain_resnet = ResNet1D(
+            in_channels=num_ifos * num_chirp_masses,
+            layers=layers,
+            classes=1,
+            kernel_size=kernel_size,
+            zero_init_residual=zero_init_residual,
+            groups=groups,
+            width_per_group=width_per_group,
+            stride_type=stride_type,
+            norm_layer=norm_layer,
+        )
+
+    def forward(self, X):
+        return self.time_domain_resnet(X)
diff --git a/libs/utils/pyproject.toml b/libs/utils/pyproject.toml
index 9c0a76ce0..bcb4f2cc5 100644
--- a/libs/utils/pyproject.toml
+++ b/libs/utils/pyproject.toml
@@ -9,7 +9,7 @@ dependencies = [
     "h5py~=3.6",
     "numpy>=2",
     "s3fs>=2024,<2025",
-    "ml4gw>=0.7.10",
+    "ml4gw>=0.8.0",
     "astropy>=6.0.1",
 ]
 
diff --git a/libs/utils/tests/test_preprocessing.py b/libs/utils/tests/test_preprocessing.py
index ae1654dda..fb29633a6 100644
--- a/libs/utils/tests/test_preprocessing.py
+++ b/libs/utils/tests/test_preprocessing.py
@@ -7,6 +7,10 @@
     PsdEstimator,
     TimeSpectrogramPreprocessor,
 )
+from utils.augmentation import HeterodyneAugmentor
+from ml4gw.transforms import Heterodyne
+import torch
+import pytest
 
 
 class TestBackgroundSnapshotter:
@@ -247,6 +251,59 @@ def simple_augmentor(x):
         kernels_aug = whitener_aug(x)
         assert torch.allclose(kernels_aug, kernels * 2)
 
+    def test_with_heterodyne_augmentor(self):
+        heterodyne_augmentor = HeterodyneAugmentor(
+            sample_rate=self.sample_rate,
+            kernel_length=self.kernel_length,
+            chirp_mass_low=1.0,
+            chirp_mass_high=2.5,
+            num_chirp_masses=10,
+            chirp_mass_spacing="log",
+            keep_last_n_seconds=None,
+        )
+
+        whitener = BatchWhitener(
+            kernel_length=self.kernel_length,
+            sample_rate=self.sample_rate,
+            inference_sampling_rate=self.inference_sampling_rate,
+            batch_size=self.batch_size,
+            fduration=self.fduration,
+            fftlength=self.fftlength,
+        )
+
+        whitener_aug = BatchWhitener(
+            kernel_length=self.kernel_length,
+            sample_rate=self.sample_rate,
+            inference_sampling_rate=self.inference_sampling_rate,
+            batch_size=self.batch_size,
+            fduration=self.fduration,
+            fftlength=self.fftlength,
+            augmentor=heterodyne_augmentor,
+        )
+
+        channels = 2
+        total_samples = int(
+            (
+                (self.batch_size - 1) * whitener.stride_size
+                + whitener.kernel_size
+            )
+            * 2
+        )
+        x = torch.randn(channels, total_samples)
+
+        heterodyne = Heterodyne(
+            sample_rate=self.sample_rate,
+            kernel_length=self.kernel_length,
+            chirp_mass=heterodyne_augmentor.chirp_mass_grid,
+            return_type="time",
+        )
+
+        kernels_heterodyned = heterodyne(whitener(x))
+        _B, _C, _M, _T = kernels_heterodyned.shape
+        kernels = kernels_heterodyned.reshape(_B, _C * _M, _T)
+        kernels_aug = whitener_aug(x)
+        assert torch.allclose(kernels_aug, kernels)
+
 
 class TestMultiModalPreprocessor:
     """Test suite for MultiModalPreprocessor module."""
diff --git a/libs/utils/utils/augmentation.py b/libs/utils/utils/augmentation.py
new file mode 100644
index 000000000..7680b7a0e
--- /dev/null
+++ b/libs/utils/utils/augmentation.py
@@ -0,0 +1,135 @@
+import math
+import torch
+from torch import Tensor
+from typing import Literal
+
+from ml4gw.transforms import Heterodyne
+
+
+class HeterodyneAugmentor(torch.nn.Module):
+    """
+    Apply a heterodyne transform over a grid of chirp masses to a batch
+    of time-series data.
+
+    Args:
+        sample_rate (float): Input sampling rate in Hz.
+        kernel_length (float): Length of output kernels in seconds.
+        chirp_mass_low (float):
+            Lower bound of chirp mass range (in solar masses).
+        chirp_mass_high (float):
+            Upper bound of chirp mass range (in solar masses).
+        num_chirp_masses (int):
+            Number of chirp mass samples to generate.
+        chirp_mass_spacing (Literal["linear", "log"]):
+            Spacing of chirp mass grid. Use "linear" for evenly spaced
+            values or "log" for logarithmic spacing.
+        keep_last_n_seconds (float):
+            If provided, only the last `n` seconds of the kernel_length are
+            returned. Otherwise, the full kernel_length is returned.
+    Shape:
+        Input: (batch_size, channels, time)
+        Output: (batch_size, channels * num_chirp_masses, time_out)
+
+        where `time_out = time` unless `keep_last_n_seconds` is set.
+
+    Note:
+        The output shape of the `BatchWhitener` is
+        (batch_size, channels, kernel_size).
+        The `HeterodyneAugmentor` changes the output shape to
+        (batch_size, channels * num_chirp_masses, time_out)
+        where time_out is determined by the `keep_last_n_seconds` parameter.
+
+    Example:
+        >>> augmentor = HeterodyneAugmentor(
+        ...     sample_rate=2048, kernel_length=8, chirp_mass_low=1.0,
+        ...     chirp_mass_high=2.5, num_chirp_masses=100,
+        ...     chirp_mass_spacing="log", keep_last_n_seconds=4.0,
+        ... )
+        >>> x = torch.randn(8, 2, 16384)  # (batch, channels, time)
+        >>> y = augmentor(x)
+        >>> # y: (8, 2 * 100, 8192) since we keep the last 4 seconds at 2048 Hz
+    """
+
+    def __init__(
+        self,
+        sample_rate: float,
+        kernel_length: float,
+        chirp_mass_low: float = 1.0,
+        chirp_mass_high: float = 2.5,
+        num_chirp_masses: int = 100,
+        chirp_mass_spacing: Literal["linear", "log"] = "log",
+        keep_last_n_seconds: float = None,
+    ):
+        super().__init__()
+        self.sample_rate = sample_rate
+        self.kernel_length = kernel_length
+        self.keep_last_n_seconds = keep_last_n_seconds
+        self.num_chirp_masses = num_chirp_masses
+        self.keep_last_n_seconds = keep_last_n_seconds
+
+        self.chirp_mass_grid = self._create_chirp_mass_grid(
+            chirp_mass_low,
+            chirp_mass_high,
+            num_chirp_masses,
+            chirp_mass_spacing,
+        )
+
+        if self.keep_last_n_seconds is not None:
+            self.keep_last_n_samples = int(
+                self.keep_last_n_seconds * sample_rate
+            )
+
+        self.heterodyne_transform = Heterodyne(
+            sample_rate=sample_rate,
+            kernel_length=kernel_length,
+            chirp_mass=self.chirp_mass_grid,
+            return_type="time",
+        )
+
+    def _create_chirp_mass_grid(
+        self,
+        chirp_mass_low: float,
+        chirp_mass_high: float,
+        num_chirp_masses: int,
+        chirp_mass_spacing: Literal["linear", "log"],
+    ) -> torch.Tensor:
+        if chirp_mass_spacing == "linear":
+            return torch.linspace(
+                chirp_mass_low, chirp_mass_high, num_chirp_masses
+            )
+        elif chirp_mass_spacing == "log":
+            return torch.logspace(
+                math.log10(chirp_mass_low),
+                math.log10(chirp_mass_high),
+                num_chirp_masses,
+            )
+        else:
+            raise ValueError(
+                f"Invalid chirp mass spacing: {chirp_mass_spacing}"
+            )
+
+    def forward(self, x: Tensor) -> Tensor:
+        """
+        Args:
+            x (Tensor): Input data of shape (batch, channels, time).
+
+        Returns:
+            Tensor: Output data of shape
+                (batch, channels * num_chirp_masses, time_out),
+                where `time_out` is either the same as input time
+                or determined by `keep_last_n_seconds`.
+        """
+        _B, _C, _T = x.shape
+        x_heterodyned = torch.empty((_B, _C * self.num_chirp_masses, _T))
+        # Heterodyne the whitened timeseries
+        x = self.heterodyne_transform(x)
+        # Reshaping x from (batch_size, channels, num_chirp_mass, kernel_size)
+        # to (batch_size, channels x num_chirp_mass, kernel_size)
+        x = x.reshape(_B, _C * self.num_chirp_masses, _T)
+        x_heterodyned[:, :, :] = x
+        # Returning the desired length of heterodyned strain in the
+        # time dimension
+        if self.keep_last_n_seconds is not None:
+            return x_heterodyned[..., -self.keep_last_n_samples :]
+        else:
+            return x_heterodyned
diff --git a/libs/utils/uv.lock b/libs/utils/uv.lock
index 43a5e8406..a6882d09b 100644
--- a/libs/utils/uv.lock
+++ b/libs/utils/uv.lock
@@ -418,7 +418,7 @@ wheels = [
 
 [[package]]
 name = "ml4gw"
-version = "0.7.10"
+version = "0.8.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jaxtyping" },
@@ -427,9 +427,9 @@ dependencies = [
     { name = "torch" },
     { name = "torchaudio" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/42/4b/d04a579fe0009f29f336af329d231dfd0da32c1f03560cbfdcd1d3081b86/ml4gw-0.7.10.tar.gz", hash = "sha256:20c54524b9f44669ef2b320832c62fac9766d2374019cfd798ec89a83adf1e99", size = 119139, upload-time = "2025-12-03T01:34:50.761Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/2a/56/abb490d353f989802f918ee92cf6c9a37336483aa20c7f17df7730d81744/ml4gw-0.8.0.tar.gz", hash = "sha256:43a2411ae348f8f911fdc0e2defd4fa54370414fa8b51c63518de3cb805754ba", size = 121709, upload-time = "2026-04-17T13:15:20.347Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/04/a8/174f4112c2dae1c61f0ed1c5eb32b7b7dd0c34b8050ec1a67d45480f34b3/ml4gw-0.7.10-py3-none-any.whl", hash = "sha256:f9edaa40365ee4441ffb42b4bf542a70522990495659bef441fbb2eba0efbcf4", size = 129192, upload-time = "2025-12-03T01:34:49.491Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/22/64102f10ad7f9043083d8bafcf84b8f2e8abc84bfc214fd04dd8d243ece9/ml4gw-0.8.0-py3-none-any.whl", hash = "sha256:0b4377541d5a90dcf9c728efa4008b4d57b942452acf564856ac58f599273070", size = 132926, upload-time = "2026-04-17T13:15:18.751Z" },
 ]
 
 [[package]]
@@ -1095,7 +1095,7 @@ dev = [
 requires-dist = [
     { name = "astropy", specifier = ">=6.0.1" },
     { name = "h5py", specifier = "~=3.6" },
-    { name = "ml4gw", specifier = ">=0.7.10" },
+    { name = "ml4gw", specifier = ">=0.8.0" },
     { name = "numpy", specifier = ">=2" },
     { name = "s3fs", specifier = ">=2024,<2025" },
 ]
diff --git a/projects/data/uv.lock b/projects/data/uv.lock
index b1e64cc12..ca4d5a28c 100644
--- a/projects/data/uv.lock
+++ b/projects/data/uv.lock
@@ -1448,7 +1448,7 @@ wheels = [
 
 [[package]]
 name = "ml4gw"
-version = "0.7.11"
+version = "0.8.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jaxtyping" },
@@ -1457,9 +1457,9 @@ dependencies = [
     { name = "torch" },
     { name = "torchaudio" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/6f/0a/722f553635ffc91b32623e69a4c93591c11ce2c24a10e4bda35ab0d8e6ae/ml4gw-0.7.11.tar.gz", hash = "sha256:8df9ebecd97ed6a6e8ba07fab40882f5966e646897f5187a9ccf7913faf6464e", size = 119593, upload-time = "2026-01-29T20:34:30.794Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/2a/56/abb490d353f989802f918ee92cf6c9a37336483aa20c7f17df7730d81744/ml4gw-0.8.0.tar.gz", hash = "sha256:43a2411ae348f8f911fdc0e2defd4fa54370414fa8b51c63518de3cb805754ba", size = 121709, upload-time = "2026-04-17T13:15:20.347Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/89/7d/f8c3e695d52cd9e70fd3f7bb51efd29848a3eb481dc1b94228f481dd05f8/ml4gw-0.7.11-py3-none-any.whl", hash = "sha256:0a6645f27444d266fb94afe988450bc2d00e24bd70328b0a5903194e1900acdb", size = 129588, upload-time = "2026-01-29T20:34:29.357Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/22/64102f10ad7f9043083d8bafcf84b8f2e8abc84bfc214fd04dd8d243ece9/ml4gw-0.8.0-py3-none-any.whl", hash = "sha256:0b4377541d5a90dcf9c728efa4008b4d57b942452acf564856ac58f599273070", size = 132926, upload-time = "2026-04-17T13:15:18.751Z" },
 ]
 
 [[package]]
@@ -2753,7 +2753,7 @@ dependencies = [
 requires-dist = [
     { name = "astropy", specifier = ">=6.0.1" },
     { name = "h5py", specifier = "~=3.6" },
-    { name = "ml4gw", specifier = ">=0.7.10" },
+    { name = "ml4gw", specifier = ">=0.8.0" },
     { name = "numpy", specifier = ">=2" },
     { name = "s3fs", specifier = ">=2024,<2025" },
 ]
diff --git a/projects/export/export_heterodyne.yaml b/projects/export/export_heterodyne.yaml
new file mode 100644
index 000000000..1e3414634
--- /dev/null
+++ b/projects/export/export_heterodyne.yaml
@@ -0,0 +1,43 @@
+# commented args (i.e. comments with colon at end 
+# to illustrate a value needs to be filled out)
+# represent values filled out
+# by the export task at run time. To build a functional
+# standalone config, add these in yourself.
+
+logfile:
+weights:
+batch_file:
+repository_directory:
+num_ifos: 2
+kernel_length: 4.0
+inference_sampling_rate: 4
+sample_rate: 2048
+batch_size: 128
+fduration: 2
+psd_length: 64
+preprocessor:
+  class_path: utils.preprocessing.BatchWhitener
+  init_args:
+    kernel_length: 4.0
+    sample_rate: 2048
+    inference_sampling_rate: 4
+    batch_size: 128
+    fduration: 2
+    fftlength: 2
+    augmentor:
+      class_path: utils.augmentation.HeterodyneAugmentor
+      init_args:
+        sample_rate: 2048
+        kernel_length: 4.0
+        chirp_mass_low: 1.0
+        chirp_mass_high: 2.5 
+        num_chirp_masses: 100
+        chirp_mass_spacing: "log" 
+        keep_last_n_seconds: null
+    highpass: 32.0
+streams_per_gpu: 12
+# num_outputs:
+aframe_instances: 1
+platform: TENSORRT
+clean: true
+verbose: true
diff --git a/projects/export/pyproject.toml b/projects/export/pyproject.toml
index 408c4266c..48bfe77d9 100644
--- a/projects/export/pyproject.toml
+++ b/projects/export/pyproject.toml
@@ -6,7 +6,7 @@ authors = [{ name = "Ethan Jacob Marx", email = "ethan.marx@ligo.org" }]
 requires-python = ">=3.11,<3.14"
 license = "MIT"
 dependencies = [
-    "ml4gw>=0.7.7",
+    "ml4gw>=0.8.0",
     "boto3~=1.30",
     "fsspec[s3]>=2024,<2025",
     "ml4gw-hermes[torch]",
diff --git a/projects/export/uv.lock b/projects/export/uv.lock
index d408303d0..db182e34f 100644
--- a/projects/export/uv.lock
+++ b/projects/export/uv.lock
@@ -357,7 +357,7 @@ requires-dist = [
     { name = "boto3", specifier = "~=1.30" },
     { name = "fsspec", extras = ["s3"], specifier = ">=2024,<2025" },
     { name = "jsonargparse", specifier = ">=4.27.1,<5" },
-    { name = "ml4gw", specifier = ">=0.7.7" },
+    { name = "ml4gw", specifier = ">=0.8.0" },
     { name = "ml4gw-hermes", extras = ["torch"], git = "https://github.com/ML4GW/hermes?branch=dev" },
     { name = "nvidia-cudnn-cu12" },
     { name = "tensorrt", specifier = ">=10.0" },
@@ -732,7 +732,7 @@ wheels = [
 
 [[package]]
 name = "ml4gw"
-version = "0.7.10"
+version = "0.8.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jaxtyping" },
@@ -741,9 +741,9 @@ dependencies = [
     { name = "torch" },
     { name = "torchaudio" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/42/4b/d04a579fe0009f29f336af329d231dfd0da32c1f03560cbfdcd1d3081b86/ml4gw-0.7.10.tar.gz", hash = "sha256:20c54524b9f44669ef2b320832c62fac9766d2374019cfd798ec89a83adf1e99", size = 119139, upload-time = "2025-12-03T01:34:50.761Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/2a/56/abb490d353f989802f918ee92cf6c9a37336483aa20c7f17df7730d81744/ml4gw-0.8.0.tar.gz", hash = "sha256:43a2411ae348f8f911fdc0e2defd4fa54370414fa8b51c63518de3cb805754ba", size = 121709, upload-time = "2026-04-17T13:15:20.347Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/04/a8/174f4112c2dae1c61f0ed1c5eb32b7b7dd0c34b8050ec1a67d45480f34b3/ml4gw-0.7.10-py3-none-any.whl", hash = "sha256:f9edaa40365ee4441ffb42b4bf542a70522990495659bef441fbb2eba0efbcf4", size = 129192, upload-time = "2025-12-03T01:34:49.491Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/22/64102f10ad7f9043083d8bafcf84b8f2e8abc84bfc214fd04dd8d243ece9/ml4gw-0.8.0-py3-none-any.whl", hash = "sha256:0b4377541d5a90dcf9c728efa4008b4d57b942452acf564856ac58f599273070", size = 132926, upload-time = "2026-04-17T13:15:18.751Z" },
 ]
 
 [[package]]
@@ -1692,7 +1692,7 @@ dependencies = [
 requires-dist = [
     { name = "astropy", specifier = ">=6.0.1" },
     { name = "h5py", specifier = "~=3.6" },
-    { name = "ml4gw", specifier = ">=0.7.10" },
+    { name = "ml4gw", specifier = ">=0.8.0" },
     { name = "numpy", specifier = ">=2" },
     { name = "s3fs", specifier = ">=2024,<2025" },
 ]
diff --git a/projects/online/online/main.py b/projects/online/online/main.py
index a21a14d75..e8fe56ba4 100644
--- a/projects/online/online/main.py
+++ b/projects/online/online/main.py
@@ -1,6 +1,7 @@
 import atexit
 import logging
 import signal
+from math import floor
 import traceback
 from collections.abc import Iterable
 from pathlib import Path
@@ -55,6 +56,7 @@ def load_model(model: Architecture, weights: Path):
     arch_weights = {
         k.removeprefix("model."): v
         for k, v in checkpoint["state_dict"].items()
+        if k.startswith("model.")
     }
     model.load_state_dict(arch_weights)
     model.eval()
@@ -64,8 +66,9 @@ def load_model(model: Architecture, weights: Path):
 def load_amplfi(model: FlowArchitecture, weights: Path, num_params: int):
     model, checkpoint = load_model(model, weights)
     scaler_weights = {
-        k.removeprefix("scalar."): v
+        k.removeprefix("scaler."): v
         for k, v in checkpoint["state_dict"].items()
+        if k.startswith("scaler.")
     }
     scaler = ChannelWiseScaler(num_params)
     scaler.load_state_dict(scaler_weights)
@@ -242,15 +245,15 @@ def search(
             # but don't search for events
             if X is not None:
                 logging.debug(
-                    f"Frame {t0} is not analysis ready. Using dummy values "
-                    "for inference and ignoring any triggers"
+                    f"Frame {floor(t0)} is not analysis ready. Using dummy "
+                    "values for inference and ignoring any triggers"
                 )
                 pass
             # or if it's because frames were dropped within the stream
             # in which case we should reset our states
             else:
                 logging.warning(
-                    f"Missing frame files after timestep {t0}, "
+                    f"Missing frame files after timestep {floor(t0)}, "
                     "resetting states"
                 )
 
@@ -264,7 +267,7 @@ def search(
         elif not in_spec:
             # the frame is analysis ready, but previous frames
             # weren't, so reset our running states
-            logging.info(f"Frame {t0} is ready again, resetting states")
+            logging.info(f"Frame {floor(t0)} is ready again, resetting states")
             state = snapshotter.reset()
             input_buffer.reset()
             output_buffer.reset()
diff --git a/projects/online/online/monitor/main.py b/projects/online/online/monitor/main.py
index 914e0e660..22104af6c 100644
--- a/projects/online/online/monitor/main.py
+++ b/projects/online/online/monitor/main.py
@@ -48,7 +48,7 @@ def main(
         detected_events = [
             event
             for event in detected_event_dir.iterdir()
-            if float(event.name.split("_")[1])
+            if float(event.name.split("_")[1]) > summary_page.start_time
         ]
 
         # The event page will be created/updated only if the event directory
diff --git a/projects/online/online/subprocesses/events.py b/projects/online/online/subprocesses/events.py
index c8a8001da..bec57a9a3 100644
--- a/projects/online/online/subprocesses/events.py
+++ b/projects/online/online/subprocesses/events.py
@@ -3,6 +3,9 @@
 from queue import Queue
 from typing import TYPE_CHECKING
 
+import certifi
+from ligo.gracedb.kafka import GraceDbKafkaProducer
+
 from .utils import subprocess_wrapper
 
 if TYPE_CHECKING:
@@ -23,6 +26,13 @@ def event_creation_subprocess(
 
     # override with subprocesses logger
     gdb.logger = logger
+
+    # Need to create the producer within the subprocess that uses it
+    gdb.kafka_producer = GraceDbKafkaProducer(
+        bootstrap_servers="kafka-dev.ligo.org:9092",
+        service_url=gdb.server.service_url,
+        ca_cert_path=certifi.where(),
+    )
     while True:
         event = event_queue.get()
         logger.debug("Putting event in pastro queue")
diff --git a/projects/online/online/utils/gdb.py b/projects/online/online/utils/gdb.py
index 28eeb3bdc..44bc1c5f1 100644
--- a/projects/online/online/utils/gdb.py
+++ b/projects/online/online/utils/gdb.py
@@ -81,7 +81,12 @@ def __init__(
             **kwargs,
             service_url=server.service_url,
             use_auth="scitoken",
+            api_version="v2",
         )
+
+        # The kafka producer will be set in the subprocess that uses it
+        self.kafka_producer = None
+
         self.server = server
         self.write_dir = write_dir
         if logger is None:
@@ -99,6 +104,8 @@ def submit(self, event: Event):
             pipeline="aframe",
             filename=str(filename),
             search="AllSky",
+            kafka=self.kafka_producer,
+            http_fallback=True,
         )
 
         self.logger.debug("Event created")
diff --git a/projects/online/pyproject.toml b/projects/online/pyproject.toml
index 720b2f669..2188dba2e 100644
--- a/projects/online/pyproject.toml
+++ b/projects/online/pyproject.toml
@@ -22,7 +22,7 @@ dependencies = [
     "scipy>=1.9",
     "matplotlib>=3.9.4",
     "ligo-skymap>=2.4.0,<3",
-    "ligo-gracedb>=2.14.1",
+    "ligo-gracedb[kafka]>=2.15.4",
     "tables>=3.9",
     "gwpy>=3.0.12",
     "jsonargparse>=4.18.0",
diff --git a/projects/online/uv.lock b/projects/online/uv.lock
index 2bdb1613e..fcd0d93f0 100644
--- a/projects/online/uv.lock
+++ b/projects/online/uv.lock
@@ -713,6 +713,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e6/75/49e5bfe642f71f272236b5b2d2691cf915a7283cc0ceda56357b61daa538/comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3", size = 7180, upload-time = "2024-03-12T16:53:39.226Z" },
 ]
 
+[[package]]
+name = "confluent-kafka"
+version = "2.13.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7a/38/f5855cae6d328fa66e689d068709f91cbbd4d72e7e03959998bd43ac6b26/confluent_kafka-2.13.2.tar.gz", hash = "sha256:619d10d1d77c9821ba913b3e42a33ade7f889f3573c7f3c17b57c3056e3310f5", size = 276068, upload-time = "2026-03-02T12:53:31.457Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/71/a7/7dfee75b246f5e5f0832a27e365cd9e8050591c5f4301714672bea2375ce/confluent_kafka-2.13.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:e85dc2aaf08dcac610d20b24d252a24891440cf33c09396c957781b8a1f24015", size = 3629660, upload-time = "2026-03-02T12:52:42.595Z" },
+    { url = "https://files.pythonhosted.org/packages/93/77/bc6bca93f455e91b41b196bb208b9cbfc517442a65abae2391f1af64cd2f/confluent_kafka-2.13.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:eb1b218beeaae36b3fc94927e30df5f6d662858e766eada2369b290df0b1bff0", size = 3190013, upload-time = "2026-03-02T12:52:44.193Z" },
+    { url = "https://files.pythonhosted.org/packages/2e/ce/2ee04c1b2707b6dd7177eab40fced00b474671d2303e5096d96f3bf7e231/confluent_kafka-2.13.2-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:69b4286296504b89c0c3cd1e531d12053c633e56d2c5b477ff9000524fe24eb5", size = 3719524, upload-time = "2026-03-02T12:52:45.488Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/93/5c40e2f7eae52774db6b14060254d001ae8c4ef8d4385bf2f13294dd929f/confluent_kafka-2.13.2-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:e47be4267d3feda5bf1c066f140f61e61ea28bd6ecdb60c2a52ec1a91b8903e7", size = 3976453, upload-time = "2026-03-02T12:52:47.787Z" },
+    { url = "https://files.pythonhosted.org/packages/46/85/a3d25b67470abbd4835fca714a419465323ba79dceefcdda65dfa4415c80/confluent_kafka-2.13.2-cp311-cp311-win_amd64.whl", hash = "sha256:84dd6e7f456910aa4d4763d86efa0dded7167fdf1251b51d808dec0f124f5e13", size = 4097308, upload-time = "2026-03-02T12:52:49.083Z" },
+    { url = "https://files.pythonhosted.org/packages/d9/d3/a845c6993a728b8b6bdce9b500d15c3ec3663cd95d2bbf9c1b8cfd519b17/confluent_kafka-2.13.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:e259c0d2b9a7e16211b45404f62869502246ac3d03e35a1f80720fd09d262457", size = 3635348, upload-time = "2026-03-02T12:52:50.927Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/22/1cb998f7b3ee613d5b29f4b98e4a7539776eb0819b89d7c3cdd19a685692/confluent_kafka-2.13.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:77ea4ceccdbb67498787b7c02cc329c32417bb730e9383f46c74eb9c5851763c", size = 3194667, upload-time = "2026-03-02T12:52:53.468Z" },
+    { url = "https://files.pythonhosted.org/packages/11/38/8a1b12321068e8ae126e62600a55d7a1872f969e1de5ec7f602e0dba8394/confluent_kafka-2.13.2-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:a64a8967734f865f54b766553d63a40f17081cd3d2c6cfe6d3217aa7494d88fb", size = 3724453, upload-time = "2026-03-02T12:52:55.187Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/06/3effa66c59a69e17cc48c69ae2533699f4321fac1b46741f2e4b1aefb1e7/confluent_kafka-2.13.2-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:e4cb7d112463ec15a01a3f0e0d20392cda6e46156a6439fcaaad2267696f5cde", size = 3980919, upload-time = "2026-03-02T12:52:56.852Z" },
+    { url = "https://files.pythonhosted.org/packages/98/22/f76a8b85fad652b4d5c0a0259c8f7bb66393d2d9f277631c754c9ebe5092/confluent_kafka-2.13.2-cp312-cp312-win_amd64.whl", hash = "sha256:44496777ff0104421b8f4bb269728e8a5e772c09f34ae813bc47110e0172ebe0", size = 4097817, upload-time = "2026-03-02T12:52:58.831Z" },
+]
+
 [[package]]
 name = "contourpy"
 version = "1.3.2"
@@ -2225,16 +2243,22 @@ wheels = [
 
 [[package]]
 name = "ligo-gracedb"
-version = "2.14.2"
+version = "2.15.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cryptography" },
     { name = "igwn-auth-utils" },
     { name = "requests" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ad/63/d42ee081193d7bbd0f449d3eb46566669207d35117e68d74518cf5e41c27/ligo_gracedb-2.14.2.tar.gz", hash = "sha256:cd93fd50c7a999f88ef969826e678bdf99e242090b6d439d230e5f4f8043f39b", size = 2396894, upload-time = "2025-04-11T15:44:18.597Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/2d/7a/6fa8ee52bb99b7d510a51686e7bd935be263031464eb38a710d458893916/ligo_gracedb-2.15.4.tar.gz", hash = "sha256:06c246f76bdded0bbb4c0471e77a69b0da95cdc97f2c6736a3966ad77667a8bb", size = 2444113, upload-time = "2026-03-25T20:23:00.73Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/d6/ad/e00cc582adb7d0f41363e94a894624a357bfeded7aa143ed9c1820ef1d2e/ligo_gracedb-2.14.2-py3-none-any.whl", hash = "sha256:6463af190ac27c136df0a074c4ea1261b407fc568a2a71a4fa04686797e725f5", size = 2454508, upload-time = "2025-04-11T15:44:17.177Z" },
+    { url = "https://files.pythonhosted.org/packages/3c/a6/075ee856218dcd9597fbfb829a60b7daf55ed877f2c0493735d7d9a8ea91/ligo_gracedb-2.15.4-py3-none-any.whl", hash = "sha256:3ac1b4dcac86d7f533b931a4ab140f37a09497eea2fbb314c40c34aac57211c2", size = 2516860, upload-time = "2026-03-25T20:22:59.234Z" },
+]
+
+[package.optional-dependencies]
+kafka = [
+    { name = "certifi" },
+    { name = "confluent-kafka" },
 ]
 
 [[package]]
@@ -2505,7 +2529,7 @@ wheels = [
 
 [[package]]
 name = "ml4gw"
-version = "0.7.13"
+version = "0.8.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jaxtyping" },
@@ -2514,9 +2538,26 @@ dependencies = [
     { name = "torch" },
     { name = "torchaudio" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ba/af/bab88ca4f54386735a64502a990f1bc4edb6a0353aa2b910efd9aa244919/ml4gw-0.7.13.tar.gz", hash = "sha256:4e6264fcdb9cbf5ed6a83910a231a946770687bbc6c576f8e6dc811af7102f24", size = 121893, upload-time = "2026-04-07T00:22:25.566Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/2a/56/abb490d353f989802f918ee92cf6c9a37336483aa20c7f17df7730d81744/ml4gw-0.8.0.tar.gz", hash = "sha256:43a2411ae348f8f911fdc0e2defd4fa54370414fa8b51c63518de3cb805754ba", size = 121709, upload-time = "2026-04-17T13:15:20.347Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a8/7e/1499d3835e9fb9adeeb8b5b5107dcdee79c37d6a61cd9db912fe59100efa/ml4gw-0.7.13-py3-none-any.whl", hash = "sha256:53132028674bb079759f97b664ba8d1ab1e53ecb21a50660b9015ae7c518bde0", size = 132944, upload-time = "2026-04-07T00:22:24.325Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/22/64102f10ad7f9043083d8bafcf84b8f2e8abc84bfc214fd04dd8d243ece9/ml4gw-0.8.0-py3-none-any.whl", hash = "sha256:0b4377541d5a90dcf9c728efa4008b4d57b942452acf564856ac58f599273070", size = 132926, upload-time = "2026-04-17T13:15:18.751Z" },
+]
+
+[[package]]
+name = "mldatafind"
+version = "0.1.8"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "boto3" },
+    { name = "cloudpathlib" },
+    { name = "gwpy" },
+    { name = "htgettoken" },
+    { name = "law" },
+    { name = "luigi" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e6/fe/8600fae835cad7c850da58c8b8765c8cefac25dfbf5e8cf87476ad6218cc/mldatafind-0.1.8.tar.gz", hash = "sha256:7e33d5da1ebeff5908c6551c5c6ae0ac02c6543f908657da3be8b4d2020ea8e8", size = 119699, upload-time = "2025-06-21T13:36:12.607Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2d/8f/cb9af0a0c61148afb3ac403c996da28d0ee598366ac1abcf0d6c36283fca/mldatafind-0.1.8-py3-none-any.whl", hash = "sha256:04c641f046caeb8a6e14ae9eb79d007c85aa97753fbf2b1cb594ecfcd28a4251", size = 15078, upload-time = "2025-06-21T13:36:11.37Z" },
 ]
 
 [[package]]
@@ -3077,7 +3118,7 @@ dependencies = [
     { name = "gwpy" },
     { name = "jsonargparse" },
     { name = "ledger" },
-    { name = "ligo-gracedb" },
+    { name = "ligo-gracedb", extra = ["kafka"] },
     { name = "ligo-skymap" },
     { name = "matplotlib" },
     { name = "ml4gw" },
@@ -3106,7 +3147,7 @@ requires-dist = [
     { name = "gwpy", specifier = ">=3.0.12" },
     { name = "jsonargparse", specifier = ">=4.18.0" },
     { name = "ledger", editable = "../../libs/ledger" },
-    { name = "ligo-gracedb", specifier = ">=2.14.1" },
+    { name = "ligo-gracedb", extras = ["kafka"], specifier = ">=2.15.4" },
     { name = "ligo-skymap", specifier = ">=2.4.0,<3" },
     { name = "matplotlib", specifier = ">=3.9.4" },
     { name = "ml4gw", specifier = ">=0.7.4" },
@@ -4964,7 +5005,7 @@ dependencies = [
 requires-dist = [
     { name = "astropy", specifier = ">=6.0.1" },
     { name = "h5py", specifier = "~=3.6" },
-    { name = "ml4gw", specifier = ">=0.7.10" },
+    { name = "ml4gw", specifier = ">=0.8.0" },
     { name = "numpy", specifier = ">=2" },
     { name = "s3fs", specifier = ">=2024,<2025" },
 ]
diff --git a/projects/train/configs/time_heterodyne.yaml b/projects/train/configs/time_heterodyne.yaml
new file mode 100644
index 000000000..d2928f781
--- /dev/null
+++ b/projects/train/configs/time_heterodyne.yaml
@@ -0,0 +1,148 @@
+# commented args represent values filled out
+# by train task at run time. To build a functional
+# standalone config, add these in.
+ 
+# To start training from a checkpoint, uncomment the below argument
+# and specify a path to the desired checkpoint
+# ckpt_path: ""
+model:
+  class_path: train.model.SupervisedAframe
+  init_args:
+    # architecture
+    arch:
+      class_path: architectures.supervised.SupervisedHeterodyneTimeDomainResNet
+      init_args:
+        layers: [3, 4, 6, 3]
+        kernel_size: 25
+        norm_layer:
+          class_path: ml4gw.nn.norm.GroupNorm1DGetter
+          init_args:
+            groups: 16
+    metric:
+      class_path: train.metrics.TimeSlideAUROC
+      init_args:
+        max_fpr: 1e-3
+        pool_length: 8
+
+    # optimization params
+    weight_decay: 3e-5
+    learning_rate: 4e-4
+    pct_lr_ramp: 0.115
+    # early stop
+data:
+  class_path: train.data.supervised.HeterodyneTimeDomainSupervisedAframeDataset
+  init_args:
+    # loading args
+    background_dir:
+    waveforms_dir:
+    ifos: [H1,L1]
+    sample_rate: 2048
+    batches_per_epoch: 100
+    num_files_per_batch: 10
+    chunk_size: 1000
+    chunks_per_epoch: 20
+    # preprocessing args
+    batch_size: 100
+    kernel_length: 8
+    psd_length: 20
+    fduration: 2
+    # augmentation args
+    waveform_prob: 0.277
+    swap_prob: 0.014
+    mute_prob: 0.055
+    left_pad: 6.95
+    right_pad: 0.05
+    # heterodyne preprocessing args
+    chirp_mass_low: 1
+    chirp_mass_high: 2.5
+    num_chirp_masses: 100
+    chirp_mass_spacing: "log"
+    keep_last_n_seconds: null
+    # highpass: 
+    # lowpass: 
+    fftlength: 2
+    snr_sampler:
+      class_path: ml4gw.distributions.PowerLaw
+      init_args: 
+        minimum: 8
+        maximum: 100
+        index: -3
+    # curriculum learning for snr sampler
+    # snr_sampler:
+    #   class_path: train.augmentations.SnrSampler
+    #   init_args:
+    #     max_min_snr: 30
+    #     min_min_snr: 8
+    #     max_snr: 100
+    #     alpha: -3
+    #     decay_steps: 600
+    waveform_sampler:
+      class_path: train.data.waveforms.WaveformLoader
+      init_args:
+        training_waveform_path:
+        val_waveform_file:
+    dec: 
+      class_path: ml4gw.distributions.Cosine
+    psi: 
+      class_path: torch.distributions.Uniform
+      init_args:
+        low: 0
+        high: 3.14159
+        validate_args: false
+    phi: 
+      class_path: torch.distributions.Uniform
+      init_args:
+        low: 0
+        high: 6.28318
+        validate_args: false
+    # validation args
+    valid_stride: 0.5
+    num_valid_views: 5
+    valid_livetime: 57600
+
+trainer:
+  # by default, use a local CSV logger.
+  # note that you can use multiple loggers!
+  # Options in train task for appending 
+  # a wandb logger for remote logging.
+  logger:
+    - class_path: lightning.pytorch.loggers.TensorBoardLogger
+      init_args:
+        save_dir:
+        name:
+        version:
+
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        monitor: "valid_auroc"
+        mode: "max"
+        patience: 500
+    # custom model checkpoint for saving and
+    # tracing best model at end of traiing
+    # that will be used for downstream export
+    - class_path: train.callbacks.ModelCheckpoint
+      init_args:
+        monitor: "valid_auroc"
+        mode: "max"
+        save_top_k: 10
+        save_last: true
+        auto_insert_metric_name: false
+    - class_path: train.callbacks.SaveAugmentedBatch
+    - class_path: lightning.pytorch.callbacks.LearningRateMonitor
+      init_args:
+        logging_interval: "step"
+  # uncomment below if you want to profile
+  # profiler:
+    # class_path: lightning.pytorch.profilers.PyTorchProfiler
+    # dict_kwargs:
+      # profile_memory: true
+  # devices:
+  # strategy: set to ddp if len(devices) > 1
+  #precision: 16-mixed
+  accelerator: auto
+  max_epochs: 1500
+  check_val_every_n_epoch: 1
+  log_every_n_steps: 20
+  enable_progress_bar: true
+  benchmark: true
diff --git a/projects/train/pyproject.toml b/projects/train/pyproject.toml
index 133569ab3..a5c05b4e8 100644
--- a/projects/train/pyproject.toml
+++ b/projects/train/pyproject.toml
@@ -22,7 +22,7 @@ dependencies = [
     "fsspec[s3]>=2024,<2025",
     "urllib3>=2",
     "utils",
-    "ml4gw>=0.7.7",
+    "ml4gw>=0.8.0",
     "aframe",
     "ledger",
     "priors",
diff --git a/projects/train/train/cli.py b/projects/train/train/cli.py
index 3953dbc81..e0dfd1b2e 100644
--- a/projects/train/train/cli.py
+++ b/projects/train/train/cli.py
@@ -57,6 +57,16 @@ def add_arguments_to_parser(self, parser):
         except Exception:
             pass
 
+        # TODO: This is a workaround for linking num_chirp_masses between
+        # the model and the architecture for in_channels.
+        try:
+            parser.link_arguments(
+                "data.init_args.num_chirp_masses",
+                "model.init_args.arch.init_args.num_chirp_masses",
+            )
+        except Exception:
+            pass
+
         parser.link_arguments(
             "data.init_args.sample_rate",
             "data.init_args.waveform_sampler.init_args.sample_rate",
diff --git a/projects/train/train/data/supervised/__init__.py b/projects/train/train/data/supervised/__init__.py
index 41b42c9b9..947d9122e 100644
--- a/projects/train/train/data/supervised/__init__.py
+++ b/projects/train/train/data/supervised/__init__.py
@@ -6,3 +6,9 @@
     SpectrogramDomainSupervisedAframeDataset,
     TimeSpectrogramDomainSupervisedAframeDataset,
 )
+from .multimodal import MultiModalSupervisedAframeDataset
+from .supervised import SupervisedAframeDataset
+from .time_domain import (
+    TimeDomainSupervisedAframeDataset,
+    HeterodyneTimeDomainSupervisedAframeDataset,
+)
diff --git a/projects/train/train/data/supervised/time_domain.py b/projects/train/train/data/supervised/time_domain.py
index a26806614..c31bbbafe 100644
--- a/projects/train/train/data/supervised/time_domain.py
+++ b/projects/train/train/data/supervised/time_domain.py
@@ -1,6 +1,9 @@
+import math
 import torch
+from typing import Literal
 
 from train.data.supervised.supervised import SupervisedAframeDataset
+from ml4gw.transforms import Heterodyne
 
 
 class TimeDomainSupervisedAframeDataset(SupervisedAframeDataset):
@@ -20,3 +23,118 @@ def inject(self, X, waveforms=None):
         X, y, psds = super().inject(X, waveforms)
         X = self.whitener(X, psds)
         return X, y
+
+
+class HeterodyneTimeDomainSupervisedAframeDataset(SupervisedAframeDataset):
+    """
+    A derived class from BaseAframeDataset and SupervisedAframeDataset, it
+    applies heterodyning to strain data and returns heterodyned timeseries
+    for loading data to train Aframe models. If `keep_last_n_seconds` is
+    passed, returns only the final portion of the heterodyned strain.
+
+    Args:
+        chirp_mass_low (float):
+            Lower bound of chirp mass range (in solar masses).
+        chirp_mass_high (float):
+            Upper bound of chirp mass range (in solar masses).
+        num_chirp_masses (int):
+            Number of chirp mass samples to generate.
+        chirp_mass_spacing (Literal["linear", "log"]):
+            Spacing of chirp mass grid. Use "linear" for evenly spaced
+            values or "log" for logarithmic spacing.
+        keep_last_n_seconds (float):
+            If provided, only the last `n` seconds of the kernel_length are
+            returned. Otherwise, the full kernel_length is returned.
+    """
+
+    def __init__(
+        self,
+        chirp_mass_low: float = 1.0,
+        chirp_mass_high: float = 2.5,
+        num_chirp_masses: int = 100,
+        chirp_mass_spacing: Literal["linear", "log"] = "log",
+        keep_last_n_seconds: float = None,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+
+        self.chirp_mass_grid = self._create_chirp_mass_grid(
+            chirp_mass_low,
+            chirp_mass_high,
+            num_chirp_masses,
+            chirp_mass_spacing,
+        )
+
+        self.keep_last_n_seconds = keep_last_n_seconds
+
+        if self.keep_last_n_seconds is not None:
+            self.keep_last_n_samples = int(
+                self.keep_last_n_seconds * self.hparams.sample_rate
+            )
+
+    def build_transforms(self, *args, **kwargs):
+        super().build_transforms(*args, **kwargs)
+        self.heterodyne_transform = Heterodyne(
+            sample_rate=self.hparams.sample_rate,
+            kernel_length=self.hparams.kernel_length,
+            chirp_mass=self.chirp_mass_grid,
+            return_type="time",
+        )
+
+    def _create_chirp_mass_grid(
+        self,
+        chirp_mass_low: float,
+        chirp_mass_high: float,
+        num_chirp_masses: int,
+        chirp_mass_spacing: Literal["linear", "log"],
+    ) -> torch.Tensor:
+        if chirp_mass_spacing == "linear":
+            return torch.linspace(
+                chirp_mass_low, chirp_mass_high, num_chirp_masses
+            )
+        elif chirp_mass_spacing == "log":
+            return torch.logspace(
+                math.log10(chirp_mass_low),
+                math.log10(chirp_mass_high),
+                num_chirp_masses,
+            )
+        else:
+            raise ValueError(
+                f"Invalid chirp mass spacing: {chirp_mass_spacing}"
+            )
+
+    def build_val_batches(self, background, signals):
+        X_bg, X_inj, psds = super().build_val_batches(background, signals)
+        X_bg = self.whitener(X_bg, psds)
+        X_bg = self.heterodyne_transform(X_bg)
+        _B_bg, _C_bg, _M_bg, _T_bg = X_bg.shape
+        X_bg = X_bg.view(_B_bg, _C_bg * _M_bg, _T_bg)
+        # whiten each view of injections
+        X_fg = []
+        for inj in X_inj:
+            inj = self.whitener(inj, psds)
+            inj = self.heterodyne_transform(inj)
+            X_fg.append(inj)
+        X_fg = torch.stack(X_fg)
+        _V_fg, _B_fg, _C_fg, _M_fg, _T_fg = X_fg.shape
+        X_fg = X_fg.view(_V_fg, _B_fg, _C_fg * _M_fg, _T_fg)
+
+        if self.keep_last_n_seconds is not None:
+            return X_bg[..., -self.keep_last_n_samples :], X_fg[
+                ..., -self.keep_last_n_samples :
+            ]
+        else:
+            return X_bg, X_fg
+
+    def inject(self, X, waveforms=None):
+        X, y, psds = super().inject(X, waveforms)
+        X = self.whitener(X, psds)
+        X = self.heterodyne_transform(X)
+        _B, _C, _M, _T = X.shape
+        X = X.view(_B, _C * _M, _T)
+
+        if self.keep_last_n_seconds is not None:
+            return X[..., -self.keep_last_n_samples :], y
+        else:
+            return X, y
diff --git a/projects/train/uv.lock b/projects/train/uv.lock
index 09499776a..ec3713e33 100644
--- a/projects/train/uv.lock
+++ b/projects/train/uv.lock
@@ -2100,7 +2100,7 @@ wheels = [
 
 [[package]]
 name = "ml4gw"
-version = "0.7.11"
+version = "0.8.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jaxtyping" },
@@ -2109,9 +2109,9 @@ dependencies = [
     { name = "torch" },
     { name = "torchaudio" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/6f/0a/722f553635ffc91b32623e69a4c93591c11ce2c24a10e4bda35ab0d8e6ae/ml4gw-0.7.11.tar.gz", hash = "sha256:8df9ebecd97ed6a6e8ba07fab40882f5966e646897f5187a9ccf7913faf6464e", size = 119593, upload-time = "2026-01-29T20:34:30.794Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/2a/56/abb490d353f989802f918ee92cf6c9a37336483aa20c7f17df7730d81744/ml4gw-0.8.0.tar.gz", hash = "sha256:43a2411ae348f8f911fdc0e2defd4fa54370414fa8b51c63518de3cb805754ba", size = 121709, upload-time = "2026-04-17T13:15:20.347Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/89/7d/f8c3e695d52cd9e70fd3f7bb51efd29848a3eb481dc1b94228f481dd05f8/ml4gw-0.7.11-py3-none-any.whl", hash = "sha256:0a6645f27444d266fb94afe988450bc2d00e24bd70328b0a5903194e1900acdb", size = 129588, upload-time = "2026-01-29T20:34:29.357Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/22/64102f10ad7f9043083d8bafcf84b8f2e8abc84bfc214fd04dd8d243ece9/ml4gw-0.8.0-py3-none-any.whl", hash = "sha256:0b4377541d5a90dcf9c728efa4008b4d57b942452acf564856ac58f599273070", size = 132926, upload-time = "2026-04-17T13:15:18.751Z" },
 ]
 
 [[package]]
@@ -4101,7 +4101,7 @@ requires-dist = [
     { name = "ledger", editable = "../../libs/ledger" },
     { name = "lightning", specifier = ">=2.2.1" },
     { name = "lightray", specifier = ">=0.2.3" },
-    { name = "ml4gw", specifier = ">=0.7.7" },
+    { name = "ml4gw", specifier = ">=0.8.0" },
     { name = "priors", editable = "../../libs/priors" },
     { name = "ray", extras = ["default", "tune"], specifier = ">=2.8.0,<3" },
     { name = "s3fs", specifier = ">=2024,<2025" },
@@ -4237,7 +4237,7 @@ dependencies = [
 requires-dist = [
     { name = "astropy", specifier = ">=6.0.1" },
     { name = "h5py", specifier = "~=3.6" },
-    { name = "ml4gw", specifier = ">=0.7.10" },
+    { name = "ml4gw", specifier = ">=0.8.0" },
     { name = "numpy", specifier = ">=2" },
     { name = "s3fs", specifier = ">=2024,<2025" },
 ]
diff --git a/uv.lock b/uv.lock
index dd41a31ba..729278c81 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1723,7 +1723,7 @@ wheels = [
 
 [[package]]
 name = "ml4gw"
-version = "0.7.11"
+version = "0.8.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jaxtyping" },
@@ -1732,9 +1732,9 @@ dependencies = [
     { name = "torch" },
     { name = "torchaudio" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/6f/0a/722f553635ffc91b32623e69a4c93591c11ce2c24a10e4bda35ab0d8e6ae/ml4gw-0.7.11.tar.gz", hash = "sha256:8df9ebecd97ed6a6e8ba07fab40882f5966e646897f5187a9ccf7913faf6464e", size = 119593, upload-time = "2026-01-29T20:34:30.794Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/2a/56/abb490d353f989802f918ee92cf6c9a37336483aa20c7f17df7730d81744/ml4gw-0.8.0.tar.gz", hash = "sha256:43a2411ae348f8f911fdc0e2defd4fa54370414fa8b51c63518de3cb805754ba", size = 121709, upload-time = "2026-04-17T13:15:20.347Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/89/7d/f8c3e695d52cd9e70fd3f7bb51efd29848a3eb481dc1b94228f481dd05f8/ml4gw-0.7.11-py3-none-any.whl", hash = "sha256:0a6645f27444d266fb94afe988450bc2d00e24bd70328b0a5903194e1900acdb", size = 129588, upload-time = "2026-01-29T20:34:29.357Z" },
+    { url = "https://files.pythonhosted.org/packages/2a/22/64102f10ad7f9043083d8bafcf84b8f2e8abc84bfc214fd04dd8d243ece9/ml4gw-0.8.0-py3-none-any.whl", hash = "sha256:0b4377541d5a90dcf9c728efa4008b4d57b942452acf564856ac58f599273070", size = 132926, upload-time = "2026-04-17T13:15:18.751Z" },
 ]
 
 [[package]]
@@ -3487,7 +3487,7 @@ dependencies = [
 requires-dist = [
     { name = "astropy", specifier = ">=6.0.1" },
     { name = "h5py", specifier = "~=3.6" },
-    { name = "ml4gw", specifier = ">=0.7.10" },
+    { name = "ml4gw", specifier = ">=0.8.0" },
     { name = "numpy", specifier = ">=2" },
     { name = "s3fs", specifier = ">=2024,<2025" },
 ]